Power Efficiency Research Articles

On the Viability of Hydrofluoroolefin-R245fa Mixtures as Organic Working Fluids in a Low-Temperature Solar-Powered Organic Rankine Cycle (ORC)

Abstract To address the intermittent nature of solar and other renewable energy (RE) sources, an organic Rankine cycle (ORC) can be employed to facilitate steady production of power. Organic working fluid (OWF) mixtures in ORCs are of interest due to the possibility of reducing irreversible losses in the system and having a better sense of control over its environmental impact while not compromising on the performance benefits when paired with conventional fluids such as R245fa. Hydrofluoroolefins (HFOs) are considered as one of the components due to their zero ozone depleting potential (ODP) and very low global warming potential (GWP). This study evaluates the performance of binary zeotropic mixtures of R245fa and HFOs with available thermodynamic properties (R1234yf, R1234zeE, and R1234zeZ) as OWFs in a simple low-temperature solar-powered ORC. Specifically, the optimal compositions of each pair of HFO-R245fa mixtures within the specified operating conditions are identified via the performance indicators (1 kW net power output; thermal efficiencies; and GWP); and are compared against their pure components and known conventional OWFs. The turbine power outputs and efficiencies of the mixtures, determined from ASPEN Plus simulations, were observed to be competitive as alternatives for the pure components, and conventional fluids. At optimal compositions, R245fa/R1234zeZ (0.2619 mass fraction R245fa) was shown to be the most viable OWF blend with peak efficiency at 7.5545% and GWP of 274.2, accomplishing the target 1kW turbine power output, followed closely by R245fa/R1234zeE (0.2678 R245fa; 1kW power output; 7.4824% peak efficiency; GWP = 281), and R245fa/R1234yf (0.3589 R245fa; 0.9595 kW power output; 7.4338% peak efficiency; GWP = 372.2). It is recommended that more HFO-R245fa mixtures, as well as other complex blends, be explored and be subjected to further studies.

The Characterization of A Thermoelectric Generator Type TEC1-12706 Hybridized On 50W Polycrystalline PV

The potential of solar energy is very large in generating electrical energy conversion using TEG and PV technology. Therefore, it is important to understand how the technology works. This study aims to characterize the electrical generation of 12 TEG modules type TEC1-12706 hybridized with 2 PV panels. One PV panel is left without a TEG module as a comparison. Meanwhile, on the cold side of the TEG attached to the PV, a heatsink with 3 fins is placed. The left and right fins are air allowed to flow naturally, while the middle fin is flowed with cold water fluid through a 0.01 m diameter water hose with 2 flow rates; 0.02 and 0.05 m / s. The results of the study showed that the fluid flow rate of 0.02 presented a better TEG performance effect than 0.05 m / s at an irradiance of 450 W/m2. Different things are shown by PV solar panels, where the power generation and efficiency are better in TEG at a flow rate of 0.05 m/s compared to 0.02 m/s for the same irradiance. Overall, the performance of PV-TEG is better at a TEG cooling fluid flow rate of 0.05 m/s.

Optimal design and dispatch of phosphoric acid fuel cell hybrid system with direct heat recovery through coupled calculation and artificial intelligence-based optimization

A phosphoric acid fuel cell (PAFC) is the first-generation of modern fuel cells that can use waste heat in various ways. This study presents a PAFC hybrid system with direct heat recovery. Direct heat recovery uses PAFC cooling water as the working fluid for the steam turbine, simplifying the system and improving performance. The PAFC in this hybrid system acts as a power generator and evaporator. The hybrid system operates in power generation mode (mode_pg) and combined heat and power mode (mode_chp). The overall behavior of the hybrid system was analyzed using a coupled calculation method. Under mode_pg, the electric power and efficiency of the hybrid system were 37.0% and 10.5% higher, respectively, than those of a conventional system. The thermal power and CHP efficiency in mode_chp were 35.7% and 8.8%p higher, respectively, than those of a conventional system. The optimal dispatch was obtained using an artificial intelligence-based optimization framework that considered two scenarios. As a result, the optimal demand was 25.8% higher when the hybrid system with the optimal dispatch was used. In addition, the economic and environmental indices were 7.3% and 6.8% lower, respectively, than the conventional system, highlighting the feasibility and eco-friendliness of the hybrid system.

