High Efficiency Design Research Articles

High efficiency design of metal–insulator–metal metasurface by ResNets-10

Deep learning prediction of metasurface has been a widely discussed issue in recent years. However, the prediction accuracy is still one of the challenges to be solved. In this work, we proposed using the ResNets-10 model to predict plasmonic metasurface S11 parameters. The two-stage training was performed by the k-fold cross-validation and small learning rate. After the training was complete, the predicted logarithmic losses for aluminum, gold, and silver metal–insulator–metal metasurfaces were −48.45, −46.47, and −35.54, respectively. Due to the ultralow error value, the proposed network can efficiently replace the traditional computing methods within a certain structural range. The ResNets-10 can complete training within 1100 iterations, which is highly efficient. The ResNets-10 model we proposed can also be used to design meta-diffractive devices and meta-resonance biosensors, thereby reducing the time required for the simulation process. The ultralow lose value of the network indicates that this work contributes to the development of future artificial intelligence electromagnetic devices computing software.

Research into a Marine Helicopter Traction System and Its Dynamic Energy Consumption Characteristics

As countries attach great importance to the ocean-going navigation capability of ships, the energy consumption of shipborne equipment has attracted much attention. Although energy consumption analysis is a guiding method to improve energy efficiency, it often ignores the dynamic characteristics of the system. However, the traditional dynamic analysis method hardly considers the energy consumption characteristics of the system. In this paper, a new type of electric-driven helicopter traction system is designed based on the ASIST system. Combined with power bond graph theory, a system dynamic modeling method that considers both dynamic and energy consumption characteristics is proposed, and simulation analysis is carried out. The results indicate that the designed traction system in this study displays high responsiveness, robust, steady-state characteristics, and superior energy efficiency. When it engages with helicopter-borne aircraft, it swiftly transitions to a stable state within 0.2 s while preserving an efficient speed tracking effect under substantial load force, and no significant fluctuations are detected in the motor rotation rate or the helicopter movement velocity. Moreover, it presents a high energy utilization rate, achieving an impressive energy utilization rate of 84% per single working cycle. Simultaneously, the proposed modeling methodology is validated as sound and effective, particularly apt for the dynamic and power consumption analysis of marine complex machinery systems, guiding the high-efficiency design of the transmission system.

Analyzing heat transfer in Axial Flux Permanent Magnet electrical machines: A literature review on the discretization methods-FVM and FDM

Axial Flux Permanent Magnet (AFPM) machines have gained significant attention due to their high power density, efficiency, and compact design. However, effective heat transfer analysis is critical for optimizing their performance and reliability. This paper presents a comprehensive literature review on the application of discretization methods, specifically the Finite Volume Method (FVM) and Finite Difference Method (FDM), in the thermal analysis of AFPM machines. The fundamentals of FVM and FDM are briefly explained, followed by an exploration of their applications in AFPM machine thermal analysis. The advantages and limitations of using these methods are discussed, and a comparison between FVM and FDM is provided. Advanced discretization techniques, such as the Finite Element Method (FEM), and coupled thermal-electromagnetic analyses are also examined. The paper highlights studies that have utilized FVM or FDM in developing optimized designs and effective thermal management strategies for AFPM machines. Lastly, potential future research directions are identified, including the development of more efficient discretization methods, the incorporation of advanced materials, and the investigation of novel cooling techniques. This review offers valuable insights into the current state of research and potential future directions in the thermal analysis of AFPM machines using discretization methods.

