Reduction In Power Output Research Articles

Simulation of Ground Power Unit-3 Stirling Engine With Air as Working Fluid

Abstract The immediate need to mitigate climate change presents a chance to move civilization in the direction of a more sustainable future. A Stirling engine has multifuel capabilities such as biomass, solar thermal, and waste heat and hence can contribute significantly to the energy mix of fuel sources. The most common working fluids for Stirling engines are hydrogen, helium, and air, with air being the least expensive and safest. Studies analyzing Stirling engine performance with 3D CFD are limited, and even fewer use air as the working fluid. This research presents a novel 3D CFD analysis of the Ground Power Unit-3 (GPU-3) Stirling engine with air as the working fluid using ansys fluent. The fluid domain was modeled in SolidWorks and one-eighth of the geometry was used for simulation with realizable enhanced wall treatment (EWT) k–ε as an eddy viscosity model. On average, there was a reduction in power output by 50% when air was used as working fluid against helium as working fluid. Engine's power output decreases as the engine's speed increases. The impinging effect contributes to vortex formation and temperature variation within the engine components was nonsinusoidal, this is in line with similar studies performed on GPU-3 Stirling engine.

Research on a New Maximum Power Tracking Algorithm for Photovoltaic Power Generation Systems

INTRODUCTION: Significant advances have been made in photovoltaic (PV) systems, resulting in the development of new Maximum Power Point Tracking (MPPT) methods. The output of PV systems is heavily influenced by the varying performance of solar-facing PV panels under different weather conditions. Partial shading (PS) conditions pose additional challenges, leading to multiple peaks in the power-voltage (P-V) curve and reduced output power. Therefore, controlling MPPT under partial shading conditions is a complex task. OBJECTIVES: This study aims to introduce a novel MMPT algorithm based on the ant colony incorporated bald eagle search optimization (AC-BESO) method to enhance the efficiency of PV systems. METHODS: The effectiveness of the proposed MPPT algorithm was established through a series of experiments using MATLAB software, tested under various levels of solar irradiance. RESULTS: Compared to existing methods, the proposed AC-BESO algorithm stands out for its simplicity in implementation and reduced computational complexity. Furthermore, its tracking performance surpasses that of conventional methods, as validated through comparative analyses. CONCLUSION: This study confirms the efficacy of the AC-BESO method over traditional strategies. It serves as a framework for selecting an MPPT approach when designing PV systems.

Impact of Wind-Assisted Propulsion on Fuel Savings and Propeller Efficiency: A Case Study

In order to meet current and future efforts to reduce fuel consumption and gas emissions, an increasing number of ships are being retrofitted with one of the wind-assisted propulsion solutions. In this paper, the effects of retrofitted wind-assisted propulsion on the efficiency of the propeller are investigated. The installed ship propeller is usually designed to operate under specific conditions; once the thrust force from the sails is added, the operating point of the propeller changes. Taking into consideration the reduced efficiency of the propeller, which is no longer operating in its optimal regime, the actual wind-assisted propulsion contribution can be calculated. The wind-assisted contribution is calculated with a velocity prediction program as a reduction in conventional propulsion power output by maintaining the vessel’s designed speed. From the calculated variations in sail thrust force, dependent on the wind speed, the propeller efficiency is analyzed for different operating states. The propulsion efficiency of the propeller was analyzed with a performance characteristics calculation tool that has been developed and presented in this paper. From the meteorological data obtained from Copernicus Marine Services and available ship documentation, a case study was conducted for a selected route.