Experimental and numerical investigation of a double spiral tube heat exchanger in a novel composite phase change material

Phase change energy storage technology effectively harnesses energy, and phase change materials have been garnered significant attention due to their high latent heat capacity. The heat transfer capacity and phase transition of HCNT − CH3COONa·3H2O/Na2HPO4·12H2O composites in a double spiral tube heat storage unit were investigated. The effects of different flow rates and temperatures on the heat transfer characteristics of phase change materials were studied, and the thermal performance of the storage units was evaluated by calculating the power, energy and energy efficiency ratios. The phase change process of the composite during heat transfer was further revealed via simulation, and the heat storage and heat release of the composite were compared with those of other phase change materials. The results show that the effects of the flow velocity on the transformation time and power of the phase change material are negligible. However, an increase in flow rate can significantly increase the heat storage and heat release of the phase change unit. In particular, when the flow rate is 4 LPM, the energy efficiency ratio can reach 76.6 %. When the temperature increases from 80 °C to 90 °C, the melting time decreases by 30.23 %, indicating that increasing the temperature has a significant effect on the phase transition process. Through simulation calculations, the composite phase change material shows significant performance advantages under the same working conditions; its heat storage capacity is 108 % greater than that of the other materials, and the heat release capacity is 206 % greater, which indicates that it has greater energy storage and release efficiency and a better thermal energy conversion capacity. In addition, the experimental and simulation results also reveal the existence of a diagonal region of slow phase transition in the heat storage unit. In summary, this paper analyses the heat transfer performance of a double spiral tube heat storage device, provides a theoretical basis for practical application, and provides an important reference for improving the performance of a phase–change energy storage system.

Comparative experimental analysis of monocrystalline and polycrystalline photovoltaic panel through hybrid phase change material

Photovoltaics thermal management is utmost necessary as continuous heating of the module decreases the performance. This paper presents a comparative study in terms of performance of two photovoltaic modules namely monocrystalline and polycrystalline using hybrid phase change materials (PCMs). The hybrid PCMs of RT31 and RT 44HC were prepared by method of stirring. The final prepared hybrid PCMs were poured at the back of panel and panel was covered. The purpose of these experiments is to improve the efficiency of polycrystalline and monocrystalline photovoltaic modules and comparing which technology is more efficient having hybrid PCM. The experiments were performed under the room conditions in Taxila, Pakistan. The output parameters of the photovoltaic module appeared to be strongly dependent upon the solar irradiance. The power output and efficiency of monocrystalline and polycrystalline photovoltaic module were measured. The monocrystalline panel maximum temperature was 36.59 °C as compared to polycrystalline panel which was 40.23 °C. As the radiation intensity increased till noon, electrical power was improved, and it was 6.52 W in monocrystalline panel as compared to polycrystalline panel which was 6.32 W. Electrical conversion efficiency was also observed, and it was maximum up to 11.84 % in monocrystalline panel. The higher performance was observed in case of monocrystalline panel having hybrid in terms of performance as compared to polycrystalline panel.

An Ultralow-Power Closed-Loop Distributed Beamforming Technique for Efficient Wireless Power Transfer

An Ultralow-Power Closed-Loop Distributed Beamforming Technique for Efficient Wireless Power Transfer

Growth-adaptive distillation compressed fusion model for network traffic identification based on IoT cloud-edge collaboration

