*Article* **On Numerical Models for Cube Drop Test of Bladder Fuel Tank for Aeronautical Applications**

**Domenico Cristillo 1, Francesco Di Caprio 1,\*, Claudio Pezzella 2, Carmen Paciello 2, Simone Magistro 3, Luigi Di Palma 4 and Marika Belardo 1**

**Abstract:** For some categories of aircraft, such as helicopters and tiltrotors, fuel storage systems must satisfy challenging crash resistance requirements in order to reduce or eliminate the possibility of fuel fires and thus increase the chances of passenger survival. Therefore, for such applications, fuel tanks with high flexibility (bladder) are increasingly used, which are able to withstand catastrophic events and avoid fuel leakages. The verification of these capabilities must be demonstrated by means of experimental tests, such as the cube drop test (MIL-DTL-27422). In order to reduce development costs, it is necessary to execute experimental tests with a high confidence of success, and, therefore, it is essential to have reliable and robust numerical analysis methodologies. The present work aims to provide a comparison between two explicit FE codes (i.e., Abaqus and Ls-Dyna), which are the most frequently used for such applications according to experimental data in the literature. Both codes offer different material models suitable for simulating the tank structure, and therefore, the most suitable one must be selected by means of a specific trade-off and calibration activity. Both are able to accurately simulate the complex fluid–structure interaction thanks to the use of the SPH approach, even if the resulting sloshing capabilities are quite different from each other. Additionally, the evolution of the tank's deformed shape highlights some differences, and, in particular, Abaqus seems to return a more natural and less artificial behavior. For both codes, the error in terms of maximum impact force is less than 5%, but, even in this case, Abaqus is able to return slightly more accurate results.

**Keywords:** SPH; hyperelastic; drop test; fuel tank; FEM; explicit analysis; bladder; tiltrotor

### **1. Introduction**

The runway-independent aircraft concept is progressively becoming more prominent in civil aviation thanks to its ability to improve public mobility in areas where airport infrastructures are not highly developed. Horizon 2020 Clean Sky 2 FRC IADP Next Generation of Civil Tiltrotor Technology Demonstrator (NGCTR-TD) is an example of European-funded research in the sector of VTOL aircraft. NGCTR-TD is the innovative Civil Tiltrotor Technology Demonstrator [1,2] with a configuration that will go beyond current architectures for this type of aircraft. NGCTR-TD is currently under development by Leonardo Helicopter (helicopter division of Leonardo Company, leader in Italian aerospace and defense), and it is shown in Figure 1.

**Citation:** Cristillo, D.; Di Caprio, F.; Pezzella, C.; Paciello, C.; Magistro, S.; Di Palma, L.; Belardo, M. On Numerical Models for Cube Drop Test of Bladder Fuel Tank for Aeronautical Applications. *J. Compos. Sci.* **2022**, *6*, 99. https://doi.org/ 10.3390/jcs6030099

Academic Editor: Stelios K. Georgantzinos

Received: 14 February 2022 Accepted: 18 March 2022 Published: 21 March 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Figure 1.** NGCTR tiltrotor, courtesy of Leonardo Helicopters (https://svppbellum.blogspot.com/20 20/01/il-next-generation-civil-tiltrotor.html, accessed on 28 January 2022).

Because the tiltrotor is able to operate as both a helicopter and an aircraft, the airworthiness specifications are a combination of CS-25 and CS-29 Airworthiness Requirements, and in some cases, specific tailoring is necessary. For their VTOL capability, tiltrotors have to comply with crashworthiness requirements according to CS-29, similarly to helicopters. The fuel storage system of the NGCTR-TD consists of bladder tanks installed inside the wing structures. Crashworthiness is one of the most demanding aspects to consider in the design of VTOL fuel tanks in order to satisfy passenger survivability requirements. Among the various requirements, tanks have to successfully pass a drop test from a height equal to 15.2 m according to CS 29.952 [3] by showing no leakage after the drop. Moreover, the tanks must possess sufficient flexibility and foldability in order to be installed through suitable access doors placed on the lower skin of the wing and to be used for inspection and maintenance activities. NGCTR-TD has 14 fuel bladder tanks, all located in the wing structure [4]. As an example, the V22 has 16 fuel tanks, 10 integrated into the wings and 6 in the fuselage, holding from 5489 to 13,779 L of fuel [5]. The NCGTR-TD fuel storage system is based on bladder tanks manufactured from very flexible and resistant rubberized materials. Each tank provides a fuel barrier against fuel and fuel vapor leakages and mechanical resistance in case of a crash, puncture or penetration. All tanks are completed with co-cured metallic flanges and valves to connect the fuel storage system with fuel and venting lines.

Not only must the tanks be designed according to crashworthiness requirements, but the wing itself must have crashworthy features, such as the ability to separate itself from the fuselage to ensure that a crash is survivable. This is necessary to prevent the fuselage from being crushed due to the inertial load related to the wings, fuel and nacelles and to let passengers escape from the aircraft. This feature is also present in military tiltrotors, such as Bell V-22 tiltrotor [6]. The civil application should require an experimental test to prove the effectiveness of any solutions devoted to breaking the wing in specific sections. Experimental and numerical studies of a full-scale regional fuselage drop test without any wings installed [7–9] have demonstrated that the fuselage damage is located mainly on the lower section of the fuselage, while the upper part is not critically damaged [10,11]. No evidence on the structural behavior of the wing during a crash has been obtained under crash conditions. In the 1960s, the US Army introduced the first military regulation (MIL-DTL-27422) that defined the certification requirements for fuel storage systems to be installed in a helicopter. The crashworthiness of a tiltrotor tank is based on CS-29 and MIL-DTL-27422 specifications, similarly to a helicopter [12]. As described in MIL-DTL-27422, the drop impact test must be conducted to verify the structural behavior in a dynamic regime of the fuel tank, both in the standalone configuration (cube drop test) and in the partially integrated configuration with the surrounding structure [13]. The Federal Aviation Administration (FAA) funded research on crash-resistant fuel systems for commercial aircraft with the aim of providing data that are useful in reducing the possibility of fire due to crash events [14,15]. Anghileri [16] investigated the fuel tank crashworthiness

from experimental and numerical points of view. Fuel sloshing during crash events was studied by means of numerical models, which were validated by comparing their outputs to experimental results [17]. Using parallel computing, Li et al. [18] investigated the dynamic behavior of a fuel tank in a dynamic impact event with the ground, demonstrating that such an algorithm is able to run with a high speed-up ratio and parallel efficiency. Luo et al. [19] studied the crashworthiness of a fuel tank for helicopter applications by means of the finite element method (FEM) in order to verify the energy absorption capabilities of the textile layer and protection frame. Kim et al. [20] used the commercial FE code Ls-Dyna to simulate the dynamic response of fuel tanks installed in a rotorcraft. An accurate study based on analytical, numerical and experimental results was performed to investigate the structural performance of tanks with energy-absorbing fixtures under dynamic pressure and blast load conditions [21,22].

The activities aimed at developing the next-generation fuel storage system of NGCTR-TD are part of the DEFENDER project, and within the design and analysis work package, an innovative aspect is represented by the setup of high-fidelity methodologies and models to support the tank design, especially its performance against crash and impact loads. The final aim is to provide models that are validated by means of a full-scale crash test on the most critical tank plus the wing surrounding its composite structure. The activity will be performed with an incremental approach, which passes through numerical–experimental validation, the simulation of a cube-like tank test and, finally, a full-scale test. The study presented herein is one of the basic steps foreseen for the final objective of the high-fidelity crashworthiness simulation. In particular, in this work, two numerical models for the prediction of the structural response of a cube-like tank under impact load conditions were developed and verified by comparing their results with experimental data available in the literature.

### **2. Experimental Test Case Description**

In order to validate the numerical model, experimental data available in the literature [23] were used. Yang et al. performed two drop tests on a fuel tank filled with water (to replace real fuel). The fuel tank was realized with a flexible structure made of an external fabric nylon layer and an inner sealing layer. The tank was 760 mm long and wide and its height is about 600 mm. In order to execute the drop test, according to the specification, the tank was filled with about 350 kg of water. In the drop impact test, the tank was lifted to a specific height by means of a crane and dropped on a rigid impact surface instrumented with four load cells with a capacity equal to 2000 kN. The impact surface was a steel plate and was 80 mm thick; therefore, it can be reasonably considered rigid. They performed two drop tests using two different drop heights: 15.2 and 19.8 m. The impact velocities related to these drop heights are 17.3 and 19.7 m/s, respectively. For the following numerical comparison, only the impact velocity equal to 17.3 m/s was taken into account, because it is related to a standard drop height, as reported in MIL-DTL-27422.

### **3. Numerical Model Description**

The validation of the experimental test, as previously described, was performed by means of a numerical model, realized with two different software packages: Abaqus and LS Dyna. The experimental test was simulated by means of a 3D FE model composed of three parts: the fuel tank, the ground and the fuel. In particular, the fuel tank was modeled by using shell elements with a thickness of 2 mm and fuel with SPH elements, and the ground was discretized by means of rigid elements in Abaqus and a rigid wall in Ls-Dyna.

