Output Power Efficiency Research Articles

Enhancing output characteristics of semi-active flapping airfoil energy capture device via flexible deformation strategy

ABSTRACT A concentrated-torsion flexible airfoil model is established to enhance the output characteristics of the flapping airfoil energy capture device in this work. The effect of the damping ratio C 2*, spring ratio K 2* of the torsion spring, and mass ratio M A * of the tail airfoil on the dynamic characteristics is investigated systematically. The numerical simulation results show that compared with the rigid airfoil, the time-averaged power coefficient and output efficiency of the optimized flexible airfoil (C 2* = 4, K 2* = 0.5, and M A * = 0.7) are increased by 91.85% and 63.45%, respectively. Remarkably, the output efficiency of the optimized flexible airfoil is 32.15%. The reason for the enhanced performance is that the passive deformation can ensure the airfoil generates a stronger vortex and delays the shedding of the vortex. In addition, the experimental test results (with a 36.29% performance enhancement) further demonstrate the effectiveness of using the concentrated-torsional flexible deformation strategy in enhancing the energy output characteristics of the flapping airfoil.

Part-load performance analysis of a modular biomass boiler with a combined heat and power industrial Rankine cycle and supplementary sCO2 Brayton cycle

Abstract The addition of a supplementary high efficiency cycle integrated with an existing steam power cycle may increase energy efficiency and net generation. In this paper, part load performance and operation of a modular biomass boiler with an existing industrial Rankine steam heat and power cycle and supplementary supercritical CO2 (sCO2) Brayton cycle is analysed. The aim is to leverage the high efficiency of the sCO2 cycle by retrofitting sCO2 heaters in the existing biomass boiler, increasing net power output and thermal efficiency. With nominal load performance previously investigated, understanding part-load performance and operation is vital to determine cycle feasibility. A quasi-steady-state one dimensional thermofluid network model was used to simulate the integrated cycle performance for loads ranging from 100% to 60%. The model solves the mass, energy, momentum, and species balance equations, capturing detailed component characteristics. Two control methodologies are explored for the sCO2 Brayton cycle, namely inventory control and inventory control combined with throttling valve control. Inventory control is selected as the better performing control strategy for load following, maintaining high thermal efficiency across partial loads. At 60% load, the sCO2 compressor operates near the pseudo-critical point, leading to a sharp decrease in sCO2 cycle capacity, which requires careful management of inventory control. Two sCO2 heater configurations are investigated, namely a single convective-dominant heater, and a dual heater configuration with a radiative and a convective heater. The single heater configuration is preferred to minimise adverse impacts on the Rankine cycle superheaters.

Enhancing optical output power efficiency in nitride‐based green micro light‐emitting diodes by sidewall ion implantation

AbstractThough micro‐LED (light‐emitting diode) displays are considered the emerging display technology, the micron‐scale LED chip size encounters severe efficiency degradation that may impact the power budget of the displays. This work proposes an ion implantation method to deliberately create a high‐resistivity sidewall in the InGaN/GaN green LED. Our study demonstrates that ion implantation suppresses reverse leakage current due to the mitigation of sidewall defects. For an LED mesa size of 10 × 10 μm2, optical output power density is improved by 36.2% compared to the device without implantation. Compared to a larger 100 × 100 μm2 device without implantation, we achieve only 21.3% degradation of output power density under 10 A/cm2 injection for a 10 × 10 μm2 LED with implantation. In addition, the ion implantation method can lower the wavelength shift by reducing light emission from the region near the sidewall (where the amount of Indium [In] clustering differs from the mesa region). The results show promise in addressing the efficiency challenges of micro‐LED displays by selective ion implantation.

