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Heart valve replacements are critical for patients with valve malfunctions, and the tri-leaflet mechanical heart valve (tMHV) is one of the most durable options available. The tMHV is used to replace malfunctioning heart valves, restoring normal blood flow with exceptional durability, often lasting up to 20 years without needing replacement. This durability makes tMHVs particularly suitable for patients under 60. However, mechanical issues like cavitation can undermine the valve's functionality, posing risks to both its longevity and the overall efficacy of cardiovascular treatments. While previous studies have investigated some aspects of cavitation in these valves, the combined effects of blood viscosity and fluid-structure interaction (FSI) on cavitation dynamics remain insufficiently explored.This work models and numerically simulate the influence of blood viscosity on cavitation within tMHVs, using FSI principles. A detailed geometric model of the tMHV was developed, incorporating experimental data and non-Newtonian fluid behaviour to accurately replicate blood flow. Simulations were conducted in ANSYS Fluent R1® using a transient solver to capture the dynamic FSI, with the Carreau-Yasuda model representing blood's shear-dependent viscosity. Cavitation was observed at pressures as low as 4.5 Pa—well below the 15-20 Pa range typically reported—indicating a higher vulnerability than previously recognized. The simulations further showed significant vapour bubble formation, with the maximum vapour volume fraction reaching 0.953. High-speed leakage flows, peaking at 11 m/s during valve closure, were also noted, considerably exceeding velocities observed in earlier studies.These findings demonstrate that cavitation occurs when blood pressure drops below vapour pressure, causing vapour bubbles to form. These bubbles generate shock waves that can damage the valve surfaces and surrounding tissue. Insights from this study will aid in the design of next-generation tMHVs with optimised flow dynamics, potentially reducing cavitation risks and enhancing patient outcomes by minimising valve-associated complications.

Metaheuristic optimization algorithms (MOAs) are gaining increasing interest because of their exceptional effectiveness in addressing many optimization issues. Nevertheless, these algorithms often face difficulties when used to solve real-world problems, necessitating further evaluation of these algorithms that are used to solve real-world problems. In addition, there is currently no established standard for evaluating algorithmic performance. MOAs successfully address engineering challenges due to their inherent complexity and multidimensional character. Several studies have explored the use of these algorithms to tackle electrical and civil engineering problems, highlighting their practical importance. This study thoroughly examined the most recent papers on electrical and civil engineering topics, specifically focusing on the extraction of parameters and optimization of models.

Achieving sustainable development goals requires integrating renewable energy systems (RESs) into our current energy infrastructure. However, sophisticated approaches are needed due to the complexity of decision-making. In the context of RESs, this study provides a thorough evaluation, progress report, bibliometric analysis, and assessment of metaheuristic algorithms and multi-criteria decision-making methodologies. Through an extensive review of academic literature, this study highlights the potential of metaheuristics to improve sustainable development decision-making processes by emphasizing important trends and developments. The study also examines how these approaches promote efficient resource allocation, reduce environmental impacts, and increase societal benefits to address the economic, social, and environmental pillars of sustainable development. The summary of results highlights the important roles that metaheuristics and multi-criteria decision-making play in effectively integrating and administrating RESs.

A proton exchange membrane (PEM) electrolyzer is fed with water and powered by electric power to electrochemically produce hydrogen at low operating temperatures and emits oxygen as a by-product. Due to the complex nature of the performance of PEM electrolyzers, the application of an artificial neural network (ANN) is capable of predicting its dynamic characteristics. A handful of studies have examined and explored ANN in the prediction of the transient characteristics of PEM electrolyzers. This research explores the estimation of the transient behavior of a PEM electrolyzer stack under various operational conditions. Input variables in this study include stack current, oxygen pressure, hydrogen pressure, and stack temperature. ANN models using three differing learning algorithms and time delay structures estimated the hydrogen mass flow rate, which had transient behavior from 0 to 1 kg/h, and forecasted better with a higher count (>5) of hidden layer neurons. A coefficient of determination of 0.84 and a mean squared error of less than 0.005 were recorded. The best-fitting model to predict the dynamic behavior of the hydrogen mass flow rate was an ANN model using the Levenberg–Marquardt algorithm with 40 neurons that had a coefficient of determination of 0.90 and a mean squared error of 0.00337. In conclusion, optimally fit models of hydrogen flow from PEM electrolyzers utilizing artificial neural networks were developed. Such models are useful in establishing an agile flow control system for the electrolyzer system to help decrease power consumption and increase efficiency in hydrogen generation.

Several computational algorithms have been developed to determine building heating and cooling energy requirements on a citywide scale. However, limited contemporary models can analyse all structures in an urban region, and even fewer can handle power consumption on monthly basis or at lower levels. Nevertheless, these characteristics are crucial for urban power generation planners. In light of the imperative role that accurate modelling plays in evaluating urban energy systems, this paper underscores the critical need to overcome the substantial processing costs associated with existing models, recognizing that addressing this challenge is pivotal for precise simulation of urban electricity consumption and, consequently, for informing effective urban power generation planning strategies. This study delves into the integration of solutions related to renewable energy source envelopes, focusing particularly on the strategic approach within clusters of buildings in urban energy systems. The primary objective of this research is to elucidate the cluster methodology and its defining parameters, followed by an exploration of key influencing factors and modelling methodologies. The study is delimited by specific considerations encompassing barrier dimensions, methodological intricacies, and the pivotal elements that govern renewable energy source envelope-based power provisions for building clusters. According to the conducted analysis, the processing costs of these models are a significant hurdle to accurate microsimulation of urban electricity consumption. Theoretical and numerical approximations should be used; even computational methodologies are subject to uncertainty analysis and error measurement.

