5. Optimization of FGM wings against divergence

The use of the in-plane grading in aeroelastic design was first exploited by Librescu and Maalawi [6], who introduced the underlying concepts of using material grading in optimizing subsonic rectangular wings against torsional instability. Exact mathematical models were developed allowing the material physical and mechanical properties to change in the wing spanwise direction, where both continuous and piecewise structural models were successfully implemented. In this section, analytical solutions are developed for slender tapered composite wings through optimal grading of the material volume fraction in the spanwise direction. The enhancement of the wing torsional stability is measured by maximization of the critical flight speed at which aeroelastic divergence occurs. The total structural mass is maintained at a value equals to that of a known baseline design in order not to violate other performance requirements. Figure 11 depicts a slender wing constructed from Np panels with trapezoidal planform and known airfoil cross section. The wing is considered to be made of unidirectional fiber-reinforced composites with variable fiber volume fraction in the spanwise direction. The flow is taken to be steady and incompressible, and the aspect ratio is assumed to be sufficiently large so that the classical engineering theory of torsion can be applicable and the state of deformation described in terms of one space coordinate.

The chord distribution is assumed to have the form:

given by Eq. (3). The corresponding design variable vector is defined by X

= (0.3, 0.3, 0, 0.75) and XU

Figure 10. Normalized critical speed Ω^ cr augmented with the mass constraint (M^ ¼ 1:0) in (p-Δf) design space.

maximum critical speed increased by 14% above that of the baseline design with active mass

A last optimization strategy to be addressed here is to combine the two criteria in a single

Minimize F ¼ � Ω^ cr þ T^cr

τmax τallow

Eq. (22) assumes that whirling and torsional buckling instabilities are of equal relative importance. This model resulted in a balanced improvement in both stabilities with active mass constraint. The attained optimal solution was found to have a uniform distribution of the fiber volume fraction with its upper limiting value of 70% and wall thickness = 0.935. The corresponding optimal values of the design objectives were Ω^ cr ¼ 1:135 and T^cr ¼ 1:161, representing optimization gains 13.5 and 16.1%, respectively, as measured from the baseline

Subject to M^ � 1 ≤ 0

!

!

� 1:0 ≤ 0

!

objective function subject to the mass, strength, and side constraints.

optimal design variable vector was calculated to be Xopt

with lower and upper limits XL

178 Optimum Composite Structures

constraint.

design.

!

¼ Vfð Þ0 ; Vf 1

= (0.7, 0.7, ∞, 1.25). The attained

¼ ð Þ 0:7; 0:3; 5:61; 0:955 at which the

2 ; p; H^

(32)

$$\mathbf{C}(\mathbf{x}) = \mathsf{C}\_{\mathsf{r}} \left( 1 - \beta\_{\mathsf{c}} \mathbf{x} \right), \beta\_{\mathsf{c}} = (1 - \Delta\_{\mathsf{c}}) \tag{33}$$

The symbol Δc denotes the chord taper ratio (= tip chord Ct/root chord Cr) and x (= x1/L) denotes the dimensionless spanwise coordinate. The equivalent shear modulus G of a unidirectional reinforced composite, thin-walled cross section can be determined from the relation [35]:

$$\mathbf{G} = f\_1 \,\, \mathbf{G}\_{12} \tag{34}$$

where f1 is a function that depends on the geometry and thickness ratio of the cross section (h/ C) and the ratio (G12/ G13), where G12 and G13 are the in-plane and out-of-plane shear moduli,

Figure 11. Trapezoidal wing planform and cross section geometry. (a) Multipanel, piecewise wing model, (b) airfoil section and applied airloads.

respectively (refer to Table 1). C is the chord length and h is the maximum thickness of the cross section. For many types of fibrous composites that are commonly utilized in aerospace industry [23], such as carbon/epoxy and graphite epoxy, both moduli are approximately equal, G12 ≈ G13.

