Proceedings of the Institution of Mechanical Engineers,Part A: Journal of Power and Energy

Global energy shortage has increasingly become a major issue of global concern. Since the pump is the largest component in the energy conversion device, it is very important to improve the energy characteristics. The existence of tip clearance leads to the formation of leakage vortices, which causes flow instability and performance degradation, especially in the hump region. This paper investigates the internal flow characteristics of a mixed-flow pump in the hump performance region. The boundary vorticity flow (BVF) diagnosis theory and the vorticity transport equation are utilized to analyze the influence of complex vortex structures on pressure pulsation and flow patterns. It can be found the vortex structure in the impeller is divided into primary tip leakage vortex (PTLV) and secondary tip leakage vortex (STLV) under the hump condition (0.7Q_dm). The vortex structure near the leading edge (LE) of the blade have a tendency to move to the next blade, and the scale increases first and then decreases. On the contrary, the vortex structure near the trailing edge (TE) does not change significantly with time. In the spectrum analysis of the leakage vortex and pressure pulsation, the complete evolution frequency of the leakage vortex is 1.1f_n, while the pressure pulsation is 1.0f_n. This study provides a theoretical basis and technical support for the design of the mixed-flow pump.

This paper introduces a novel control framework for a Free-Piston Stirling Linear Generator (FPSLG) system. The primary contribution is the first application of advanced sliding mode controllers specifically Super-Twisting (ST-SMC) and Third-Order Sliding Mode Control (TO-SMC) to regulate the DC-link voltage in a Space Vector Pulse Width Modulation (SVPWM) based rectifier. These controllers are implemented in the power converter to condition the FPSLG’s inherently variable electrical output, improving system performance under fluctuating conditions. The proposed ST-SMC and TO-SMC demonstrate superior performance by achieving reduced voltage/current ripple, improved robustness, and better dynamic response. Additionally, the system combines a bidirectional DC-DC converter with dual-loop PI control to maintain stable voltage for battery storage. MATLAB/Simulink simulations validate the effectiveness of this control strategy in significantly improving power quality and system stability for FPSLG-based renewable energy systems.

Rapid development in recent machinery requires a serious heat eradication system to fulfil the energy demand and improve compact heat exchanger performance. The passive approach is employed to accelerate the rapid heat removal processes and thermal efficiency. The present paper investigates the energy, exergy, and sustainable economic development of a compact Heat Exchanger (HX) operated with water, Ethylene Glycol (EG), and Propylene Glycol-based Carbon Quantum Dots (CQD) nanofluid assisted with two modified geometrical inserts, Turbulator Insert (TI) and Perforated Turbulator Insert (PTI) in a plain tube. The thermophysical properties of the coolant and HX performances have been evaluated using a mathematical model. Results show that the use of geometrical inserts with nanofluid significantly enhances the thermo-economic parameters of the compact heat exchanger. The passive PTI inserts in the core of the plain tube operated with CQD-PG nanofluid result in 11.7% higher overall heat coefficient, 10.4% higher exchange of exergy, and a superior Sustainable index at a lower Reynolds number. The heat transfer rate of HX with PTI and CQD-PG nanofluid results in 9.5% higher, and with TI, 8.5% higher than without inserts. In the meantime, PTI geometrical inserts require 93.3% higher operating cost with CQD-PG nanofluid. The operating cost of CQD-PG is found to be 17.4% than CQD-EG nanofluid. This work is the first to integrate passive heat transfer inserts with CQD nanofluids in HX. It offers a comprehensive framework for maximizing heat exchanger performance while guaranteeing economic viability by fusing thermal, exergy, and economic evaluations. The results have broad implications for sustainable engineering solutions and energy-efficient thermal systems.

NH₃/CO₂ cascade systems have become one of the most widely used kinds of industrial and commercial refrigeration systems, in which the middle temperature and condensing temperature are the most important parameters affecting the system performance, and hence chosen as control variables in optimization control. However, the existing optimization control based on data-driven models still encounters the problem of how to obtain complete data covering a large number and wide range of operating conditions as soon and low cost as possible. To solve this problem, a novel energy-saving control method of NH₃/CO₂ cascade refrigeration systems by data-driven models with varying searching boundaries was proposed innovatively. The potential of the proposed methods was evaluated from the precision and power perspectives. Results showed that the proposed energy-saving control method had the lowest RMSEs of control variables and system power, which indicated its very high optimization precision. Besides, it provided a reduction in energy consumption by 10.62% compared to the conventional constant-variable control approach and by 1.2% compared to the data-driven model with fixed search boundaries, which proved the huge potential of the proposed control method.

