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This study aims to develop an environmentally friendly multi-energy system for sustainable production of electricity, cooling and hydrogen. The study introduces a pioneering geothermal-based system, integrating an ejector refrigeration cycle, a dual-loop organic Rankine cycle, and a hydrogen production unit with proton exchange membrane electrolyzers. The study provides a thorough analysis of the system's energy and exergy performance, as well as its economic feasibility. Through sensitivity and parametric analyses, the research identifies key parameters that significantly influence system performance. The system's innovative design promises minimal environmental impact while delivering multifaceted performance: generating 1.38 MW of electricity, supplying 436 kW of cooling load, and producing 5.39 kg/h of hydrogen. In the exergy analysis, Evaporator1 is identified as the primary contributor to exergy loss, representing 34 % of the total exergy destruction. This is followed by the electrolysis unit, the condenser, and the ejector refrigeration cycle, which contribute 18 %, 14 %, and 12 %, respectively. The system achieves optimal efficiency at an organic Rankine cycle turbine1 inlet temperature of 387 K, yielding a power generation of 885.4 kW and an exergy efficiency of 26.7 %. Beyond this temperature, any further increase leads to a decline in power output due to operational disturbances. A multi-criteria optimization using genetic algorithm is applied, resulting in an optimized system with a cost rate of 18.13 $/h and an exergy efficiency of 38.96 %.

Marine transportation is a significant contributor to overall energy consumption among transportation sectors and is responsible for producing a considerable amount of greenhouse gases. A potential solution for alternative applications involves implementing combined heat integration, thereby enabling the production of additional utilities and mitigating emissions. The current work introduces an innovative heat integration process for a marine engine, focusing on implementing an optimal thermal matching technique to minimize overall irreversibility in producing liquefied hydrogen and coolant. The process incorporates an organic flash-bi-evaporator cooling cycle, a humidification dehumidification desalination, a polymer electrolyte membrane water electrolysis process, and a Claude cycle. The generated freshwater is delivered to the electrolyzer to produce gaseous hydrogen. This product and the cooling for freezing are utilized in the Claude cycle for hydrogen liquefaction. The study utilizes an advanced thermo-environmental multi-criteria investigation and optimization, considering sensitivity analysis and optimization based on artificial intelligence method. The optimization process incorporates the training and testing of artificial neural networks, NSGA-II method, and TOPSIS decision-making. The primary objective functions include exergetic efficiency and carbon dioxide emission. The findings demonstrate that the specified objectives are computed to be 0.121 and 2.67 kg/MWh, correspondingly. Besides, this condition exhibits a liquefied hydrogen flow rate of 6.44 L/h and a cooling output of 43.61 kW, showing an energy efficiency of 0.1145. Also, the total exergy destruction rate associated with the arranged structure is 124.5 kW. Furthermore, the optimum state reveals an exergoenvironmental index of 0.840 and an exergetic stability factor of 0.869.

The proposed system is designed to address the growing demand for sustainable and efficient energy solutions that can minimize greenhouse gas emissions while meeting the increasing energy needs of a modern world. The integrated approach of the system allows it to produce not only electricity but also hydrogen and fresh water from seawater, making it a comprehensive solution to multiple challenges. The system comprises several components that work together including a gas turbine unit, Rankine and organic Rankine cycles, a multi-effect desalination (MED) system, and a proton exchange membrane (PEM) electrolyzer. One significant characteristic of this system is its ability to generate hydrogen, which is later combined with the inlet fuel of the gas turbine. This novel methodology presents a more environmentally friendly fuel alternative, showcasing its potential to enhance combustion efficiency in a sustainable manner. To ensure the viability and feasibility of the system, a MATLAB function was utilized for calculating the base case scenario, followed by a GA optimization technique with the aid of a machine learning model to minimize run-time. In the given scenario, the total cost rate is measured at 3726 $/h, with an exergy efficiency of 54% and a normalized CO2 emission of 305 kg/GJ. Following the optimization process, significant enhancements can be observed. The cost rate decreases by 2202 $/h, the exergy efficiency improves from 54% to 55.61%, and the annual CO2 emissions decrease from 133,760 metric tons to 50,320 metric tons. Moreover, the implementation of blended fuel leads to a 10% decrease in normalized CO2 emissions.