Using the classical elasticity and aerodynamic strip theories, the governing differential equation of torsional stability in dimensionless form is [35]:

$$\left(\hat{\mathbf{G}}\hat{\mathbf{J}}\boldsymbol{\alpha}'\right)' + \hat{\boldsymbol{V}}^2 \boldsymbol{\alpha}(\mathbf{x}) = \mathbf{0} \tag{35}$$

Minimize � V^ div

� �

P^bk ¼ 1:0

The preassigned parameters that do not change during the optimization process include the wing semispan (b), the chord taper ration (Δc), airfoil type and geometry, and fiber and resin material types. This model has been applied to obtain wing designs with improved torsional stability by maximizing the divergence speed (Vdiv) without weight penalty. The selected material is carbon-AS4/epoxy-3501-6 composite (see Table 2), which has favorable characteristics and is highly desirable in manufacturing aircraft structures. The baseline design has uniform mass and stiffness distributions and is made of uniform unidirectional fibrous composite with equal volume fraction of the matrix and fiber materials, that is, Vfo = 50%. Figures 12 and 13 show the developed level curves of constant divergence speed (also named isodiverts) for two-panel wings with chord tapering ratio, Δc = 0.5. Actually, these curves represent the dimensionless critical speed, augmented with the equality mass constraint. Examining Figure 12, it is seen that the Vdiv function is well behaved and continuous everywhere in the selected design space except in the empty regions to the upper left and right regions, where the equality mass constraint is violated. The feasible domain is bounded from

k¼1,2,::Np

≤ ð Þ 0:75; 1:25; 1:0

http://dx.doi.org/10.5772/intechopen.82411

Optimization of Functionally Graded Material Structures: Some Case Studies

(39)

181

Subject to M^ s � 1:0 ≤ 0

ð Þ 0:25; 0:5; 0:0 ≤ Vf ; ^h; ^b

Figure 12. Isodivert in (Vf1- b1) design space for a two-panel wing model (h1 = h2 = 1.0, Δc = 0.5, M^ s ¼ 1).

The associated boundary conditions of the elastic angle of attack, α, are α(0) = 0 and α0 ð Þ¼ 1 0. The symbol G^ ¼ G12=G12, o denotes the dimensionless shear modulus, ^J ¼ J=Jr denotes the dimensionless torsion constant, and the prime denotes differentiation with respect to the dimensionless coordinate x=x1/L. The dimensionless flight speed is defined by V^ ¼ VCrb ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rae=2GJr p , where (GJ)r is the torsional stiffness of the baseline design at root. The shear modulus G12,0 of the baseline design can be calculated by taking Vfo = 50%.

Considering the K-th panel of the wing as shown in Figure 11a, and using the transformation y = (1-βx), Eq. (25) takes the form:

$$z\alpha'' + 3\alpha' + a\_k^2 \alpha = 0, \quad \text{(1-\beta x\_{k+1})} \le y \le \left(1 - \beta \mathbf{x}\_k\right) \tag{36}$$

where ak ¼ V^ =β ffiffiffiffiffiffiffiffiffiffiffi ^ hkG^ k q , ^ hk and G^ k are the normalized wall thickness and shear modulus of the kth wing panel, respectively. The general solution of Eq. (26) is:

$$\alpha(y) = A\_1 \frac{J\_2\left(2\sqrt{a\_k y}\right)}{y} - A\_2 \frac{Y\_2\left(2\sqrt{a\_k y}\right)}{y} \tag{37}$$

where J2 and Y2 are Bessel's function of the first and second kind with order 2, respectively [35], and A1 and A2 are the constants of integration. The dimensionless internal torsional moment, T, can be obtained by differentiating Eq. (27) and multiplying by the dimensionless shear rigidity. Applying the boundary conditions at stations (k) and (k + 1), the constants A1 and A2 can be expressed in terms of the state variables at station (k), which can be related to those at station (k+1) by the transfer matrix relation:

$$\begin{Bmatrix} \alpha\_{\mathbf{k}+1} \\ \mathbf{T}\_{\mathbf{k}+1} \end{Bmatrix} = \begin{bmatrix} \mathbf{E}^{(\mathbf{k})} \end{bmatrix} \begin{Bmatrix} \alpha\_{\mathbf{k}} \\ \mathbf{T}\_{\mathbf{k}} \end{Bmatrix} \tag{38}$$

