**3. EBF as treatment of insomnia**

Insomnia is a most pervasive disorder, affecting about 15% of the general population while 6% meet clinical (DSM-IV) criteria (Ohayon, 2002) and interferes with cognition, quality of life, job performance and represents a multi-billion dollar burden on healthcare providers (Daley et al., 2009; Ebben & Spielman, 2009; Edinger et al., 2004). Insomnia can be subdivided into primary and co-morbid insomnia with the most salient symptoms being difficulty initiating and/or maintaining sleep (Espie, 2007). Causes of primary insomnia include physiological, cognitive and behavioural factors (Espie, 2007). Symptoms and duration are related to severity and persistence of stressors (Morin et al., 2006).

To better understand the possible therapeutic targets of insomnia, the so-called "3P model" has been proposed (Ebben & Spielman, 2009). This model specifies three categories of factors influencing the risk at developing or worsening insomnia: predisposing, precipitating and perpetuating factors. The first category constitutes genetic factors or personality traits, such as increased basal level of anxiety or hyperarousal (Drake et al., 2004), whereas precipitating events represent work and educational stress together with health and emotional problems (Bastien et al., 2004). Finally, perpetuating factors, such as continuous stress and poor sleep hygiene, may cause the actual transition to chronic insomnia and complete the vicious circle.

Pharmacological treatment of insomnia with sedative-hypnotic agents has seen a steady decline over the past (Aldrich, 1992; Walsh & Schweitzer, 1999), because of side effects, discontinuation discomfort, and the risk of developing drug tolerance or dependency (Ebben & Spielman, 2009; Walsh & Schweitzer, 1999). Alternative treatment options that have been met with success are cognitive-behavioural therapy (CBT) (Ebben & Spielman, 2009; Espie, 1999; Morin et al., 1999; Morin et al., 1994; Murtagh & Greenwood, 1995; Siebern & Manber, 2010) or treatments

EEG-Biofeedback as a Tool to Modulate Arousal:

2010; Hoedlmoser et al., 2008).

**3.2 Target brain activity**

Trends and Perspectives for Treatment of ADHD and Insomnia 439

individual strategies (Berner et al., 2006; Cortoos et al., 2009; Hauri et al., 1982; Hoedlmoser et al., 2008). Training sessions (Table 3) are usually blocked (e.g., 3 minute intervals) during which a threshold of activity expressed as a percentage of, or within a predefined band around the baseline, must be maintained for 250–500 ms (Berner et al., 2006; Cortoos et al.,

Insomniacs differ from good sleepers in terms of their EEG profile (Fig. 3), especially exhibiting large spectral decreases in the lower frequency bands (delta, theta) (Merica et al., 1998) and attenuated sigma activity, corresponding to less occurrences of sleep spindles (Besset et al., 1998). These findings have led to the design of EBF therapies aimed at either increasing theta activity, due to its close relationship with drowsiness and early sleep stages, or SMR activity, as this rhythm overlaps with the sigma range and is believed to stimulate sleep spindle occurrence which in turn is key to further progression into deeper sleep stages

Fig. 3. Insomniacs and normal sleepers have different EEG during stage 1 sleep. Insomniacs (solid line) have reduced delta, theta and alpha activity, but higher levels of beta activity compared to normal sleepers (dashed line) during early stages of sleep. Y-axis: average power over all participants in specific frequency band. X-axis: normalized duration of sleep

stage 1, each of the 50 dots marks a 2% interval. From: Merica et al., 1998.

increasing body temperature (e.g., physical exercise, hot bath before bed), which has recently been shown to hasten sleep onset (Van Someren, 2006). Whereas CBT causes sustained improvements and reduces sleep complaints, one fifth of the patients does not respond to the intervention (Cortoos et al., 2010; Harvey & Payne, 2002; Morin, 2006). EBF therapy for insomnia could be a safer alternative to medication and may offer treatment where CBT fails.

The EEG profile of insomniacs (Fig. 3) consists of increased levels of beta activity especially during the sleep-onset period and early sleep stages (Merica et al., 1998). These observations may be interpreted as evidence of cognitive hyperarousal, which is in line with the often reported 'racing thoughts' of insomniacs (Bastien et al., 2003; Buysse et al., 2008; Buysse et al., 2008; Freedman, 1986; Harvey & Payne, 2002; Jacobs et al., 1993; Lamarche & Ogilvie, 1997; Merica, et al., 1998; Merica & Gaillard, 1992; Nofzinger et al., 1999; Perlis et al., 2001). In addition, elevated levels of alpha activity at sleep onset (Besset et al., 1998; Krystal et al., 2002) as well as a decrease in delta activity during non-REM sleep (Merica et al., 1998; Merica & Gaillard, 1992) have been reported. Furthermore, it has been demonstrated that insomniacs produce less spontaneous waking SMR activity than controls (P. Hauri, 1981; Krystal et al., 2002). One interesting aspect about the SMR is that it lies in the same frequency range as sleep spindles (Sterman, et al., 1970). Spindles are the hallmark waveform of stage 2 sleep, and their occurrence is reduced in insomniacs (Besset et al., 1998), possibly resulting in lighter and more fragmented sleep (Glenn & Steriade, 1982; Perlis et al., 2001).

#### **3.1 Training duration and feedback**

Protocols for EEG-biofeedback in insomnia are quite similar in many respects to the ones used in the treatment of ADHD, e.g. patients usually receive feedback and reward in the form of auditory and/or visual stimuli and are encouraged to search for their own

 **P/R/B**= Placebo/Randomized/Blind (- = no, + = yes). **N(m)**: Number of participants (males), **Freq.**: Target frequency (increase/decrease), **?** = data unavailable

Table 3. Overview of EBG group studies aimed at improving sleep.

individual strategies (Berner et al., 2006; Cortoos et al., 2009; Hauri et al., 1982; Hoedlmoser et al., 2008). Training sessions (Table 3) are usually blocked (e.g., 3 minute intervals) during which a threshold of activity expressed as a percentage of, or within a predefined band around the baseline, must be maintained for 250–500 ms (Berner et al., 2006; Cortoos et al., 2010; Hoedlmoser et al., 2008).

#### **3.2 Target brain activity**

438 Neuroimaging – Cognitive and Clinical Neuroscience

increasing body temperature (e.g., physical exercise, hot bath before bed), which has recently been shown to hasten sleep onset (Van Someren, 2006). Whereas CBT causes sustained improvements and reduces sleep complaints, one fifth of the patients does not respond to the intervention (Cortoos et al., 2010; Harvey & Payne, 2002; Morin, 2006). EBF therapy for insomnia could be a safer alternative to medication and may offer treatment where CBT fails. The EEG profile of insomniacs (Fig. 3) consists of increased levels of beta activity especially during the sleep-onset period and early sleep stages (Merica et al., 1998). These observations may be interpreted as evidence of cognitive hyperarousal, which is in line with the often reported 'racing thoughts' of insomniacs (Bastien et al., 2003; Buysse et al., 2008; Buysse et al., 2008; Freedman, 1986; Harvey & Payne, 2002; Jacobs et al., 1993; Lamarche & Ogilvie, 1997; Merica, et al., 1998; Merica & Gaillard, 1992; Nofzinger et al., 1999; Perlis et al., 2001). In addition, elevated levels of alpha activity at sleep onset (Besset et al., 1998; Krystal et al., 2002) as well as a decrease in delta activity during non-REM sleep (Merica et al., 1998; Merica & Gaillard, 1992) have been reported. Furthermore, it has been demonstrated that insomniacs produce less spontaneous waking SMR activity than controls (P. Hauri, 1981; Krystal et al., 2002). One interesting aspect about the SMR is that it lies in the same frequency range as sleep spindles (Sterman, et al., 1970). Spindles are the hallmark waveform of stage 2 sleep, and their occurrence is reduced in insomniacs (Besset et al., 1998), possibly resulting in lighter and more

Protocols for EEG-biofeedback in insomnia are quite similar in many respects to the ones used in the treatment of ADHD, e.g. patients usually receive feedback and reward in the form of auditory and/or visual stimuli and are encouraged to search for their own

**N (m) Age Electrodes**

Total:

Total:

**P/R/B**= Placebo/Randomized/Blind (- = no, + = yes). **N(m)**: Number of participants (males), **Freq.**:

**/Ref**

C3 /A2

/ FCz

FPz & Cz /A2

C3 /A2 **Freq. Stim/**

T7&C3/A2

SMR

SMR

SMR

 Visual & Auditory

SMR Visual &

Auditory

Visual 20/

**Reward**

Visual 24.8

Visual 25.4/ 60 min 27.8/60 min

**#Ses./ Dur.**

(15-62) / 60 min

1 / 4x10 min

10/ 24 min

20 min

fragmented sleep (Glenn & Steriade, 1982; Perlis et al., 2001).

**P/R/B**

EBF/Sham +/+/+ Cz

+/+/+

Table 3. Overview of EBG group studies aimed at improving sleep.