For both ABAQUS and Ls-Dyna models, the same mesh density was adopted in order to remove any dependency by the discretization technique. The tank structure was discretized by means of 31728 shell elements, while the number of smooth particle hydrodynamics (SPH) elements used for modeling the fuel was 537936. The platform used for the numerical simulations is an HP Z840 workstation, equipped with an Intel Xeon E5-2620 v3 CPU @ 2.40 GHz and 128 GB RAM. Considering that the simulated event time is 200 ms (even if the most relevant part is up to 50 ms), the computational time is about 4.5 h for Abaqus and about 4 h for Ls-Dyna (both codes run adopting the parallel solution with 4 cores). Obviously, this small difference is also related to the requested output; therefore, the difference in computational time is not too relevant. Figure 2 reports the adopted numerical model with schematic detail of SPH elements and the fluid–structure interaction.

### *3.1. Boundary Conditions*

The tank is subject to a free-fall form and height equal to 15.2 m; therefore, the impact velocity is about 17.3 m/s. The ground was simulated by means of rigid elements.

Further, from the reference work, it can be assumed that the impact angle is not equal to zero, but it is worth noting that such a condition is quite hard to obtain. The impact angle is about 3◦ around both the X and Y axes. Figure 3 schematically reports the adopted boundary conditions.

**Figure 3.** Boundary conditions.

### *3.2. Smooth Particle Hydrodynamics Method (SPH) and Water Material Model*

Smoothed particle hydrodynamics (SPH) are represented by three-dimensional elements with three degrees of freedom and defined by their center of mass. These elements have their own shape functions that depend on the connectivity of the particles. The interpolation distance between the particles, called smoothing length, furnishes the location and provides information about transmission among the different particles.

In the SPH approach, the water was simulated as particles with the same dimensions and distances without mass. The velocity and energy of the particles at any time can be solved by means of a function *f*(*x*). The value of this function can be approximated for particle data by Equation (1):

$$
\langle f(\mathbf{x}\_i) \rangle = \sum\_{j=1}^{N} \frac{m\_j}{\rho\_j} f(\mathbf{x}\_j) \mathcal{W}(\mathbf{x}\_i - \mathbf{x}\_j, h) \tag{1}
$$

where *h* is the smoothing length, *mi* and *ρi* are the mass and density of the particle, *xi* and *xj* are the positions of particles, and *W* is the kernel interpolation.

In particular, we consider two particles (*i* and *j*) to be nearest neighbors. If the distance between particles *i* and *j* is lower than the radius of the sphere of influence of *j*, then *i* is connected to *j*. The dimension of the sphere of influence is a multiple of the particle's smoothing length, and the multiplication factor depends on the kernel used to create the smooth particle's shape function.

Therefore, the value of a variable for particle *j* can be obtained by adding the contributions of particle *i* contained within the influence radius. The interpolation kernel used in this analysis was proposed by Monaghan [24].

The behavior of the fluid material inside the fuel tank is determined by the equation of state (EOS), which relates the pressure and the material volume change rate in a physical state.

The equation for the conservation of energy equates the increase in internal energy per unit mass, *Em,* to the rate at which work is being performed by stresses and the rate at which heat is being added. In the absence of heat conduction, the energy equation can be written as:

$$
\rho \frac{\partial E\_m}{\partial t} = (p - p\_{lv}) \frac{1}{\rho} \frac{\partial \rho}{\partial t} + S : \dot{e} + \rho \dot{Q} \tag{2}
$$

where *p* is the pressure stress, defined as positive in compression; *pbv* is the pressure stress due to the bulk viscosity; *S* is the deviatoric stress tensor; . *e* is the deviatoric part of the strain rate; and . *Q* is the heat rate per unit mass.

The equation of state is assumed for the pressure as a function of the current density, *ρ*, and the internal energy per unit mass, *Em*.

Since the internal energy can be eliminated, it is possible to write the EOS as a *p* versus *V* relationship (where *V* is the current volume) or, equivalently, a *p* versus 1/*ρ* relationship that is unique to the material described by the equation-of-state model. This unique relationship is called the Hugoniot curve and is the locus of *p-V* states achievable behind a shock (see Figure 4).

**Figure 4.** A schematic representation of a Hugoniot curve.

The Hugoniot pressure, *pH*, is a function of density only and can be determined, in general, by fitting experimental data.

There are different formulations of EOS, and the most common form for the water description is the linear *Us–Up* equation, which is given by:

$$pH = \frac{\rho\_0 c\_0^2 \eta}{\left(1 - s\eta\right)^2} \tag{3}$$

where *c*0 and s define the linear relationship between the shock velocity, *Us*, and the particle velocity, *Up*, as follows:

$$
\mathcal{U}\_s = \mathfrak{c}\_0 + s\mathcal{U}\_p \tag{4}
$$

With the above assumptions, the linear *Us*−*Up* Hugoniot form is written as

$$p = \frac{\rho\_0 c\_0^2 \eta}{\left(1 - s\eta\right)^2} \left(1 - \frac{\Gamma\_0 \eta}{2}\right) + \Gamma\_0 \rho\_0 E\_m \tag{5}$$

where *ρ*0*c*0 2 is equivalent to the elastic bulk modulus at small nominal strains.

### *3.3. Tank Material Model*

The fuel tank structure is a crash-resistant composite fabric composed of a rubber layer and a nylon layer. A different formulation is used for each of these materials.

Rubber materials have very small compressibility compared to their shear flexibility. This behavior can be modeled by means of hyperplastic material. In fact, the hyperplastic material model is isotropic and nonlinear, is valid for materials that exhibit an instantaneous elastic response to large strains and requires that geometric nonlinearity be accounted for during the analysis step since it is intended for finite-strain applications.

Hyperelastic materials are described in terms of a "strain energy potential", *U*(*ε*), which defines the strain energy stored in the material per unit of reference volume (volume in the initial configuration) as a function of the strain at that point in the material. There are several forms of strain energy potentials available in Abaqus to model approximately incompressible isotropic elastomers: the Arruda–Boyce form, the Marlow form, the Mooney–Rivlin form, the neo-Hookean form, the Ogden form, the polynomial form, the reduced polynomial form, the Yeoh form and the van der Waals form. For this material description, the Marlow form is used. A strain energy potential is constructed that will reproduce the test data exactly and that will have reasonable behavior in other deformation modes.

The form of the Marlow strain energy potential is:

$$
\mathcal{U}\mathcal{U} = \mathcal{U}\_{dcv}(\overline{\mathcal{I}}\_1) + \mathcal{U}\_{\text{vol}}\left(f^{el}\right) \tag{6}
$$

where *U* is the strain energy per unit of reference volume, with *Udev* as its deviatoric part and *Uvol* as its volumetric part; *I*1 is the first deviatoric strain invariant and is defined as:

$$
\overline{\lambda}\_1 = \overline{\lambda}\_1^2 + \overline{\lambda}\_2^2 + \overline{\lambda}\_3^2 \tag{7}
$$

where the deviatoric stretch *λi* = *J*− 1 3 *λi* is the total volume ratio, *J el* is the elastic volume and *λi* is the principal stretch. The deviatoric part of the potential is determined by providing either uniaxial, equibiaxial or planar test data, while the volumetric part is determined by providing the volumetric test data, defining Poisson's ratio or specifying lateral strains together with uniaxial, equibiaxial or planar test data.

Additionally, the fabric layer has very low compressibility compared to its shear flexibility, and for this reason, the hyperelastic material model was also adopted for the fabric layer.

The most suitable mathematical model is the Ogden form. A strain energy potential is constructed that will reproduce the test data exactly and that will have reasonable behavior in other deformation modes.

The form of the Ogden strain energy potential is:

$$MI = \sum\_{i=1}^{N} \frac{2\mu\_i}{\alpha\_i^2} \left( \overline{\lambda}\_1^{u\_i} + \overline{\lambda}\_2^{u\_i} + \overline{\lambda}\_3^{u\_i} - 3 \right) + \sum\_{i=1}^{N} \frac{1}{D\_i} \left( f^{el} - 1 \right)^{2i} \tag{8}$$

where *λi* is the deviatoric principal stretch *λi* = *J*− 1 3 *λi*; *λi* is the principal stretch; *N* is a material parameter; and *μi* , *αi* and *Di* are temperature-dependent material parameters. The initial shear modulus and bulk modulus for the Ogden form are given by:

$$
\mu\_0 = \sum\_{i=1}^{N} \mu\_i \,\, \, \mathcal{K}\_0 = \frac{2}{D\_1} \tag{9}
$$

Since the fabric layer is stiffer and stronger than the rubber layer, its behavior is predominant, and for this reason, the Ogden material model was used to reproduce the structural behavior of the tank skin.

### *3.4. Tank Material Properties*

The tank structure is made in a composite material that has a fabric nylon layer and an inner rubber layer aimed at ensuring impermeability to the fuel. Generally, the rubber layer is co-cured on the fabric layer, and the connection is very strong; possible failures involve both layers. The global response of the tank material can be related to a hyperplastic material, and therefore, a suitable material model was adopted in both Abaqus and Ls-Dyna. The best approach to defining the structural behavior is by means of obtaining the experimental stress–strain curve in a uniaxial tensile test. The density of the woven material is 1150 kg/m3, and the average thickness of the woven material is 2 mm. The stress–strain curve of the woven material, used as input, is reported in Figure 5.

**Figure 5.** Stress–strain curve of the woven material.

### **4. Numerical Results**

In the following, the comparison between Ls-Dyna and Abaqus results is shown. Figure 6 shows the deformed shape of the fuel tank in terms of global displacement (vectorial sum) for several time steps in order to provide a sort of deformation time-history.