It is now possible to compute the state variables progressively along the wing span by applying continuity requirements of the variables (α, T) among the interconnecting boundaries of the various wing panels. The divergence speed can be calculated by applying the boundary conditions and considering the nontrivial solution of the resulting equations (similar to the procedure outlined in Section 3.1.3). The associated optimization problem may be cast in the following:

$$\begin{aligned} \text{Minimize} &- \hat{V}\_{\text{div}}\\ \text{Subject to} & \hat{M}\_s - 1.0 \le 0 \end{aligned}$$

$$(0.25, 0.5, 0.0) \le \left( V\_f, \hat{h}, \hat{b} \right)\_{k=1, 2, \dots \text{Np}} \le (0.75, 1.25, 1.0) \tag{39}$$

$$\sum \hat{b}\_k = 1.0$$

The preassigned parameters that do not change during the optimization process include the wing semispan (b), the chord taper ration (Δc), airfoil type and geometry, and fiber and resin material types. This model has been applied to obtain wing designs with improved torsional stability by maximizing the divergence speed (Vdiv) without weight penalty. The selected material is carbon-AS4/epoxy-3501-6 composite (see Table 2), which has favorable characteristics and is highly desirable in manufacturing aircraft structures. The baseline design has uniform mass and stiffness distributions and is made of uniform unidirectional fibrous composite with equal volume fraction of the matrix and fiber materials, that is, Vfo = 50%. Figures 12 and 13 show the developed level curves of constant divergence speed (also named isodiverts) for two-panel wings with chord tapering ratio, Δc = 0.5. Actually, these curves represent the dimensionless critical speed, augmented with the equality mass constraint. Examining Figure 12, it is seen that the Vdiv function is well behaved and continuous everywhere in the selected design space except in the empty regions to the upper left and right regions, where the equality mass constraint is violated. The feasible domain is bounded from

respectively (refer to Table 1). C is the chord length and h is the maximum thickness of the cross section. For many types of fibrous composites that are commonly utilized in aerospace industry [23], such as carbon/epoxy and graphite epoxy, both moduli are approximately equal,

Using the classical elasticity and aerodynamic strip theories, the governing differential equa-

þ V^ 2

The symbol G^ ¼ G12=G12, o denotes the dimensionless shear modulus, ^J ¼ J=Jr denotes the dimensionless torsion constant, and the prime denotes differentiation with respect to the dimensionless coordinate x=x1/L. The dimensionless flight speed is defined by V^ ¼ VCrb ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

where (GJ)r is the torsional stiffness of the baseline design at root. The shear modulus G12,0 of the

Considering the K-th panel of the wing as shown in Figure 11a, and using the transformation

� � ≤ y ≤ 1 � βxk

hk and G^ k are the normalized wall thickness and shear modulus of

Y2 2 ffiffiffiffiffiffi aky � � p

kα ¼ 0, 1-βxkþ1

J2 2 ffiffiffiffiffiffi aky � � p y � A2

αkþ1 Tkþ1

( )

where J2 and Y2 are Bessel's function of the first and second kind with order 2, respectively [35], and A1 and A2 are the constants of integration. The dimensionless internal torsional moment, T, can be obtained by differentiating Eq. (27) and multiplying by the dimensionless shear rigidity. Applying the boundary conditions at stations (k) and (k + 1), the constants A1 and A2 can be expressed in terms of the state variables at station (k), which can be related to

¼ Eð Þ k h i αk

It is now possible to compute the state variables progressively along the wing span by applying continuity requirements of the variables (α, T) among the interconnecting boundaries of the various wing panels. The divergence speed can be calculated by applying the boundary conditions and considering the nontrivial solution of the resulting equations (similar to the procedure outlined in Section 3.1.3). The associated optimization problem may be cast in the

Tk

( )

αð Þ¼ x 0 (35)

� � (36)

y (37)

ð Þ¼ 1 0.

rae=2GJr p ,

(38)

G^^Jα0 � �0

The associated boundary conditions of the elastic angle of attack, α, are α(0) = 0 and α0

tion of torsional stability in dimensionless form is [35]:

baseline design can be calculated by taking Vfo = 50%.

zα00 þ 3α0 þ a2

the kth wing panel, respectively. The general solution of Eq. (26) is:

αð Þ¼ y A1

y = (1-βx), Eq. (25) takes the form:

ffiffiffiffiffiffiffiffiffiffiffi ^ hkG^ k q

, ^

those at station (k+1) by the transfer matrix relation:

where ak ¼ V^ =β

following:

G12 ≈ G13.