Performance analysis and optimization of a vehicular LT-PEMFC for maximum power density and efficiency based on NSGA-II algorithm

Based on finite temporal thermodynamics (FTT), a model for a low-temperature proton exchange membrane fuel cell (LT-PEMFC) is created. The link between the LT-PEMFC’s output power density and efficiency was determined using the aforementioned model. The experimental results confirm the algorithm model’s accuracy. Investigations are also conducted into how the design and operating parameters affect the LT-PEMFC’s output outcomes. Moreover, the NSGA-II technique is used to maximize the power density and efficiency of the LT-PEMFC for multi-objective optimization. The experiment outcomes demonstrate that the optimized LT-PEMFC model performs better at output. The related powertrain design scheme for the various output performances of Fuel cell vehicles (FCVs) is provided by LT-PEMFC optimization. It is demonstrated by the study of simulation data that the optimized LT-PEMFC achieves reduced hydrogen consumption and higher FCV efficiency.

Study and Analysis thermal performance of Taza gas power plant in Kirkuk –Iraq

In this research, the concept of energy balance was implemented in one of the gas turbine electricity generation units in Iraq (Kirkuk - Taza) K1. The design operating data is taken and compared with the data calculated in the computer model used by Engineering Equation Solutions (EES), and the validity of the model used was confirmed. The results indicate the release of a large amount of thermal energy into the atmosphere due to the open Brayton cycle, as energy losses amounted to 60% of the energy input. The results based on the data of the first unit of the station demonstrated that the theoretical efficiency of the unit is a function of the two variables, which are the temperature of the air entering the compressor and the Turbine inlet temperature: Increasing the compressor inlet temperature leads to a decrease in net power output and first-law efficiency and an increase in the specific fuel consumption rate. Increasing the turbine inlet temperature to 1°C leads to an improvement in both net power output and first-law efficiency by (0.24MW, and 0.04) %), respectively. The results also showed that cooling the air entering the compressor for 1°C leads to improving power output and first law efficiency by (0.72MW, and 0.12%), respectively, and reduces specific fuel consumption by 7.8kg/MWh.

Reliable Prediction of Solar Photovoltaic Power and Module Efficiency using Bayesian Surrogate Assisted Explainable Data-Driven Model

This study proposes a Bayesian surrogate-driven explainable deep neural network model to predict and interpret the module efficiency and maximum output power of three commercially available photovoltaic modules: monocrystalline silicon, polycrystalline silicon, and amorphous silicon during the winter season. In addition, the influence of the photovoltaic material and ambient conditions on the predicted power and module efficiency is investigated. The model inputs include solar irradiance, module material (monocrystalline, polycrystalline, and amorphous silicon), season of the year (months), and time of day (morning to evening). Experiments were conducted on these three modules during the winter using data from Rawalpindi, Pakistan. These data were then fed into the deep neural network model to train and yield predictions. Several preprocessing techniques such as logarithmic transformation, square root, cube root, reciprocal, and exponential transformations are employed to improve the linearity of distributed data. For hyper-parameters optimization, Gaussian process, gradient boost regression trees, and random forest are used. The results show that the optimal deep neural network using random forest surrogate model along with square root and exponential transformation is able to predict the maximum power output and module efficiency of the considered photovoltaic modules for the entire investigated period with a correlation coefficient, R2 = 0.998. The least accurate model (R2=0.991) is a Gaussian process using simple data distribution. These results hold valuable insights for predicting and optimizing the performance of other solar photovoltaic modules in various climates and different seasons of the year.

High-efficiency and thermally stable FACsPbI3 perovskite photovoltaics.

α-FA1-xCsxPbI3 is a promising absorber material for efficient and stable perovskite solar cells (PSCs)1,2. However, the most efficient α-FA1-xCsxPbI3 PSCs require the inclusion of methylammonium chloride (MACl) additive3,4, which generates volatile organic residues (i.e., MA) that limit device stability at elevated temperatures5. To date, the highest certified power-conversion efficiency (PCE) of α-FA1-xCsxPbI3 PSCs without MACl was only ~24% (ref.6,7), and has yet to exhibit any stability advantages. Here, we identify interfacial contact loss caused by the Cs+ accumulation for the conventional α-FA1-xCsxPbI3 PSCs, which deteriorates the device performance and stability. Through in-situ GIWAXS analysis and DFT calculations, we demonstrate an intermediate phase-assisted crystallization pathway enabled by acetate surface coordination to fabricate high-quality α-FA1-xCsxPbI3 film, without using MA-additive. We herein report a certified stabilized power output (SPO) efficiency of 25.94% and a reverse-scanning PCE of 26.64% for α-FA1-xCsxPbI3 PSCs, exhibiting negligible contact losses and enhanced operational stability. The devices retain >95% of their initial PCEs after over 2,000 hours operating at maximum power point under 1 sun, 85 °C, and 60% relative humidity (ISOS-L-3).