Energy, Exergy and Exergo-economic analyses of supercritical CO2 cycles for the exploitation of a geothermal resource in the Italian water dominant Amiata site

Alternative powerplant layouts to commonly applied flash technology is investigated for the water dominant geothermal location of Amiata Mountain, Italy. The proposed solution avoids flashing the brine stream by a borehole pump capable of maintaining pressurized conditions, thus reducing the amount of non-condensable gases released when reaching the ground level. Therefore, the need of a gas treatment section is completely avoided. The heat recovered from the hot brine at ground level is transferred to different supercritical CO2 cycle configurations, equipped with high efficiency Printed Circuit Heat Exchangers (PCHE). The optimal 45.5% calculated exergy efficiency was achieved for the recuperative cycle with intercooling and reheat, which however didn’t show the optimal electricity costs (8.092 c€/kWh) because of the more complex heat exchangers network. The lowest cost of electricity (7.4 c€/kWh) was found for the recuperative cycle configuration. These costs are in line with the current levels of geothermal binary cycles, but almost doubled compared to the current single flash +ORC solution, due to a 40-50% reduction of power output with sCO2 cycles. On the other hand, sCO2 binary cycles could guarantee an almost zero environmental impact, which is the main reason for social concerns related to the current flash powerplants.

Experimental study of ammonia energy ratio on combustion and emissions from ammonia-gasoline dual-fuel engine at various load conditions

Achieving carbon neutrality necessitates the adoption of zero-carbon fuels in engine applications, with ammonia emerging as an up-and-coming candidate due to its favorable safety profile and advantages in storage and transportation. This study experimentally investigated the feasibility of an ammonia-gasoline dual-fuel (AGDF) engine to achieve comparable power output and satisfactory carbon reduction without changing the main structural parameters of the engine. A four-cylinder, naturally aspirated, spark ignition engine was used to investigate the impact of ammonia energy ratio (AER), engine base torque and engine speed on the engine performance, combustion evolution and emission characteristics. The findings reveal that the brake thermal efficiency (BTE) in AGDF mode is lower than in gasoline-only mode, primarily due to the reduced combustion activity. However, this efficiency decline becomes noticeable only when the AER exceeds 15 %. Additionally, at high AERs and high engine base torques, the delayed effect of ammonia fuel on the main combustion period results in a double-peak pattern, which limits the energy output but presents opportunities for phase optimization. The study also examined three incomplete combustion emissions, each exhibiting distinct behaviors. Except for ammonia slip, adding ammonia fuel does not significantly affect carbon monoxide (CO) and unburned hydrocarbons (UHC) emissions, particularly at AERs below 25 %. Nevertheless, nitrogen oxide (NOx) emissions under AGDF combustion are significantly higher than under gasoline alone in most instances. Crucially, the study demonstrates the carbon reduction potential of ammonia fuel across different engine loads, with a maximum carbon dioxide (CO2) reduction of 46.8 % at a 35 % AER. It is anticipated that further optimization of the combustion phase will improve the capability for carbon reduction.

Thermodynamic and advanced exergy analysis of Rankine Carnot battery with cascaded latent heat storage

Cascaded latent heat storage offers significant advantages in Rankine Carnot battery systems by minimizing exergy destruction during heat transfer processes. However, the system performance and the sources of exergy destruction remain unclear, and the configuration of multiple phase change materials remains ambiguous. This paper investigates a Rankine Carnot battery system integrated with cascaded latent heat storage using energy, conventional exergy, and advanced exergy analysis methods. First, a novel phase change material partitioning configuration scheme is proposed, considering the phase change of the working fluid, significantly improving temperature matching within the storage. The number of stages in heat storage devices significantly affects system performance, with roundtrip and exergy efficiencies increasing by 32.2 % and 15.2 % as the number of stages rises from 1 to 4, though further increases have minimal impact. Advanced exergy analysis on a 4-stage system indicates that the endogenous avoidable exergy destruction of the expander, compressor, evaporator, and condenser constitutes 12.4 %, 12.0 %, 10.5 %, and 7.0 % of the total system exergy destruction, respectively, highlighting the priority components for system improvement. Furthermore, raising the heat storage temperature by 45 °C reduces roundtrip efficiency by 26.5 % while increasing power output by 63.2 %. Increasing the heat pump’s evaporation temperature by 30 °C improves roundtrip efficiency by 151.1 % but reduces power output by 33.7 %. Reducing the amount of waste heat and surplus electricity results in a significant decrease in the roundtrip efficiency and exergy efficiency, which is mainly caused by the reduction in the isentropic efficiencies of compressor and expander under partial load conditions. The findings on the priority components for system improvement and the configuration of cascaded latent heat storage provide an effective approach for enhancing Rankine Carnot batteries.