The next figure shows that, generally, the two models behave quite similarly to each other. In particular, since the structure is very flexible, it begins to deform and is crushed in the lower area (the one that first comes into contact with the rigid ground). The internal fluid, under the action of inertial forces, pushes the tank structure downwards, introducing a moderate deformation state to the upper part. The magnitude of the global displacement is comparable between the two models, and therefore, the overall deformation of the structure does not present striking differences. However, it is equally evident that the structural response starts to exhibit some differences at around 40 ms. In fact, the Abaqus model seems to be softer, and therefore, the upper central part is subject to greater displacements. This is explained by the fact that the liquid begins to press towards the lateral walls in the bottom region (see also the following figures that report a sectional view). The fluid then generates an empty region that is unable to support the tank structure, and therefore, it collapses. On the other hand, in the LS-Dyna model, this peculiarity is not evident, and this is associated with the greater rigidity of the structure, which is therefore unable to accommodate fluid dislocations.

**Figure 6.** Global displacement contour plot. Left: Ls-Dyna model; right: Abaqus model.

Figure 7 shows the time histories of the strain of the bladder fuel tank for both the Ls-Dyna and Abaqus models. The distribution of the strain is quite different between the two models, even if, in terms of absolute values, they are still comparable. The biggest differences occur after the rebound stage. At 20 ms, the LS-Dyna model has a wide band with fairly high strain values (about 38%), so the area subject to high stretching is quite extensive. In the Abaqus model, on the other hand, only the areas close to the vertical edges show high strain values (around 32%). It is reasonable to assume that the largest strain values should be concentrated just in these areas, because the structure will try to eliminate the existing bends in order to accommodate fluid sloshing. The parts far from the edges, on the other hand, will be subject almost exclusively to a tensile stress state.

**Figure 7.** Strain distribution on tank structure. Left: Ls-Dyna model; right: Abaqus model.

After the rebound, at 40 ms, in the LS-Dyna model, a wide region with high strain values remains (obviously, the values are smaller than those related to the maximum crushing step). On the other hand, in the Abaqus model, the regions with high strain values are always very reduced and localized in the bending areas. However, it should be noted that at 40 ms, the deformation state of the bottom regions presents comparable values between the two models (about 12% for Ls-Dyna and 14% for Abaqus). The top of the Ls-Dyna structure has an average strain state of about 4%, while in Abaqus, this area is not notably stressed since the average strain is about 0.4%. All of this, again, highlights the greater flexibility of the Abaqus model compared to the Ls-Dyna model.

Finally, at 50 ms, although the colors could be misleading, the maximum values on the top are comparable and are equal to about 10%. This is due to the fluid, which, after the rebound, is concentrated towards the area with minimum resistance, i.e., the central area, and pushes the tank from the inside upwards.

Figure 8 shows a section of the tank in which it is possible to see the global displacement of the SPH elements. The sectional view provides a clear overview of the behavior of the fluid inside the tank. Prior to 20 ms, there are no significant differences between the two models, as observed in the previous results. Even in this case, the greatest differences occur after the rebound phase. The Abaqus model allows for greater mobility of fluid particles, and thus, the sloshing is much greater. In fact, it is possible to note a clear mixing of the particles. Further, the 50 ms frame can explain the strain distribution on the top region. In Abaqus, the particles are more able to concentrate towards the central region, and therefore, in this area, the pressure is much greater. It is equally important to note that both models are able to accurately simulate the fluid–structure interaction. The particles remain contained within the tank and are able to slide easily on the internal walls. For example, at 10 ms, it is evident that there are large tank portions in which the fluid is not in direct contact with the structure.

**Figure 8.** SPH global displacement field. Left: Ls-Dyna model; right: Abaqus model.

The force time-history curve (Figure 9) provides a global depiction of the good correlation level achieved by both models. The whole phenomenon is dominated by two force peaks, which are related to the first contact time and to the maximum crushing time. In

reference to this, the Abaqus model is more accurate since the Ls-Dyna model estimates the presence of a third peak (although with a reduced value). This could be related to the lower liquid fluidity (less sloshing), which leads to the concentration of inertial forces in more than two events. In addition, the overestimation of the second force peak by the LS-Dyna model could be related to the fact that the greater force is due to greater mass (therefore, more fluid is concentrated in the contact region due to a lower mobility capability and therefore a decreased ability to spread itself over a larger surface).

**Figure 9.** Numerical–experimental comparison in terms of impact force time-history.

In particular, the first experimental impact load peak is about 622 kN, whereas the value determined from the Abaqus model is 637.15 kN (+2.40%), and the value related to the Ls-Dyna model is 595.29 kN (−4.33%). As said before, the second force peak is well estimated by the Abaqus model since the error is about −7.45%, and it is quite overestimated by the Ls-Dyna model, which has an error of about +36%. Finally, both models overestimate the contact time by about 10 ms.

Another aspect to underline is that the experimental results do not show zero force after the rebound (an event that should occur at around 60 ms). This could be due to a poor damping capacity of the impact surface, since such structures are subject to significant rebounds if no catastrophic failures occur. Therefore, the comparison after that time step is not realistic.

### **5. Conclusions**

This paper provides a comparison between two numerical models developed by means of two different explicit FE codes: Abaqus and LS-Dyna. The quality of the numerical results was verified by numerical–experimental comparison with respect to experimental data available in the literature related to the standard cube drop test for fuel tank certification.

impact force is smaller than 5% (a slightly better result was obtained with the Abaqus model). Both codes overestimate the duration of the main crash event (i.e., up to the rebound) to a similar extent. More significant differences can be found regarding the second peak of the impact force (relative to the instant of maximum crushing). In this case, Abaqus is clearly more accurate since the second peak is estimated with an error of about 7%.

**Author Contributions:** Conceptualization, D.C., F.D.C., C.P. (Claudio Pezzella), C.P. (Carmen Paciello), S.M. and M.B.; methodology, D.C., F.D.C., C.P. (Claudio Pezzella) and C.P. (Carmen Paciello); validation, D.C., C.P. (Carmen Paciello) and S.M.; formal analysis, D.C. and C.P. (Carmen Paciello); data curation, D.C., F.D.C. and S.M.; writing—original draft preparation, D.C., F.D.C., C.P. (Claudio Pezzella), C.P. (Carmen Paciello), S.M. and M.B; writing—review and editing, F.D.C.; funding acquisition L.D.P.; supervision, L.D.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation program, Grant Agreement number: 738078—DEsign, development, manufacture, testing and Flight qualification of nExt geNeration fuel storage system with aDvanced intEgRated gauging and self-sealing capabilities (DEFENDER).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


**3.2. Core layer**

This layer is responsible for managing the federated environment. Among their functions are: identification of new providers with their corresponding hardware and software resources; task scheduling and controlling; definition, establishment and monitoring of SLA (Service Level Agreement); storage and managing of input and output files; maintainance of the online environment; and the election of new coordinators for each requested service. To each function implemented in this layer, a controller service was included in the architecture, as described next.

#### *3.2.1. Discovery service*

This service identifies the cloud providers integrating the federation, and consolidates information about storage and processing capabilities, network latency, availability of resources, available bioinformatics tools, details of parameters and input and output files. To realize this, the discovery service waits for information published by providers about their infrastructure and available tools. To consolidate these data, the discovery service maintains a data structure that is updated whenever new data is received. Furthermore, the discovery service has a policy of controlling each provider, removing from the federation those providers not regularly sending updated information, which guarantees the correct and update task execution on the federated cloud. Regarding to the entrance of a cloud provider in BioNimbus, *a priori*, at any time a peer participating in the P2P network can start the process of publishing its resources (storage and processing capabilities) and available bioinformatics applications. However, for security and controlling purposes, permission to join the federation as a provider must be verified with the support of the security service whenever any information about a new provider arrives to the discovery service.

As can be seen from the above description, an efficient resource discovery mechanism plays a central role in our federated cloud, since the information gathered by this service is essential to other services to properly perform their functions. According to [49], in large-scale distributed services, a resource discovery infrastructure has to meet the following key requirements: it must be scalable so it can handle thousands of machines without being unavailable or losing performance; it must be able to handle both static and dynamic resources; and it must be flexible enough so its queries could be extended in order to handle different types of resources. Possible implementations of a resource discovery service could be developed using central or hierarchical approaches, but these are known to have serious limitations of scalability, fault-tolerance and network congestion [56].

In BioNimbus, we plan to use a publish/subscribe mechanism, in which providers publish information about their resources to a decentralised resource discovery system. This system will use a Distributed Hash Table (DHT) data structure [7] in order to achieve low management costs and network overhead, efficient resource searching and fault-tolerance. For resource information handling, we plan to use serializable and extendable formats such as the JSON format [23]. In this way it will be possible for the federated cloud to deal with different types of information, thus causing the least impact possible.