The development of the Internet of Things (IoT) has led to the rapid growth of the types and number of connected devices and has generated large amounts of complex and diverse traffic data. Traffic identification on edge servers solves the real-time and privacy requirements of IoT management and has attracted much attention, but still faces several problems: (1) traditional machine learning (ML) models rely on artificially constructed features, and the existing deep learning (DL) traffic identification models have reached their performance limit; and (2) insufficient computing resources of edge servers limit the possible improvement in the performance of deep learning models by increasing the number of parameters and structural complexity. To address these issues, we propose a lightweight fusion model. First, the Network-in-Network (NiN) model and Random Forest (RF) model are used on the cloud server to construct a traffic identification fusion model. The excellent representation extraction capability of the NiN compensates for the RF’s dependence on manual feature extraction, and its modular structure is suitable for the subsequent model compression operations. Then, the NiN was distilled. We propose Growth-Adaptive Distillation to lightweight the NiN model, which can reduce the operation of manually adjusting the structure of the student model and ensure the efficiency and low power consumption of the fusion model deployment. In addition, both the RF in the cloud and the distilled NiN are deployed on the edge server. Comparisons with multiple algorithms on two network traffic datasets show that the proposed model achieves state-of-the-art performance while ensuring the use of minimal computational resources.

Parametric study of combustion characteristics of diesel/methanol dual fuel engine using global sensitivity analysis and multi-objective optimization

The pursuit of clean and efficient internal combustion engine power necessitates the substitution of fossil fuels with methanol and the adoption of advanced combustion strategies. This study examines the combustion and emission characteristics of methanol/diesel dual-fuel compression ignition engines. To understand the intricate interplay between critical parameters and engine performance, the global sensitivity analysis (GSA) method is employed, utilizing Sobol sensitivity as the quantitative metric. The investigated parameters include methanol substitution rate, diesel injection parameters, and five thermodynamics parameters concerning in-cylinder conditions. A validated computational fluid dynamics (CFD) model, coupled with a chemical kinetic mechanism, is developed to predict the combustion behavior of the dual-fuel engine accurately. This predictive capability forms the basis for utilizing the tree-based process optimization (TPOT) algorithm. By implementing TPOT, a significant reduction in the mean square error (MSE) of the predicted characteristics model is achieved, averaging a decrease of 73.59%. Subsequently, a multi-objective optimization is conducted, exploring the impact of varying target weights on the optimal outcomes. Results show the initial pressure significantly impacts the engine's economy, power and emission characteristics, and NOx formation should be carefully considered in the combustion optimization of dual-fuel engines.

Effect of current spreading layer on Internal Quantum efficiency and optical power of flip chip gallium nitride LEDs with circular contacts

The unique properties of the Indium Tin Oxide (ITO) make it an excellent choice as a current spreading layer in Flip Chip Light Emitting Diodes (FCLEDs) and other optoelectronic devices. Herein, the performance of FCLEDs is analyzed by a precise mathematical model for the current spreading length (Ls) produced by the ITO layer under circular-shaped contacts. The expressions are formulated without approximations using ABC-model for extracting the Internal Quantum Efficiency (IQE, ηint), optical power (Pint) and Emission Intensity (EI). The thickness (tITO) and resistivity (ρITO) of the ITO layer are varied for different current densities, and their adverse effects on IQE are determined. At lower current densities, IQE increases with thickness and decreases for high resistivity of the ITO layer. At higher current densities, there is a gradual decrease in IQE irrespective of the ITO layer presence due to “Efficiency Droop”. The IQE in the proposed work is 82 % at a thickness of 50–200 nm and current density of 8 A/cm2, and the optical power is around 40 mW, showing good agreement with the experimental data, making it feasible for future high-performance FCLEDs.

Surface functionalized porous MXene (Ti3C2Tx)/Polyethyleneimine membrane for efficient osmotic energy conversion

The development of ion-selective membranes is crucial for achieving efficient osmotic power conversion in reverse electrodialysis system. However, balancing ion selectivity and ion permeability in membranes remains a challenge, limiting improvements in energy conversion efficiency for practical applications. In this study, we develop efficient cation exchange membranes by integrating versatile aromatic hydroxyl groups with MXene nanosheets and polyethyleneimine (PEI) into their lamellar structure. This approach holds promise for achieving non-swelling, high selectivity, and enhanced power density for osmotic energy conversion. The abundance positive surface charge within PEI molecules, combined with the 2D sub-nanochannels of MXene nanosheets, facilitates the biomimetic replication of channel size, chemical groups, and adjustable charge density in the resulting membranes. The fabrication of these membranes is easily achieved through simple hydrothermal, functionalization, and surface-charged techniques, suggesting promising scalability. These membranes exhibit remarkable stability against swelling in aqueous solutions for up to 25 days and demonstrate a high selectivity of 0.95 and a superior output power density of 18.3 W/m2 in artificial river water and seawater.