Target frequency (increase/decrease), **?** = data unavailable

 Total:

**3.1 Training duration and feedback**

**Study Conds. Control**

[1] EBF() [2] EBF(SMR)

[1] EBF [2] Sham

[1] EBF [2] EMG [3] Control

Hauri,1981 [1] EBF+EMG [2] EBF [3] EMG [4] Control

Hauri et al.,1982

Berner et al., 2006

Hoedlmoser et al., 2008

Cortoos et al., 2009

Insomniacs differ from good sleepers in terms of their EEG profile (Fig. 3), especially exhibiting large spectral decreases in the lower frequency bands (delta, theta) (Merica et al., 1998) and attenuated sigma activity, corresponding to less occurrences of sleep spindles (Besset et al., 1998). These findings have led to the design of EBF therapies aimed at either increasing theta activity, due to its close relationship with drowsiness and early sleep stages, or SMR activity, as this rhythm overlaps with the sigma range and is believed to stimulate sleep spindle occurrence which in turn is key to further progression into deeper sleep stages

Fig. 3. Insomniacs and normal sleepers have different EEG during stage 1 sleep. Insomniacs (solid line) have reduced delta, theta and alpha activity, but higher levels of beta activity compared to normal sleepers (dashed line) during early stages of sleep. Y-axis: average power over all participants in specific frequency band. X-axis: normalized duration of sleep stage 1, each of the 50 dots marks a 2% interval. From: Merica et al., 1998.

EEG-Biofeedback as a Tool to Modulate Arousal:

compilation of strategies feasible.

*322*(5903), 876-880.

**5. References**

Trends and Perspectives for Treatment of ADHD and Insomnia 441

treatment (i.e., medication) is in conflict with the Declaration of Helsinki (Vernon et al., 2004). Employing sham (random frequency) feedback (Hoedlmoser et al., 2008; Logemann et al., 2010) is therefore not always an option when treating patients. Thus, apart from reaching certain endpoints of treatment, the further validation of EBF therapy is likely to depend on the observation of complimentary physiological changes, e.g., obtained from neuroimaging

Motivation and cognitive strategies are also important aspects to consider (Bregman & McAllister, 1982; Meichenbaum, 1976). If participants are motivated and rewarded for their success they will put effort into the therapy, whereas lack thereof leads to frustration and possibly resignation (Huang et al., 2006). Good methodology can compensate for possible expectancy effects, i.e., improved symptoms like decreases in sleep onset latency induced by the sheer hope of becoming better through therapy (Hauri et al., 1982). However, providing sham feedback, which lacks obvious rewards, bears the risk of the participant becoming unmotivated, ceasing effort and thus confounding the comparison between control and experimental condition (Logemann et al., 2010). In addition, the instructions given to participants in the EBF studies reviewed here do not go beyond the direction to meet some specified criterion, i.e., increasing an onscreen bar towards a target value. The general idea is that participants need to search for their own strategies to modulate their brain activity. In our view, this is unfortunate, because good instructions/guidance can increase participant compliance and speed of learning (Weinert et al., 1989). While individual strategies are likely to vary greatly, an opportunity for future research presents itself in the collection of these strategies and finding patterns that may be useful to guide participants towards success more efficiently. Interestingly, Gevensleben et al. (2009) report on having queried individual strategies of their participants (albeit without further analysis), making future

Technological advances have made it possible to record high-density EEG data from several hundred electrodes at once (Dornhege et al., 2006). However, current EBF studies seldom record from more than two active electrodes (Tables 2 and 3). With ongoing developments towards ever more powerful and cost-effective computational equipment, it is feasible that future research should focus on the opportunities these advances can offer EBF, possibly in combination with tools from the field of BCI (e.g., more sophisticated algorithms, spatial filtering allowing feedback on localized anatomical structures and less artefacts). Despite some (methodological) issues that have subjected the field to scepticism, recent developments give rise to optimism, as stricter guidelines are increasingly being adhered to and new avenues continue to be explored (e.g., SCP feedback and tele-neurofeedback as in Cortoos et al., 2009). Overall, from the studies reviewed here we conclude that EBF is a promising tool for treating

disorders of arousal, which offers many opportunities for future research.

Aldrich, M. S. (1992). Sleep disorders. *Curr Opin Neurol Neurosurg, 5*(2), 240-246.

Alhambra, M. A., Fowler, T. P., & Alhambra, A. A. (1995). EEG biofeedback: A new treatment option for ADD/ADHD. *Journal of Neurotherapy, 1*(2), 39-43. Alkire, M. T., Hudetz, A. G., & Tononi, G. (2008). Consciousness and anesthesia. *Science,*

Angelakis, E., Stathopoulou, S., Frymiare, J. L., Green, D. L., Lubar, J. F., & Kounios, J.

(2007). EEG neurofeedback: a brief overview and an example of peak alpha

experiments or other biomarker assays (Frank & Hargreaves, 2003).

(Berner et al., 2006; Budzynski, 1973; Hauri, 1981; Sittenfeld, 1972; Steriade, 2003). The application of either protocol depends on the insomnia sub-population: theta feedback (enhancement training) is used for patients with difficulty initiating sleep, whereas SMR/sigma feedback is best used on patients that have problems maintaining sleep. The importance of disentangling insomnia subtypes is further illustrated by the studies of Hauri et al. (1981,1982). Even though all participants showed a trend towards improvement, the experimental groups (i.e. theta feedback, SMR feedback) did not differ, which could be attributed to participants having received treatment unsuitable to the underlying symptoms (Hauri, 1981; et al., 1982).

#### **3.3 Efficacy of EEG-biofeedback in the treatment of insomnia**

A pioneering case study used theta training to treat an insomnia patient and observed a near doubling of theta activity by the end of the 11-week (one session per week) EBF training, together with vastly decreased sleep-onset latency (from 54 to 16 minutes), an increase in total sleep time and a halving in intrusive thoughts (Bell, 1979). Recent studies have compared SMR training with pseudo-EBF training and reported positive results with respect to the total sleep time and the sleep latency (Berner et al., 2006; Hoedlmoser et al., 2008). Cortoos et al. (2009) compared electromyography (EMG) biofeedback, aimed at reducing muscle tension and relaxation, with an EBF protocol of SMR increase and simultaneous theta-, and beta-band suppression. Both groups showed decreases in sleep latency (-8.5 and -12.3 minutes respectively) and time awake after sleep onset. It is noteworthy that participants were trained to apply electrodes and initiate training in their home environment and experimental control was established remotely through the internet, making this "tele-neurofeedback" protocol an interesting example of fusing established knowledge with advanced technology.

In contrast to the case of ADHD where subjective ratings largely define outcome measures (Table 2), efficacy and validity of EBF-therapy for insomniacs is easier to assess through objective measures such as total sleep time, sleep-onset latency and the number of nightly awakenings. In 1998, the American Academy of Sleep Medicine recommended biofeedback in general, including EMG-biofeedback, as treatment for insomnia and classified it as "probably efficacious", based on the Guidelines for Evaluation of Clinical Efficacy of Psychophysiological Interventions (Table 3). In the update of 1999–2004, this rating was maintained (Morgenthaler et al., 2006; Morin et al., 2006; Morin et al., 1999).


*Article* **Numerical and Experimental Study of Microchannel Performance on Flow Maldistribution**

**Jojomon Joseph 1,2,\*, Danish Rehman 3, Michel Delanaye 1, Gian Luca Morini 3, Rabia Nacereddine 1, Jan G. Korvink 2 and Juergen J. Brandner 2**

Received: 27 February 2020; Accepted: 19 March 2020; Published: 20 March 2020

**Abstract:** Miniaturized heat exchangers are well known for their superior heat transfer capabilities in comparison to macro-scale devices. While in standard microchannel systems the improved performance is provided by miniaturized distances and very small hydraulic diameters, another approach can also be followed, namely, the generation of local turbulences. Localized turbulence enhances the heat exchanger performance in any channel or tube, but also includes an increased pressure loss. Shifting the critical Reynolds number to a lower value by introducing perturbators controls pressure losses and improves thermal efficiency to a considerable extent. The objective of this paper is to investigate in detail collector performance based on reduced-order modelling and validate the numerical model based on experimental observations of flow maldistribution and pressure losses. Two different types of perturbators, Wire-net and S-shape, were analyzed. For the former, a metallic wire mesh was inserted in the flow passages (hot and cold gas flow) to ensure stiffness and enhance microchannel efficiency. The wire-net perturbators were replaced using an S-shaped perturbator model for a comparative study in the second case mentioned above. An optimum mass flow rate could be found when the thermal efficiency reaches a maximum. Investigation of collectors with different microchannel configurations (s-shaped, wire-net and plane channels) showed that mass flow rate deviation decreases with an increase in microchannel resistance. The recirculation zones in the cylindrical collectors also changed the maldistribution pattern. From experiments, it could be observed that microchannels with S-shaped perturbators shifted the onset of turbulent transition to lower Reynolds number values. Experimental studies on pressure losses showed that the pressure losses obtained from numerical studies were in good agreement with the experiments (<4%).