#### *3.2.2. Job controller*

6 Will-be-set-by-IN-TECH:Bioinformatics, ISBN: 980-953-307-202-4

This layer provides the service of interaction with users, which can be implemented by web pages, command lines, graphical interfaces (GUI) or workflow management systems (WfMS). Users can execute workflows or a single application, choosing among available services. Job controller service has the function of collecting user requests and sending the input data to the core layer. Moreover, this layer is responsible for showing to each user the current status of his running applications. Users can list the files stored in the federation, upload or download

This layer encompasses the cloud providers belonging to BioNimbus. The previous described core layer ensures a unified view of the cloud, which allows users to see all the resources

A plug-in service is used to integrate a cloud provider (public or private) in the federation. Each plug-in service is an interface that aims at communicating the BioNimbus core with each cloud provider. Cloud providers can communicate among themselves also using the core layer. Each plug-in needs to map the requests sent by the core components to the corresponding actions that have to be realized in each cloud provider. This implies that each cloud requires a special plug-in service. Furthermore, to integrate distinct providers (public or private), each plug-in needs to treat three different kinds of requests: information about the

This layer is responsible for managing the federated environment. Among their functions are: identification of new providers with their corresponding hardware and software resources; task scheduling and controlling; definition, establishment and monitoring of SLA (Service Level Agreement); storage and managing of input and output files; maintainance of the online environment; and the election of new coordinators for each requested service. To each function implemented in this layer, a controller service was included in the architecture, as

This service identifies the cloud providers integrating the federation, and consolidates information about storage and processing capabilities, network latency, availability of resources, available bioinformatics tools, details of parameters and input and output files. To realize this, the discovery service waits for information published by providers about their infrastructure and available tools. To consolidate these data, the discovery service maintains a data structure that is updated whenever new data is received. Furthermore, the discovery service has a policy of controlling each provider, removing from the federation those providers not regularly sending updated information, which guarantees the correct and update task execution on the federated cloud. Regarding to the entrance of a cloud provider in BioNimbus, *a priori*, at any time a peer participating in the P2P network can start the process of publishing its resources (storage and processing capabilities) and available bioinformatics applications. However, for security and controlling purposes, permission to

files, create and execute workflows in the BioNimbus cloud environment.

available on each cloud as if they were one unified system.

provider infrastructure, task management and file transfer.

*3.1.1. Application layer*

*3.1.2. Cloud provider layer*

**3.2. Core layer**

described next.

*3.2.1. Discovery service*

The job controller links the core and the application layers of BioNimbus. It first calls the security service to verify if a user has permission (authentication) to execute jobs in BioNimbus and what are the credentials of this user. Moreover, the job controller's main function is to manage distinct and simultaneously running workflows, noting that the workflows may belong to the same or to different users. Thus, for each accepted workflow, the job controller generates an associated ID and controls each workflow execution using this ID.

#### *3.2.3. SLA controller*

According to [74], SLA is a formal contract between service providers and consumers to guarantee that consumers' service quality expectations can be achieved. In BioNimbus, the SLA controller is responsible for implementing the SLA lifecycle, which has six steps: discovers service providers, defines SLA, establishes agreement, monitors SLA violation, terminates SLA and enforces penalties for violation. A SLA template represents, among others, the QoS parameters that a user has negotiated with BioNimbus. The user can populate, through an user interface, a suitable template with required values or even define a new SLA from scratch in order to describe functional (e.g. CPU cores, memory size, CPU speed, OS type and storage size) and non-functional (e.g. response time, budget, data transfer time, availability and completion time) service requirements. In bioinformatics, functional requirements are number of cores, amounts of memory and storage, CPU speed, bioinformatics programs and databases and respective versions. Non-functional requirements are latency (transfer rate) and uptime (reliability, in the sense that it measures how frequently a cloud provider is running tasks or if it is not entering and leaving the federation).

The SLA controller has the responsibility to investigate whether the SLA template submitted by the user can be supported by the federated cloud platform. For this, the SLA controller

#### 8 Will-be-set-by-IN-TECH:Bioinformatics, ISBN: 980-953-307-202-4 114 Bioinformatics Towards a Hybrid Federated Cloud Platform to Efficiently Execute Bioinformatics Workflows 9

retrieves the SLA level published through the provider plug-in (e.g. gold, silver or bronze SLA level).

service. Furthermore, as previously described, the monitoring service has to verify agreement

Towards a Hybrid Federated Cloud Platform to Effi ciently Execute Bioinformatics Work ows 115

This service decides how to distribute and replicate data among the cloud providers integrating the federation [8, 39, 73], particularly model the storage strategy of the files consumed and produced by the jobs executed in BioNimbus. To realize this, the storage service can communicate with the discovery service to access information about the federation, since the discovery service knows the actual storage conditions of each provider integrating the federation. Thus a storage policy is defined, so that this choice can be made based on receiving information about the file (FileInfo) and returns at least one cloud provider

Some characteristics of biological data for bioinformatics applications are: large volume; it is not necessary to guarantee the ACID transaction properties since there are no users simultaneously updating data during execution; according to a particular bioinformatics application, fragmentation and replication can use different models; and data provenance

Considering these characteristics, the BioNimbus storage service proposal is based on the HBase NoSQL (Not only SQL) database. Among distinct noSQL databases, like HBase [38], Dynamo [26], Bigtable [17], Cassandra [41], PNUTS [22], monogDB [19] and cloudDB [5], we adopted HBase since its basic data storage is Apache's Hadoop Distributed File System (HDFS) [13] and it is a column-oriented database [24, 50, 51], which allows joining data

Replication will be done copying data to at most three clouds in order to ensure recovery in case of failure. Total or partial fragmentation depends on the biological data and the application. On the top of HBase, we propose to create an Analyzer module, which will decide where the replication has to be done. The objective of the Analyzer module is to reduce data transfers among the cloud providers, based on three criteria: disk space, geographic position and data transfer speed. The most important analysis is the available space, which should be sufficient to store input and output. The geographic position criterion has the objective of reducing data transfer on the network, being the closer clouds used first if possible. Finally, the Analyzer module uses data transfer speed, which must be computed using the time to

This service guarantees integrity among the distinct tasks executed in the federated clouds. A federated cloud needs to include the security policies of each cloud provider while avoiding strong inter-dependency among the clouds. A security context can be partitioned into three main topics: authentication, authorization and confidentiality. We address those requirements

• **Authentication**: The descentralized federated cloud infrastructure should not make a centralized authentication, which is a not a good choice because it limits the scalability and

(biological data file) and sets of information (e.g. data provenance).

using standard algorithms and protocols as described next.

violations.

is essential.

transfer packages.

*3.2.6. Security service*

*3.2.5. Storage service*

(PluginInfo) to store this file.

So, if the service agreement required by the SLA template can not be satisfied, a negotiation phase starts. The SLA negotiation phase is done as follows: the user submits a service request with the new SLA template to the job controller. Next, after parsing the SLA definition, the SLA controller asks the monitoring service if it could execute the service with the specified requirements. In order to respond to this request, the monitoring service requests the scheduling service to find the best suitable provider by matching the gathered resource properties to the service requirements by applying predefined scheduling algorithms. If none of the providers can be matched, the monitoring service enables the discovery service, which must seek new cloud providers to be integrated into the federated environment, aiming at satisfying the SLA template requested by the user. However, if this is not possible, the mentioned steps must be repeated for renegotiation, with a new SLA template, until reaching an agreement.

After establishing an agreement, the SLA controller generates an ID for the agreement, and sends the ID to the job controller, which records this ID agreement. Then, the job controller forwards both the request and the agreement ID to the monitoring service, which sends the tasks for the scheduling service. The monitoring service is responsible for checking if a violation of the agreement occurred and in this case it immediately has to inform the SLA controller, which terminates the SLA and enforce penalties for violation.

#### *3.2.4. Monitoring service*

This service verifies if a requested service is available in a cloud provider, searching for another cloud in the federation if it is not; receives the tasks to be executed from the job controller, and sends them to the scheduling service that will distribute them, guaranteeing that all the tasks of a process are correctly executed; and informs the job controller when a task successfully finishes its execution. To ensure the monitoring of all the requested tasks, this service periodically sends messages to the clouds that are executing tasks, and informs the user the current status of each submitted task.

To perform the activities described above, the monitoring service must be able to gather information about resource allocation and task execution, which depends on the application being executed [28]. Therefore, we have to establish some criteria about the frequency that data are obtained and their corresponding format, so that the decision-making process performed by this service can be made with reliability with respect to data timeliness and flexibility towards distinct applications. In BioNimbus, the monitoring service was planned to send messages at regular intervals to all the federation members or whenever needed. The latter case happens when a decision has to be taken for a specific federation member or when data update is necessary. All information exchange is done with timestamps so only the updated data are sent in order to save network bandwidth. We also plan to use an extensible and flexible format, such as JSON [23], like in the discovery service.

In federated clouds the monitoring service must have other characteristics, such as: scalability, to handle a large number of resources and tasks to be monitored; elasticity, to handle addition and removal of resources in a transparent manner; and federation, to handle entering and leaving providers [20]. In order to meet these requirements, we propose to use a decentralized information indexing infrastructure, which would be the same DHT available to the discovery service. Furthermore, as previously described, the monitoring service has to verify agreement violations.