Because of the harsh environmental impacts of fossil fuels, price fluctuation, and resource limitation, renewable energy resources (RERs) are considered the ultimate solution to overcome these challenges. RERs are also abundant and environmentally friendly. It has been reported that the growth rates in renewable energy installations have increased by 8–9% annually since 2010, reaching a cumulative capacity of 2.9 TW by 2020 and exceeding the growth rate for fossil resources. In this work, the different RERs have been critically discussed in terms of their operation principles, advantages, disadvantages, and associated environmental impacts, focusing on wind and solar energies as the most deployed RERs. Barriers to the full deployment of RERs to replace fossil resources are discussed. Based on the discussion presented on RERs, the contribution of deploying RERs towards the achievement of the United Nations (UN) Sustainable Development Goals (SDGs) is also critically discussed. A total of 80 indicators have been identified for RERs' contribution toward the achievement of SDGs.

Abstract Carbon dioxide (CO2) emissions contribute considerably towards increasing greenhouse effect. Carbon capture and storage can reduce CO2 emissions to a great extent but lacks economic feasibility. The economic feasibility of CO2 capture could be boosted by utilizing the captured gas to produce valuable end products. CO2 is a highly stable molecule; therefore, special catalysts and elevated conditions of temperature and pressure are required for its conversion. This review presents the current status of CO2 utilization processes from various aspects, including thermodynamic, economic, and environmental impacts. The use of process systems engineering (PSE) tools and techniques in a broad spectrum, to improve the technical, economic, and environmental feasibility of these processes, is the major focus of this review. In this regard, a framework has also been presented showing the integration of various PSE techniques. All the related information in the form of tabulated data as well as qualitative and quantitative plots have been presented and critically analyzed.

The optimum output power of the proton exchange membrane fuel cell (PEMFC) is dependent on operational conditions such as fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate. Therefore, the aim of this paper is to enhance performance of PEMFC by identifying optimal operating parameters of PEMFC. The proposed strategy includes both modelling and optimization stages. An adaptive network-based fuzzy inference system (ANFIS) is utilized in creating the model based on experimental datasets. Whereas, the grey wolf optimizer (GWO) is used to identify the best values of fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate corresponding to maximum power PEMFC. The obtained results demonstrated the superiority of the integration between ANIFS based modelling and GWO. Regarding the modelling accuracy, The RMSE values are 0.017 as well as 0.0262 respectively for treating and testing phases. The coefficient of determination values is 0.9921 as well as 0.9622 respectively for treating coupled with testing phases. The optimal parameters are 1.0 bar, 0.8 bar, 117.03 mL/min, 150.0 mL/min respectively fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate corresponding to maximum power of PEMFC. Thanks to the integration between ANFIS-based modelling and GWO, the output power of PEMFC has been increased from 0.587 W using experimental work to 0.92 W.

Polymer Electrolyte Membrane fuel cell (PEMFC) uses hydrogen as fuel to generate electricity and by-product water at relatively low operating temperatures, which is environmentally friendly. Since PEMFC performance characteristics are inherently nonlinear and related, predicting the best performance for the different operating conditions is essential to improve the system’s efficiency. Thus, modeling using artificial neural networks (ANN) to predict its performance can significantly improve the capabilities of handling multi-variable nonlinear performance of the PEMFC. This paper predicts the electrical performance of a PEMFC stack under various operating conditions. The four input terms for the 5 W PEMFC include anode and cathode pressures and flow rates. The model performances are based on ANN using two different learning algorithms to estimate the stack voltage and power. The models have shown consistently to be comparable to the experimental data. All models with at least five hidden neurons have coefficients of determination of 0.95 or higher. Meanwhile, the PEMFC voltage and power models have mean squared errors of less than 1 × 10−3 V and 1 × 10−3 W, respectively. Therefore, the model results demonstrate the potential use of ANN into the implementation of such models to predict the steady state behavior of the PEMFC system (not limited to polarization curves) for different operating conditions and help in the optimization process for achieving the best performance of the system.

The recovery of waste heat from Proton Exchange Membrane (PEM) Fuel cell is sin qua non to the development of organic Rankin cycle units. Despite the appreciable increase in the sale of PEM fuel cell units in 2021, the waste heat from some of these fuel cell units is typified by large fluctuations in mass flow rate as well as temperature which is more likely to affect the overall performance of an organic Rankine cycle (ORC) unit when coupled to a fuel cell. It is therefore imperative that the dynamic modelling of the Proton Exchange Membrane Fuel cell and organic Rankine cycle integrated system is developed to analyse the performance of the integrated system. This also involves the development of an appropriate control strategy for guaranteeing safer and optimum performance of the integrated system. The developed Proportional, Integral, Derivative (PID) control unit is able to maintain the thermal efficiency of the ORC system at 10% subject to the mass flow rate of the waste heat as well as the working fluid and also ensure safe operation of the integrated system. There is a 0.9% increase in the output power of the PEMFC after 2000 seconds of operation clearly highlighting the contribution of the integrated system in improving the overall output power being harnessed.