**Keywords:** micro channel; reduced model; wire-net perturbators; s-shaped perturbators; high-temperature heat exchangers

#### **1. Introduction**

In recent years, Computational Fluid Dynamics (CFD) has been widely used to analyze heat exchanger performance and optimize it for specific applications. In the micro-gas turbine (MGT) industry, it is challenging to maintain laminar flow in microchannels. On the other hand, it is well-known that perturbators lead to local turbulences and, therefore, increase the heat transfer performance. Min et al. [1] presented different heat exchanger design schemes for gas turbine applications. They suggested that heat transfer effectiveness must be higher than 90% and pressure losses must be less

than 3%, along with excellent resistance to oxidation and creep for temperatures above 650 ◦C. Based on a recent market study on MGT for decentralized energy systems made by Xiao [2], the target for thermal effectiveness must be higher than 90% and pressure losses must be less than 5%. Shah [3], in his review paper, noted similar performance requirements.

Microchannel entrance length effects, as well as flow maldistribution [4], plays an essential role in compact heat exchanger performance. The temperature distribution effect on the flow maldistribution for a parallel microchannel system was meticulously investigated by Siva [5]. He figured out that a high heat flux or highly heated areas induced a reduction in viscosity of fluid, resulting in a higher flow maldistribution. Different types of collectors were analyzed by Siddique [6], and it was found that the creation of a stagnation zone, the growth of a boundary layer along the collector wall and low/high-velocity zones in the collector were the prime causes of flow maldistribution.

Microchannel performance plays a vital role in heat exchanger assembly. Tao [7] suggested heat transfer enhancement by decreasing the thermal boundary layer, introducing protrusions inside the microchannels and increasing the velocity gradient near the heated surface. Transverse vortex generators and longitudinal vortex generators play a significant role in local and global heat transfer enhancements, as shown in [8]. Valencia [9] showed that at Reynolds number 2000 heat transfer was enhanced by 30% due to turbulence together with a five-fold increase in friction factor. Increasing the blockage ratio and reducing the aspect ratio increased the global Nusselt number. An analysis by Paolo [10] of Conjugate Heat Transfer (CHT) on a cube revealed that the heat transfer was minimum when the boundary layer was undisturbed.

Smooth pipes (with smaller roughness) have friction factor behavior similar to the conventional theory. On the contrary, rough tubes have showed an earlier turbulent transition (Re = 350) [11–14]. Thus, adding perturbators in microchannels can induce turbulent transitions at smaller Reynolds numbers. At the same time, near-wall turbulence causes a significant share of pressure losses. Increasing the area density will increase the pressure losses and result in an increased fouling effect. Studies have showed that a high level of turbulence intensifies the self-cleaning inside complex microchannels [15]. On the contrary, increasing size to overcome pressure losses results in higher capital and reduces the performance advantages of miniaturization.

Yang [16,17] compared the performance of heat exchange devices with different microstructures manufactured on thin foils to enhance heat transfer. He found that "partition foils having low thermal conductivity can enhance the heat transfer by decreasing the axial conduction" and that, at a microscale, cross-flow configuration tends to provide similar results to the counter-current arrangement at a large Reynolds number (>2400). This is because, even if the arrangement is counter-current, there is always a cross-flow part in microchannel arrangements due to the microchannels with plane partition foils [17]. This fact led to the wire-net arrangement in our study, which was created to suppress the cross-flow pattern in the counter-flow channels.

Microchannel performances of wire-net and S-shape perturbators were presented by Joseph [18]. Joseph [18–21] also proposed a Reduced Order Model (ROM) to investigate the collector performance. The global parameters of the heat exchanger (overall thermal efficiency and pressure losses) were investigated using the ROM and were validated experimentally based on high-temperature testing of the full-scale heat exchanger. The experimental study used to validate the employed numerical model in terms of flow maldistribution, microchannel pressure loss and so on was not presented. In this paper, an experimental study is performed to investigate flow maldistribution and microchannel pressure losses based on a pressure-loss study (without temperature effects). In addition, an experimentally validated ROM was utilized to investigate the influence of collector performance on microchannel characteristics like foil thickness, type of perturbators and collector recirculation zones.

#### **2. Numerical Model**

A single heat exchanger block comprises of a secondary collector with 60 microchannels. The number of blocks is calculated based on the total mass flow rate, required pressure losses and thermal efficiency. A primary collector supplies and collects the mass flow rate to each block of heat exchangers. Thus, flow maldistribution in both primary and secondary collectors play a crucial role in the performance of the heat exchanger assembly. A computationally inexpensive methodology that has been presented by Joseph [20] will be utilized for a detailed investigation of collector performance.

#### *2.1. Microchannel Design*

Two types of microchannel configurations, wire-net and S-shape perturbators, were selected for detailed microchannel CFD analysis. A comparative study was carried out by replacing the wire-net perturbators with S-shape perturbators. The wire-net heat exchanger consist of partition foils that are separated by bobbing wire-net structures. The patented wire-net heat exchanger [18–21] is assembled as a stack of counter-flow channels with optimized thicknesses separated by thin partition foils. The frames are brazed together with the partition foils (see Figure 1a). Cylindrical collectors provide cylindrical-shaped microchannel inlets that distribute mass flow rates uniformly to the wire-net microchannels. Cylindrical collectors with wire-net microchannels are depicted in Figure 1a.

High computational effort is needed to simulate entire counter-flow wire-net microchannels. Two types of geometrical symmetry exist in a plane that is parallel to the inlet flow direction (see Figure 1a). The primary symmetrical domain (see Figure 1b) is at half of the secondary collector's diameter, while the secondary symmetrical domain is located at the center of the wire-net compact heat exchanger (see Figure 1c). Again, high computational effort is needed to carry out a Conjugate Heat Transfer (CHT) analysis by holding the primary symmetry (see Figure 1b). The objective of the preliminary analysis based on the primary symmetrical domain is to investigate the homogeneity in the wire-net flow physics and verify the second symmetry assumption for the CHT analysis. Thus, a turbulent K-ω Reynolds averaged Navier-Stokes (NS) model without energy equations was adopted for the preliminary study to reduce solver complexity. Apparently, the second analysis based on the second symmetrical domain was performed to investigate the Conjugate Heat Transfer effects in microchannels.

Similarly, periodic boundary conditions were implemented in the plane that is perpendicular to the inlet flow direction. A periodicity exists in half of the microchannel foil thickness on every hot and cold side alternatively. Thus, the counter-flow arrangement was simplified into single hot and cold microchannels with partition foil above, as shown in Figure 1c.

Three-dimensional, ideal gas, steady Conjugate Heat Transfer (CHT) simulations were carried out in counter-flow ducts to investigate the effect of thermal efficiency and pressure losses for various inlet mass flow rates. A turbulent K-ω shear stress transport model was utilized for all the CHT models and collectors simulations. The free microchannel simulations were performed using laminar models. The k-ε model was not accurate in the near-wall region, while the K-ω model was appropriate for near-wall turbulent flows. In the K-ω shear-stress turbulent transport model, both conventional models (K-ω & k-ε) are mixed to take advantage of their unique advantages [22].

The microchannel thermal efficiency ε [%] is calculated using the following:

$$\varepsilon[\%] = \frac{\text{C}\_{\text{h}}(\text{T}\_{\text{in}}^{\text{h}} - \text{T}\_{\text{out}}^{\text{h}})}{\min(\text{C}\_{\text{c}}, \text{C}\_{\text{h}})(\text{T}\_{\text{in}}^{\text{h}} - \text{T}\_{\text{in}}^{\text{c}})} \times 100 \tag{1}$$

$$\mathbf{C}\_{\mathbf{c}} = \mathbf{m}\_{\mu\mathbf{c}} \mathbf{C} \mathbf{p}\_{\mathbf{c}} ; \mathbf{C}\_{\mathbf{h}} = \mathbf{m}\_{\mu\mathbf{h}} \mathbf{C} \mathbf{p}\_{\mathbf{h}} \tag{2}$$

where Cpc/h are the hot and cold fluid capacity rates, respectively; Th in/out and Tc in/out are the inlet and outlet temperatures of the counter-flow channels, respectively; and mμc/μh are the microchannel mass flow rates at the hot and cold inlets, respectively. The microchannel pressure losses ΔP [%] were calculated using the following:

$$
\Delta\text{P} \,\left[\%\right] = \frac{\text{P}\_{\text{in}} - \text{P}\_{\text{out}}}{\text{P}\_{\text{ab}}} \times 100\tag{3}
$$

where Pin/out are the inlet and outlet absolute pressure, respectively; and Pab is the absolute pressure at the inlet. The influence of pressure losses on density is relatively low if the total pressure loss is less than 5%. Morini [23] considered in his studies that flow is only incompressible when the Mach number is less than 0.3, or when the relative pressure loss Δ*P*/Pab is less than 5%. In our present study, the maximum pressure loss expected was 5%. As a result, the flow regime remains incompressible, although density variations as a function of the inverse of temperature as well as the fluid were dilatable [23]. As a result, a segregated solver is adopted.