#### *3.2.5. Storage service*

8 Will-be-set-by-IN-TECH:Bioinformatics, ISBN: 980-953-307-202-4

retrieves the SLA level published through the provider plug-in (e.g. gold, silver or bronze

So, if the service agreement required by the SLA template can not be satisfied, a negotiation phase starts. The SLA negotiation phase is done as follows: the user submits a service request with the new SLA template to the job controller. Next, after parsing the SLA definition, the SLA controller asks the monitoring service if it could execute the service with the specified requirements. In order to respond to this request, the monitoring service requests the scheduling service to find the best suitable provider by matching the gathered resource properties to the service requirements by applying predefined scheduling algorithms. If none of the providers can be matched, the monitoring service enables the discovery service, which must seek new cloud providers to be integrated into the federated environment, aiming at satisfying the SLA template requested by the user. However, if this is not possible, the mentioned steps must be repeated for renegotiation, with a new SLA template, until reaching

After establishing an agreement, the SLA controller generates an ID for the agreement, and sends the ID to the job controller, which records this ID agreement. Then, the job controller forwards both the request and the agreement ID to the monitoring service, which sends the tasks for the scheduling service. The monitoring service is responsible for checking if a violation of the agreement occurred and in this case it immediately has to inform the SLA

This service verifies if a requested service is available in a cloud provider, searching for another cloud in the federation if it is not; receives the tasks to be executed from the job controller, and sends them to the scheduling service that will distribute them, guaranteeing that all the tasks of a process are correctly executed; and informs the job controller when a task successfully finishes its execution. To ensure the monitoring of all the requested tasks, this service periodically sends messages to the clouds that are executing tasks, and informs

To perform the activities described above, the monitoring service must be able to gather information about resource allocation and task execution, which depends on the application being executed [28]. Therefore, we have to establish some criteria about the frequency that data are obtained and their corresponding format, so that the decision-making process performed by this service can be made with reliability with respect to data timeliness and flexibility towards distinct applications. In BioNimbus, the monitoring service was planned to send messages at regular intervals to all the federation members or whenever needed. The latter case happens when a decision has to be taken for a specific federation member or when data update is necessary. All information exchange is done with timestamps so only the updated data are sent in order to save network bandwidth. We also plan to use an extensible

In federated clouds the monitoring service must have other characteristics, such as: scalability, to handle a large number of resources and tasks to be monitored; elasticity, to handle addition and removal of resources in a transparent manner; and federation, to handle entering and leaving providers [20]. In order to meet these requirements, we propose to use a decentralized information indexing infrastructure, which would be the same DHT available to the discovery

controller, which terminates the SLA and enforce penalties for violation.

and flexible format, such as JSON [23], like in the discovery service.

the user the current status of each submitted task.

SLA level).

an agreement.

*3.2.4. Monitoring service*

This service decides how to distribute and replicate data among the cloud providers integrating the federation [8, 39, 73], particularly model the storage strategy of the files consumed and produced by the jobs executed in BioNimbus. To realize this, the storage service can communicate with the discovery service to access information about the federation, since the discovery service knows the actual storage conditions of each provider integrating the federation. Thus a storage policy is defined, so that this choice can be made based on receiving information about the file (FileInfo) and returns at least one cloud provider (PluginInfo) to store this file.

Some characteristics of biological data for bioinformatics applications are: large volume; it is not necessary to guarantee the ACID transaction properties since there are no users simultaneously updating data during execution; according to a particular bioinformatics application, fragmentation and replication can use different models; and data provenance is essential.

Considering these characteristics, the BioNimbus storage service proposal is based on the HBase NoSQL (Not only SQL) database. Among distinct noSQL databases, like HBase [38], Dynamo [26], Bigtable [17], Cassandra [41], PNUTS [22], monogDB [19] and cloudDB [5], we adopted HBase since its basic data storage is Apache's Hadoop Distributed File System (HDFS) [13] and it is a column-oriented database [24, 50, 51], which allows joining data (biological data file) and sets of information (e.g. data provenance).

Replication will be done copying data to at most three clouds in order to ensure recovery in case of failure. Total or partial fragmentation depends on the biological data and the application. On the top of HBase, we propose to create an Analyzer module, which will decide where the replication has to be done. The objective of the Analyzer module is to reduce data transfers among the cloud providers, based on three criteria: disk space, geographic position and data transfer speed. The most important analysis is the available space, which should be sufficient to store input and output. The geographic position criterion has the objective of reducing data transfer on the network, being the closer clouds used first if possible. Finally, the Analyzer module uses data transfer speed, which must be computed using the time to transfer packages.

#### *3.2.6. Security service*

This service guarantees integrity among the distinct tasks executed in the federated clouds. A federated cloud needs to include the security policies of each cloud provider while avoiding strong inter-dependency among the clouds. A security context can be partitioned into three main topics: authentication, authorization and confidentiality. We address those requirements using standard algorithms and protocols as described next.

• **Authentication**: The descentralized federated cloud infrastructure should not make a centralized authentication, which is a not a good choice because it limits the scalability and

#### 10 Will-be-set-by-IN-TECH:Bioinformatics, ISBN: 980-953-307-202-4 116 Bioinformatics Towards a Hybrid Federated Cloud Platform to Efficiently Execute Bioinformatics Workflows 11

creates a strong interdependency among the clouds. We intend to use a Single Sign-On (SSO) protocol [52] so that no central authority is in charge of its users' authentication, which prevents a single point of failure and allows scalability according to the number of users. We chose the OpenID standard [57] as our SSO mechanism, since it has been used by corporate and academic sites around the world. OpenID allows each "site" (e.g. a cloud) to provide an authentication facility to its users so that they do not need to authenticate with each other cloud integrating the federation. Instead, each cloud provider acts as an identity provider for user credentials, so that each user should authenticate with its affiliated provider.

Besides using this gossip based failure detector, we use a coordination service based on atomic broadcast protocol [59]. The open source system Apache Zookeeper [34] runs on each cloud and allows our system to detect node failures and realize an election of leaders among the cloud machines, in order to guarantee the services availability, including discovery and fault tolerance services. Zookeeper is used to elect some of the nodes that are known as gossip servers. Those servers are dinamically chosen so that they can exchange the list of nodes among the cloud providers. This helps to reduce the bandwidth between two clouds to a few

Towards a Hybrid Federated Cloud Platform to Effi ciently Execute Bioinformatics Work ows 117

This service dynamically distributes tasks among the cloud providers belonging to the federation, maintaining a register for the allocated tasks, controlling load of each cloud provider, and redistributing the tasks when resources are overloaded. The scheduling service is responsible for receiving the tasks created from the user requests, and maintaining a record about the status of each executed task. Before being executed in a cloud provider, a task is sent to the scheduling service, which uses one or more scheduling policies to choose the cloud provider that will execute this task, according to the negotiated SLA. Each policy receives a list of tasks to be scheduled and an agreement ID, and returns a mapping of the tasks and the cloud providers where these tasks will be executed. To do this, the scheduling policy communicates with the discovery service. The scheduling policy should consider the SLA QoS parameters and the margin values accepted by the cloud providers (e.g. gold, silver and bronze SLA level). These parameters are important for a matching of a cloud provider that is done by the scheduling policy, and therefore the user needs to give reasonable values for them. Some typical SLA parameters used in context of a cloud provider are CPU cores, CPU speed, memory size, in/ou bandwidth, OS type, storage size, response time, budget, data transfer time, completion time and availability. In BioNimbus, the scheduling service can be

We implemented a new *DynamicAHP* algorithm in BioNimbus [12]. The key idea of DynamicAHP is to map available resources of the cloud providers to the requested tasks, then associating a cloud to execute each task. This algorithm is based on a decision making strategy proposed by [61]. DynamicAHP worked well on a first BioNimbus prototype, since it was capable to dinamically scale using only the knowledge about the length of each task input file, while performing load balancing among the cloud providers. Since BioNimbus stores information about the cloud providers such as network latency and wait time in the execution queues, DynamicAHP reduced costs and execution time of the tasks. The promising results obtained from developing DynamicAHP in BioNimbus showed that good scheduling algorithms can really lower the time to execute bioinformatics applications in federated

It is interesting to investigate new scheduling metrics, mainly related to costs. For example, a public cloud can be associated to a lower priority due to its associated costs, when compared to other public clouds integrating the federation. Another idea is to assign weights to the metrics that could set a priority order among them. To develop and analyze a model capable of storing information about the executed tasks, such that the scheduling service could combine this information to estimate the execution time of a particular task is another challenging

servers.

clouds.

project.

*3.2.8. Scheduling service*

easily modified to use different scheduling policies.

#### *3.2.7. Fault tolerance service and high availability*

This service guarantees that all the core services are always available. In a cloud environment, machine failures occur, and it is well known among the cloud community that those failures are the norm rather than the exception. Thus, any federated cloud should be designed for fault recovering and system availability. Therefore, a fault tolerance service is an essential part of our federated cloud, and has the objective of providing high availability and resiliency against periodic or transient failures.

There are extensive studies in the literature on failure detection systems [16, 31, 45, 70]. On the other hand, few systems are designed to scale with a large number of nodes as those found on clouds. Thus, an important requirement of our fault detection service is to be scalable with a large number of machines. We adopted a modified gossip based failure detector proposed by Renesse et al [70], which works as described. Each host runs the gossip failure detector service, which maintains a list of known hosts in the cloud. Every *T*seconds, a host increases a heartbeat, and at random chooses a set of nodes for sending a list of known nodes. When received, each list is merged with the host current list, assuming the largest heartbeat for each node in the list. If a node does not update its heartbeat for a *T*elapsed time, then it will be marked as failed. Note that a node may be marked as failed due to slow network links or even in presence of a fractioned network. But our failure detection service is conservative so that it only purges a host from the list after a *T >*= 2 ∗ *T*elapsed.

Besides using this gossip based failure detector, we use a coordination service based on atomic broadcast protocol [59]. The open source system Apache Zookeeper [34] runs on each cloud and allows our system to detect node failures and realize an election of leaders among the cloud machines, in order to guarantee the services availability, including discovery and fault tolerance services. Zookeeper is used to elect some of the nodes that are known as gossip servers. Those servers are dinamically chosen so that they can exchange the list of nodes among the cloud providers. This helps to reduce the bandwidth between two clouds to a few servers.