Coordinated frequency control strategy for modern power system considering engagement willingness

The large scale integration of renewable energy sources (RES) is posing unprecedented challenges to the frequency stability of modern power systems, with system inertia being the primary concern. Under such a landscape, this paper introduces a coordinated control strategy aiming at enhancing frequency stability by swiftly adjusting the power output of flexible resources to mitigate power imbalances and further dampen frequency fluctuations. Firstly, a communication-free center of inertia (COI) frequency estimation method is proposed. An equivalent inertia estimation method tailored for modern power systems is then developed by exploiting the correlation between synchronous inertia and active power fluctuations. By integrating the COI frequency variation rate with the estimated equivalent inertia, the imbalance power is calculated and compensated through the proposed coordinated control strategy. In this work, two typical flexible resources including photovoltaic (PV) generation and electric vehicles (EVs) are modeled and integrated into the testing system in PSCAD/EMTDC. Considering the contradiction between maximizing generation profit and reducing power output to mitigate frequency variation, the Corporate Social Responsibility (CSR) of renewable Generation Companies (GENCOs) is modeled to sketch their willingness of engaging into frequency support. Moreover, the frequency modulation potential of EVs in both commercial and residential areas are quantified for practical implementation. The simulations results successfully validate the effectiveness of the proposed control strategy.

Modeling and simulation of a photovoltaic generator for analyzing the impact of faults on the I-V curve

The rapid expansion of the solar industry has underscored the importance of photovoltaic installations in the ongoing transition to sustainable energy. With this growth comes the crucial task of effectively monitoring and controlling the power generated. Photovoltaic systems are particularly vulnerable to defects due to their exposure to challenging environmental conditions, which can lead to reduced power output and an increased risk of fire. Therefore, a thorough analysis of any faults is essential in order to mitigate potential damage to the system. The present study proposes a comprehensive analysis of the behavior of a photovoltaic generator comprising four modules. MATLAB/Simulink software is used to model the generator in healthy operation. Subsequently, a simulation of the generator in faulty conditions is conducted, considering four fault cases: partial shading (PS), open circuit fault (OCF), bypass diode disconnected (PSBD), and twinned fault bypass diode disconnected plus open circuit (PSBDOC). A detailed examination of the simulation results for the faults above reveals that the twinned fault results in a substantial reduction in the output current, as well as an elimination of the open circuit voltage of the photovoltaic generator. This contrasts the behavior observed in a system comprising two modules, wherein the open circuit voltage remains unaltered. This particular fault offers a compelling rationale for the monitoring of photovoltaic installations, to enhance overall productivity while avoiding any potential damage to the system.

Study of ash deposition characteristics of photovoltaic arrays taking into account the effect of photovoltaic module spacing and its effect on output characteristics: Indoor experiment

The problem of ash deposition on the surface of photovoltaic (PV) arrays during actual operation seriously affects their power generation efficiency. Because traditional research does not consider the PV module spacing, it cannot reflect the actual PV array ash deposition problem. This study explored the influence of PV module spacing, combined with different tilt angles and inlet wind speeds, on the characteristics of ash deposition and power output of the PV array through indoor experiments. The study designed a set of experimental equipment for simulating the formation process of ash deposition on the surface of PV arrays and set up an experimental platform to measure the influence of ash deposition on the maximum output power (Pmax), open-circuit voltage (Uoc), and short-circuit current (Isc) of the PV modules. The findings indicate that as the tilt angle increases, the PV array experiences reduced ash deposition and increased output power. Conversely, higher inlet wind speeds lead to greater ash deposition and decreased output power. Moreover, widening the spacing between PV modules results in increased ash deposition and reduced output power. Notably, within the PV array, the front PV module accumulates more ash than the rear module, with larger particle sizes. These research findings have significant implications for optimizing PV array design and enhancing power generation efficiency.