180 Optimum Composite Structures

Figure 12. Isodivert in (Vf1- b1) design space for a two-panel wing model (h1 = h2 = 1.0, Δc = 0.5, M^ s ¼ 1).

above by the two curved lines representing the upper and lower limiting constraints imposed on the volume fraction of the outboard blade panel. The contours inside the feasible domain are not allowed to penetrate these borderlines and obliged to turn sharply to be asymptotes to them, in order not to violate the mass constraint. The final attained optimal solutions are summarized in Table 4. It can be observed that good wing patterns shall have the lower limit of the fiber volume fraction at the tip and the upper limit at root. Using material and wall thickness grading together results in a considerable enhancement of the wing torsional stability.

6. Optimization of composite thin-walled pipes conveying fluid

modulus of elasticity (E) can be determined using the formulas of Table 1.

þ U2 V00

ð Þ EI kV0000

Figure 14. Multimodule composite pipe conveying flowing fluid.

equation [39]:

The associated eigenvalue problem is described by the fourth-order ordinary differential

ffiffiffiffi βo q

V0 � mkω2

V ¼ 0 (40)

þ 2iωU

where V(x) is the dimensionless mode shape satisfying boundary conditions, and ω is the corresponding dimensionless frequency of oscillation, which will be, in general, a complex number to be determined by the requirement of nontrivial solutions, V(x) 6¼ 0. More details for

The subject of vibration and stability of thin pipes conveying flowing fluids is of a considerable practical interest. An advanced textbook by Paїdoussis [36] gives an excellent review of the several developments made in this research area. Practical models for enhancing static and dynamic stability characteristics of pipelines constructed from uniform modules were addressed by Maalawi et al. [37, 38], where the relevant design variables were selected to be the mean diameter, wall thickness, and length of each module composing the pipeline. The general case of an elastically supported pipe, covering a variety of boundary conditions, was also investigated. Distinct domains of the flutter instability boundaries were presented for different ratios of the fluid-to-pipe mass, and the variation of the critical flow velocity with support flexibility was examined and discussed. Concerning pipelines made of advanced FGMs, this section presents a mathematical model for enhancing the overall system stability against flutter and/or divergence under mass constraint. Figure 14 shows a FGM pipe conveying flowing fluid with the coordinate system chosen such that the x-axis coincides with the longitudinal centroidal axis in its undeformed position, while the y- and z-axes coincide with the cross section principal axes. The pipe model consists of rigidly connected thin-walled circular tubes made of unidirectional fibrous composite material. Each pipe module has different material properties, wall thickness, and length. Such a configuration results in a piecewise axial grading of either the material of construction or the wall thickness in the direction of the pipe axis. Assuming no voids are present, the distributions of the mass density (r) and

Optimization of Functionally Graded Material Structures: Some Case Studies

http://dx.doi.org/10.5772/intechopen.82411

183

Figure 13. Isodivert in (Vf1- b1) design space for a two-panel wing model (h2 = 0.5, Vf2 = 0.3, Δc = 0.5, M^ s ¼ 1).


**2.2 Meta-heuristics**

In recent times, meta-heuristic optimization-based methods, using Artificial-Intelligence (AI) inspired algorithms, have attracted the care of researchers to obtain with good precision, the unknown PV parameters values.

The metaheuristic methods use bio-inspired algorithms in the search process to identify the PV parameters values at real-time, using the errors between the real experimental data and the simulated data. These approaches are based on an experimental process and are known as identification methods [20]. These approaches are graphically based on curve characteristics fitting.

Meta-heuristics are categorized into four main sets such as evolution-based [21], physic-based [22], immune-human-based [23] and swarm-based intelligence methods [24]. Some of each category is used for obtaining PV parameters values as presented on what follow.


**3.3 Total systems**

Element technologies as described above can be combined with sub-elements and subsystems (e.g. partial exoskeletons, exoskeleton for lower body part/feet, and exoskeleton for upper body part) to more complex total systems as full body exoskeletons or mobility robots.


*4.1. Comparison with Partially Observation-Based Studies*

There are extremely limited observation-based snow albedo feedback studies, so only three of the compatible ones (with similar study area of the North Hemisphere) are chosen for further comparison: snow and ice albedo radiative forcing estimation by Flanner et al. for 1979–2008 [33], both snow albedo radiative forcing and feedback estimations by Chen et al. for 1982–2013 [57] and snow albedo feedback estimation by Peng et al. for 1980–2006 [58]. It should be noted that, although feedback was also quantified in Flanner and coworkers' study, the regional mean value of the combining snow and ice albedo feedback makes it incomparable with our snow albedo feedback results.