Artificial intelligence-based power market price prediction in smart renewable energy systems: Combining prophet and transformer models

With the increasing integration of smart renewable energy systems and power electronic converters, electricity market price prediction is particularly important. It is not only crucial for the interests of power suppliers and market regulators but also plays a key role in ensuring the reliable and flexible operation of the power system, particularly during extreme weather events or abnormal conditions. This study develops a hybrid time series forecasting model that combines Prophet and Transformer, which takes advantage of deep learning to provide a new solution for electricity market price forecasting. By introducing the Stacking optimization strategy, this study improves the accuracy and stability of electricity market price sequence prediction. In addition, the study tries to integrate traditional time series forecasting methods (such as the Prophet model) with deep learning models (such as the Transformer model), aiming to make full use of their respective advantages to achieve more accurate and stable predictions. Through experimental evaluation on four electricity market data sets, this study finds that the hybrid forecast model exhibits significant performance improvements in enhancing the accuracy and stability of electricity market price predictions. This method not only provides a more accurate tool for power market price prediction, but also provides solid technical support for the efficient operation and sustainable development of smart renewable energy systems. Experimental results also show that the combination of deep learning models with traditional time series methods and the introduction of Stacking strategies is crucial to improving the performance of power market price prediction, and also helps us better understand and design smart renewable energy systems, price and energy management strategies, thereby providing an effective method for achieving efficient and reliable power and energy transmission.

Construction of data base containing flow field information under different blade placement of pumping station in digital twin

Abstract The data base in digital twin technology includes various data such as the structure, performance, and operating status of the physical object. And the data base is the key point for the construction of the digital twin system. However, the data bases of different digital twin systems usually contain different information. In this paper, the flow state of the pumping station under different angles of four blades is selected as the research object. The research methods in this paper mainly include Latin hypercube sampling, numerical simulation and neural network prediction. The Latin hypercube sampling is used to select the typical Angle difference model. The numerical simulation is used to obtain flow field information. The neural network prediction method is used to construct a complete data base. The study incorporates an actual large-scale mixed-flow pump station to head, efficiency, and shaft power as the prediction targets. It utilizes the different angle difference data of ten sets of four blades as the training set, forty sets of remaining samples as the experiment set, and employs the backpropagation neural network method for constructing the prediction network. By changing the experiment set and test set, the method can efficiently complete the construction of data base including actual test and numerical simulation, which provides a solid foundation for the construction of digital twin platform.

Radiometric characterization of daytime luminescent materials directly under the solar illumination

The materials efficiently emitting photoluminescence (PL) under solar irradiation could find broad applications in passive radiative cooling of buildings, urban heat mitigation, and solar energy harvesting. In this work, we discuss the limitations of common laboratory characterization of such materials (using tunable light sources or solar simulators) and propose a methodology for direct outdoor characterization of PL efficiency under full solar irradiation. A simple, portable radiometry setup with an integrating sphere is described, and its capability of rapid determination of the spectral distribution of the absorbed and emitted power, as well as the overall PL power efficiency and quantum yield, is demonstrated. By characterization of three materials developed for daytime radiative cooling applications, we reveal deviations of obtained parameters caused by the replacement of the real solar irradiation by that of solar simulators. The described method is suitable for studies of long-term stability, photo-induced degradation, or thermal effects.

Addressing Power Efficiency and Stability in SRAM: A 4x4 Cell Array Design with Enhanced Power Gating

In order to lower power consumption and leakage currents during active operation, the suggested SRAM architecture with power gating design trims the source voltage across the SRAM cell, ranging from 50 to 150 mV. Power gating based on sectors is utilized, using a self-biasing approach where the gate terminal and source of a PMOS transistor act as a diode, controlling the virtual ground. However, three challenges arise with this method in nanometer technology: the additional self- biasing transistor (SBT) occupies 5% more space, the source voltage adjustment mechanisms are not effectively implemented, and the increase in virtual ground voltage leads to bias temperature instability. To implement this design, a 4x4 SRAM cell array is constructed, consisting of 4 rows and 4 columns of 10T SRAM cells. A decoder addresses these cells, and each row represents half a byte, with control circuitry managing input and output data. Additionally, the outputs of individual cells in each column are combined using a 4-bit OR, producing a single data output point. This architecture effectively reduces power consumption while maintaining operational efficiency, making it suitable for nanometer-scale SRAM designs.