**Figure 1.** Wire-net brazed heat exchanger arrangement (**a**); microchannels with wire-net perturbators (**b**); Conjugate Heat Transfer (CHT) domain (**c**); wire-net microchannels replaced by S-shaped perturbators (**d**); CHT domain (**e**).

#### *2.2. Porous Medium Model*

The CFD methodology based on a porous medium approximation was implemented to investigate the flow maldistribution. A general scheme of the heat exchanger arrangement is depicted in Figure 2a. The microchannels of the wire-net/S-shape fins have a complicated structure that again leads to high

simulation needs. The best method is to split the heat exchanger configuration part by part and analyze the performance using a Reduced Order Model (ROM).

**Figure 2.** Scheme of the heat exchanger assembly (**a**); numerical methodology (**b**); secondary collector model (**c**); primary collector mesh model with 16 porous mediums replicating 16 collectors (**d**).

The modelling was performed in three different stages (see Figure 2b):

In the first stage of the CFD modelling, a detailed CHT analysis was carried out for a microchannel section with specific boundary conditions. Microchannel performance characteristics were utilized to model the microchannels as a porous medium, with inertial and viscous coefficients as the main parameters.

In the second stage of modelling, the microchannels were considered a porous medium, and the cylindrical secondary collector performance was evaluated. The cylindrical cross-section of the secondary collectors was extruded at both the outlet and inlet for better convergence, as depicted in Figure 2c. The Constant Integration Method (CIM) based on the Darcy-Forchheimer law was utilized to calculate the porous medium characteristics that replicate the Conjugate Heat Transfer model. The computational domain was sharply reduced, since the microchannels were examined as a porous medium with a free-slip boundary condition.

In the third stage of modelling, the microchannels with secondary collectors were considered a porous medium, and the scalability of the ROM provided an easiness with which to create the temperature and pressure jump across circular of cross-sections. Scalability of the ROM was developed to characterize the primary collector performance and thereby the overall heat exchanger performance for the micro combined heat and power (CHP) system.

The primary and secondary collector mesh model is depicted in Figure 2a,b, respectively. Half a section of the cylindrical collectors that feeds 60 microchannels is depicted in Figure 2a. The global performance of a secondary collector with 60 microchannels was implemented into a cylindrical duct with a trapezoidal primary collector configuration (see Figure 2b). A cylindrical channel with a porous medium approximation replaced the effects of the secondary collector and 60 microchannels.

The number of microchannels, together with the microchannel size reduction (γ) coefficient, was utilized to calculate the microchannel inlet mass flow rate from the collector inlet mass flow rate,

$$\mathbf{m}\_{\rm \mu \rm CHT} = \frac{\mathbf{m}\_{\rm co}}{\mathbf{\varUpsilon}\_{\rm Pl}} \tag{4}$$

where mμCHT is the microchannel mass flow rate, mco is the collector mass flow rate, npl is the number of plates/channels and γ is the microchannel size-reduction coefficient calculated due to simplification of the computational domain (symmetric and periodic boundary conditions). The primary collector inlet mass flow rate was calculated using the following:

$$\mathbf{m}\_{\rm co} = \frac{\mathbf{m}\_{\rm to}}{2\mathbf{n}\_{\rm bl}} \tag{5}$$

where mco is the secondary collector mass flow rate, mto is the total mass flow rate and nbl is the number of blocks/modules.

#### Reduced Order Model (ROM)

Global parameters such as pressure loss, thermal efficiency and turbulent viscosity were determined from a three-dimensional CHT analysis of microchannels. This was utilized to calculate the inertial and viscous coefficients (porous medium model) of the reduced model using CIM. The Darcy-Forchheimer law was modified and implemented to account for the significant temperature variation (T = 600 K) and localized turbulence effect in the microchannels [19]:

$$\begin{split} & \frac{\left(\frac{\text{P}\_{\text{in}}-\text{P}\_{\text{out}}}{\text{L}}\right)}{\text{L}} \\ &= \frac{1}{\alpha} \frac{\text{L}}{\Lambda \text{T}} \frac{\text{m}}{\Lambda} \frac{\text{R}}{\text{P}} \left\{ \left[ \text{T}^{\frac{1}{2}} \left( 3\text{T}^{2} - 5\text{S}\_{1}\text{T} + 15\text{S}\_{1}^{2} \right) - 15\text{S}\_{1}^{\frac{5}{2}} \arctan \left( \frac{\text{T}}{\text{S}\_{1}} \right)^{\frac{1}{2}} \right) + \epsilon\_{\text{m}} \frac{\text{PT}}{\text{R}} \right\}\_{\text{T}\_{0}}^{\text{T}\_{1}} \\ &+ \frac{1}{\alpha} \text{C}\_{2} \text{m} \left[ \text{T}^{2} \right]\_{\text{T}\_{0}}^{\text{T}\_{1}} \end{split} \tag{6}$$

Power per unit volume was implemented as a source term along the microchannels (see Equation (7)). Joseph [19,20] applied the new approach and validated the porous medium model (with modified inertial and viscous coefficients) for secondary collectors.

$$\mathbf{Q} = \mathbf{m}\_{\text{μCHT}} (\mathbf{T}\_{\text{in}}^{\text{h}/\text{c}} - \mathbf{T}\_{\text{out}}^{\text{h}/\text{c}}) \mathbf{C}\_{\text{Ph}/\text{c}} \tag{7}$$

The model was validated numerically and experimentally [18–20]. Rehman et al. [24] used a similar approach for microchannels with large density variations (compressible flows). The collector's flow maldistribution influenced the mass flow rate and thereby the heat transfer prediction was biased along with total pressure losses.

#### **3. Results and Discussion**

#### *3.1. Microchannel Performance*

The non-dimensional velocity contours (with and without the wire-net) from the preliminary analysis based on the primary symmetrical domain are depicted in Figure 3. The non-uniform velocity distribution in the microchannels (without the wire-net) adversely affect the compact heat exchanger performance, as mentioned by Yang et al. [25], due to lower resistance from the inlet to the outlet. This reduced the benefits of counter-flow passages, since the maximum velocity gradients lied at two extreme inlets (see Figure 3). However, the microchannels with the wire-net showed a more homogeneous velocity distribution that retained the counter-flow effects (see Figure 3a). These effects were similar to the conventional grid turbulence theory [26], where a grid generates turbulence that is nearly homogeneous and isotropic. Apparently, conventional grid turbulence theory is the major assumption in most of the turbulence models [27]. Thus, the numerical model can be simplified (see Figure 1c,e) for a detailed CHT analysis.

**Figure 3.** Non-dimensional velocity contours of microchannels with and without the wire-net.

CHT analysis was performed for counter-flow microchannels with symmetric boundary conditions on both sides and periodic boundary conditions of the partition foils. The acceptable thickness for metal 3D-printing is 0.25 mm. Thus, a 0.25 mm thick partition foil was selected for microchannels with S-shaped perturbators. Obviously, there is an optimum mass flow rate where the thermal efficiency reaches a maximum [19]. However, a substantial decrease in efficiency at higher mass flow rates was encountered. The steepness of this efficiency curve pattern was stronger for the S-shape perturbator than the wire-net protrusions (see Figure 4). This steepness increases the working range and thereby reduces the size and cost of the compact heat exchanger. As the steepness decreases, the microchannels

can work at high mass flow rates with higher efficiency and, thereby, the number of microchannels required will be lower. As a result, heat exchangers became more compact and cost-effective. However, the pressure loss was relatively high for the wire-net at higher Reynolds numbers. The varying pressure-loss pattern (along with an increasing mass flow rate) was quadratic, which is similar to the conventional Darcy-Forchheimer law where the inertial and viscous terms are well-balanced. The free microchannels had the smallest pressure losses, as expected. The wire-net introduced higher pressure losses than the S-shaped fins (see Figure 4).

**Figure 4.** Thermal efficiency and pressure losses of free channels with the wire-net and S-shape microchannels for various mass flow rates based on CHT simulations.

#### *3.2. Collector Performance*

#### 3.2.1. Secondary Collector

#### Influence on Perturbators

The secondary collectors distributed the flow to the 60 microchannels, and the flow maldistribution is depicted in Figure 5. The collectors with lower microchannel resistance (channels with S-shape perturbators) showed a maximum deviation from the ideal CHT mass flow rate. This coincides with the studies made by Thansekhar [28] and Teng [17,29,30], which showed that the higher the flow resistance through the channels, the better the flow distribution, From these studies it can be observed that flow distribution becomes more uniform at higher flow rates, and that flow resistance increases with an increased mass flow rate.