#### *3.2.8. Scheduling service*

10 Will-be-set-by-IN-TECH:Bioinformatics, ISBN: 980-953-307-202-4

• **Authorization**: The authorization of a federated cloud resource is provided by the Access Control Lists (ACLs) [69] provided by each cloud provider. An ACL determines who can access a given resource, e.g. disk storage, CPU cycles and bioinformatics services. Therefore, each cloud is able to determine access patterns so that it can control its resource's

• **Confidentiality**: Communication between each two cloud providers is established using TLS/SSL [68] connection. The use of secure connections between two clouds in the federation is not enforced by our model, but it can be provided as well. As far as we know, few cloud systems provide secure intra-cloud communication. Each cloud should provide a certificate that will be used by hosts in two clouds to establish a secure connection. As we improve BioNimbus, we plan to include audit trails so that each required resource can

This service guarantees that all the core services are always available. In a cloud environment, machine failures occur, and it is well known among the cloud community that those failures are the norm rather than the exception. Thus, any federated cloud should be designed for fault recovering and system availability. Therefore, a fault tolerance service is an essential part of our federated cloud, and has the objective of providing high availability and resiliency

There are extensive studies in the literature on failure detection systems [16, 31, 45, 70]. On the other hand, few systems are designed to scale with a large number of nodes as those found on clouds. Thus, an important requirement of our fault detection service is to be scalable with a large number of machines. We adopted a modified gossip based failure detector proposed by Renesse et al [70], which works as described. Each host runs the gossip failure detector service, which maintains a list of known hosts in the cloud. Every *T*seconds, a host increases a heartbeat, and at random chooses a set of nodes for sending a list of known nodes. When received, each list is merged with the host current list, assuming the largest heartbeat for each node in the list. If a node does not update its heartbeat for a *T*elapsed time, then it will be marked as failed. Note that a node may be marked as failed due to slow network links or even in presence of a fractioned network. But our failure detection service is conservative so

user account or login information.

be available when needed.

against periodic or transient failures.

*3.2.7. Fault tolerance service and high availability*

that it only purges a host from the list after a *T >*= 2 ∗ *T*elapsed.

uses.

creates a strong interdependency among the clouds. We intend to use a Single Sign-On (SSO) protocol [52] so that no central authority is in charge of its users' authentication, which prevents a single point of failure and allows scalability according to the number of users. We chose the OpenID standard [57] as our SSO mechanism, since it has been used by corporate and academic sites around the world. OpenID allows each "site" (e.g. a cloud) to provide an authentication facility to its users so that they do not need to authenticate with each other cloud integrating the federation. Instead, each cloud provider acts as an identity provider for user credentials, so that each user should authenticate with its affiliated provider. Once this user is authenticated, each time his/her credentials are required, OAuth [10] allows a user's site to forward authorization without exposing the

> This service dynamically distributes tasks among the cloud providers belonging to the federation, maintaining a register for the allocated tasks, controlling load of each cloud provider, and redistributing the tasks when resources are overloaded. The scheduling service is responsible for receiving the tasks created from the user requests, and maintaining a record about the status of each executed task. Before being executed in a cloud provider, a task is sent to the scheduling service, which uses one or more scheduling policies to choose the cloud provider that will execute this task, according to the negotiated SLA. Each policy receives a list of tasks to be scheduled and an agreement ID, and returns a mapping of the tasks and the cloud providers where these tasks will be executed. To do this, the scheduling policy communicates with the discovery service. The scheduling policy should consider the SLA QoS parameters and the margin values accepted by the cloud providers (e.g. gold, silver and bronze SLA level). These parameters are important for a matching of a cloud provider that is done by the scheduling policy, and therefore the user needs to give reasonable values for them. Some typical SLA parameters used in context of a cloud provider are CPU cores, CPU speed, memory size, in/ou bandwidth, OS type, storage size, response time, budget, data transfer time, completion time and availability. In BioNimbus, the scheduling service can be easily modified to use different scheduling policies.

> We implemented a new *DynamicAHP* algorithm in BioNimbus [12]. The key idea of DynamicAHP is to map available resources of the cloud providers to the requested tasks, then associating a cloud to execute each task. This algorithm is based on a decision making strategy proposed by [61]. DynamicAHP worked well on a first BioNimbus prototype, since it was capable to dinamically scale using only the knowledge about the length of each task input file, while performing load balancing among the cloud providers. Since BioNimbus stores information about the cloud providers such as network latency and wait time in the execution queues, DynamicAHP reduced costs and execution time of the tasks. The promising results obtained from developing DynamicAHP in BioNimbus showed that good scheduling algorithms can really lower the time to execute bioinformatics applications in federated clouds.

> It is interesting to investigate new scheduling metrics, mainly related to costs. For example, a public cloud can be associated to a lower priority due to its associated costs, when compared to other public clouds integrating the federation. Another idea is to assign weights to the metrics that could set a priority order among them. To develop and analyze a model capable of storing information about the executed tasks, such that the scheduling service could combine this information to estimate the execution time of a particular task is another challenging project.


6.2.1. Nanofluids Influence on Flow Boiling

During the normal operating condition, the thermal efficiency of a reactor system depends mainly on the flow boiling of the working fluid, where the working fluid is forced to flow by means of a pump and buoyancy effects. Flow boiling consists of several flow regimes [315] such as liquid single-phase flow, bubbly flow, slug flow, annular flow, mist flow, and vapor single-phase flow, as shown in Figure 20. The existence of each regime is affected by several factors such as the type of working fluid, surface orientation, degrees of liquid subcooling, system pressure, wall temperature, mass flux, surface microstructure (including porosity), wettability, oxidation, and surface roughness [316,317]. Moreover, this mode of thermal transport can remarkably improve the energy efficiency of the system when the HTC and CHF of the working fluid are enhanced [318,319]. Researchers through

their intensive studies, have shown that a passive and a safe approach to achieve higher HTC and CHF can be accomplished using nanofluids in the system [320]. The level of enhancement that accompanies the utilization of nanofluids over their other conventional counterparts is tuned by altering nanoparticle morphology (i.e., shape and size), thermophysical properties and different flow parameters (i.e., mass flow rate, channel size, flow direction, and flow regime) [321,322]. Published reports described that nanofluids of enhanced thermal conductivities also showed better convective flow performance [319,323]. Carbon allotropes based nanofluids have higher thermal conductivities compared to the metallic and oxide based nanofluids [324–326]. Therefore, nuclear scientists have already started to investigate the flow boiling performance of various carbon-based nanofluids. to achieve higher HTC and CHF can be accomplished using nanofluids in the system [320]. The level of enhancement that accompanies the utilization of nanofluids over their other conventional counterparts is tuned by altering nanoparticle morphology (i.e., shape and size), thermophysical properties and different flow parameters (i.e., mass flow rate, channel size, flow direction, and flow regime) [321,322]. Published reports described that nanofluids of enhanced thermal conductivities also showed better convective flow performance [319,323]. Carbon allotropes based nanofluids have higher thermal conductivities compared to the metallic and oxide based nanofluids [324–326]. Therefore, nuclear scientists have already started to investigate the flow boiling performance of various carbonbased nanofluids.

Moreover, this mode of thermal transport can remarkably improve the energy efficiency of the system when the HTC and CHF of the working fluid are enhanced [318,319]. Researchers through their intensive studies, have shown that a passive and a safe approach

*Nanomaterials* **2021**, *11*, x FOR PEER REVIEW 42 of 79

**Figure 20.** Flow boiling regimes inside a horizontal tube from liquid to vapor phases [327]. **Figure 20.** Flow boiling regimes inside a horizontal tube from liquid to vapor phases [327].

Some of the work done on carbon-based nanofluids in flow boiling includes the research conducted by Hashemi and Noie [328]. They experimentally used MWCNTs–water suspensions produced through the two-step fabrication method and stabilized by adding the AG surfactant, of the 1:1 surfactant to the nanomaterial ratio, to the mixture. The MWCNT used had a 10–20 nm outer diameter and 30 µm length, and base fluid concentrations between 0.0005 and 0.005 vol. %. Furthermore, the stability of their dispersions was determined through the zeta potential method, and the testing section consisted of a horizontal stainless steel (SS) tube of 10 mm (diameter) and 200 cm (length). Their findings showed that the HTC of the as-prepared nanofluids was significantly higher than the base fluid, and that the enhancement in the HTC for both types of fluids was influenced by the changes in heat and mass fluxes. In addition, the CHF in the nanofluids case showed a maximum improvement over pure water by approximately 4.3%, when using the 0.005 vol. % suspensions. The same research group [329] also presented the feasibility of AG stabilized MWCTs–water nanofluids at various concentrations (0.001, 0.005, and 0.01 wt %) for flow boiling inside a 2 m long tube placed horizontally under atmospheric condition. The zeta potential analysis of test samples confirmed the well dispersion of nanoparticles in base fluid. Their report describes that CHF of nanoparticles increased significantly due to particle inclusion as well as the increase in mass flux. The CHF enhancement was observed better than water due to nanoparticle deposition and enhancement in wettability of the heating surface. Another flow boiling study under forced convective and nucleate boiling regions using CNTs nanofluid was conducted by Sarafraz and Abad [323]. Their work was performed using statistical, regression and experimental analyses for commercial heat transfer oil based CNTs nanofluids. The nanofluids were prepared by dispersing 0.1 wt % and 0.3 wt % of the dry powder in therminol 66 for a total duration of 25 min, using a two-step procedure (magnetic stirring 15 min then sonication 10 min). The stability of the suspensions was achieved by adding nonylphenol ethoxylates surfactant of 0.1 Some of the work done on carbon-based nanofluids in flow boiling includes the research conducted by Hashemi and Noie [328]. They experimentally used MWCNTs–water suspensions produced through the two-step fabrication method and stabilized by adding the AG surfactant, of the 1:1 surfactant to the nanomaterial ratio, to the mixture. The MWCNT used had a 10–20 nm outer diameter and 30 µm length, and base fluid concentrations between 0.0005 and 0.005 vol. %. Furthermore, the stability of their dispersions was determined through the zeta potential method, and the testing section consisted of a horizontal stainless steel (SS) tube of 10 mm (diameter) and 200 cm (length). Their findings showed that the HTC of the as-prepared nanofluids was significantly higher than the base fluid, and that the enhancement in the HTC for both types of fluids was influenced by the changes in heat and mass fluxes. In addition, the CHF in the nanofluids case showed a maximum improvement over pure water by approximately 4.3%, when using the 0.005 vol. % suspensions. The same research group [329] also presented the feasibility of AG stabilized MWCTs–water nanofluids at various concentrations (0.001, 0.005, and 0.01 wt %) for flow boiling inside a 2 m long tube placed horizontally under atmospheric condition. The zeta potential analysis of test samples confirmed the well dispersion of nanoparticles in base fluid. Their report describes that CHF of nanoparticles increased significantly due to particle inclusion as well as the increase in mass flux. The CHF enhancement was observed better than water due to nanoparticle deposition and enhancement in wettability of the heating surface. Another flow boiling study under forced convective and nucleate boiling regions using CNTs nanofluid was conducted by Sarafraz and Abad [323]. Their work was performed using statistical, regression and experimental analyses for commercial heat transfer oil based CNTs nanofluids. The nanofluids were prepared by dispersing 0.1 wt % and 0.3 wt % of the dry powder in therminol 66 for a total duration of 25 min, using a two-step procedure (magnetic stirring 15 min then sonication 10 min). The stability of the suspensions was achieved by adding nonylphenol ethoxylates surfactant of 0.1 vol. % to the mixture matrix, after which the zeta potential of the prepared samples was measured. The results showed that the stability of the prepared nanofluids was maintained for 5 days. Additionally, the presence of carbon nanotube within the oil increased the convective