Ultrafast dynamics and electroluminescence performance of a novel tetraphenylethylene-based aggregation induced emission luminogen

Efficient emission and confined molecular conjugation in aggregated/solid form, are two beneficial characteristics of blue organic light-emitting materials (OLEMs). Aggregation-induced emission molecules (AIEgens) have evolved as auspicious OLEMs that possess these properties. Herein, we discover an innovative sky-blue tetraphenylethene-based AIEgen, 4′,4‴-(1-phenyl-2-(4-(1,2,2-triphenylvinyl)phenyl)-1H-imidazole-4,5-diyl)bis(N,N-diphenyl-[1,1′-biphenyl]-4-amine) (2TPA-ITPE), with high thermal stability. By employing this AIEgen as a non-doped emitter, the constructed organic light-emitting diode demonstrates a maximum external quantum efficiency of 3.35 % with a power efficiency of 4.1 lm/W and high luminescence of 15896.92 cd/m2 2TPA-ITPE exhibits a weak donor-acceptor character. Moreover, to probe the photo-physical nature of this AIEgen, its excited-state dynamics are examined through ultrafast transient absorption spectroscopy (TAS). The TAS results of this OLEM unveil the generation of a resonant structure species owing to the elongation of C=C double bond. The excited state dynamics reveal that 2TPA-ITPE undergoes three processes after excitation (internal conversion, C=C elongation and vibrational cooling). These findings offer innovative insights into the ultrafast mechanisms underlying excited TPE-based OLEMs.

Modelling and experimental investigation of cooling of field-operating PV panels using thermoelectric devices for enhanced power generation by industrial solar plants

The performance of commercial solar power plants degrades due to an increase in module temperatures for which standard PV-T air or water-cooling techniques are mostly used. In this study, a thermoelectric cooling system is studied for improving photovoltaic cell power efficiency and hence solar power generation. The cooling optimization requires solar cell temperature prediction of field operating PV modules, for which analysis of six models, is presented. The experimentation results show that TEC cooling maintains PV cell at 25 °C whereas PV cell without TEC operates at 55–63 °C, a higher temperature range, showing the effectiveness of the thermoelectric cooling system in precisely controlling PV cell temperature to operate at or near STC conditions in the field creating a temperature difference of 30–38 °C. The NOCT and Faiman model results are found close to the experimental values in comparison to other models. The potential for cooling and a corresponding increase in solar plant energy production is assessed using PV Syst modeling and simulation for three practical PV installation scenarios for 31 different climatic zone locations worldwide showing 6–27 % power loss due to elevated temperatures, which is not studied in previous studies adding novelty to the analysis. The results show that PV-TECS is an effective system to control the temperature of field operating PV modules, which can be used in future photovoltaic power plants. Field results and analysis of PV temperature models is crucial for the optimization and future development of PV-thermoelectric systems deployed under actual outdoor conditions as well as the expected cooling gains in different climatic locations. These aspects are collectively studied in the current work adding to the novelty of the study.

Time-modulated planar arrays enabled directional modulation symbol synthesis

Different from linear arrays that can resolve only one angular component, two-dimensional (2-D) array patterns enable to remove the cone of uncertainty and right/left ambiguities generated in a linear array. Motivated by this, a novel structure of 2-D time-modulated planar arrays (TMPAs) is developed to synthesize directional modulation (DM) symbols in this paper. The proposed 2-D TMPA DM transmitter can synthesize multicarrier DM symbols with suppressing the mirror harmonic frequencies and provide a higher power efficiency. This is demonstrated by way of simulations of a multi-beam 8*8 element planar antenna array and its security performance is shown via the bit error rate (BER) metric.