Influence on Recirculation Zones

Three different configurations were selected to investigate the collector performance:

The first two configurations were selected to investigate and compare the recirculation zone of cylindrical secondary collectors for different microchannel pressure losses. Further, the second and third configurations were selected to study the effect on collector inlet mass flow rates. Figure 6a shows the vorticity strength (calculated based on the Lambda 2 velocity criteria) of the recirculation zone for three different mass flow rates. The vortices originated at the beginning of the recirculation zone and remained steady enough to dissipate the integral vortex near the collector beds.

Due to the intense mixing of the recirculating fluid with the entraining fluid, strong vortices originated along the recirculation zone. The intensity of the recirculation zones of the cylindrical collector was clear from the vorticity contour (see Figure 6a, left). Thus, the mass flow rate had a strong effect on flow maldistribution, as stated above. As microchannel pressure losses increased (see Figure 6a, center and right), vorticity strength increased. As a result, the mass flow rate distribution near the collector beds became irregular, and deviation from the CHT mass flow rate increased. In addition, the vorticity strength increased with increases in the collector inlet mass flow rate.

**Figure 6.** Cylindrical secondary collector performance for various microchannel pressure losses and mass flow rates (**a**); primary collector performance for different heights (**b**) based on a computational study.

#### 3.2.2. Primary Collector

The primary trapezoidal collector inlet distributed the flow to 16 secondary collectors and then to 60 microchannels. Collector flow maldistribution strongly depended on the secondary flows in the primary collectors. A parametric study of different primary collector heights was performed, and the flow maldistribution is depicted in Figure 6b. As the collector height increased, the maldistribution decreased and the optimum collector height was found to be at 100 mm. The velocity contours of the minimum and maximum collector heights are represented in Figure 7a,b, respectively. As the height increased, the velocity near the secondary collector inlet decreased, as did the recirculation strength. The highest turbulence production for the maximum height configuration was near the primary collector outlet. The primary collector height configuration profoundly influenced the flow distribution as well as the turbulence characteristics.

**Figure 7.** Velocity contour height = 50 mm (**a**); velocity contour height = 160 mm (**b**).

**;ďͿ**

#### **4. Experimental Studies**

Using numerical modeling it has been established that the generation of local turbulence serves as the prime reason for enhanced heat transfer when the perturbators are used within microchannels. Such enhanced turbulence in the flow increases the friction factor, but fortunately this increase in the friction factor can easily be observed using an experimental pressure-loss analysis of adiabatic flow, without the need of a high-temperature gas flow. The thermal performance of the micro heat exchanger was numerically analyzed above using two different perturbators. Of those, this section details a microchannel with an S-shaped perturbator that puts into evidence the increased frictional losses and an earlier onset of laminar to turbulent flow transition. A complete CFD investigation of the experimental microchannel is also performed in order to validate the earlier usage of the k-ω SST turbulence model during CHT analysis to model the local turbulent flow around the perturbator. Similarly, usage of the same turbulence model in the collector studies in which microchannels are modeled as porous media can also be justified through a comparison of the experimental and numerical maldistribution patterns of the adiabatic flow. It is acknowledged that the extent of maldistribution in a diabatic operational condition would differ from that of an adiabatic case, but the selection of turbulence model can be well justified using the adiabatic case only. Therefore, trapezoidal collectors with two layers of 33 microchannels were tested experimentally using an adiabatic flow to demonstrate the close agreement between experimental and numerical evaluations of maldistribution. The details of the fabrication, test bench and experimental data reduction, along with the results of both experimental studies, are discussed in this section.

#### *4.1. Microchannel*

The test bench and apparatus for microchannels with S-shaped perturbators is depicted in Figure 8. Microchannels with S-shaped perturbators were microfabricated using a micro-milling process as shown in Figure 8b. After passing through a particle filter (2), gas stored in a high-pressure tank (1) was directed to the desired mass flow rate controller using a three way valve (3). A mass flow rate controller (4) with an operating range of 0–5000 NmL/min controlled the inlet flow. Gas was then allowed to enter the microchannel test assembly (5) and finally exit through an outer port. The total pressure loss between the inlet and the outlet of the microchannel assembly was measured utilizing a differential pressure transducer (Validyne DP15) (6) with an interchangeable sensing element that allowed accurate measurements over the whole range of encountered pressures. Atmospheric pressure was measured using an absolute pressure sensor (Validyne AP42) (7). The pressure sensor calibration was performed using a comparison test pump (Giussani BT 400) that was capable of imposing the desired pressure difference relative to the atmosphere.

**Figure 8.** Experimental test bench (**a**) and microchannel assembly (**b**).

To measure the temperature at the entrance of the microchannel, a calibrated K-type thermocouple (8) was used. The thermocouple voltage and an amplified voltage of pressure sensors were fed to the internal multiplexer board of Agilent 39470A and were read through a PC using a Labview program. Uncertainties associated with all the measuring instruments are tabulated in Table 1.

**Table 1.** Characteristics and uncertainties of the measurement instruments.

The experimental friction factor was established using the following [31]:

$$\mathbf{f}\_{\rm f} = \left(\frac{\mathbf{D}\_{\rm h}}{\mathbf{L}}\right) \left| \frac{\mathbf{P}\_{\rm in}\,^2 - \mathbf{P}\_{\rm out}\,^2}{\mathbf{R}\mathbf{T}\_{\rm av}\left(\frac{\mathbf{m}}{\mathbf{T}}\right)^2} - 2\ln\left(\frac{\mathbf{P}\_{\rm in}}{\mathbf{P}\_{\rm out}}\right) + 2\ln\left(\frac{\mathbf{T}\_{\rm in}}{\mathbf{T}\_{\rm out}}\right) \right| \tag{8}$$

where L is the length, Tav is the average temperature of the gas between the inlet and outlet and Ω represents the cross-sectional area of the microchannel. Hydraulic diameter was defined as

$$\mathbf{D}\_{\mathbf{h}} = \frac{2\Omega}{\mathbf{w} + \mathbf{h}} \tag{9}$$

where w and h are width and height of the microchannel, respectively. Finally, Reynolds number at the microchannel inlet was defined as Re = mμDh μΩ , where μ represents the dynamic viscosity of the nitrogen gas at the inelt. Two separate theoretical correlations were used to compare the experimental friction factor for each of the laminar and turbulent flow regimes. For a fully developed flow in a rectangular channel, the laminar friction factor can be calculated using the Shah & London (S&L) correlation as [31,32]

$$\mathbf{F\_{[SI]}} = \frac{96\mathbf{\hat{i}} - 1.3553\ \mathbf{\hat{j}} + 1.9467\mathbf{\hat{j}}^2 - 1.7012\mathbf{\hat{j}}^3 + 0.9564\mathbf{\hat{j}}^4}{\text{Re}}\tag{10}$$

where aspect ratio β is defined as the ratio of the depth over the width of the microchannel. The experimental friction factor was compared to the Blasius law in the turbulent regime, which is given as follows:

$$\mathbf{F}\_{\text{Turb}} = 0.3164 \,\text{Re}^{-0.25} \tag{11}$$

CFD simulations were also performed on microchannels with perturbators for the specific experimental mass flow rates. The temperature drop was negligible and not taken into account for CFD analysis. A single side of the counter-flow channels was taken for the pressure-loss studies, and 3D-Ideal gas equations were utilized using the K-ω turbulence model for CFD analysis. The microchannel length (L) with the S-shaped perturbator was 74 mm. An additional length of 9.696 mm was added to the inlet side, and 16.575 mm was added to the outlet part. This was implemented to fit the existing testbench. In conclusion, the microchannels were 101 mm in length, with 74 mm having S-shaped perturbators.

A comparison between the experimental and numerical pressure loss is shown in Figure 9a. The CFD and experimental results coincided with reasonable accuracy (<4%). The primary vertical axis denotes the pressure losses and the secondary vertical axis shows the deviation of CFD from

experiments in %. The maximum deviation was larger at lower mass flow rates where the turbulent transition occurred. The RANS turbulent transition model was not reasonably accurate to predict turbulent transition. At higher mass flow rates, the CFD pressure losses were more reliable (<2%) when the flow was fully turbulent. The experimental friction factor evaluated using Equation (8) is shown in Figure 9b. The perturbators cause an increased surface roughness in the microchannels. Thus, as expected, the evaluated friction factor stayed higher than the S&L correlation in the early laminar regime. The slope of the experimental friction factor curve started deviating from the slope of laminar friction factor (S&L) and became parallel to the Blasius correlation at a considerably lower Re when compared to a smooth microchannel. The deviation in the current case began at an Re ~300, as shown in Figure 9b. This implies that the anticipated turbulent transition occurred at a lower Re, and is in agreement with several studies conducted by Shah and Wanniarachchi [11] and Heggs [12] on corrugated plate heat exchanger microchannel geometries. For Shah, the critical Reynolds number was less than 200, while Heggs [12] suggests that flow is never laminar after Re = 150. The length scale in this paper refers to the surface roughness and perturbator size in microchannels. If the length scale was too small, the turbulent transition occurred at the conventional Reynolds number. As the length scale increased, the critical Reynolds number reduced to a lower Reynolds number.