Techno-Economic evaluation and multi-objective optimization of a filter equipped solar chimney system

Urban air pollution has recently become a pressing challenge, and innovative solutions are needed to reduce it. The current study proposes and investigates the performance of integrating a solar chimney system with an air filtration unit to mitigate urban air pollution and generate electricity simultaneously. A comprehensive computational fluid dynamics model coupled with optimization techniques is proposed to analyze and optimize the design parameters from system performance and cost perspectives. A multi-objective optimization is performed to determine the optimal placements of the air filter and geometric parameters of the hybrid solar chimney to maximize power output and filtration efficiency while minimizing the capital cost. Results indicated that placing the filter in the middle of the chimney resulted in optimal configuration with a power output of 18.83 kW, and filter efficiency of 53.73 % at a cost of $ 4.83 million, whereas placing the filter close to the inlet of the collector achieved the highest output power of 100.4 kW, a filter efficiency of 89.67 % at a capital cost of $ 7.35 million. It is concluded that placing the filter on the collector side is more beneficial in terms of performance and cost per unit of power produced.

Research of a 0.14 THz Dual-Cavity Parallel Structure Extended Interaction Oscillator.

This paper presents a method to enhance extended interaction oscillator (EIO) output power based on a dual-cavity parallel structure (DCPS). This stucture consists of two conventional ladder-line structures in parallel through a connecting structure, which improves the coupling efficiency between the cavities. The dual output power fusion structure employs an H-T type combiner as the output coupler, which can effectively combine the two input waves in phase to further increase the output power. The dispersion characteristics, coupling impedance, and field distribution of the DCPS are investigated through numerical and simulation calculations, and the optimal operating parameters and output structure are obtained by PIC simulation. At an operating voltage of 12.6 kV, current density of 200 A/cm2, and longitudinal magnetic field of 0.5 T, the DCPS EIO exhibits an output power exceeding 600 W at a frequency of 140.6 GHz. This represents a nearly three-fold enhancement compared with the 195 W output of the conventional ladder-line EIO structure. These findings demonstrate the significant improvement in output power and interaction efficiency achieved by the DCPS for the EIO device.

Research on PV array reconstruction and Full-cycle maximum power point tracking technology of space solar power station

Space solar power station is an energy system that converts solar energy into electrical energy in the space environment and then transmits it to the space platform or ground using wireless power transmission technology. To improve the power generation and system efficiency of the space solar power station, an adaptive and reconfigurable photovoltaic array with multi-configuration is proposed, which can avoid large attenuation of the output power and efficiency of the photovoltaic array when the photovoltaic modules have a fault occurs or the receive different irradiation intensity. Then, according to the orbit area and light condition of the space solar power station, the operation mode are divided in detail. Furthermore, a novel full-cycle and multi-mode GMPPT (maximum power point tracking) strategy is proposed. Compared to the single mode MPPT, the control strategy has shorter response time, faster convergence and higher tracking accuracy. Through the above research, the output power and photoelectric conversion efficiency of space solar power station can be significantly improved.

A stochastic averaging mathematical framework for design and optimization of nonlinear energy harvesters with several electrical DOFs

Energy harvesters for mechanical vibrations are electro-mechanical systems designed to capture ambient dispersed kinetic energy, and to convert it into usable electrical power. The random nature of mechanical vibrations, combined with the intrinsic non-linearity of the harvester, implies that long, time domain Monte-Carlo simulations are required to assess the device performance, making the analysis burdensome when a large parameter space must be explored. Therefore a simplified, albeit approximate, semi-analytical analysis technique is of paramount importance. In this work we present a methodology for the analysis and design of nonlinear piezoelectric energy harvesters for random mechanical vibrations. The methodology is based on the combined application of model order reduction, to project the dynamics onto a lower dimensional space, and of stochastic averaging, to calculate the stationary probability density function of the reduced variables. The probability distribution is used to calculate expectations of the most relevant quantities, like output voltage, harvested power and power efficiency. Based on our previous works, we consider an energy harvester with a matching network, interposed between the harvester and the load, that reduces the impedance mismatch between the two stages. The methodology is applied to the optimization of the matching network, allowing to maximize the global harvested power and the conversion efficiency. We show that the proposed methodology gives accurate predictions of the harvester’s performance, and that it can be used to significantly simplify the analysis, design and optimization of the device.