Direct methanol fuel cell (DMFC) is fueled with liquid methanol coupled with air to produce power at reasonably lower operational conditions while resulting in by-products of carbon dioxide and water, which is more environmentally friendly. Due to the complexity associated with the performance of direct methanol fuel cell, the application of artificial neural network (ANN) can significantly predict the characteristic performance of the cells. Nevertheless, limited studies have delved into the exploration of artificial neural network in the prediction of the transient characteristics of direct methanol fuel cells. The current study however presents a detailed investigation into the prediction of the dynamic thermal characteristics of a direct methanol fuel cell stack subjected to varying operational environment. Parameters considered in the study as input include methanol concentration, anode as well as cathode inlet flow rates, coupled with current. Outcomes for the artificial neural network models for three varying learning algorithms were ascertained for anode and cathode temperatures, which were forecasted closely by models with higher number of hidden neurons. Such models have coefficients of determination of 0.95 or more and mean square error less than 0.04. Thus, the outcome of the study presents prospects for artificial neural network methods as optimum control approach in direct methanol fuel cell development.

Micromobility is dominant in urban areas, enhancing the transportation sustainability and assisting in fulfilling the United Nations Sustainable Development Goals (SDGs). This work provides an overall assessment of micromobility: its role under SDGs, policy options, micromobility regulations, emerging technologies, utilisation determinants, energy source, and energy storage. The analysis shows that micromobility could play a major role in achieving the SDGs, specifically SDG 3 (Good Health and Well-being) by lowering toxic gas emissions and reducing projected traffic accidents. Also, the effect on SDG 8 (Decent Work and Economic Growth) by reducing the transportation footprint, on SDG 11 (Sustainable Cities and Communities) by increasing transposition accessibility, reducing traffic congestion and improving the air quality, and equally on SDG 12 (Responsible Consumption and Production) by reducing transportation footprint and increase the sources efficiency. Moreover, micromobility affects SDG 13 (Climate Action) by reducing the greenhouse gases. Furthermore, the analysis shows a clear gap in literature and publications on micromobility, especially in energy management and energy storage area. This review shows that new technology of renewable energy and energy storage could play a significant role in achieving the sustainability of micromobility therefore achieving the SDGs.

Nowadays heat pipes are considered to be popular passive heat transfer technologies due to their high thermal performance. The heat pipe is a superior heat transfer apparatus in which latent heat of vaporization is employed to transfer heat for an extended distance under a limited operating temperature difference. Numerical simulation of heat transfer devices is a principal step before implementing in real-life applications as many parameters can be tested in cost-and time-effective behaviors. The present study provides a review of the numerical simulations of various heat pipes in different applications such as cooling of electronic components, heating, ventilation, and air conditioning (HVAC), nuclear reactors, solar energy systems, electric vehicles, waste heat recovery systems, cryogenic, etc. Firstly, this work introduces a background about the main components of heat pipes such as an evacuated tube, wick, and working fluid. The fluid flow and thermal performance characteristics of heat pips are discussed, considering the optimum parameters. Finally, the critical challenges and recommendations for future work encountering the broad application of heat pipes are thoroughly studied.

The rely on internal combustion engines is gradually decreased with the recent evolution of electric vehicles (EVs) in the automotive industry. Electric motors are replacing the energy systems mainly to improve the powertrain's efficiency and ensure they are environmentally friendly. These novel powertrains are designed to operate solely on batteries or supercapacitors. For these types of EVs, the battery is charged using an alternating current supply in connection to the grid in the case of plug-in electric vehicles. Internal combustion engines are equally used for some hybrid vehicles. Charging of the battery can also be carried out via regenerative braking from the traction motor. This study presents a brief background about the different available EVs, detailed information on various power converter electronics used in battery electric vehicles, and a summary of the strengths (S), weaknesses (W), opportunities (O), and threats (T) of the EV is presented. Moreover, SWOT analysis of the battery electric vehicles (BEV) and their prospects in the automotive industry are introduced.

The adoption of Proton Exchange Membrane (PEM) fuel cells (FCs) is of great significance in diverse industries, as they provide high efficiency and environmental advantages, enabling the transition to sustainable and clean energy solutions. This study aims to enhance the output power of PEM-FCs by employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) and modern optimization algorithms. Initially, an ANFIS model is developed based on empirical data to simulate the output power density of the PEM-FC, considering factors such as pressure, relative humidity, and membrane compression. The Salp swarm algorithm (SSA) is subsequently utilized to determine the optimal values of the input control parameters. The three input control parameters of the PEM-FC are treated as decision variables during the optimization process, with the objective to maximize the output power density. During the modeling phase, the training and testing data exhibit root mean square error (RMSE) values of 0.0003 and 24.5, respectively. The coefficient of determination values for training and testing are 1.0 and 0.9598, respectively, indicating the successfulness of the modeling process. The reliability of SSA is further validated by comparing its outcomes with those obtained from particle swarm optimization (PSO), evolutionary optimization (EO), and grey wolf optimizer (GWO). Among these methods, SSA achieves the highest average power density of 716.63 mW/cm2, followed by GWO at 709.95 mW/cm2. The lowest average power density of 695.27 mW/cm2 is obtained using PSO.