**Figure 9.** Experimental and CFD pressure-loss comparison and deviations (%) of the CFD results from the experimental pressure losses (dashed lines are for the numerical results and symbols are for the experimental readings) (**a**); experimental friction factor (**b**).

#### *4.2. Collector*

A collector with a microchannel configuration was fabricated using an Ultimaker 3+ extended version 3D printer based on fused deposition modeling (FDM). An open source software known as "Cura" was used to generate the 3D printer slicing application. The 3D printer transformed the design into several cross-sections then provided successive layers, until a three-dimensional object was formed. The plastic filaments were fed into the printer and the melted plastic extruded through the nozzle head and was directed on the building plate, where it created an object layer by layer. The biodegradable polylactic acid (PLA) plastic material was selected because PLA is having additives such as color pigments, plasticizers, nucleating agents and so on to optimize the properties for the specific application. The 3D printer had two nozzles: a primary nozzle to print the body and a secondary nozzle to print supporting structures for better surface finish and quality printing. Calibration was carried out to level the building plate. The building plate is the plate in which the microchannel is extruded. The building plate was heated to 40 ◦C. In addition, the nozzle was heated to 210 ◦C. In order to avoid the adhesion issues of the first layer, normal glue was provided on the building plate. The quality of the first layer was paramount, and it was guaranteed that the first layer had nicely adhered onto the glass plate with flat lines of filament. PLA prints at moderate temperatures, specifically 210 ◦C, depend on the selected nozzle size and print profile. Profiles for 0.25-mm nozzles use a slightly lower temperature. A temperature of 60 ◦C was maintained on the building plates. An ultrasonic cleaner was used for postprocessing to remove the supporting materials. However, it was difficult to remove the supporting structures from the microchannels. Apparently, the primary nozzle AA was only used for the collector fabrication. In order to investigate the surface finish and avoid overhang structures, two layers of microchannels were fabricated separately and glued. Figure 10a shows the experimental test design without collector covering. The steps in the collectors were introduced to decrease the flow maldistribution.

**Figure 10.** Experimental test design for collector flow maldistribution (**a**); comparison plot between experimental and CFD pressure losses (dashed lines are for the numerical results and symbols are for the experimental readings) (**b**).

An experimental test apparatus of secondary collectors with microchannels is depicted in Figure 10a. The inlet of the secondary collectors was extruded to have a uniform flow at the collector inlets. The extruded height of the collector inlet was selected based on a CFD optimization study. Six pressure taps (P1–P6) were installed for pressure readings (see Figure 10a). Five pressure taps were installed near the microchannel inlets (P2–P6), and the remaining one (P1) was placed at the collector inlet covering. Experimental testing was carried out for five different cases. The inlet mass flow rates are shown in Table 2. The size of the pressure tap hole was 60 microns, and it was ensured that the pressure taps were placed precisely at the center of the duct.

**Table 2.** Collector inlet mass flow rates for different cases.

It is relatively easy to measure the mass flow rate of selected microchannels in a heat sink or heat exchanger if liquids are used as the working fluid [28]. Moreover, optical techniques such as micro particle image velocimetry can also be employed to quantify the maldistribution of liquid flows. For a multichannel heat exchanger with gas as the working medium, however, it is challenging to measure the mass flow rate of each microchannel. Therefore, an indirect method was used in the current work where pressure loss was measured across five microchannels amongst 33 parallel microchannels. An unequal pressure profile in the trapezoidal manifold suggests an opposite profile of maldistribution, as the mass flow through each microchannel was directly linked with the associated pressure loss. Experiments were therefore carried out to investigate the collector pressure losses and compare them

with CFD. The results of the comparison between CFD and the microchannel measurements are shown in Figure 10b. Microchannel inlets (P2) near the collector inlets had more pressure losses. On the contrary, the microchannel inlets (P6) that were far away from the collector inlets had lower pressure losses. The pressure-loss decreasing pattern (from the collector inlet (P1) to the last microchannel (P6)) was nearly quadratic. In addition, there was a good agreement between CFD and the experiments, and the relative error was less than 4%. This validates the employed turbulence model in the collector studies presented earlier, and therefore the maldistribution pattern predicted by the CFD model can be considered to be representative of reality.

#### **5. Conclusions**

The influences of wire-net and S-shape perturbators on microchannel and collector performances were evaluated separately based on a Reduced Order Model (ROM). Microchannels with a wire-net were considered for comparative studies. Furthermore, the microchannel and collector performances were experimentally analyzed based on pressure-loss studies. The wire-net microchannels showed a better performance in terms of thermal efficiency, while S-shape fins provided a better performance in terms of pressure loss. Collectors with wire-net microchannels provided a more uniform flow distribution compared to the S-shape microchannels. Collector flow maldistribution decreased with higher microchannel resistance (i.e., smaller microchannels).

The recirculation zones in the collectors influenced the flow maldistribution. The strength of the recirculation zone near the collector beds was suppressed at higher mass flow rates. From the primary collector analysis, it was found that there was an optimum height where the pressure losses were saturated and the collector maldistribution decreased. The choice of a turbulent model was verified by comparing the CFD results with experimental tests performed for a single microchannel with S-shape perturbators and a micro heat exchanger with triangular collectors. Experimental testing of adiabatic flow showed that as a result of perturbators the onset of turbulent transition shifts to lower Reynolds number values compared to free channels. Apart from that, the microchannel and collector simulation results coincided with the experimental results with good accuracy (<4%).

**Author Contributions:** Simulations and Analysis, J.J.; Supervision, M.D., J.J.B., R.N. and G.L.M.; Funding acquisition, M.D., J.J.B., G.L.M. and G.J.K.; Experimental Investigation, D.R. and J.J.; Validation, J.J. and D.R.; Writing—original draft, J.J.; Review: J.J.B., J.G.K., M.D. and D.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This ITN Research Project MIGRATE (www.migrate2015.eu) is supported by European Community H2020 Framework under the Grant Agreement No. 643095. The research of MITIS has been funded by grants Nanocogen+ and Nanocogen+2 from DGO4 and DGO6 directorates of Wallonia.

**Acknowledgments:** Microfabrication of microchannels with S-shaped perturbators from Institute of Microstructure Technology is acknowledged. Technical support of Franceso Vai for fabrication of the MC test section assembly is acknowledged.

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

#### **Abbreviations**

#### **References**

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).


Meet the editor

Idah Sithole Niang graduated from Michigan State University, USA. She is a professor in the Department of Biotechnology and Biochemistry, University of Zimbabwe, Africa, where she teaches Molecular Biology and Biotechnology and conducts research on exploiting the biotechnological potential of bacteriophages and bacterial and fungal endophytes. She maintains a keen interest in biotechnology and biosafety issues. She has edited and

co-edited several books and is the former chairperson of the University of Zimbabwe Publications Committee.

Contents

**Section 1**

*and Aloysius A. Beah*

*by Richard Mundembe*

*and Melaku Addisu*

**Section 2**

*by Robert Redden*

Genetic Engineering for Oil Modification

**Preface XI**

Transformation Methodology and Biotechnology Applications **1**

**Chapter 1 3**

**Chapter 2 25**

**Chapter 3 49**

**Chapter 4 83**

**Chapter 5 95**

**Chapter 6 107**

Biosafety Framework and Consumer Perceptions **131**

**Chapter 7 133**

*by Endale Gebre Kedisso, Nicolas Barro, Lilian Chimphepo, Tahani Elagib,*

Proposed Revision of the National Gene Technology Scheme for Australia

*by Muthulakshmi Chellamuthu, Kokiladevi Eswaran and Selvi Subramanian*

Genetic Modification and Application in Cassava, Sweetpotato and Yams *by Prince Emmanuel Norman, Daniel K. Dzidzienyo, Kumba Yannah Karim*

Next Generation of Transgenic Plants: From Farming to Pharming

Site-Specific Recombination and the Development of Cisgenic Plants

*by Alp Ayan, Sinan Meriç, Tamer Gümüş and Çimen Atak*

Genetic Conservation and Importance of Ginger in Ethiopia

Crop Biotechnology and Smallholder Farmers in Africa

*Rose Gidado, Ruth Mbabazi, Bernard Oloo and Karim Maredia*

*by Girma Hailemichael, Mohammedsani Zakir*


**2. Nutritional status**

#### **2.1. Malnutrition rates and weight loss**

Esophageal cancer patients often suffer from malnutrition, the manifestation of which is strongly linked to the stage of the disease and therapeutic regimens. Excessive weight loss is partly attributed to the disease process itself and is often deteriorated due to chemotherapy/ radiotherapy treatment [11]. Malnutrition occurs in 60–85% of esophageal cancer patients, which is one of the highest reported rates when compared to other malignancies, such as lung, head and neck, stomach, and pancreatic cancers [12]. According to recent publications, 32% of patients who underwent esophagectomy experienced more than 10% weight loss preoperatively [13], while 90% of patients had a 5% weight loss at 3 months postoperatively [14]. It is noteworthy that in many patients weight loss persists for at least 3 years after surgi‐ cal intervention [15]. Another study revealed that 43.8% of patients with esophageal cancer were underweight based on BMI values, 29.7% of patients were undernourished as indicated from anthropometric measurements and 69% had weight loss within 2 weeks before hos‐ pital admission [16]. Chemotherapy and radiotherapy affect nutritional status by promot‐ ing weight loss and muscle wasting. More specifically, malnutrition developed in 83.8% of patients after the end of radiotherapy [17] and the number of patients requiring nutritional intervention increased from 56 to 75% during induction chemotherapy [10]. These facts high‐ light the need of nutritional assessment at several time points in order to identify patients who are candidates for nutritional support.