HTC together with a substantial augmentation in the nucleate boiling HTC. The degree of subcooling also increased the HTC in both boiling regions due to enhancements in thermophysical properties of nanofluids. Furthermore, the thermal the thermo-hydraulic performance of the system enhanced by 56% using the carbon nanotube nanofluid compared to the same with pure oil. Sarafraz and Hormozi [330] experimentally investigated flow boiling heat transfer in MWCNT, alumina, and copper oxide flow. Different techniques were implemented for stabilizing based on the type of the dispersed nanomaterial. The results showed that MWCNT–water nanofluids had higher thermal conductivity and boiling thermal performance compared to the other suspensions. It was also found that, as the heat and mass fluxes and the concentration of nanofluids increased, the boiling performance of MWCNT nanofluids intensified. However, the HTC was deteriorated in force convection and nucleate boiling regimes due to the deposition of the nanoparticle on the surface. As a result, the surface roughness decreased over the time since it is affected by the size of nanoparticles, thickness of deposited layer and size of microcavities. This was confirmed by the minimal amount of bubble generation due to reduction in nucleation sites and surface roughness. The researchers finally concluded that the MWCNT–water nanofluids outperformed the other candidates for utilization in cooling applications.

Other studies have found that addition of graphene oxide (GO) into base fluid improve the CHF in flow boiling due to its hydrophilicity feature [331,332]. Lee et al. [331] examined 0.01 vol. % of GO–water nanofluid in round tubes with a length of 0.5 m and an inner diameter of 0.01041 m at two inlet temperatures (25 and 50 ◦C) and four different mass fluxes (100, 150, 200, and 250 kg/m2 ·s) for low pressure and low flow scenarios. Comparing to other oxide nanofluids from the literature [333], the research group showed maximum CHF enhancements at mass flux of 250 kg/m2 ·s increased up to 100% and 72% at fixed temperatures of 25 ◦C and 50 ◦C, respectively. This significant improvement was due to the liquid film's wettability enhancement caused by the deposition of GO nanoparticles. However, Park and Bang [332] reported limited improvement in CHF of up to 20% when testing GO–water nanofluid in advanced light water reactors (ALWRs) at 50 and 100 kg/m2 .s and subcooling condition of 10 K compared to distilled water. The results showed that GO deposited on the heated surface and changed phase to reduced GO (RGO) during nucleate flow boiling, which might constrain the thermal activity improvement. Zhang et al. [334] examined the deposition of GO in water nanofluids over heating surface with nanoparticle concentration ranging from 0 to 0.05 wt %. They reported that the increase in GO concentration depreciated the heat transfer performances (CHF and HTC) up to 100% and 73% (at 0.05 wt % with 40 mL/min), respectively. In another study, Mohammed [335] varied graphene particle concentration from 0 to 0.5 vol. % in zinc bromide and acetone solution (acetone–ZnBr2).The CHF and HTC on the heated surface increased with GO concentration by up to approximately 52% and 58%, respectively. However, the increase in particle concentration involved a decrease in pressure drop up to 11% approximately.

In terms of nanofluids containing NDs, there were a limited amount of literature covering this topic [336–338]. For instance, DolatiAsl et al. [338] proposed a mathematical model using Kim et al. [336] results to estimate the different parameters that were affecting the CHF when utilizing ND suspensions. Their correlation only required the properties of the nanomaterial and the vol. % employed to predict the CHF. Furthermore, the numerical findings showed that the most effecting parameters on the CHF were the length of the tube (decreasing) and the mass flux (increasing), whereas the particles concentration and thermal conductivity had the lowest influence. They extended the work by developing another correlation that contains the particles property data, and thus only required an input of the type of the particles (i.e., NDs) and the vol. % to perform the prediction. The new model predicted the actual values with a mean absolute error of 9.8% for CHF ranging from 500 to 2500 kW/m2 .

#### 6.2.2. Dispersions Effect on Pool Boiling 6.2.2. Dispersions Effect on Pool Boiling

.

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CHF ranging from 500 to 2500 kW/m2

Pool boiling heat transfer plays a crucial role in numerous technological and industrial applications. It consists of four different regimes [339]: natural convection (single-phase), nucleate boiling, transition boiling, and film boiling as shown in Figure 21. When a surface is sufficiently heated and plunged in a water pool, film boiling regime occurs where the heated surface is physically separated from the coolant by a stable vapor blanket. This region is denoted by the film boiling regime. In this regime, heat transfers by conduction and radiation only leading to a gradual decrease in the surface temperature. The performance of the pool boiling is evaluated by the enhancement in CHF, HTC, Tmin, and vapor film thickness. In literature, the effects of the following parameters have been studied: substrate material [340], surface conditions and oxidation [341,342], system pressure [343,344], initial wall temperature [345], shape and dimension of the testing specimen [346,347], degrees of liquid subcooling [348–350], surface wettability and vapor–liquid contact angle [351], surface roughness and wickability [352], and type of quenchant such as water, oil, or nanofluids [353,354]. Recently, researchers have been focused on the effects of the later parameter on pool boiling heat transfer performance. Pool boiling heat transfer plays a crucial role in numerous technological and industrial applications. It consists of four different regimes [339]: natural convection (singlephase), nucleate boiling, transition boiling, and film boiling as shown in Figure 21. When a surface is sufficiently heated and plunged in a water pool, film boiling regime occurs where the heated surface is physically separated from the coolant by a stable vapor blanket. This region is denoted by the film boiling regime. In this regime, heat transfers by conduction and radiation only leading to a gradual decrease in the surface temperature. The performance of the pool boiling is evaluated by the enhancement in CHF, HTC, Tmin, and vapor film thickness. In literature, the effects of the following parameters have been studied: substrate material [340], surface conditions and oxidation [341,342], system pressure [343,344], initial wall temperature [345], shape and dimension of the testing specimen [346,347], degrees of liquid subcooling [348–350], surface wettability and vapor–liquid contact angle [351], surface roughness and wickability [352], and type of quenchant such as water, oil, or nanofluids [353,354]. Recently, researchers have been focused on the effects of the later parameter on pool boiling heat transfer performance.

**Figure 21.** Boiling curve for stagnant water at atmospheric pressure (1 atm), where (**a**) shows the boiling curve and (**b**–**e**) illustrates the bubble formation within the free convection, nucleate boiling, transition boiling, and film boiling regimes. **Figure 21.** Boiling curve for stagnant water at atmospheric pressure (1 atm), where (**a**) shows the boiling curve and (**b**–**e**) illustrates the bubble formation within the free convection, nucleate boiling, transition boiling, and film boiling regimes.