The magnitude of snow albedo feedback is examined first. By using a linear fit between snow albedo radiative forcing change and temperature increase during the study period, Chen et al. calculated snow albedo feedback over the North Hemisphere to be 0.17 ± 0.008 W·m−2·K−1 [57]. By multiplying the sensitivity of end date of snow cover to temperature by difference in black surface albedo before and after snowmelt, the result is 0.19 ± 0.17 W·m−2·K−1, according to Peng et al. [58]. With the method of block bootstrap test, snow albedo feedback of this study is 0.18 ± 0.08 W·m−2· ◦C−1. Despite the different methods and datasets applied in each work, results of the three studies agree well.

Snow albedo radiative forcing is also compared, as it's a key parameter of feedback and offers valuable spatial information. Both Flanner and coworkers' work and our work show similar snow albedo radiative forcing pattern in terms of spatial distribution and temporal variation [33]. Large snow albedo radiative forcing is found in the northern part of Eurasia, the mid-high latitude of North America and the Tibetan Plateau in both works, though the Tibetan Plateau exhibits even larger values in our work. In addition, seasonal cycle of snow albedo radiative forcing in both works show a single peak (peaks in April), with the largest value in spring months. Chen and coworkers' work, however, shows a relatively weaker consistency with us [57]: smaller value in the Tibetan Plateau as compared with Flanner et al. and our work, and a clear tendency of snow albedo radiative forcing with latitude, i.e., the higher latitudes exhibit larger radiative forcing.

While different surface albedo kernels used in these studies have proven to vary only a little [33,56], differences among the results of snow albedo radiative forcing are considered mainly due to albedo change caused by snow cover change ( *∂α ∂S* (*t*,*r*)). In Chen and coworkers' work, surface albedo instead of snow albedo data was used, thus the influence of vegetation change was imported to snow albedo change [57]. In addition, according to the conclusions of Singh et al., the coarse resolution of snow data (1◦ and monthly [33]) is likely to result in an overestimate of snow albedo radiative forcing [53].


**3. Data-Driven Redundant Transform Model Based on Tight Frame**

In this section, we present our data-driven redundant transform based on Parseval frames (DRTPF, Section 3.1) model along with an efficient redundant transform learning algorithm (Section 3.2) which contains the sparse coding algorithm (Section 3.2.1) and the transform pair update algorithm (Section 3.2.2).


**Chapter 4**

*Bioactive Compounds - Biosynthesis, Characterization and Applications*

s11101-012-9242-8. Published

[25] Domínguez, H In Functional Ingredients from Algae for Foods and Nutraceuticals; Domínguez, H., Ed.; Woodhead Publishing: 2013, p 1.

[26] Puri, M; Sharma, D; Barrow, C: Enzyme-assisted extraction of bioactives from plants, Trends in biotechnology 2011, 30, 37.DOI:

10.1016/j.tibtech.2011.06.014. Published

[27] Sandesh J. Marathe, S B J, Sandip B.

Bankar and Rekha S. Singhal.

Published Online: 2013

Published Online: 2019

Published Online: 2017

2013, p 113.

[28] Ademola Monsur Hammed , I J, Azura Amid , Zahangir Alam , Tawakalit Tope Asiyanbi-H & Nazaruddin Ramli: Enzymatic Hydrolysis of Plants and Algae for Extraction of Bioactive Compounds, Food Reviews International 2013. DOI: 10.1080/87559129.2013.818012.

[29] Akdağ, A; Ozturk, E: Distillation Methods of Essential Oils, 2019.

[30] Božović, M; Navarra, A; Garzoli,

[31] Destandau, E; Michel, T; Elfakir, C

rjmp.2011.21.31. Published Online: 2011

[32] Jaiswal, P T a Y: An Overview of Microwave Assisted Extraction and its Applications in Herbal Drug Research, Research Journal of Medicinal

Plants 2011, 5, 21.DOI: 10.3923/

S; Pepi, F; Ragno, R: Esential oils extraction: a 24-hour steam distillation systematic methodology, Natural Product Research 2017.DOI: 10.1080/14786419.2017.1309534.