Data-driven study/optimization of a solar power and cooling generation system in a transient operation mode and proposing a novel multi-turbine modification concept to reduce the sun's intermittent effect

This study explores the integration of solar energy via parabolic trough collectors (PTC) with the Goswami cycle, focusing on evaluating life cycle costs (LCC) and energy performance in both steady-state and transient conditions. A suitable system configuration of a single-turbine Goswami cycle is designed for integration with PTC. Then, in the system's steady-state, a parametric study is performed based on the change in the design components. Subsequently, a multi-objective optimization method using energy and lifetime cost objectives are defined to identify the best system performance. This optimization technique utilizes artificial neural network (ANN) and the grey wolf optimizer (GWO) algorithm. Accordingly, the transient performance of the system is investigated during its optimal operating point using the TRNSYS software. Suitable controllers are considered for regulating power production and cooling system. Annual transient performance graphs of the systems are obtained. Following these investigations, a novel multi-turbine system is proposed. This configuration aims to enhance system reliability and diminish the reliance on storage devices by optimizing performance. The proper configuration of the five-turbine system with the required controllers is also provided. Hence, the method of dividing the turbine into similar turbines with lower capacity is a new concept proposed in this study. In the optimal state, it is observed that for 100 kW of net power output, 161 kW of effective cooling load can be achieved. At this configuration, the system's LCC amounts to 2.91 M. Moreover, during transient operation, an average power of 90 kW is attainable with a cooling load of 120 kW. The findings indicate that modifying the system from single turbine to five-turbines incurs an additional cost of approximately 9.2 % and reduces power output by 6 %. However, this modification significantly mitigates the transient impact of solar fluctuations.

Research on supply-demand balance in China’s five southern provinces amidst fluctuations in regional wind and solar power generation and transmission faults

To investigate the supply-demand balance of regional power systems under extreme scenarios, this study employs the high-resolution power optimization model SWITCH-China to simulate the regional heterogeneity and randomness of extreme weather events in detail. Focusing on the five southern provinces, this study explores various impacts on the power generation side and the grid side under scenarios of reduced wind and solar power output, transmission line failures, and combined scenarios, proposing strategies for constructing a new power system. The main conclusions are: the reduction in wind and solar power output significantly affects provinces with a high proportion of these installations, like Guizhou, necessitating other stable power generation forms to compensate. Transmission line failures notably impact provinces like Guangdong, which rely heavily on imported electricity, requiring increased investment in new wind and solar installations and more self-generated power to offset the reduction in imported electricity. The combination of these factors amplifies their individual impacts, leading to the highest carbon reduction and electricity costs. The simulation results of this study are valuable for China’s five southern provinces in coping with extreme scenarios. As these provinces work on building a new power system and gradually retire fossil fuel units, they should expand the number and capacity of inter-provincial high-voltage transmission lines while considering system economics. Additionally, accelerating the deployment of energy storage is crucial for maintaining power system stability.

Enhancing solar thermoelectric power generation with supercritical CO2 cooling: Hydraulic, thermal, and exergy analysis