Data-driven multi-objective optimization of aerodynamics and aeroacoustics in dual Savonius wind turbines using large eddy simulation and machine learning

Savonius rotor is a popular form of vertical-axis wind turbine (VAWT) for small-scale and urban applications because of its straightforward design and self-starting ability. Dual VAWTs present challenges in terms of wake interactions and noise, particularly in urban areas. Optimizing these parameters is essential for future wind energy adoption. This research is the first to analyze how the interaction of wakes from adjacent rotors, combined with a deflector, affects both the aerodynamic performance and noise levels of dual Savonius rotors. Large Eddy Simulation is applied, as it effectively captures detailed turbulent wind flows and their interactions with wind turbines. A multi-objective optimization method combining Machine Learning and Computational Fluid Dynamics (CFD) is developed to optimize rotors for maximum power efficiency and minimum noise, considering their wake interactions with a unique deflector system. First, the influence of geometric parameters on aerodynamics and aeroacoustics characteristics of rotors is analyzed, and the database is generated using Design of Experiment approach. Next, the CFD model is replaced by Artificial Neural Network (ANN) model established for predicting rotor performances. A Multi-Objective Genetic Algorithm method is used to optimize aerodynamics and aeroacoustics characteristics of rotors. Finally, optimal design parameters are identified from the Pareto front using the technique for order of preference by similarity to ideal solution decision-making method. The ANN model demonstrated high accuracy with an RANN2 of 0.995 and 0.971 for the average power coefficient (CP) and overall sound pressure level (OSPL) predictions, respectively. Multi-objective optimization revealed the best configuration of the deflector with bleed jets, improving the average CP up to 57.5% and reducing OSPL to an almost 5.2% compared to the dual rotor case at TSR = 0.8.

Reactive Power Compensation for Standalone Hybrid Power System Using Facts Devices

Reactive power compensation is essential in hybrid grid-connected systems because power electronic inverters utilized for supplying DC energy into the grid causes a reduction in the overall power factor of the power systems. Due to the ability to provide a reliable and efficient power supply to remote and off-grid areas, hybrid power systems are growing in popularity. It can provide greater opportunities for operational growth by combining economic and technology advancement. To meet the demand for electrical energy in both regular and critical situations, operators must be in charge of the power systems reliable and secure operation. The primary objective of the work is to control the reactive power flow in a grid-connected hybrid renewable energy system (PV-wind-battery). The power quality problems that these systems frequently experience include voltage sags, harmonics, and flicker. A FACTS device Unified Power Quality controller (UPQC) with a Genetic Algorithm based PI controller is suggested to handle these power quality issues. In order to improve the performance of the power system, the proposed optimization approaches are used to tune the UPQC in a multiline transmission system. The model was developed with the help of the MATLAB/Simulink work framework.

Hydrodynamic Flexible Spindle (HydroFlex) Polishing of turbine blade internal cooling channels for oxide removal

Internal cooling channels are essential to turbine blades for high efficiency power generation. Effective removal of aluminum oxide build-up in turbine blade cooling channels is of critical importance to refurbishment and prolonged service life of turbine blades. Conventional internal polishing processes, including abrasive flow machining, chemical polishing, and electrical discharge machining cannot effectively remove the oxide layer within the internal cooling channels due to the complex geometry with high aspect ratio and diameter variation and the electric insulation of the oxide layer. In this case study, we investigated the application of a novel hydrodynamic flexible-spindle (HydroFlex) polishing process to remove the oxide layer within the internal cooling channels of an Inconel 738 turbine blade that was taken out of serve due to oxide build-up. For a 350 mm long cooling channel featured with an inner diameter transition from ϕ4 mm to ϕ2.5 mm, within 12 min, at the grinding wheel rotational speed of 50,000 rpm and 30,000 rpm, HydroFlex was able to completely remove the 14.31 µm thick oxide layer off from the wall of the turbine blade internal cooling channel, improve the channel circularity by 54.7 %, and decrease the channel surface roughness by up to 64.3 %. The results demonstrated the effectiveness of HydroFlex in polishing complex internal cooling channels of turbine blades for oxide removal and potential blade service life extension.