Online: 2012

Online: 2011

[17] In Encyclopædia Britannica; Adam Augustyn, P. B., Brian Duignan, Alison Eldridge, Erik Gregersen, Amy McKenna, Melissa Petruzzello, John P. Rafferty, Michael Ray, Kara Rogers, Amy Tikkanen, Jeff Wallenfeldt, Adam Zeidan, and Alicja Zelazko., Ed.; Encyclopædia Britannica, inc.: 2018.

[18] Latif, S; Chiapusio, G; Weston, L A In Advances in Botanical Research; Becard, G., Ed.; Academic Press: 2017;

[19] Gonçalves Paterson Fox, E; Russ Solis, D; Delazari dos Santos, L;

[20] Balasundram, N; Sundram, K; Samman, S: Phenolic compounds in plants and agri-industrial by-products: Antioxidant activity, occurrence, and potential uses, Food Chemistry 2006, 99, 191.DOI: https://doi.org/10.1016/j. foodchem.2005.07.042. Published

Aparecido dos Santos Pinto, J R; Ribeiro da Silva Menegasso, A; Cardoso Maciel Costa Silva, R; Sergio Palma, M; Correa Bueno, O; de Alcântara Machado, E: A simple, rapid method for the extraction of whole fire ant venom (Insecta: Formicidae: Solenopsis), Toxicon 2013, 65, 5.DOI: https://doi.org/10.1016/j. toxicon.2012.12.009. Published

Vol. 82, p 19.

Online: 2013

Online: 2006

Amrani, I 2017.

Online: 2019

[21] Giada, M 2013, p 87.

[22] Abdelkader, B; Belkacem, N;

[24] Cheynier, V: Phenolic compounds: from plants to foods, Phytochemistry Reviews 2012, 11, 153.DOI: 10.1007/

[23] Tian, S; Sun, Y; Chen, Z; Yang, Y; Wang, Y: Functional Properties of Polyphenols in Grains and Effects of Physicochemical Processing on Polyphenols, Journal of Food Quality 2019, 2019, 1.DOI: 10.1155/2019/2793973. Published

**50**


**2. Methodology**

This section describes one method to survey and analyze the energy use of an industry. The industrial energy audit may be divided into three main parts: the survey, the analysis and the proposed measures. The expected result of the energy audit is a number of measures that will increase the energy efficiency, switch from non-renewable to renewable sources and decrease the energy use of the company or in the energy system as a whole. The result is normally presented in a report.

In the three parts of the audit you have different tasks:

Energy survey – What do we have?

Energy analysis – How "bad" is the utilization of the supplied energy? Where does it go wrong? How far can we reach?

Suggested measures – What can we do about it?

**•** Identify possible solutions.

may be dimensioned both for ventilation and cooling. If you see that the device is taking care of two different processes you also see that there is a point in adjusting the flow depending on

The concept is based on the objective of the industrial process, the mixing of materials, cooling or drying a product, the production of compressed air or to carry goods, etc. Unit processes are considered to be the smallest components within an industrial energy system. Unit proc‐ esses are general for all industries, thus providing opportunities for comparisons of a given unit process, such as forming, between different industries. Using the unit processes to rep‐ resent the "building blocks" of energy use also enables modelling, e.g. simulation or optimi‐

This section describes one method to survey and analyze the energy use of an industry. The industrial energy audit may be divided into three main parts: the survey, the analysis and the proposed measures. The expected result of the energy audit is a number of measures that will increase the energy efficiency, switch from non-renewable to renewable sources and decrease the energy use of the company or in the energy system as a whole. The result is normally

Energy analysis – How "bad" is the utilization of the supplied energy? Where does it go wrong?

**•** Identify mismatching energy quality, where high quality energy is used for non-demanding

**•** Sum up the potential for energy efficiency and conversion for the system as a whole.

**•** Identify technologies that are far from BAT (Best Available Technology).

purposes, for example, electricity for low temperature heating.

zation modelling of industrial energy use. For example, see [16].

In the three parts of the audit you have different tasks:

**•** Allocate energy use to different unit processes.

**•** Identify both system errors and detail problems.

the cooling demand.