Temperature plays a crucial role in various industries, equipment, and human life, and heat exchangers emerge as fundamental devices in managing this essential parameter. Heat exchangers come in various types, each tailored for specific applications. One commonly utilized variant is the finned-tube heat exchanger, which excels in transferring heat between liquid and gas. This study involved a numerical investigation of a finned-tube heat exchanger equipped with four bow-shaped vortex generators. The primary objective of this study was to assess the influence of three distinct geometric parameters associated with vortex generators on the performance of the heat exchanger. The key performance indicators investigated in this research were the Nusselt number and friction factor, which respectively represent the thermal and hydraulic performance of the device. Also, the relative efficiency index parameter was investigated to evaluate thermal and hydraulic performances simultaneously. Artificial Neural Network and Genetic Algorithm methods were employed to develop response models to simplify and increase the analysis accuracy and determine the optimal geometry. The artificial neural network's prediction models demonstrated high accuracy and effectively forecasted the desired outcomes. It was determined that the relative efficiency index of the finned-tube heat exchanger with vortex generators, even in its weakest-performing design, reached 1.0467. This means that by adding the bow-shaped vortex generators to the heat exchanger, there is a 4.67% increase in the overall performance in the worst case. Also, after predicting the optimal design for the heat exchanger, it was observed that this device has a 13.12% improvement in overall performance compared to the case without vortex generators. To adjust the Nusselt number of the finned-tube heat exchanger lacking vortex generators with that of the finned-tube heat exchanger featuring the optimal design, it is necessary to increase the inlet flow rate of the vortex generators-lacking heat exchanger by 53.20%. This increase in flow rate leads to a significant escalation of 108.73% in the heat exchanger's pumping power compared to the pumping power of the optimal design heat exchanger.

This research aims to explore the economic and energy efficiency ramifications of combining the supercritical carbon dioxide oxy-fuel cycle with the Goswami cycle in the context of integrated heating and cooling systems, as well as power generation. Oxy-fuel systems, known for their high operating temperatures. In oxy-fuel systems, gas turbine exhaust gases typically preheat incoming flows to the combustion chamber. Nevertheless, even after preheating the combustion chamber inlets, the exhaust gases from the gas turbine retain a relatively high temperature, demonstrating considerable potential for energy production. Through a comprehensive parametric study, the impact of independent parameters on system performance is systematically explored. Subsequently, a three-stage economic analysis, total capital investment cost estimation, total operating cost estimation, and income evaluation, is conducted. The investment return time is assessed based on the selling price of system products. In the final step, a genetic algorithm, combined with artificial intelligence, optimizes the system for rational and optimal economic and energy conditions. The research findings reveal that in the absence of the Goswami cycle, the system yields 13.5 MW of power and 8.5 MW of heating, with an investment return time of approximately 16.32 years. However, with the inclusion of the Goswami cycle, power production increases to 15 MW, accompanied by 5 MW of cooling and 6.7 MW of heating. Despite augmented initial and operational costs, the investment return time is significantly reduced to 9.9 years. The results of a three-objective optimization strategy, focusing on maximizing power, heating, and cooling, highlight the system's versatility. Opting for maximum power production enhances power by 12.53 % and cooling by 27.48 %, compared to the reference mode. However, heating production decreases by 37.01 %. Notably, total capital investment costs show improvement by about 4.54 %, and the pay-back time experiences a substantial reduction of approximately 27.68 %. Moreover, the levelized cost of energy decreases from 111.4 $/MWh in the reference state to 106.9 $/MWh, underscoring the economic efficiency of the integrated system.