Three-dimensional hydrogel membranes for boosting osmotic energy conversion: Spatial confinement and charge regulation induced by zirconium ion crosslinking

Ion-exchange membranes have been widely used to harvest osmotic energy in the past decades. However, conventional ion-exchange membranes suffer from low output power and poor conversion efficiency due to their limited pores and high membrane resistance. Herein, a sodium alginate (SA)/3-sulfopropyl acrylate potassium salt (SPAK) hydrogel membrane which has good cationic selectivity and can effectively harvest osmotic energy is designed, yielding a maximum power density of 16.44 W/m2 under a 50-fold NaCl concentration gradient and 36.85 W/m2 with ion selectivity of 0.73 at 500-fold. Furthermore, by introducing Zr4+, post-crosslinking reaction was employed to prepare tougher hydrogel membranes at room temperature for breaking a trade-off between selectivity and permeability, boosting a maximum power density up to 25.07 W/m2 under a 50-fold NaCl concentration gradient and 121.66 W/m2 with a high cation selectivity of 0.87 at 500-fold. Importantly, the resultant SA/SPAK/Zr4+ membrane reveals excellent osmotic energy harvesting property with the largest thickness of 500 μm, exceeding other reported porous nanofluidic membranes. Theoretical calculations correlate the enhanced power density of SA/SPAK/Zr4+ membranes with the enriched Cl- and smaller pore size after the introduction of Zr4+. This work paves an avenue to design and develop the 3D hydrogel membranes for high-performance osmotic energy generators.

Heat Pipe-Based Cooling Enhancement for Photovoltaic Modules: Experimental and Numerical Investigation

High temperatures in photovoltaic (PV) modules lead to the degradation of electrical efficiency. To address the challenge of reducing the temperature of photovoltaic modules and enhancing their electrical power output efficiency, a simple but efficient photovoltaic cooling system based on heat pipes (PV-HP) is introduced in this study. Through experimental and numerical investigations, this study delves into the temperature characteristics and power output performance of the PV-HP system. Orthogonal tests are conducted to discern the influence of different factors on the PV-HP system. The experimental findings indicate that the performance of the PV-HP system is superior to that of the single system without heat pipes. The numerical simulation shows the effects of system structural parameters (number of heat pipes, angle of heat pipe condensation section) on system temperature and power output performance. The numerical simulation results show that increasing the angle of the heat pipe condensation section and the number of heat pipes leads to a significant drop in system temperature and an increase in the efficiency of the photovoltaic cells.

Data-Driven Generative Model Aimed to Create Synthetic Data for the Long-Term Forecast of Gas Turbine Operation

Abstract The prediction of gas turbine (GT) future health state plays a strategic role in the current energy sector. However, training an accurate prognostic model is challenging in case of limited historical data (e.g., new installation). Thus, this paper develops a Generative Adversarial Network (GAN) model aimed to generate synthetic data that can be used for data augmentation. The GAN model includes two neural networks, i.e., a generator and a discriminator. The generator aims to generate synthetic data that mimic the real data. The discriminator is a binary classification network. During the training process, the generator is optimized to fool the discriminator in distinguishing between real and synthetic data. The real data employed in this paper were taken from the literature, gathered from three GTs, and refer to two quantities, i.e., corrected power output and compressor efficiency, which are tracked during several years. Three different analyses are presented to validate the reliability of the synthetic dataset. First, a visual comparison of real and synthetic data is performed. Then, two metrics are employed to quantitively evaluate the similarity between real and synthetic data distributions. Finally, a prognostic model is trained by only using synthetic data and then employed to predict real data. The results prove the high reliability of the synthetic data, which can be thus exploited to train a prognostic model. In fact, the prediction error of the prognostic model on the real data is lower than 2.5% even in the case of long-term prediction.