The parameter identification of a PEMFC is the process of using optimization algorithms to determine the ideal unknown variables suitable for the development of an accurate fuel-cell-performance prediction model. These parameters are not always available from the manufacturer’s datasheet, so they need to be determined to accurately model and predict the fuel cell’s performance. Five optimization methods—bald eagle search (BES) algorithm, equilibrium optimizer (EO), coot (COOT) algorithm, antlion optimizer (ALO), and heap-based optimizer (HBO)—are used to compute seven unknown parameters of a PEMFC. During optimization, these seven parameters are used as decision variables, and the fitness function to be minimized is the sum square error (SSE) between the estimated cell voltage and the actual measured cell voltage. The SSE obtained for the BES algorithm was noted to be 0.035102. The COOT algorithm recorded an SSE of 0.04155, followed by ALO with an SSE of 0.04022 and HBO with an SSE of 0.056021. BES predicted the performance of the fuel cell accurately; hence, it is suitable for the development of a digital twin for fuel-cell applications and control systems for the automotive industry. Furthermore, it was deduced that the convergence speed for BES was faster compared to the other algorithms investigated. This study aims to use metaheuristic algorithms to predict fuel-cell performance for the development and commercialization of digital twins in the automotive industry.

The main target is the maximization of the output power of PEM “proton exchange membrane” fuel cell via fuzzy modelling and optimization. In the beginning, using the experimental data, a robust fuzzy model is designed for simulating the PEM fuel cell using the relative humidity (%) and stoichiometric ratio at the anode and cathode. Then, the artificial ecosystem optimiser (AEO) is applied to determine the best values of the controlling input parameters. During the optimization process, the four controlling input parameters of the PEMFC are used as the decision variables, whereas as the cost function is required to be at the maximum of the output power density of the PEMFC. For the fuzzy model of the power, the RMSE values are 1.5588 and 3.1906, respectively, for training and testing data. The coefficient of determination values are 0.9826 and 0.8743 for training and testing, respectively. This confirms a successful modelling phase. Finally, the integration between fuzzy and AEO boosted the power of the PEMFC from 57.95 W to 78.44 W (by around 35%). Under this optimal condition, the controlling input parameters values are 26.65%, 56.77%, 1.14, and 1.68, respectively, for anode relative humidity, cathode relative humidity, anode stoichiometric ratio and cathode stoichiometric ratio. The present study, however, intends to highlight the importance of fuzzy modelling and metaheuristic algorithms in the development of digital twins to accelerate the commercialization of fuel cells as well as its applicability in diverse global economic sectors where a higher power requirement is needed. It is also aimed at informing the fuel cell research community and policy makers on strategies that could be adopted in boosting fuel cell performance and therefore could be a good reference source in decision-making for fuel cell commercialization and its practical implementation.

Living organisms' energy conversion is considered as an essential and sustainable green energy source and future bio-hybrid technologies. Recently, plants were used after harvesting as biomass in bio-fermentation as an energy source. In bio-electrochemical systems, microorganisms work with plants to generate electricity, hydrogen, or methane. This work discusses the simultaneous pollutant removal and electricity generation in plant-based bio-electrochemical systems (P-BES). Factors affecting the P-BES performance and the removal efficiencies of the different organic and inorganic pollutants were illustrated. Furthermore, the plant-based bioelectrochemical systems' role in achieving the sustainable development goals (SDGs) was discussed. The SDGs contribution of plant-based bioelectrochemical systems were presented and discussed to evaluate such systems' ability to achieve the three pillars of sustainable development, i.e., economic, environmental, and social.

The consumption of hydrogen could increase by sixfold in 2050 compared to 2020 levels, reaching about 530 Mt. Against this backdrop, the proton exchange membrane fuel cell (PEMFC) has been a major research area in the field of energy engineering. Several reviews have been provided in the existing corpus of literature on PEMFC, but questions related to their evolutionary nuances and research hotspots remain largely unanswered. To fill this gap, the current review uses bibliometric analysis to analyze PEMFC articles indexed in the Scopus database that were published between 2000–2021. It has been revealed that the research field is growing at an annual average growth rate of 19.35%, with publications from 2016 to 2012 alone making up 46% of the total articles available since 2000. As the two most energy-consuming economies in the world, the contributions made towards the progress of PEMFC research have largely been from China and the US. From the research trend found in this investigation, it is clear that the focus of the researchers in the field has largely been to improve the performance and efficiency of PEMFC and its components, which is evident from dominating keywords or phrases such as ‘oxygen reduction reaction’, ‘electrocatalysis’, ‘proton exchange membrane’, ‘gas diffusion layer’, ‘water management’, ‘polybenzimidazole’, ‘durability’, and ‘bipolar plate’. We anticipate that the provision of the research themes that have emerged in the PEMFC field in the last two decades from the scientific mapping technique will guide existing and prospective researchers in the field going forward.