Low-energy cases can be designed more efficiently by using analytical optimization. The non-linear thermal performance of buildings has led to the development of optimization techniques based on simulation. In building optimization, it is important to achieve superior solutions while minimizing calculation expenses. This study aims to optimize an Australian office building using the Balanced Tree Growth Optimizer (BTGO). It has resulted that more than 11.7 % of energy can be saved by the optimization process and also some energy-saving measures. A comparison of the utilized algorithm with benchmark algorithms including the Nelder-Mead method, hybrid Particle Swarm Optimization, Hooke-Jeeves, and Ant Colony Optimization for continuous domain showed that the BTGO can achieve better solutions and needs less computational time.

In modern technology, anticipating the optimal thermal performance in microchannel heat sinks (MCHSs) is a vital response to overheating challenges in high-performance electronic devices. The Response Surface Methodology (RSM) application presents an innovative way of modeling such equipment's behavior. In the current study, an MCHS with pin fins was chosen to examine the Nusselt number (Nu) and pressure drop (ΔP). Using two RSM models and multi-objective optimization, the study tried to discern the optimal values of the responses in the MCHS. The RSM models examined three key parameters: length, installation angle, and the spacing between pin fins. The interactive impacts of independent parameters and their consequent effect on the responses were also explored. The RSM models attained exceptional predictive accuracy, with the determination coefficient values of 0.9946 and 0.9986 for Nu and ΔP, respectively. The working fluid applied in the present work was water with an inlet temperature of 288 K and a Reynolds number of 300. Besides, the constant heat flux of 100 W/cm2 was exposed to the bottom wall of the MCHS. In pursuit of an optimal MCHS design that maximizes Nu while minimizing ΔP, the study employed the relative efficiency index (η) parameter. Considering heat transfer and ΔP in the pin-fin MCHS, the optimal design showed an impressive improvement of about 64.5 % compared to the fin-free heat sink. Moreover, the study provided Pareto optimal points for Nu, ΔP, and η, adding depth to understanding the complex trade-offs within the investigated MCHS.

In order to achieve a sustainable energy future, it is of utmost importance to harness solar energy for the production of liquid hydrogen. This undertaking is justified by the significant role that liquid hydrogen plays as a clean and highly efficient fuel source, effectively addressing the pressing concerns regarding greenhouse gas emissions and reducing reliance on conventional fossil fuels. An additional advantage lies in the easy storage and transportation capabilities of liquid hydrogen, making it a viable solution for energy storage across diverse sectors. In this regard, the current research presents a novel solar-powered setup consisting of parabolic trough solar collectors (PTSCs), a sequential organic Rankine cycle (ORC), a liquefied natural gas (LNG) regasification unit, and a module dedicated to the production and subsequent liquefaction of hydrogen. A meticulously crafted MATLAB code is utilized to replicate the operations of the proposed system, facilitating a thorough examination of its energy, exergy, environmental, and economic performance. The proposed system exhibits a net output power of 1.13 MW and possesses the capacity to produce hydrogen at a rate of 34.92 kg/h, featuring a levelized cost of hydrogen (LCOH) of 3.59 $/kg. Additionally, the system encompasses a cooling capability of 192 kW. From an environmental perspective, this configuration contributes to a decrease in carbon dioxide emissions by 255.96 kg/h. After conducting a dynamic analysis with meteorological data, it was determined that the system has the potential to generate an annual hydrogen output of 536.84 tons in San Francisco.