#### **2.2. Methods of nutritional assessment and impact on clinical outcome**

Excess alcohol consumption and consumption of hot food and beverages increase the risk of esophageal squamous cell carcinoma, whereas many components of Western type diet, such as red and processed meat increase the risk of esophageal adenocarcinoma [2–4]. Moreover, obesity, especially central type, and gastroesophageal reflux disease are risk factors toward developing esophageal adenocarcinoma [5, 6]. On the other hand, healthy dietary habits could be a shield against some types of esophageal cancer [7], while nutritional support could

Esophageal cancer is commonly associated with malnutrition and impaired nutritional intake. Nutritional management of these patients may differ according to the type of therapy and stage of disease, in order to alleviate symptoms, ameliorate nutritional status, and improve quality of life. Furthermore, cancer cachexia affects a great percentage of esophageal can‐ cer patients emerging as a significant factor the multidisciplinary team should deal with [8]. Regarding the perioperative care, the proper type of feeding (i.e. enteral/parenteral nutrition (EN/PN), immunonutrition, oral supplements, etc.) as well as the right time of feeding is a matter of controversy due to lack of consistent evidence for patients undergoing esophagec‐ tomy [9]. Special nutritional needs in the long run are also of great significance for patients with both resectable and unresectble disease, bearing in mind that the majority of these patients strive to meet their caloric and protein needs. Considering the treatment approach, nutritional screening and assessment leads to early detection of malnourished patients in need of nutritional support. Taking into account patient's specific needs helps to choose the most suitable routes of delivering nutritional support (nutritional counseling and artificial nutrition). Personalized nutritional support could modify poor nutritional status resulting in reduced postoperative complications and improved survival. Moreover, the implementation of nutritional protocols could reduce toxicity of treatment regimens and in conjunction with nutritional monitoring could have impact on patients' daily living [10]. Therefore, nutritional therapy should be an essential part of a multidisciplinary approach in the clinical setting, in

The aim of the current review is to focus on the etiology of malnutrition, review the various methods of nutritional assessment, and analyze the aspects of nutritional management of

Esophageal cancer patients often suffer from malnutrition, the manifestation of which is strongly linked to the stage of the disease and therapeutic regimens. Excessive weight loss is partly attributed to the disease process itself and is often deteriorated due to chemotherapy/ radiotherapy treatment [11]. Malnutrition occurs in 60–85% of esophageal cancer patients, which is one of the highest reported rates when compared to other malignancies, such as lung, head and neck, stomach, and pancreatic cancers [12]. According to recent publications, 32% of patients who underwent esophagectomy experienced more than 10% weight loss

esophageal cancer patients as a fundamental part of multimodality therapy.

be beneficial for the management of these patients after cancer diagnosis.

order to improve short‐ and long‐term outcomes.

**2. Nutritional status**

90 Esophageal Abnormalities

**2.1. Malnutrition rates and weight loss**

There are various methods of nutritional assessment in the clinical setting providing cli‐ nicians with tools for the evaluation of the nutritional status and the estimation of nutri‐ tional needs of esophageal cancer patients. One commonly used criterion of malnutrition is the percentage of weight loss in a certain period of time. Α weight loss of more than 5% in the previous month or more than 10% in the last 3–6 months is considered significant malnutrition [18]. One retrospective study concluded that weight loss <10% and BMI>18 kg/m2 were significantly correlated with a better response to chemoradiotherapy, while BMI>18 kg/m2 was predictive of survival at both univariate and multivariate analysis [19]. Other anthropometric measurements, such as mid‐arm circumference and mid upper‐arm muscle area can give information about the nutritional status and body composition of these patients.

Biochemical markers, such as plasma proteins are often used as nutritional markers. For instance, albumin is commonly used for the assessment of protein status, but given its long half‐life (14–20 days), it has a slow response to dietary interventions and cannot detect subtle changes in nutritional status. Furthermore, albumin reflects an acute phase response and is not always a reliable marker of malnutrition [20]. However, albumin is an independent risk factor for complications after esophagectomy, since patients with hypoalbuminemia have twice the risk of postoperative infection and increased incidence of acute respiratory distress syndrome [21]. A recent review examined the association of serum albumin with postop‐ erative complications in patients undergoing esophagectomy, suggesting that low serum albumin does increase the risk of postoperative complications, but there is still conflicting evidence regarding the prognostic value of this biomarker [22].

Other methods of nutritional assessment include questionnaires that incorporate many factors that impede adequate nutritional intake, as well as laboratory parameters, and unintentional weight loss. For example, Subjective Global Assessment (SGA), a question‐ naire based on four parameters of patient's history (percentage of weight loss, changes in habitual diet, presence of significant gastrointestinal symptoms, and changes in patient's functional capacity) and three elements of their physical examination (loss of subcutane‐ ous fat, muscle wasting, and presence of edema or ascites) is one the most commonly used tool for nutritional screening in malnourished hospital patients with cancer. SGA is strongly correlated with performance status in esophageal cancer patients [16] as well as with The Glasgow prognostic score and with complications during cancer treatment [23]. Other tools that have been studied in cancer patients is the Prognostic Nutritional Index (PNI), the Nutritional Risk Screening 2002 (NRS 2002), the Controlling Nutritional Status (CONUT) and the Nutritional Risk Index (NRI). Esophagectomized patients with a high Prognostic Nutritional Index—a tool which includes serum albumin and absolute peripheral lymphocyte count—had a higher prevalence of postoperative complications [24]. These results are in accordance with later studies, indicating that nutritional status preoperatively, expressed as PNI, was significantly related with the occurrence of severe complications and was a predictive factor for long‐term survival [25, 26]. Nevertheless, Han‐Geurts et al. showed that PNI was not associated with postoperative infectious com‐ plications in patients who underwent esophageal resection for malignancy [27]. NRS‐2002 is recommended by the European Society for Clinical Nutrition and Metabolism as a stan‐ dard tool for the assessment of surgical patients [28], but it is not tailored to esophageal cancer patients, thus limiting its prognostic value in this population. The CONUT score, which is calculated by serum albumin concentration, total peripheral lymphocyte count, and total cholesterol concentration, was developed as a screening tool for early detec‐ tion of patients at risk of malnutrition [29]. Patients classified preoperatively as moderate or severe malnourished had a higher incidence of pulmonary and other severe morbidi‐ ties, surgical site infections, and reoperation. Consequently, the duration of hospital stay in patients with moderate or severe malnutrition was significantly longer compared to well‐nourished or slightly malnourished patients [30]. Similarly, Hirahara et al. [31] and Toyokawa et al. [32] demonstrated that CONUT was significantly associated with cancer death and poorer disease‐free survival in patients with resectable esophageal squamous cell carcinoma. NRI is another tool used for the assessment of malnutrition based on serum albumin concentration and weight loss. Ιncreased nutritional risk, derived from NRI, is associated with reduced survival in esophageal cancer patients treated with definitive chemoradiotherapy [10, 33], but is not associated with postoperative infectious complica‐ tions in patients treated with esophagectomy [27]. The Geriatric Nutritional Risk Index (GNRI) is a new index recently introduced for the assessment of nutritional status of elderly patients. Patients diagnosed with poor nutritional status according to GNRI had significantly higher rate of respiratory complications after esophagectomy and gastric tube reconstruction [34]. In conclusion, there are several nutrition assessment tools for esopha‐ geal cancer patients, but since many of the studies mentioned above are of retrospective nature, the gold standard for the evaluation of nutritional status in this cancer subpopula‐ tion is yet to be determined (**Table 1**).