A computational analysis of the impact of thin undoped channels in surface-related current collapse of AlGaN/GaN HEMTs

Abstract This study provides an insight into the impact of thin purely undoped GaN channel thickness (t ch) on surface-related trapping effects in AlGaN/GaN high electron mobility transistors. Our TCAD study suggests that in cases where parasitic gate leakage is the driving trapping mechanism that promotes the injection of electrons from the Schottky gate contact into surface states, this effect can be alleviated by reducing t ch of the undoped GaN channel. We show that by decreasing t ch from 130 to 10 nm, devices exhibit a reduction in gate-related current collapse under the specific class-B RF operating bias conditions as a consequence of a substantial decrease in the off-state gate leakage with reducing t ch. Large-signal simulations revealed an increase by 3 W mm−1 and about 12% output power and power-added efficiency due to the decrease of gate-related collapse. This work, for the first time, highlights the role of a proper purely undoped GaN t ch selection to alleviate gate-related surface trapping in the design of GaN-based microwave power amplifiers.

Energy-efficient Vienna rectifier for electric vehicle battery charging stations

Global DC fast-charging infrastructure is being rapidly developed to accommodate the ever-increasing demand from the electric vehicle market. A major complication in the electric power system's process is the proliferation of ultra-fast charging (UFC) stations, which are known for their enormous power consumption and unpredictable, intermittent performance. By implementing grid-supportive features and ensuring an improved power consumption profile for the grid, installing regional energy storage can solve these challenges. This study presents a control approach for managing the grid-side AC/DC converters of rapid charging stations focused on future-oriented regulation. The paper primarily concentrates on various Vienna rectifier topologies. The technology, characteristics, benefits, and operational aspects of Vienna rectifier topologies are vital to improving the performance, efficiency, and grid integration of electric vehicle charging systems. Fast charging, grid stability, energy economy, and the smooth integration of electric vehicles into the electrical grid are all made possible by Vienna rectifiers. When used in battery energy storage systems (BESS) for electric vehicle charging infrastructure, Vienna rectifiers allow for effective discharge and charging of the batteries. The configurations and assessments of these converters are examined, assessed, and compared based on power output parameters, element count, power factor, THD, and efficiency. The Vienna rectifier has been identified as the best suitable DC fast-charging converter architecture for power levels exceeding 15 kW due to its exceptional efficiency, limited output voltage ripples, high power density, reduced current ripples, and reliable performance. Research on ways to improve electric vehicle charging infrastructure can be spurred by a better grasp of the Vienna rectifier's technological complexities and performance benefits, leading to greener and more efficient transportation options. As a result, the system's power density will eventually rise and elemental stress will decrease.

Design and Performance Evaluation of Multi-Generation System based on Transcritical CO2 Rankine Cycle and Helium Gas Turbine with Hydrogen Production

The advancement in nuclear energy embodied by the gas-cooled modular reactor (GCMR), incorporating the transcritical CO2 Rankine cycle (tRC) and a helium turbine (He tur.) for hydrogen (H2) production, signifies a substantial leap forward in this domain. This research endeavor aimed to amalgamate various technologies to enhance energy conversion efficiency and generate clean hydrogen, a versatile energy carrier. Helium, selected as the GCMR coolant, boasts advantageous properties such as superior heat transfer capabilities, chemical inertness, and the capacity to operate at elevated temperatures. These attributes facilitate effective heat extraction from the reactor core, mitigating corrosion risks while boosting both power output and energy efficiency. A pivotal aspect of this design lies in integrating the tRC with the helium turbine, maximizing energy conversion efficiency and resource utilization by harnessing waste heat from the He turbine to generate additional power through the CO2 Rankine cycle. Furthermore, the system incorporates a hydrogen production module, enabling the clean generation of hydrogen as a byproduct of the nuclear power generation process. According to analysis results, the net power obtained from the Helium turbine was calculated as 241679 kW, and the net power produced from the tRC was calculated as 9902 kW. Additionally, with this developed system, 23.11 kg/h H2 and 183.4 kg/h O2 can be produced. The energetic and exergetic performance of the overall system is computed as 41.8% and 54.28%, while the total amount of exergy destruction is determined as 212199 kW. Moreover, analytical findings reveal that the reactor core exhibits the highest exergy destruction among system components at 91282 kW, whereas the heat exchanger (HEx) registers the lowest exergy destruction at 3.56 kW. In addition, in this study, parametric analyses are also performed to determine the effect of helium outlet temperature analysis and pressure ratio on system performance.