The sudden increase in the concentration of carbon dioxide (CO2) in the atmosphere due to the high dependency on fossil products has created the need for an urgent solution to mitigate this challenge. Global warming, which is a direct result of excessive CO2 emissions into the atmosphere, is one major issue that the world is trying to curb, especially in the 21st Century where most energy generation mediums operate using fossil products. This investigation considered a number of materials ideal for the capturing of CO2 in the post-combustion process. The application of aqueous ammonia, amine solutions, ionic liquids, and activated carbons is thoroughly discussed. Notable challenges are impeding their advancement, which are clearly expatiated in the report. Some merits and demerits of these technologies are also presented. Future research directions for each of these technologies are also analyzed and explained in detail. Furthermore, the impact of post-combustion CO2 capture on the circular economy is also presented.

This work presents a mathematical modelling of a proton-exchange membrane fuel cell (PEMFC) system integrated with a resistive variable load. The model was implemented using MATLAB Simulink software, and it was used to calculate the fuel cell electric current and voltage at various steady-state conditions. The electric current was determined by the intersection of its polarisation curve and applied as an input value for the simulation of the PEM fuel cell performance. The model was validated using a Horizon H-500xp model fuel cell stack system, with the following main components: a 500 W PEM fuel cell, a 12 V at 12 A battery for the start-up, a super-capacitor bank to supply peak loads and a 48 V DC-DC boost converter. The generated power was dissipated by a variable resistive load. The results from the model shows a qualitative agreement with test bench results, with similar trends for stack current and voltage in response to load and hydrogen flow rate variation. The discrepancies ranged from 2% to 6%, depending on the load resistance applied. A controlled current source was utilised to simulate the variation of fan power consumption with stack temperature, ranging from 36.5 W at 23 °C to 52 W at 65 °C. Both model and experiments showed an overall PEMFC system maximum efficiency of about 48%.

A proton exchange membrane fuel cell (PEMFC) is a more environmentally friendly alternative to deliver electric power in various applications, including in the transportation industry. As PEMFC performance characteristics are inherently nonlinear and involved, the prediction of the performance in a given application for different operating conditions is important in order to optimize the efficiency of the system. Thus, modelling using artificial neural networks (ANNs) to predict its performance can significantly improve the capabilities of handling the multi-variable nonlinear performance of the PEMFC. However, further investigation is needed to develop a dynamic model using ANNs to predict the transient behavior of a PEMFC. This paper predicts the dynamic electrical and thermal performance of a PEMFC stack under various operating conditions. The input variables of the PEMFC stack for the analysis consist of the cathode inlet temperature, anode inlet pressure, anode and cathode inlet flow rates, and stack current. The performances of the ANN models using three different learning algorithms are determined based on the stack voltage and temperature, which have been shown to be consistently predicted by most of these models. Almost all models with varying hidden neurons have coefficients of determination of 0.9 or higher and mean squared errors of less than 5. Thus, the results show promise for dynamic modelling approaches using ANNs for the development of optimal operation of a PEMFC in various system applications.

Heat transfer operations are very common in the process industry to transfer a huge amount of thermal energy, i.e., heat, from one fluid to another for different purposes. Many fluids are used as heat transfer fluid (HTF), in which water is the most common HTF due to its high specific heat, availability, and affordability. However, conventional HTFs, including water, have a lower thermal conductivity, which is the most critical thermophysical property, hence decreased heat transfer efficiency. The addition of solid particles of highly thermally conductive material, specifically at nano-size, i.e., nanoparticles NPs, result in nanofluid NF, which has evolved over the last two decades as efficient HTF and have been investigated in a wide range of applications. Among NPs, graphene (Gr) based materials have shown very high potential as NF due to the very high thermal conductivity up to 5,000 W/m.K, hence higher thermal conductivity NF. This work aims to thoroughly discuss the thermophysical properties of Gr-based NFs, including thermal conductivity, heat capacity, density, and viscosity. The discussion focus on the thermophysical properties as it is the ultimate determinator of the heat transfer characteristics of the HTF, such as the convective and the overall heat transfer coefficient as well as the heat transfer capacity of the NF. The discussion expands to the relative enhancement in such thermophysical properties reaching up to a 40% increase in thermal conductivity, as the most critical thermophysical property. The discussion shows that Gr-based NF has a much higher thermal conductivity relative to widely studied metal oxide NF and at much lower content, and lower density and viscosity increase, which is critical for determining the pumping power requirements. Critical challenges facing the application of Gr-based NFs such as cost, stability, increased density and viscosity, and environmental impacts are thoroughly discussed with mitigation recommendations given.

This investigation summarized the application of nanofluids (NFs) in heat exchangers (HExs) with different geometries. The quest for heat devices with quick response for the industrial sector is still a major challenge that has been an active research direction over the years. Addressing this issue is likely to increase the capacity of several industries. There is a direct relationship between expanding the heat capacity and the pressure drop. The common approach in increasing the rate of heat transfer often leads to an increment in pressure drop. This study reviews and summarizes the investigations on various geometrical effects inside the channel combined with NF in HExs. This review explored the potential of NFs as possible heat transfer fluid in HExs. From a detailed literature review compiled and evaluated, it has been deduced that NFs application significantly improves the thermal efficiency of HExs. The investigation further evaluated plate, helical, as well as shell and tube HExs. The review explored NFs application in HExs and how they can significantly improve the HExs' thermal characteristics. It was deduced that the use of NFs improved the heat transfer both experimentally and numerically . This equally has a direct relation to energy savings as well as industrial waste heat.