This research investigates the dynamic behavior and impact of various factors on the hydraulic, thermal, and exergetic characteristics of a solar-based thermoelectric device using a pin–fin heatsink cooled by supercritical CO2. A comprehensive numerical model analyzes the heat dissipation and performance of the power generator, integrating a thermoelectric generator and a pin–fin heatsink with various pin shapes. Key geometric and operational parameters, such as the height of PN (P-type and N-type semiconductor) legs of the TEG, the number of thermocouples, operating pressure, Reynolds number, and CO2 temperature, are examined for a comprehensive performance assessment. The study highlights the superior performance of CO2 coolant over traditional water-cooling system. Near the critical temperature of CO2, enhanced heat transfer significantly boosts power output, conversion efficiency, and exergetic efficiency. For example, at 8 MPa, the TEG’s (thermoelectric generator) power output increases from 1.31 mW at 295 K to 2.35 mW at 310 K. Comparisons reveal that while water coolant lowers the cold side temperature more effectively, it results in reduced power output due to decreased temperature differentials and increased pressure loss. Conversely, CO2 coolant maintains higher cold side temperatures while having advantages in power output. At 315 K, the cold side temperature with water is 315.7 K compared to 346.7 K with CO2. Increasing the number of thermocouples from 18 to 32 for a leg height of 1 mm leads to an approximate 102.3 % increase in voltage. Raising the PN leg height from 1 mm to 2 mm for an NTC of 50 results in a nearly 99.8 % increase in voltage. Lozenge-shaped fin produces a peak power output of 2.73 mW, while square fin generates 2.62 mW. This research underscores CO2’s potential as a high-performance coolant in solar thermoelectric applications, offering insights into optimizing system design for maximum efficiency.

Supervised classification and fault detection in grid-connected PV systems using 1D-CNN: Simulation and real-time validation

Photovoltaic (PV) systems are prone to various faults, including short-circuit, open-circuit, partial shading, and inverter bypass diode issues, which reduce power output and can damage components. This study presents an innovative fault detection and online monitoring technique for grid-connected PV (GCPV) systems, combining Internet of Things (IoT) technology with a one-dimensional convolutional neural network (1D-CNN) deep learning approach. The method involves developing a temperature-dependent PV system model using series resistance and ideality factor, capturing real-time data from a 15kWp GCPV system with optimally placed sensors to minimize sensor count while maintaining data accuracy, and validating the model through MATLAB/Simulink simulations and real-time experiments under various fault scenarios. The collected data is used to train the 1D-CNN model to classify different fault types. The model is then implemented on an IoT platform for real-time monitoring and fault detection, displaying system status and alerts via a dashboard. The proposed system achieves a high fault detection accuracy of 98.15 % and 93.12 % during cyberattacks, with an uncertainty of ±4 %, significantly enhancing fault detection reliability and efficiency compared to existing methods. The IoT dashboard provides an effective tool for monitoring system performance and issuing alerts under abnormal conditions.

A Review of Thermal Management and Heat Transfer of Lithium-Ion Batteries

With the increasing demand for renewable energy worldwide, lithium-ion batteries are a major candidate for the energy shift due to their superior capabilities. However, the heat generated by these batteries during their operation can lead to serious safety issues and even fires and explosions if not managed effectively. Lithium-ion batteries also suffer from significant performance degradation at low temperatures, including reduced power output, a shorter cycle life, and reduced usable capacity. Deploying an effective battery thermal management system (BTMS) is crucial to address these obstacles and maintain stable battery operation within a safe temperature range. In this study, we review recent developments in the thermal management and heat transfer of Li-ion batteries to offer more effective, secure, and cost-effective solutions. We evaluate different technologies in BTMSs, such as air cooling, liquid cooling, phase change materials, heat pipes, external preheating, and internal preheating, discussing their advantages and disadvantages. Through comparative analyses of high-temperature cooling and low-temperature preheating, we highlight the research trends to inspire future researchers. According to the review of the literature, submerged liquid BTMS configurations show the greatest potential as a research focus to enhance thermal regulation in Li-ion batteries. In addition, there is considerable research potential in the innovation of air-based BTMSs, the optimization of liquid-based BTMSs, the coupling of heat pipes with PCMs, the integration of PCMs and liquid-cooled hybrid BTMSs, and the application of machine learning and topology optimization in BTMS design. The application of 3D printing in lithium-ion battery thermal management promises to enhance heat transfer efficiency and system adaptability through the design of innovative materials and structures, thereby improving the battery’s performance and safety.