Optimization of heat transfer and friction characteristics of a rotating U-channel with a combination of variable size connecting bridges

Gas turbine blades typically feature a U-shaped or multi-flow snake channel in their mid-chord region, both incorporating a 180° turn. However, the addition of fins and dimple or protrusion turbulent flow structures can result in excessive pressure losses within the flow channel. This study aims to reduce pressure loss and improve flow uniformity by inserting one to three connecting bridges of varying sizes in the gap between the two channels. The suitability of various connecting bridges with different angles and sizes is assessed in both stationary and rotating states through simulations. Subsequently, the cooling efficiency and reduction in resistance performance of different-sized connecting bridge combinations are evaluated. A layout scheme for optimal connecting bridge placement within the U-shaped channel is proposed. Results indicate that the incorporation of a single minimum-size connecting bridge leads to an average 22.6 % decrease in overall flow resistance within the U-shaped channel, and simultaneously yields a 3.5 % increase in the overall thermal performance. When using the recommended connecting bridge angle and layout combinations, the incorporation of connecting bridges leads to an average 46 % and 49.7 % reduction in overall flow resistance within the U-shaped channel in order to counterbalance pressure losses generated by turbulent structures. This effect consequently results in a 6.5 % and 6.6 % increase of the overall thermal performance. This study offers new insights and data-backed evidence that can potentially facilitate the design of high-efficiency and low-resistance cooling methods in the mid-chord region of high-temperature turbine blades.

Bayesian design with sampling windows for complex spatial processes

Abstract Optimal design facilitates intelligent data collection. In this paper, we introduce a fully Bayesian design approach for spatial processes with complex covariance structures, like those typically exhibited in natural ecosystems. Coordinate exchange algorithms are commonly used to find optimal design points. However, collecting data at specific points is often infeasible in practice. Currently, there is no provision to allow for flexibility in the choice of design. Accordingly, we also propose an approach to find Bayesian sampling windows, rather than points, via Gaussian process emulation to identify regions of high design efficiency across a multi-dimensional space. These developments are motivated by two ecological case studies: monitoring water temperature in a river network system in the northwestern United States and monitoring submerged coral reefs off the north-west coast of Australia.

High-Efficiency Low Noise Amplifier Design for Enhanced Receiver Sensitivity in S- and C- band Frequencies

High-Efficiency Low Noise Amplifier Design for Enhanced Receiver Sensitivity in S- and C- band Frequencies

Highly compact tunable hourglass-shaped graphene band-stop filter at terahertz frequencies

In this research paper, we introduce a band-stop filter based on an hourglass-shaped graphene nanoribbon topology. Employing the three-dimensional finite difference time domain (3D-FDTD) method, we conduct comprehensive numerical simulations to investigate transmission spectra and electromagnetic field distributions. Our primary objectives are to achieve high transmission efficiency, increase tunability, and ensure compact dimensions. The hourglass-shaped band-stop filter exhibits a remarkable bandwidth of 1.2 THz, a 90 % transmission efficiency in the passband, an impressive attenuation of −52.5 dB at the resonance frequency of 32.3 THz, and a compact footprint of 400×300 nm2. By manipulating the chemical potential (or bias voltage) to control the conductivity and dielectric constant of the graphene surface, we set the center frequency of the filter in the range of 28 to 36 terahertz. The remarkable combination of high transmission efficiency, high tunability, and compact design makes our proposed filter an ideal candidate for integration.

TSW-YOLO-v8n: Optimization of detection algorithms for surface defects on sawn timber

The goal of this work was to better meet the demand for rapid detection of surface defects in sawn timber in forestry production. This paper introduces a two-way feature fusion network based on the YOLO-v8 algorithm and proposes a feature fusion network model that combines the attention mechanism and loss function optimization. In this way it increases the tiny target detection head in order to more effectively detect small defective targets in the wood, thus realizing the model’s high-efficiency and low-consumption functional design. The results show that the improved TSW-YOLO-v8n model realized the identification of eight kinds of defects in sawn timber with a high efficiency of 91.10% mAP50 and an average detection 6 ms, which is 5.1% higher than the original model’s mAP50 and 1 ms shorter than the original model’s average detection time. The comparison of the original model and its mainstream algorithms shows that the model of this paper had better performance and better detection capability. Thus, the improved model achieved better overall performance and stronger detection ability, which provides a new idea for the development of detection technology in the forestry industry.