The proficient management and supervision of thermal conditions in microelectronic equipment are becoming progressively challenging owing to the imposition of heightened heat fluxes. The exploration of recently developed microchannel heat sinks (MCHSs) is currently underway to effectively tackle this matter. Modifying the geometric characteristics of these gadgets has proven to be highly effective in augmenting their comprehensive performance. In the present study, a novel arc-curved microchannels engraved on segmented circular heat sink and introduced configurations comprising of six discrete segments of spiral microchannels. The study focused on examining the influence of individual partitions on the Nusselt number (Nu) and pressure drop (ΔP) of the MCHS. The obtained results demonstrate that all the proposed configurations exhibit considerably higher values of the Nu in comparison to the reference MCHS consisting of 21 rectangular channels. Nevertheless, the notable augmentation in the value of the parameter Nu occurred simultaneously with an escalated ΔP. Hence, the relative efficiency index was employed as a more precise measure for evaluations. All of the designs suggested in this investigation were found to be effective when subjected to volume flow rates surpassing 47.303 ml/min. In an illustrative case, the segmented circular MCHS encompassing 10 spiral microchannels exhibited an enhanced overall efficiency of 25.9 % when operated at a volume flow rate of 66.225 ml/min, as compared to the benchmark design. In addition, artificial neural networks (ANN) were utilized to comprehensively scrutinize the influence of variables and construct a prognostic model to ascertain the overall effectiveness of the MCHS, obviating the necessity for convoluted, time-consuming, and costly experimental procedures and computations. The presentation of the Pareto optimal points effectively portrayed the optimal and unfavorable outcomes under consideration.

Customizing the configuration of Tesla valves should be adjusted to match the specific goals of the desired purpose. Channels with Tesla design have various applications in hydraulic and thermal fields. If the purpose is hydraulic application, this design of channels is used in the construction of one-way valves. The main focus may not be on thermal efficiency, instead, the emphasis lies on attaining a strong diode characteristic. However, in situations involving thermal applications, and considering that the thermal performance of these channels is suitable, they are used in the construction of heat sinks and heat exchangers. Microchannel heat sinks with Tesla design are used in cooling of CPUs, batteries, etc. In these situations, the goal is to minimize the pressure drop, while maximizing convective heat transfer. This research introduces an innovative structural design for the Tesla valve and presents two artificial neural network (ANN) models for predicting and optimizing its thermal and hydraulic capabilities. The design parameters being studied are the inner radius of the fins (R), the length of the folded segment of the divider (L), and the angle of the folded segment of the divider (θ). The researchers are examining how these variables affect the Nusselt number (Nu) and pressure drop (ΔP) in both forward and reverse flow directions. Based on the findings, by utilizing variables of optimal design 4 (θ = 130°, L = 3 mm, and R = 0.258 mm) and optimal design 5 (θ = 130°, L = 3 mm, and R = 0.586 mm) during the valve manufacturing process and altering the flow direction from forward to reverse, the thermal performance and diodicity increased by about 26.6% and 78.2%, respectively.

As a nuclear power source, gas turbine modular helium reactors (GT-MHR) can play a major role in the energy industry due to their high safety index and low operating costs. In light of the GT-MHR's high heat capacity, this study integrates a GT-MHR cycle with a Stirling engine to achieve a high-efficiency electricity production framework in an eco-friendly framework. Multi-aspect study from energy, exergy, economic and exergoenvironmental viewpoints are conducted. Detailed heat exchanger modeling is performed to accurately evaluate the system's performance conditions as part of the design process. The outcomes of this study reveal that in the optimum operating mode, the turbine's inlet temperature and expansion ratio are 1300 K and 2, respectively. The compressor compression ratio is 2.1, the Stirling engine inlet temperature is 850.6 K, and the regenerative effectiveness is 0.866. Consequently, the system's total electricity becomes 325.57 MW with 54.26 % energetic and 75.13 % exergetic efficiencies. The economic assessment of the system reveals that the unit electricity product cost is 8.9 $/GJ, and the total investment cost is 12737 $/h. Also, the optimal exergoenvironmental results show an exergoenvironmental index of 0.237, an environmental damage effectiveness index of 0.0031, and an exergetic stability factor of 0.24. Compared to GT-MHR, GT-MHR/KC, GT-MHR/APC, and GT-MHR/ORC, the newly designed system produces 44.57, 21.47, 29.74, and 25.17 MW more electricity and is more energetically efficient by 28.3 %, 24.45 %, 25.8 %, and 25.07 %, respectively.