Ground heat exchangers are surrounded by grout material, making it one of the most important components in geothermal energy applications since it significantly affects the system's thermal performance. The current study reviews the different types of grout materials and compares their thermophysical properties. The most critical parameter is the grout's thermal conductivity in which it always presents a proportional relation with the system's efficiency. Numerous factors are involved in this review to ascertain theier impact on the grouts’ performance such as flowability, shrinkage, moisture content, freezing, heat capacity, strength, permeability, solubility and thermal imbalance. The different grouts compared are bentonite, cement, sand, graphite, controlled low-strength material, dolomite, and phase change materials. The literature shows that phase change materials are the best choices of grouting since they can provide high storage capacity, stability and temperature uniformity. The major problem of such materials is their low thermal conductivity. Thus, it is recommended to use composite phase change materials to enhance their thermal conductivity and increase the storage/retrieval rate.

Heat exchangers are widely utilized in different thermal systems for diverse industrial aspects. The selection of HEx depends on the thermal efficiency, operating load, size, flexibility in operation, compatibility with working fluids, better temperature and flow controls, and comparatively low capital and maintenance costs. Heat transfer intensification of heat exchangers can be fulfilled using passive, active, or combined approaches. Utilizing nanofluids as working fluids for heat exchangers have evolved recently. The performance of heat exchangers employed different nanofluids depends mainly on the characteristics and improvement of thermophysical properties. Regarding the unique behavior of different nanofluids, researchers have attended noteworthy progress. The current study reviews and summarizes the recent implementations carried out on utilizing nanofluids in different types of heat exchangers, including plate heat exchangers, double-pipe heat exchangers, shell and tube heat exchangers, and cross-flow heat exchangers. The results showed that nanofluids with enhanced thermal conductivity, although accompanied by a considerable decrease in the heat capacity and raising viscosity, has resulted in performance enhancement of different heat exchangers types. So, the performance evaluation criterion that combines the thermal enhancement and increases the pumping power for any type of heat exchangers is requisite to evaluate the overall performance properly. The challenges and opportunities for future work of heat transfer and fluid flow for different types of heat exchangers utilizing nanofluids are discussed and presented.

The aim of this research is to develop research methodology and provide insight into the viability of using Open Pore Cellular Foam (OPCF) material in Polymer Electrolyte Membrane (PEM) Electrolysers. Analysis have therefore been carried out on three different types of electrolyser geometries. A PEM electrolyser is considered with serpentine, mesh and OPCF flow channels, whilst all the other physical and operational parameters are kept constant. Three dimensional models have been created in solid works and computational fluid dynamic simulations have been carried out on all the three types of electrolysers in ANSYS Fluent. Experimental investigations have also been carried out using all the three different flow plate geometries. ANSYS simulation show that the performance of the OPCF flow channel electrolyser is 1.5 times higher than that of the mesh channel electrolyser. Experimental results have shown that using OPCF flow channel the performance of the electrolyser improves significantly by 17% to that compared with conventional mesh flow plate electrolysers.

Fuel cells (FCs) have received huge attention for development from lab and pilot scales to full commercial scale. This is mainly due to their inherent advantage of direct conversion of chemical energy to electrical energy as a high-quality energy supply and, hence, higher conversion efficiency. Additionally, FCs have been produced at a wide range of capacities with high flexibility due to modularity characteristics. Using the right materials and efficient manufacturing processes is directly proportional to the total production cost. This work explored the different components of proton exchange membrane fuel cells (PEMFCs) and their manufacturing processes. The challenges associated with these manufacturing processes were critically analyzed, and possible mitigation strategies were proposed. The PEMFC is a relatively new and developing technology so there is a need for a thorough analysis to comprehend the current state of fuel cell operational characteristics and discover new areas for development. It is hoped that the view discussed in this paper will be a means for improved fuel cell development.

A sector that contributes immensely to global warming due to the release of harmful gases into the atmosphere annually is the automotive industry. This has led to several optimization of diesel engines from their initial inert system with lower power density to fast running types with higher power densities. Despite these modifications coupled with the introduction of Diesel Oxidation Catalyst (DOC) and Diesel Particulate Filter (DPF), there are still environmental concerns that need to be critically analyzed. The current types of diesel engines have challenges pertaining to NOx reduction due to their lean operations. These limitations further led to the technological evolution of Selective Catalytic Reduction and NOx storage catalysts (NSC). This investigation therefore explored the current state of Selective Catalytic Reduction (SCR) and NOx storage catalyst (NSC). The effect of temperature and uniform distribution of ammonia associated to these technologies were critically reviewed. Factors impeding the integration of these technologies in the automotive industry were also discussed Possible solutions to mitigate these challenges were proposed. From the review gathered, there is still the need for further research activities in this field in terms of optimization to enhance the overall performance of diesel engine cars with lower toxic emission.