Bayesian neural networks for solar power forecasts in advanced thermoelectric systems

This study presents a Bayesian neural network for the comprehensive performance forecast of an advanced thermoelectric system featuring segmented thermocouples with variable area shape for optimal solar energy conversion. The training data is obtained from a three-dimensional numerical model developed in ANSYS software to account for the energy, exergy, and entropy flows in the thermoelectric module to maximize the power output, energy, and exergy efficiencies while minimizing the entropy generation and system irreversibilities. The thermodynamic model optimizes key parameters, including geometry (height, cross-section, and percentage skutterudite content) across realistic diverse heat fluxes and cooling coefficients. To facilitate a faster and more efficient optimization pipeline, various neural networks, leveraging algorithms like Levenberg-Marquardt, scaled conjugate gradient, and Bayesian regularization, are utilized for efficient data management and accurate performance prediction. The Bayesian neural network emerges as the most accurate and efficient model providing the lowest training and testing loss of ∼10−9. Furthermore, key findings highlight the importance of skutterudite content and solar concentration ratio. For instance, an optimal skutterudite content of 6.2 % at 25 Suns produces maximum power of 29.7 W, while 37 % skutterudite at 75 Suns minimizes exergy destruction. This study's methodology and findings contribute to designing more efficient clean energy systems and advancing sustainable energy goals.

Performance evaluation of photovoltaic-phase change material-thermoelectric system through parametric study

The dual-directional photovoltaic-thermoelectric generator (PV-TEG) system offers higher performance. However, there may be scenarios where the PV and TEG devices receive different levels of irradiation, leading to variations in performance. Under these conditions, the impact of using a PCM heat sink on the system's thermal management effectiveness has not yet been clearly established. Given these considerations, this study builds a coupled heat transfer-laminar flow-electromagnetic numerical model to explore the variations in system performance when subjected to different radiation intensities. The investigation also explores the implications of integrating PCM with varying thicknesses and melting points. Based on the obtained data, it can be concluded that when the PV and TEG devices are exposed to different radiation intensities, the system exhibits distinctly different performance. The highest power output and conversion efficiency are more than 0.16 W and 16 % for the PV device, and 0.08 W and 1 % for the TEG device. Furthermore, using thicker PCM allows the PV and TEG devices to maintain higher output power and efficiency for a longer period, surpassing the performance of systems with thinner PCM. Moreover, the PCM with a melting point of 301.15K has demonstrated the best potential for thermal management in the PV-TEG system.

Low-high-low doped Ga2O3 Schottky barrier IMPATT diodes on various crystal orientations for terahertz applications

This article investigates low-high-low doped Ga2O3 Schottky barrier IMPATT diodes based on [100], [010], and [001] crystal orientations to promote oscillation power at the low-frequency band of terahertz regime. Simulation results demonstrate a conversion power of 4.88 MW/cm2 along [100] at its optimum frequency of 100 GHz, increasing by 0.73 times and 2.2 times compared to the [100] at 150 GHz and the [001] at 100 GHz, and exhibit the highest conversion efficiency of 6.83 % along [010] as indicated by the DC-RF conversion ability promoting 37 % and 30 % than that of [100] and [001] orientations, respectively. When considering a parasitic resistance of 1 × 10−5 Ω cm2, the optimal frequencies of along three orientations decrease to 90 GHz, 127 GHz, and 91 GHz, correspondingly, yielding the peak output power and total efficiency decrease by 0.3–0.4 times compared to those of the conversion characteristics.