An XAI Framework for Predicting Wind Turbine Power under Rainy Conditions Developed Using CFD Simulations

Renewable energy sources are essential to address climate change, fossil fuel depletion, and stringent environmental regulations in the subsequent decades. Horizontal-axis wind turbines (HAWTs) are particularly suited to meet this demand. However, their efficiency is affected by environmental factors because they operate in open areas. Adverse weather conditions like rain reduce their aerodynamic performance. This study investigates wind turbine power prediction under rainy conditions by integrating Blade Element Momentum (BEM) theory with explainable artificial intelligence (XAI). The S809 airfoil’s aerodynamic characteristics, used in NREL wind turbines, were analyzed using ANSYS FLUENT and symbolic regression under varying rain intensities. Simulations at a Reynolds number (Re) of 1 × 106 were performed using the Discrete Phase Model (DPM) and k–ω SST turbulence model, with liquid water content (LWC) values of 0 (dry), 10, 25, and 39 g/m3. The lift and drag coefficients were calculated at various angles of attack for all the conditions. The results indicated that rain led to reduced lift and increased drag. The innovative aspect of this research is the development of machine learning models predicting changes in the airfoil coefficients under rain with an R2 value of 0.97. The proposed XAI framework models rain effects at a lower computational time, enabling efficient wind farm performance assessment in rainy conditions compared to conventional CFD simulations. It was found that a heavy rain LWC of 39 g/m3 could reduce power output by 5.7% to 7%. These findings highlight the impact of rain on aerodynamic performance and the importance of advanced predictive models for optimizing renewable energy generation.

Improving the Long-Term Stability of PbTe-Based Thermoelectric Modules: From Nanostructures to Packaged Module Architecture.

Nanostructured lead telluride PbTe is among the best-performing thermoelectric materials, for both p- and n-types, for intermediate temperature applications. However, the fabrication of power-generating modules based on nanostructured PbTe still faces challenges related to the stability of the materials, especially nanoprecipitates, and the bonding of electric contacts. In this study, in situ high-temperature transmission electron microscopy observation confirmed the stability of nanoprecipitates in p-type Pb0.973Na0.02Ge0.007Te up to at least ∼786 K. Then, a new architecture for a packaged module was developed for improving durability, preventing unwanted interaction between thermoelectric materials and electrodes, and for reducing thermal stress-induced crack formation. Finite element method simulations of thermal stresses and power generation characteristics were utilized to optimize the new module architecture. Legs of nanostructured p-type Pb0.973Na0.02Ge0.007Te (maximum zT ∼ 2.2 at 795 K) and nanostructured n-type Pb0.98Ga0.02Te (maximum zT ∼ 1.5 at 748 K) were stacked with flexible Fe-foil diffusion barrier layers and Ag-foil-interconnecting electrodes forming stable interfaces between electrodes and PbTe in the packaged module. For the bare module, a maximum conversion efficiency of ∼6.8% was obtained for a temperature difference of ∼480 K. Only ∼3% reduction in output power and efficiency was found after long-term operation of the bare module for ∼740 h (∼31 days) at a hot-side temperature of ∼673 K, demonstrating good long-term stability.

Aerothermodynamic similarity criteria for turbines operating within non-periodic transition processes

The aerothermodynamic performance test is a prerequisite for investigating the turbine transition process. To enhance the safety and feasibility of the experiments while reducing energy costs, the target high-temperature transition processes must be converted to low-temperature ones based on the similarity criteria. However, the commonly used similarity criteria for turbine aerothermal performance are only applicable to steady states, rather than transition processes. This study presents a set of similarity criteria for turbines operating within non-periodic transition processes, according to the similarity of the dimensionless Navier-Stokes equations and time-variant definite conditions. Furthermore, the proposed similarity method ensures the similarity of the hysteresis effect by maintaining the uniformity of the dimensionless moments between the high- and low-temperature operating conditions. Comprehensive validations are carried out on two different turbines operating within transition processes, covering periodic processes with different pulsating frequencies and amplitudes, as well as non-periodic ones. Both the proposed and commonly used similarity methods are applied to obtain low-temperature performance curves, and the two curves are compared with the target high-temperature curve to evaluate the similarity accuracy of the corresponding methods. Regarding the deviations in the reduced power output between the high- and low-temperature operating conditions, the proposed similarity method exhibits similarity errors of no more than 2 %, which is only approximately 20 % of the errors produced by the commonly used method. Thus, the proposed similarity method has a high accuracy and is applicable to non-periodic transition processes.