Review papers have intensively presented research studies that cover the preparation methods of various nanofluids and test their effect on CHF and HTC [321,355–359]. It was mentioned that carbon-based nanomaterials such as graphene dispersed in water enhanced the heat transfer as compared to any other nanoparticle [355]. Nevertheless, there are many reasons that contribute to the CHF enhancement such as surface roughness [360], deposition of nanoparticles [361], concentration of nanoparticle beyond a certain limit [362,363], increase in surface wettability [364], and capillary effect [365]. Some researchers have explained that the enhancement was a result of the occurrence of cavities on the surface due to the deposition of the dispersed nanomaterial on the surface, especially on surfaces of rough structure. Others mentioned that the increase in surface area of the formed porous layer because the nanoparticle accumulation enhanced heat transfer Review papers have intensively presented research studies that cover the preparation methods of various nanofluids and test their effect on CHF and HTC [321,355–359]. It was mentioned that carbon-based nanomaterials such as graphene dispersed in water enhanced the heat transfer as compared to any other nanoparticle [355]. Nevertheless, there are many reasons that contribute to the CHF enhancement such as surface roughness [360], deposition of nanoparticles [361], concentration of nanoparticle beyond a certain limit [362,363], increase in surface wettability [364], and capillary effect [365]. Some researchers have explained that the enhancement was a result of the occurrence of cavities on the surface due to the deposition of the dispersed nanomaterial on the surface, especially on surfaces of rough structure. Others mentioned that the increase in surface area of the formed porous layer because the nanoparticle accumulation enhanced heat transfer by disturbing the flow. Active nucleation sites decreased with nanoparticle layers, which significantly increase surface wettability, and therefore enhance CHF. As for HTC enhancement, thermophysical properties has a major role on it. It has been observed that nanoparticle affects the thermal conductivity and surface tension of the quenchant whereas viscosity, density, and

heat capacity remain nearly constant [366]. A vapor blanket layer formed during film boiling on the heating surface, which significantly affect the conduction heat transfer, and hence the HTC. Yang and Liu [367] found that as the surface tension decreased, the HTC increase due to the reduction in the formation of bubbles and the active nucleation sites. The effect of the modified surface topologies such as surface roughness was observed to enhance HTC [133,368]. Furthermore, the high particle concentration led to nanoparticle deposition on the surface and thus porous surfaces occur. Depending upon either original surface condition or size of nanoparticle, the surface roughness can be increased [360] or decreased [369]. Generally, HTC was found to be maximum when carbon nanoparticles were used for boiling [355]. Since the current work focuses on the effect of carbon-based nanofluids (i.e., graphene, ND, and CNT) on heat transfer application, various selected studies on CHF and HTC in pool boiling heat transfer have been listed in Tables 7 and 8, respectively.

**Table 7.** Summary of selected CHF enhancement for various pool boiling studies in water base.

Note: f-SWCNT, and f-MWCNT refer to functionalized SWCNT, and functionalized MWCNT, respectively.

The third important parameter in pool boiling is Tmin to be considered in the reactor core under the extreme environment and severe accidents such as LOCA. Understanding this parameter can lead to an improved nuclear cladding performance that provides more efficient and safer future nuclear reactors. Physically, Tmin is defined as the boundary between film boiling and transition boiling, beyond which temperature liquid loses physical contact with the solid surface and the heat transfer significantly reduces. Fewer studies for the quenching behavior of nanofluids have been conducted in the literature that was focused on Tmin.

A study by Gerardi [387] showed that Tmin of a quenched indium-tin-oxide (ITO) rod in a nanofluid pool (ND–water) was found to increase by 30 ◦C compared to the water pool. Another experimental investigation by Fan et al. [388] was performed in aqueous nanofluids in the presence of four CNTs having various lengths and diameters. It was concluded that the accelerated quenching was clearly related to the enhancement in boiling heat transfer. An increase in Tmin was exhibited for all cases. The modified quenching and boiling behaviors were elucidated by the accumulative changes in surface properties due to the deposition of CNTs. Given the nearly unvaried contact angles, the consistently increased surface roughness and the formation of porous structure seem to be responsible for quenching and boiling enhancement. In order to achieve better performance, the use of longer and thicker CNTs tends to form a highly porous layer, even upon consecutive quenching, which may induce rewetting by the entrapped liquid in the pores and serve as vapor ventilation channels as well. In another experimental study, Fan et al. [389] examined transient pool boiling heat transfer in aqueous GO nanofluids. They tested various dilute concentrations of the nanofluids up to 0.1 wt %. It was shown that the quenching processes could be accelerated using GO nanofluids as compared to pure water. The boiling behavior during quenching was analyzed in relation to the modified surface properties of the quenched surfaces. Tmin values were found to increase with raising the concentration of GOs compared to the baseline case of pure water. The results suggested that surface property changes due to the deposition of GOs were responsible for the modified boiling behavior of the nanofluids. In addition, the surface wettability was a nondominant factor in most cases. The surface effects of the deposited layer of GOs were strongly dependent on the material properties, finish, and treatment of the original surfaces. Kim et al. [390] quenched metal spheres made from SS and zircaloy in water-based nanofluid containing low concentration (less than 0.1 vol. %) of ND. They showed that film boiling heat transfer in nanofluids was almost identical to that in pure water. However, subsequent quenches proceeded faster due to the gradual accumulation of nanoparticle deposition on the sphere tended to destabilize the vapor film but, Tmin remained unchanged. A summary of the previous research studies is listed in Table 9.

**Table 9.** *Cont*.


*2.7.4 Anti-obesity activity*

This activity includes quercetin which may be mediated by mitogen-activated protein kinases signaling pathways (MAPK) and the adenosine monophosphateactivated protein kinase (AMPK), respectively in mature adipocytes and pre adipocytes [4, 31].


**11.7 Drug resistance reversers**

The discovery and production of drug resistance reversers not only revitalises the use of chloroquine and other antimalarial drugs, but also offers a new way to keep existing drugs effective [113]. For example calcium channel blockers [114] like verapamil and diltiazem have been shown to reverse chloroquine resistance.


*2.2. Electrospinning*

PLLA (molecular weight 85−160 kg mol−1, Sigma-Aldrich, Munich, Germany) was dissolved in a mixture of dichloromethane and acetone (80:20 *v*/*v*) at a concentration of 20% (*w*/*v*) at room temperature. The solution was stirred overnight, then SBA2 was added with a concentration of 7% *w*/*v*. The resulting suspension was vigorously mixed and then stirred again overnight to obtain a homogeneous dispersion of SBA2 in the polymer solution. The solution was put in an ultrasound bath for 1 hour prior the electrospinning with the aim to reduce SBA2 clustering and then transferred in a 1 mL syringe. The spinning process was performed by a 21G stainless steel needle and a syringe pump (Harvard Apparatus, Holliston, MA, United States) with feeding rate 0.8 mL h−1 and by applying a positive high-voltage of 12 kV (EL60R0.6-22, Glassman High Voltage, XP Power, Milano, Italy) between the needle and a metal collector. Random fibre mats were collected on a static grounded 10 × 10 cm2 collector meanwhile aligned fibre mats were collected on a grounded disk (8 cm diameter, 1 cm thickness) rotating at a speed of 5000 rpm, maintaining constant all the other processing parameters. The air relative humidity and temperature during the electrospinning process were about 40% and 20◦C, respectively. The needle-collector distance was adjusted to 15 cm. Neat PLLA fibres were fabricated as reference material by using identical set-up parameters.


**4. Markers of inflammatory reaction connected with acute renal failure**

**Neutrophil gelatinase-associated lipocalin (NGAL)** is a glycoprotein expressed and secreted by immune cells, trachea, stomach, colon, and injured kidney epithelial cells as an monomer (22 kDa), dimer or trimer. NGAL may complex with collagenase type IV of human neutrophils named gelatinase B or metalloproteinase 9 (MMP-9) creating heterodimer (125 kDa) (Flower, 1996). NGAL binds and transports small lipophilic molecules e.g. free fatty acids, retinoids, arachidonic acid, and steroids (Mishra et al., 2003). NGAL is considered as provider iron to proximal renal tubules. Iron stimulates oxygenase synthesis which protects renal tubules cells. NGAL may be applied as a predictor of ischemic or toxic renal damage, before development of a full symptomatic renal insufficiency. Increase in urinary NGAL concentration is early, sensitive and non-invasive marker of renal damage correlating with intensity and time of ischemia and preceding increase in other markers such as hexosaminidase or β2-microglobu‐ lin. A strict correlation between NGAL concentration and degree of proteinuria was demon‐ strated (Flower, 1996). NGAL activated formation of nephrons in early step of renal development demonstrates a protective action in kidney (Mori & Nakao, 2007). Low molecular weight, resistant to degradation NGAL is easily excreted by cells of thick ascending arm of Henry loops and collective tubules into urine both free and in complex with MMP-9. Increase in urinary excretion of NGAL is observed several hours after stimuli of nephrotoxic factor. As concentration of urinary NGAL correlates with plasma NGAL concentration, NGAL may be an useful marker in diagnostics of renal diseases (Nickolas et al., 2008). There is an opinion that NGAL is the most promising biomarker for diagnosis of acute renal injury (AKI) in acute renal graft dysfunction (Halawa, 2011; Ting et al., 2012; Hollmen, 2011).

**Kidney injury molecule-1 (KIM-1)** is a transmembrane glycoprotein receptor (104 kDa) appearing as KIM-1a and KIM 1b. KIM-1 is produced in large quantities in renal proximal tubules after a toxic or ischemic damage. It is assumed that direct cause of KIM-1 induction is an increase of the protein concentration in glomerular ultrafiltration and presence of urinary protein casts favoring tubular obstruction, mechanical stress and an increase in glomerular pressure. An increase in urinary KIM-1 excretion is specific to the ischemic renal damage and is practically independent of chronic renal insufficiency or renal tract infection (Nickolas et al., 2008; Melnikov & Molitoris, 2008). It was reported that KIM-1 extracellular domain (fragment 90 kDa) reaches urine after cleavage by metalloproteinase (Han et al., 2002; Waanders et al., 2010). Urinary KIM-1 is particularly important in the diagnosis of the acute transplanted kidney insufficiency(AKI) (Halawa, 2011). As in renal graft recipients, contrary to urinary NGAL or IL-18, KIM determination gives better possibility for predicting a rate of the trans‐ planted kidney deterioration (Szeto et al., 2010), KIM-1 was proposed as an independent predictor of the long term renal graft survival (Ting et al., 2012).