**Table 1.** Nutritional assessment of esophageal cancer patients.

#### **2.3. Sarcopenia and cancer cachexia**

used tool for nutritional screening in malnourished hospital patients with cancer. SGA is strongly correlated with performance status in esophageal cancer patients [16] as well as with The Glasgow prognostic score and with complications during cancer treatment [23]. Other tools that have been studied in cancer patients is the Prognostic Nutritional Index (PNI), the Nutritional Risk Screening 2002 (NRS 2002), the Controlling Nutritional Status (CONUT) and the Nutritional Risk Index (NRI). Esophagectomized patients with a high Prognostic Nutritional Index—a tool which includes serum albumin and absolute peripheral lymphocyte count—had a higher prevalence of postoperative complications [24]. These results are in accordance with later studies, indicating that nutritional status preoperatively, expressed as PNI, was significantly related with the occurrence of severe complications and was a predictive factor for long‐term survival [25, 26]. Nevertheless, Han‐Geurts et al. showed that PNI was not associated with postoperative infectious com‐ plications in patients who underwent esophageal resection for malignancy [27]. NRS‐2002 is recommended by the European Society for Clinical Nutrition and Metabolism as a stan‐ dard tool for the assessment of surgical patients [28], but it is not tailored to esophageal cancer patients, thus limiting its prognostic value in this population. The CONUT score, which is calculated by serum albumin concentration, total peripheral lymphocyte count, and total cholesterol concentration, was developed as a screening tool for early detec‐ tion of patients at risk of malnutrition [29]. Patients classified preoperatively as moderate or severe malnourished had a higher incidence of pulmonary and other severe morbidi‐ ties, surgical site infections, and reoperation. Consequently, the duration of hospital stay in patients with moderate or severe malnutrition was significantly longer compared to well‐nourished or slightly malnourished patients [30]. Similarly, Hirahara et al. [31] and Toyokawa et al. [32] demonstrated that CONUT was significantly associated with cancer death and poorer disease‐free survival in patients with resectable esophageal squamous cell carcinoma. NRI is another tool used for the assessment of malnutrition based on serum albumin concentration and weight loss. Ιncreased nutritional risk, derived from NRI, is associated with reduced survival in esophageal cancer patients treated with definitive chemoradiotherapy [10, 33], but is not associated with postoperative infectious complica‐ tions in patients treated with esophagectomy [27]. The Geriatric Nutritional Risk Index (GNRI) is a new index recently introduced for the assessment of nutritional status of elderly patients. Patients diagnosed with poor nutritional status according to GNRI had significantly higher rate of respiratory complications after esophagectomy and gastric tube reconstruction [34]. In conclusion, there are several nutrition assessment tools for esopha‐ geal cancer patients, but since many of the studies mentioned above are of retrospective nature, the gold standard for the evaluation of nutritional status in this cancer subpopula‐

tion is yet to be determined (**Table 1**).

**Table 1.** Nutritional assessment of esophageal cancer patients.

*Biochemical markers* (albumin, prealbumin, total peripheral lymphocyte count)

*Questionnaires and indices:* SGA, NRS2002, CONUT, NRI, PNI, Glasgow Prognostic Score

*Percentage of unintentional*

92 Esophageal Abnormalities

*weight loss*

Patients with esophageal cancer often witness loss of muscle mass and/or muscle strength, a condition described as sarcopenia. Although there are many different definitions of the term sarcopenia, all of them place emphasis on the impaired physical function following decreased muscle mass [35–37]. Sarcopenia is a component of cancer cachexia, especially in advanced stage cancer patients. Cancer cachexia is a complex syndrome which combines anorexia, early satiety, weakness, anemia, inflammation, weight loss, and loss of muscle mass with or with‐ out loss of fat mass [38]. A recent study pointed out that the prevalence of cancer cachexia in advanced esophageal cancer patients was 52.9% [39]. There is lack of consensus on the defini‐ tion, diagnostic criteria, and classification of cancer cachexia, but the most commonly used definition includes one of the following: Weight loss >5% over past 6 months (in absence of simple starvation); or BMI <20 and any degree of weight loss >2%; or appendicular skeletal muscle index consistent with sarcopenia (males <7.26 kg/m2 ; females <5.45 kg/m2 ),and any degree of weight loss >2% [40]. Furthermore, assessment of sarcopenia plays an emerging role in cancer patients owing to the fact that CT scanning is a gold standard imaging method of body composition analysis at the tissue‐organ level [41]. CT scans can identify reduced muscle mass and predict negative cancer outcomes in people with abdominal malignancies, where traditional methods of assessment are less effective [42]. Handgrip strength is another method used to measure muscle strength, which is directly related to the physiologic status of the individual and reflects patient's nutritional status. It could be easily used in patient's nutri‐ tional assessment due to the fact that it is an inexpensive and not time‐consuming method.

Sarcopenia and cachexia are prognostic factors for surgical complications, decreased survival, and poor response to chemotherapy. More specifically, patients with weak handgrip strength had higher risk of complications and mortality after elective esophagectomy [43]. Decreased muscle mass, assessed by preoperative computed tomography scans, seems to be an indepen‐ dent predictor of both overall survival (**Figure 1**) [44] and disease‐free survival, as significant as tumor stage, in patients following esophagectomy [45, 46]. In addition, sarcopenic patients who underwent esophagectomy had significantly higher rate of respiratory complications compared to nonsarcopenic subjects, but there was no difference in the incidence of overall complications between the two groups [47–49]. Sarcopenia has also impact on chemotherapy outcome since decreased muscle mass is associated with dose‐limiting toxicity and patho‐ logical chemotherapy response in patients receiving neoadjuvant chemotherapy [50–52]. Therefore, it is imperative to estimate patients' muscle mass not only preoperatively but also before the οnset of chemotherapy, bearing in mind that sarcopenia is frequently masqued by obesity making it more difficult to define patients' needs for intervention.

#### **2.4. Mechanisms of malnutrition**

Malnutrition, as mentioned above, affects a great percentage of esophageal cancer patients. Most patients are not able to achieve a positive energy balance and in many cases, initial body weight cannot even be maintained. This is ascribed mainly to impaired metabolism and side effects caused by esophagectomy or chemotherapy/radiation treatment. Dysphagia is a very common mechanical cause of malnutrition and is accompanied by dietary changes in order to

**Figure 1.** Example of CT scan analysis by the Slice‐O‐Matic software. Lumbar vertebrae 3 slice with Hounsfield units used to measure area of skeletal muscle, subcutaneous fat and visceral fat. White outer ring, subcutaneous fat; Light grey inner ring, muscle; Dark grey central area, visceral fat. Reproduced from Gibson et al. [42].

avoid foods that worsen symptoms leading to inadequate intake of calories. The surgical pro‐ cedure often causes deficiencies in macronutrients and micronutrients, with the most prevail‐ ing side effects being postprandial dumping syndrome, dysphagia, anorexia, reflux, and early satiety [53–55]. Furthermore, chemotherapy affects rapidly proliferating cells preferentially and, consequently, affects the cells of the gastrointestinal tract. Chemotherapy‐related causes of reduced food intake include nausea, vomiting, diarrhea, mucositis, and decreased appetite. Radiotherapy also contributes to malnutrition, and combined with chemotherapy exacerbates patients' nutritional status. Common side effects of radiotherapy include mucositis, esopha‐ gitis, odynophagia, mouth and throat soreness, and hypogeusia [56]. Tumor‐related causes of malnutrition are also of great significance, but the mechanisms involved are still under inves‐ tigation. Cancer‐induced anorexia may result from circulating factors produced by the tumor or by the host in response to the tumor. For instance, cytokines, such as interleukins (IL) and tumor necrosis factor‐alpha (TNF‐α) cause anorexia [57], whereas other tumor‐secreted fac‐ tors promote central‐ and peripheral‐mediated cancer cachexia. Tumor growth results in the secretion of pro‐inflammatory factors that promote cachexia by signaling anorexia, muscle wasting, and white adipose tissue atrophy. Tumors also secrete both the proteolysis‐inducing factor and activin, which promote skeletal muscle degradation and sarcopenia [58, 59].

Even though the mechanisms behind muscle wasting have been widely studied, less is known about the factors implicated in adipose tissue loss in cancer cachectic patients, such as lipid‐ mobilizing factor, as well as about the derangement in the neuroendocrine regulation of food intake and anorexia [60]. Additionally, changes in carbohydrate, lipid, and protein metabo‐ lism account for altered substrate metabolism. Changes in resting energy expenditure (REE) are considered one of the causes of nutritional depletion in cancer. In particular, measured REE by indirect calorimetry (IC) was elevated in patients with newly detected esophageal cancer, compared to healthy individuals [61]. A cross‐sectional study involved 30 patients admitted with a diagnosis of squamous cell carcinoma who underwent IC before starting cancer therapy. The basal energy expenditure (BEE) was evaluated using IC and was also estimated using the Harris‐Benedict Equation (HBE). The results showed that BEE of patients with squamous cell carcinoma was underestimated when using the HBE [62]. However, cur‐ rent evidence is inconsistent, since some studies suggest that REE is normal and is not affected by stage of disease [63–65]. Consequently, more research should be conducted in order to shed light on this controversial field.