Electrification of Agricultural Machinery: One Design Case of a 4 kW Air Compressor

In response to the global pursuit of net-zero carbon emissions, the electrification of agricultural machinery is becoming a significant research and development trend. This study introduces the overall design of a 4 kW air compressor aimed at achieving a green vision for agricultural machinery. The design focuses on providing continuous and stable power and air output using a lithium-ion battery. Durability and cost-effectiveness are prioritized, with a particular emphasis on the Arduino system for integrating battery and motor systems to withstand harsh conditions and ensure ease of maintenance. A permanent magnet brushless motor was selected as the power source, paired with an optimized pulley to supply the proper torque to the air compressor. The system employs an Arduino-based feedback control sensor for air pressure regulation, ensuring energy efficiency. The primary energy source is a 48 V lithium iron phosphate battery, known for its high energy density and safety. The battery design focuses on system integration, addressing specific environmental discharge requirements. The embedded battery management system provides thermal and lifecycle parameter estimation, guaranteeing long-duration power supply and safe operation under various conditions. Unlike traditional fuel-driven systems, lithium iron phosphate batteries do not emit harmful gases, aligning with environmental standards. System integration testing demonstrated that the air pressure feedback control effectively meets the energy-saving requirements by digitally reducing power output as air accumulates in the chamber. Bench testing confirmed that the system performs as designed, achieving the desired results and advancing the goal of sustainable agricultural machinery.

Simulation and experimental study of thermoelectric generators with an axial gradient metal foam heat exchanger

The utilization of metal foam for heat transfer augmentation is regarded as a highly efficient technique, albeit associated with significant pressure losses. To enhance the feasibility of employing metal foam in thermoelectric generators and mitigate the high-pressure drop, we propose an enhancement strategy involving the partial axial filling of gradient metal foam. Both analytical modeling and experimental investigation were employed to evaluate the effects of porosity, pore density, and gradient structure at various filling rates on the overall performance of thermoelectric generators. The results show that arranging metal foam with increasingly high frame density in the direction of fluid flow, rather than adopting increasingly sparse or constant structures, leads to improved voltage uniformity and reduced pressure drop. A positive gradient configuration with a pore density distribution of 5-10-20 PPI yielded the highest net power at 118.3 W, which is 12.5 % higher than that of metal foam with constant 20 PPI. Ultimately, empirical verification substantiates the comprehensive performance advantages of positive gradient configuration. For filling rates of 30 %, 60 %, and 100 %, pressure drop is reduced by 35.9 %, 33.4 %, and 29.2 %, respectively, in comparison to constant 20 PPI metal foam, despite a modest reduction in output power, which remains less than 3 %.

Detection of Demagnetization Faults in Electric Motors by Analyzing Inverter Based Current Data Using Machine Learning Techniques

Demagnetization of the rotor magnets is a significant failure mode that can occur in permanent magnet synchronous machines (PMSMs). Early detection of demagnetization faults can help change system parameters to reduce power output or ensure safety. In this paper, the effects of demagnetization faults were analyzed both in simulation and experiments using the example of drone motors. An approach was investigated to detect even minor demagnetization faults that does not require any additional sensing effort. Machine learning (ML) techniques are used to analyze the phase current data directly received from the inverter to enable anomaly detection. For this purpose, the phase current is transformed by the Fast Fourier Transform (FFT), the spectral data is then reduced in dimensionality, followed by an anomaly detection algorithm using a one-class support vector machine (OC-SVM). To ensure simplified initialization of the ML model without the need for training sets of damaged drives, only data from magnetically undamaged motors was used to train the models for anomaly detection. Different selections of considered harmonics and different metrics were investigated using the experimental data, achieving a precision of up to 99%, a specificity of up to 98%, and an accuracy of up to 90%.