Over the past few years, the distinctive design and versatile applications of Tesla valves have captured considerable interest across diverse industries. In contrast to conventional check valves, Tesla valves employ interconnected channels, establishing a highly efficient and reliable fluid flow control mechanism. This research delves into an investigation of the optimum geometric parameters that significantly influence the performance of a novel Tesla valve. The study focuses on three key geometric characteristics: the valve angle (α), the distance between consecutive stages (D), and the distance between the divider wall in the second stage of each step group and the wall of the straight channel (H). The authors carried out a numerical study using computational fluid dynamics to acquire the results. Four multi-layer perceptron models, each with a structure of 3-2-2-1, were applied to predict the selected responses of Nusselt numbers in the forward (Nuf) and reverse (Nur) directions, as well as pressure drops in the forward (ΔPf) and reverse (ΔPr) directions. The findings revealed that among all the variables examined, the parameter H exerted the most substantial influence on all measured responses. It was concluded that by incorporating specific values of α = 34.065°, D = 9 mm, and H = 5.624 mm during the manufacturing process of the valve and altering the flow direction from forward to reverse, a remarkable improvement of approximately 271.7% in pressure diodicity was achieved.

Produced CO2 from fossil fuel-based transportation vehicles and energy systems can have a direct impact on people who live in adjacent of the power plants. So, in the present work, we proposed and modeled an integrated system comprising a gas engine, a carbon dioxide capturing unit, an absorption chiller, and a greenhouse. The greenhouse uses directly the CO2 produced in gas engine through the carbon dioxide capturing unit to mitigate the emitted CO2 into the atmosphere. Besides that, the produced heating in gas engine could be exerted in a cycle to render the greenhouse space in cold weather condition. In warm seasons this thermal energy could also be delivered to an absorption chiller for producing required cooling for inside space of the greenhouse. Herein, the generated electricity is sold to grid, and an economic analysis is brought about. To evaluate and estimate the system performance and interest of selling electricity, a mathematical function was applied. The results show that, during cold seasons, the maximum possible net interest is 800,000 $, and for warm seasons is 2,500,000 $. The maximum COP is also achieved by 1.007. Finally, the amount of CO2 emission rate is mitigated at least 50%.

Microchannel heat sinks (MCHSs) are widely utilized in various industries, including electronics, power systems, and aerospace, to dissipate heat generated by high-power components effectively. The efficient cooling of these devices is crucial for maintaining their optimal performance, reliability, and longevity. This research aimed to augment the overall efficiency of the MCHS by utilizing 25 baffles with varying geometric parameters. The aim was to identify the optimum geometry for these baffles using an Artificial Neural Network (ANN) model. The ANN model was applied to propose two optimal designs for the baffles, specifically targeting the maximum Nusselt number and overall performance of the MCHS. It was noticed that the vertical pitch of the baffles ''d'' had the most substantial influence on the device's behavior among the investigated geometric parameters. The higher value of ''d'' allowed the fluid to spread over the entire space of the heat sink rather than being confined to a specific area. This spreading effect enabled the fluid to contact all the baffles and channel walls, facilitating efficient heat transfer. The applied ANN model with R2 values of about 0.98 and lower values of MAE and RMSE successfully fitted the data. The findings of this study demonstrated that applying the specified input variables (horizontal pitch of 8.496 mm, vertical pitch of 5 mm, and attack angle of 210. 560°), as defined in the optimal overall design, in manufacturing of the baffles resulted in a remarkable enhancement of about 46% in the overall performance of the MCHS in comparison with the design proposed in the reference paper. The heat transfer improvement of the MCHS with baffles were due to the vortices in the baffles bring about chaotic advection and could greatly enhance the convective fluid mixing.