118 Sustainable Energy - Recent Studies

**2. Methodology**

presented in a report.

Energy survey – What do we have?

**•** Identify idling power and energy.

**•** Identifying barriers and driving forces.

Suggested measures – What can we do about it?

**•** Define the system boundary.

**•** Identify unit processes. **•** Quantify energy supply.

How far can we reach?


*3.1. Observed Temporal and Spectral Poperties of GRB Prompt Emission*

The GRB observed duration distribution is bimodal, which leads to the division into short and long *duration* events (with an observer frame separation at ∼2 s). The average spectral properties of events in the two duration classes display some differences, with short GRBs featuring on average harder spectra with respect to long ones [155], the difference residing primarily in the low-energy part of the spectrum [156,157]. Short events are detected at a lower rate with respect to long events (with an instrument-dependent short/long ratio of 1:3 for *CGRO*/BATSE, 1:5 for *Fermi*/GBM and of 1:9 for *Swift*/BAT). However, several different instrumental and cosmological effects shape (and hence bias) these observed properties [53,158,159].

The prompt emission has no apparent periodicity [160] and features a power density spectrum consistent [161,162] with turbulent dissipation processes. Different methods were employed to measure the minimum *variability* timescale of prompt emission light curves, resulting in different distributions [163,164]. Ref. [164] reports similar rest-frame distributions for the minimum variability timescale in long and short GRBs, centered around 0.5 s and extending down to 10 ms in ∼10% of the events. Another interesting feature of the prompt emission is the presence of *spectral lags*. These consist in the fact that pulses observed in the lower energy bands of the gamma-ray instruments are seen to lag behind the corresponding pulses in the higher energy bands [165]. This feature seems to be more commonly present in long GRBs, while short events typically have lags consistent with zero [166].

Most attempts at identifying the fundamental building blocks of GRB prompt emission light curves adopted parametric functions to represent pulses (e.g., [165,167]). With the caveat that these methods are applied to large samples of light curves of GRBs with unknown redshift, the results show apparent (observer frame) differences between short and long GRBs [168].

On the longer timescales, periods of activity can at times be separated by quiescent phases. In a sizable fraction (∼ 15%) of long GRBs, a long quiescence phase (reaching, in some cases, >100 s) separates *precursor* activity from the main emission episode [169]. Precursors have also been identified in short GRBs [170]. Long apparent quiescences also separate the main event from late time pulses, or *flares*, often observed in the X-ray band by *Swift*/XRT. X-ray flares share some common properties with the prompt emission [171] and are thus often interpreted as linked to late-time central engine activity. No significant differences in the average spectral properties of precursors and main emission episodes have been identified [172], while flares appear clearly softer.

The prompt emission of GRBs is characterized by a *non-thermal* spectrum. The presence of thermal-like emission has been identified in a few cases either during the initial phases of the burst [173] or along its full duration [174], with no evidence of such emission component in short bursts [29]. A combination of multiple emission components (e.g., the sum of a power law with a high energy cutoff and a black body) was also adopted to interpret observed spectra [175,176]. Observationally, the spectral energy distribution of GRB prompt emission typically peaks at 0.1–1 MeV and the low (resp. high) energy spectrum, below (resp. above) the peak, is consistent with a power-law with photon index ∼−1 (resp. −2.5).


*Long-Term Toxicities among Wilms Tumor Survivors DOI: http://dx.doi.org/10.5772/intechopen.110130*

The International Late Effects of Childhood Cancer Guideline Harmonization Group have recently published evidence-based consensus recommendations for fertility preservation, including testicular and ovarian cryopreservation, in young cancer patients [40]. Currently, fertility preservation for WT patients is largely experimental, expensive and not widely available. Most patients are prepubertal, and there are no established criteria and standard guidelines for fertility preservation in males and ovarian cryopreservation in prepubertal females. Clinicians should proactively initiate conversations around standard and experimental options for fertility preservation in high risk WT children. Other options that exist for WT survivors of both genders include adoption, surrogacy, and the use of donor sperm/ eggs or embryos.

The COG LTFU guidelines, version 5.0, provide extensive recommendations for the appropriate surveillance of childhood cancer survivors for common RT-induced toxicities observed in WT survivors (http://survivorshipguidelines.org). A summary of these guidelines is provided in **Table 3**.