The high temperature heat pump (HTHP) plays an important role in improving the efficiency of electric heating and thereby achieving a peaking and then reduction of global carbon emissions. Applications of HTHP in industrial heating is able to replace the boilers directly. Refrigerant has significant impact on system performance and then the efficiency of industrial heating. Low global warming potential (GWP) refrigerants R600, R601, R601a, R1224yd(Z), R1233zd(E), R1234ze(Z) and R1336mzz(Z) were selected and compared with R245fa. The two-stage compression HTHP system model was established. Energy, exergy, environment, and economy analysis with different refrigerants were conducted. R601, R601a, and R1233zd(E) have significant advantages over R245fa when the evaporation temperature is 50°C and condensation temperature is 100∼130°C. When the condensation temperature is 130°C, compared with R245fa, coefficient of performance (COP) of R601, R601a and R1233zd(E) increased by 11.08%, 9.14% and 6.14%, respectively. Exergy efficiency increased by 8.71%, 7.24% and 4.77%, respectively. Total equivalent warming impact (TEWI) decreased by 10.42%, 8.83% and 6.25%, respectively. Investment payback period (PBP) shortened by 69.73%, 65.93% and 40.12%, respectively.

This study presents an optimisation simulation-based approach for the ideal solar absorption cooling system design, including a thermal storage tank and a solar thermal collector. The strategy aims to reduce the costs of solar chilling systems by determining the optimal collector area and storage capacity while minimising electricity consumption to operate system. A hybrid approach is used to achieve the optimal configuration by combining dynamic simulation with TRNSYS and an optimisation algorithm using Gen-Opt. The system’s life cycle cost, over 20 years, serves as the optimisation goal. The study examines the effects of three economic factors: solar collector area, storage capacity, and electricity prices, on the design. The outcomes are analysed from technical and economic perspectives across various African locations. Additionally, techno-economic optimisation was conducted to identify the best set of system design parameters. The findings illustrate how electricity prices and climatic conditions influence the techno-economic feasibility of the system. Alkufra demonstrates and achieves the best techno-economic performance due to its high solar radiation and lower reliance on auxiliary power, which reduces electricity costs throughout the system’s lifetime. Cairo achieves a fairly reasonable performance, providing satisfactory economic viability compared to Lagos or Accra, due to sun availability and electricity costs.

Syria’s prolonged energy crisis, compounded by infrastructure destruction and fuel shortages, underscores the urgent need for sustainable and reliable energy solutions. Given the country’s exceptional solar potential exceeding 300 sunny days annually solar energy represents a key pathway for post-conflict energy recovery and long-term resilience. This study presents a national-scale comparative techno-economic evaluation of concentrated solar power (CSP) and photovoltaic (PV) systems across 14 Syrian governorates using the GREENIUS v4.5.0 simulation platform. A benchmark 50 MW parabolic trough CSP plant, modeled after the Andasol-1 configuration, along with an equivalent 50 MW PV system, was simulated using high-resolution meteorological data from Meteonorm 7.3. The analysis investigates the influence of thermal energy storage (TES) on the CSP plant’s performance, dispatchability, and levelized cost of electricity (LCOE) under varying climatic conditions. The results indicate that Rif Dimashq, Homs, and As-Suwayda exhibit the most favorable conditions for CSP deployment, characterized by the lowest LCOE values and the highest net annual electricity generation. Incorporating a 7-h TES system enhanced energy dispatchability and reduced the LCOE by up to 20% in regions with high direct normal irradiance (DNI). In contrast, PV systems showed stable performance across all governorates, demonstrating superior economic viability in coastal areas where diffuse irradiance predominates. The findings provide a comprehensive and validated framework for solar energy planning in Syria, offering practical guidance for policymakers, investors, and researchers to accelerate the nation’s transition toward a sustainable and resilient energy future.

This study addresses the temperature-induced performance degradation in solar photovoltaic (PV) modules by developing and integrating a graphene-enhanced Glauber’s salt eutectic phase change material (PCM) for advanced thermal management. The composite PCM was synthesized and rigorously characterized using Thermal Conductivity Analysis (TCA), X-ray Diffraction (XRD), and Scanning Electron Microscopy (SEM). These analyses confirmed the material’s superior thermal stability, phase uniformity, and reliable cyclic stability, crucial for long-term thermal energy storage. Comparative outdoor testing was conducted under real-environmental conditions at the KPR Institute of Engineering and Technology, Coimbatore, India (11.0834° N, 77.1402° E), utilizing two identical PV modules: a reference panel and a panel integrated with the modified PCM. Under peak irradiance (∼940 W/m²) and an ambient temperature of ∼32.5°C, the PCM-integrated module consistently operated 5–7°C cooler. This thermal mitigation resulted in a significant electrical efficiency enhancement, achieving a peak efficiency of 13.4% compared to 11.4% for the conventional reference panel. The sustained lower operating temperature also led to measurable improvements in key electrical parameters, including the open-circuit voltage (Voc) and short-circuit current (Isc). The primary contribution of this work is the experimental validation of a graphene-enhanced Glauber’s salt composite PCM that effectively mitigates PV thermal stress, establishing a scalable and sustainable pathway toward improving solar energy harvesting and offering potential for broader thermal applications.