Commercialization of proton exchange membrane fuel cells can only materials provided its performance is closely related to existing technologies useful in commercial application. Other critical parameters like the utilization of cheaper materials should be taken into account during the manufacturing of the cell. A key component in the cell that has direct correlation to the cell performance is the flow plate. The weight coupled with cost of the cell revolves around the flow plate used in the manufacturing of the cell. This study explores materials ideal for the manufacturing of fuel cells in order to improve the overall cell performance. The investigation highlights the critical impact of varying materials used in the manufacturing of flow plates for PEM fuel cells. Stainless steel (SS), aluminium (Al) and copper (Cu) were the materials considered. The flow plate designs considered were serpentine and open pore cellular foam channel. Machine learning using python for the validation of the results with Linear regression, Ridge regression and Polynomial regression algorithm was carried out. The performance of both flow field channels was compared using different bipolar plate materials. The results show that metal foam flow channels overall performance was better than serpentine flow channels with all the various bipolar plate material used and Al material outperformed Cu and SS material. There is a direct correlation in terms of the outcome of the study and literature based on the data generated experimentally. It can however be concluded that molecules of hydrogen are stable on aluminium plates compared to copper and stainless steel.

Metal–air batteries are a promising technology that could be used in several applications, from portable devices to large-scale energy storage applications. This work is a comprehensive review of the recent progress made in metal-air batteries MABs. It covers the theoretical considerations and mechanisms of MABs, electrochemical performance, and the progress made in the development of different structures of MABs. The operational concepts and recent developments in MABs are thoroughly discussed, with a particular focus on innovative materials design and cell structures. The classical research on traditional MABs was chosen and contrasted with metal–air flow systems, demonstrating the merits associated with the latter in terms of achieving higher energy density and efficiency, along with stability. Furthermore, the recent applications of MABs were discussed. Finally, a broad overview of challenges/opportunities and potential directions for commercializing this technology is carefully discussed. The primary focus of this investigation is to present a concise summary and to establish future directions in the development of MABs from traditional static to advanced flow technologies. A systematic analysis of this subject from a material and chemistry standpoint is presented as well.

To meet the increasing energy demand, renewable energy is considered the best option. Its patronage is being encouraged by both the research and industrial community. The main driving force for most renewable systems is solar energy. It is abundant and pollutant free compared to fossil products. Wind energy is also considered an abundant medium of energy generation and often goes hand in hand with solar energy. The last few decades have seen a sudden surge in wind energy compared to solar energy due to most wind energy systems being cost effective compared to solar energy. Wind turbines are often categorised as large or small depending on their application and energy generation output. Sustainable materials for construction of different parts of wind turbines are being encouraged to lower the cost of the system. The turbine blades and generators perform crucial roles in the overall operation of the turbines; hence, their material composition is very critical. Today, most turbine blades are made up of natural fiber-reinforced polymer (NFRP) as well as glass fiber-reinforced polymer (GFRP). Others are also made from wood and some metallic materials. Each of the materials introduced has specific characteristics that affect the system’s efficiency. This investigation explores the influence of these materials on turbine efficiency. Observations have shown that composites reinforced with nanomaterials have excellent mechanical characteristics. Carbon nanotubes have unique characteristics that may make them valuable in wind turbine blades in the future. It is possible to strengthen carbon nanotubes with various kinds of resins to get a variety of different characteristics. Similarly, the end-of-life treatment methods for composite materials is also presented.

Advances in building-integrated photovoltaic (BIPV) systems for residential and commercial purposes are set to minimize overall energy requirements and associated greenhouse gas emissions. The BIPV design considerations entail energy infrastructure, pertinent renewable energy sources, and energy efficiency provisions. In this work, the performance of roof/façade-based BIPV systems and the affecting parameters on cooling/heating loads of buildings are reviewed. Moreover, this work provides an overview of different categories of BIPV, presenting the recent developments and sufficient references, and supporting more successful implementations of BIPV for various globe zones. A number of available technologies decide the best selections, and make easy configuration of the BIPV, avoiding any difficulties, and allowing flexibility of design in order to adapt to local environmental conditions, and are adequate to important considerations, such as building codes, building structures and loads, architectural components, replacement and maintenance, energy resources, and all associated expenditure. The passive and active effects of both air-based and water-based BIPV systems have great effects on the cooling and heating loads and thermal comfort and, hence, on the electricity consumption.

The building block of all economies across the world is subject to the medium in which energy is harnessed. Renewable energy is currently one of the recommended substitutes for fossil fuels due to its environmentally friendly nature. Wind energy, which is considered as one of the promising renewable energy forms, has gained lots of attention in the last few decades due to its sustainability as well as viability. This review presents a detailed investigation into this technology as well as factors impeding its commercialization. General selection guidelines for the available wind turbine technologies are presented. Prospects of various components associated with wind energy conversion systems are thoroughly discussed with their limitations equally captured in this report. The need for further optimization techniques in terms of design and materials used for the development of each component is highlighted.