Graphical Abstract

This work investigates the optimization of oxygen-side catalyst loading in an unitized regenerative fuel cell (URFC). The analysis is performed using a detailed three-dimensional numerical model developed in COMSOL Multiphysics 5.4. The model incorporates an agglomerate-based structure for the bifunctional oxygen catalyst layer (OCL), chosen specifically to capture the heterogeneous distribution of catalytic sites and effective transport pathways, which homogeneous layer models fail to represent and which offers computational advantages over pore-network models by balancing accuracy and tractability. The model incorporates an agglomerate-based structure for the bifunctional oxygen catalyst layer (OCL), accounting for catalyst volume fraction, electrochemically active surface area (ECSA), and effective transport properties. Coupled simulations of the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) were carried out to examine how varying catalyst loading densities affect reactants distribution uniformity, transport resistance, and the distribution profile of reactants under steady-state conditions. Round-trip energy efficiency (RTE), calculated from voltage ratios at 500 mA/cm², was 40.8%, 43.0%, 43.5%, and 34.9% for catalyst loadings of 0.2, 0.4, 1.0, and 2.0 mg/cm², respectively. The optimum RTE of 43.5% was achieved at 1.0 mg/cm², in close agreement with the experimentally reported value of 47%, thereby validating the model’s predictive accuracy. Sensitivity analyses confirmed this optimum was robust against ±20% ECSA and ±15% volume fraction variations and stable across current densities from 100 to 800 mA/cm². Additionally, effective diffusivity and tortuosity analyses quantitatively support transport limitations at high loadings. This work provides valuable insights for designing cost-effective and high-performance URFC systems.

This study introduces a novel prediction method that utilizes graph neural networks (GNN) to enhance the prediction accuracy and thereby improve the durability of PEM fuel cell. This method leverages the ability of GNN to extract features from graph-structured data, effectively capturing the implicit variables and time dependencies in the operating parameters. The primary findings indicate that when the number of graph convolution layers, the number of LSTM layers, the batch size, and the learning rate are 3, 1, 32, and 0.000272 respectively, the mean square error after 18 iterations of the model is 4.8957 × 10⁻⁶. At this point, the performance of the GNN model is the best. This method is compared with five other classic benchmark models. The root mean square error on the training set is 2.213 × 10⁻³, and the error on the test set is 5.798 × 10⁻³. Compared with traditional methods, the error is reduced by 74.12% and 62.63% respectively. Its accuracy and fit are also superior to other models. By comparing the actual values of the output voltage of PEM fuel cell under dynamic load conditions with the output voltage predictions based on the GNN model, the GNN model is able to accurately fit the output voltage of the fuel cell under dynamic load conditions over a time span of 1008 h. By analyzing the average weight distribution of the graph attention layer, the GNN not only can capture the instantaneous correlation through short-term time information, but also extract long-term time information to predict the turning point of irreversible damage in PEM fuel cell, ultimately achieving highly accurate prediction results.

This paper introduces a novel two-stage identification algorithm for estimating the state of health (SOH) of batteries. The proposed approach leverages the unique structural properties of the SOH model by employing a sequential parameter estimation strategy. In the first stage, the parameters of the linear component are updated using a least squares algorithm. In the second stage, the nonlinear parameters are estimated via a gradient descent algorithm, building upon the previously identified linear parameters. This innovative methodology offers notable advantages over conventional gradient descent methods, including simpler implementation and improved convergence efficiency. The effectiveness of the algorithm is verified through comprehensive simulation studies, which confirm its superior performance characteristics.

This study presents a comprehensive theoretical and experimental evaluation of Photovoltaic–Thermal (PVT.) systems, focusing on the performance trade-offs between water and high-viscosity heat transfer fluids. Two identical custom-designed PVT. collectors were constructed to compare the thermodynamic behavior of water (SET-1) and Mobiltherm 605 thermal oil (SET-2) against a conventional uncooled PV panel. A steady-state mathematical model was developed to predict system performance, and its accuracy was rigorously validated against experimental data, achieving Root Mean Square Error (RMSE) values of 0.93°C for water and 2.27°C for thermal oil. Experimental results obtained under clear-sky conditions revealed that the conventional PV panel exhibited approximately 6% higher electrical efficiency compared to the PVT. systems. This disparity is attributed to optical losses caused by the additional glazing layer and heat accumulation due to rear thermal insulation in the PVT. design. However, among the PVT. configurations, the water-based system demonstrated superior performance. At a mass flow rate of 0.007 kg/s, the water-based system achieved an electrical efficiency 4% higher and a thermal efficiency nearly double that of the oil-based system approximately 8.6%. This performance gap is governed by the lower thermal conductivity and higher viscosity of the thermal oil, which suppresses turbulent flow and heat transfer coefficients at low velocities. Conversely, the oil-based system provided higher outlet temperatures, validating its potential for specific high-temperature applications where freeze protection is critical. The study concludes that while water is thermodynamically superior for maximizing instantaneous efficiency, thermal oils offer operational stability for harsh climates, provided that flow rates are optimized to compensate for their viscous nature.