This review presents a detailed summary of the latest technologies used in flywheel energy storage systems (FESS). This paper covers the types of technologies and systems employed within FESS, the range of materials used in the production of FESS, and the reasons for the use of these materials. Furthermore, this paper provides an overview of the types of uses of FESS, covering vehicles and the transport industry, grid leveling and power storage for domestic and industrial electricity providers, their use in motorsport, and applications for space, satellites, and spacecraft. Different types of machines for flywheel energy storage systems are also discussed. This serves to analyse which implementations reduce the cost of permanent magnet synchronous machines. As well as this, further investigations need to be carried out to determine the ideal temperature range of operation. Induction machines are currently stoutly designed with lower manufacturing cost, making them unsuitable for high-speed operations. Brushless direct current machines, the Homolar machines, and permanent magnet synchronous machines should also be considered for future research activities to improve their performance in a flywheel energy storage system. An active magnetic bearing can also be used alongside mechanical bearings to reduce the control systems’ complications, thereby making the entire system cost-effective.

A low cost bipolar plate materials with a high fuel cell performance is important for the establishment of Proton Exchange Membrane (PEM ) fuel cells into the competitive world market. In this research, the effect of different bipolar plates material such as Aluminum (Al), Copper (Cu), and Stainless Steel (SS) of a single stack of proton exchange membrane (PEM) fuel cells was investigated both numerically and experimentally. Firstly, a three dimensional (3D) PEM fuel cell model was developed, and simulations were conducted using commercial computational fluid dynamics (CFD) ANSYS FLUENT to examine the effect of each bipolar plate materials on cell performance. Along with cell performance, significant parameters distributions like temperature, pressure, a mass fraction of hydrogen, oxygen, and water is presented. Then, an experimental study of a single cell of Al, Cu, and SS bipolar plate material was used in the verification of the numerical investigation. Finally, polarization curves of numerical and experimental results was compared for validation, and the result shows that Al serpentine bipolar plate material performed better than Cu and SS materials. The outcome of the investigation was in tandem to the fact that due to adsorption on metal surfaces, hydrogen molecules is more stable on Al surface than Cu and SS surfaces.

This review critically evaluates the latest trends in fuel cell development for portable and stationary fuel cell applications and their integration into the automotive industry. Fast start-up, high efficiency, no toxic emissions into the atmosphere and good modularity are the key advantages of fuel cell applications. Despite the merits associated with fuel cells, the high cost of the technology remains a key factor impeding its widespread commercialization. Therefore, this review presents detailed information into the best operating conditions that yield maximum fuel cell performance. The paper recommends future research geared towards robust fuel cell geometry designs, as this determines the cell losses, and material characterization of the various cell components. When this is done properly, it will support a total reduction in the cost of the cell which in effect will reduce the total cost of the system. Despite the strides made by the fuel cell research community, there is a need for public sensitization as some people have reservations regarding the safety of the technology. This hurdle can be overcome if there is a well-documented risk assessment, which also needs to be considered in future research activities.

Combined heat and power (CHP) in a single and integrated device is concurrent or synchronized production of many sources of usable power, typically electric, as well as thermal. Integrating combined heat and power systems in today’s energy market will address energy scarcity, global warming, as well as energy-saving problems. This review highlights the system design for fuel cell CHP technologies. Key among the components discussed was the type of fuel cell stack capable of generating the maximum performance of the entire system. The type of fuel processor used was also noted to influence the systemic performance coupled with its longevity. Other components equally discussed was the power electronics. The thermal and water management was also noted to have an effect on the overall efficiency of the system. Carbon dioxide emission reduction, reduction of electricity cost and grid independence, were some notable advantages associated with fueling cell combined heat and power systems. Despite these merits, the high initial capital cost is a key factor impeding its commercialization. It is, therefore, imperative that future research activities are geared towards the development of novel, and cheap, materials for the development of the fuel cell, which will transcend into a total reduction of the entire system. Similarly, robust, systemic designs should equally be an active research direction. Other types of fuel aside, hydrogen should equally be explored. Proper risk assessment strategies and documentation will similarly expand and accelerate the commercialization of this novel technology. Finally, public sensitization of the technology will also make its acceptance and possible competition with existing forms of energy generation feasible. The work, in summary, showed that proton exchange membrane fuel cell (PEM fuel cell) operated at a lower temperature-oriented cogeneration has good efficiency, and is very reliable. The critical issue pertaining to these systems has to do with the complication associated with water treatment. This implies that the balance of the plant would be significantly affected; likewise, the purity of the gas is crucial in the performance of the system. An alternative to these systems is the PEM fuel cell systems operated at higher temperatures.

Environmental concerns of greenhouse gases (GHG) effect from fossil commodities and the fast increase in global energy demand have created awareness on the need to replace fossil fuels with other sources of clean energy. PEM fuel cell (PEMFC) is a promising source of energy to replace fossil fuels. The commercialization of the cell depends on its price, weight and mechanical strength. Bipolar plates are among the main components of PEMFC which perform some significant functions in the fuel cell stack. Metal bipolar plate is considered by the research community as the future material for fuel cells. However, surface coating is required for metals to enhance its corrosion resistance, hydrophilicity and interfacial contact resistance (ICR) in PEM fuel cells. Open pore cellular metal foam (OPCMF) materials have been used to replace the conventional flow field channel in recent times due to its low electrical resistance, high specific area and high porosity; however, it endures the same corrosion problem as the metallic bipolar plate. This investigation offers an overview on different types of bipolar plates and techniques in coating metallic bipolar platse and open pore metal foam as flow field channel materials to improve the corrosion resistance which will eventually increase the efficiency of the fuel cell appreciably.