Design Of aesthetic acoustic metamaterials window panel based on Sierpiński triangle for sound-silencing with free airflow

Design of high-efficiency, low frequency (<1000Hz) soundproof window or wall absorber which is transparent to airflow is presented. Due to the massive rise in human population and modernization, environmental noise has significantly risen globally. Prolonged noise exposure can cause severe physiological and psychological symptoms like nausea, headaches, fatigue, and insomnia. There has been continuous growth in building construction and infrastructure like offices, bus stops, and airports due to urban population. Generally, a ventilated window is used for getting fresh air into the room, but at the same time, unwanted noise comes along. Researchers used traditional approaches like noise barrier mats in front of the window or designed the entire window using sound-absorbing materials. However, this solution is not aesthetically pleasing, and at the same time, it is heavy and not adequate for low-frequency noise shielding. To address this challenge, we design a transparent hexagonal panel based on Sierpiński fractal triangle which is aesthetically pleasing, demonstrates normal incident sound absorption coefficient more than 0.96 around 700 Hz and transmission loss around 23 dB, while maintaining air circulation through triangular cutout. Next, we present a concept of fabrication of large acoustic panel for large-scale applications which lead to suppressing the urban noise pollution

The High-Efficiency Design Method for Capacitive MEMS Accelerometer.

In this research, a high-efficiency design method of the capacitive MEMS accelerometer is proposed. As the MEMS accelerometer has high precision and a compact structure, much research has been carried out, which mainly focused on the structural design and materials selection. To overcome the inconvenience and inaccuracy of the traditional design method, an orthogonal design and the particle swarm optimization (PSO) algorithm are introduced to improve the design efficiency. The whole process includes a finite element method (FEM) simulation, high-efficiency design, and verification. Through the theoretical analysis, the working mechanism of capacitive MEMS accelerometer is clear. Based on the comparison among the sweep calculation results of these parameters in the FEM software, four representative structural parameters are selected for further study, and they are le, nf, lf and wPM, respectively. le and lf are the length of the sensing electrode and fixed electrode on the right. nf is the number of electrode pairs, and wPM is the width of the mass block. Then, in order to reduce computation, an orthogonal design is adopted and finally, 81 experimental groups are produced. Sensitivity SV and mass Ma are defined as evaluation parameters, and structural parameters of experimental groups are imported into the FEM software to obtain the corresponding calculation results. These simulation data are imported into neural networks with the PSO algorithm. For a comprehensively accurate examination, three cases are used to verify our design method, and every case endows the performance parameters with different weights and expected values. The corresponding structural parameters of each case are given out after 24 iterations. Finally, the maximum calculation errors of SV and Ma are 1.2941% and 0.1335%, respectively, proving the feasibility of the high-efficiency design method.

Performance of Electro-Fenton Process for Phenol Degradation Using Nickel Foam as a Cathode

Toxic substances have been released into water supplies in recent decades because of fast industrialization and population growth. Fenton electrochemical process has been addressed to treat wastewater which is very popular because of its high efficiency and straightforward design. One of the advanced oxidation processes (AOPs) is electro-Fenton (EF) process, and electrode material significantly affects its performance. Nickel foam was chosen as the source of electro-generated hydrogen peroxide (H2O2) due to its good characteristics. In the present study, the main goals were to explore the effects of operation parameters (FeSO4 concentration, current density, and electrolysis time) on the catalytic performance that was optimized by response surface methodology (RSM). According to the results, nickel foam made an excellent choice as cathode material. The pH value was adjusted at 3 and the airflow at 10 L/h for all experiments. It was found that the optimal conditions were current density of 4.23 mA/cm2, Fe2+ dosage of 0.1 mM, and time of 5 h to obtain the removal rates of phenol and chemical oxygen demand (COD) of 81.335% and 79.1%, respectively. The results indicated that time had the highest effect on the phenol and COD removal efficiencies, while the impact of current density was the lowest. The high R2 value of the model equation (98.03%) confirmed its suitability.

Accurate inverse design for high-efficiency and broadband terahertz devices by co-simulation with genetic algorithms

In this study, we combined MATLAB with the rigorous electromagnetic field simulation software Computer Simulation Technology to perform a co-simulation method for inverse design of high-efficiency and broadband THz metasurface devices. In the proposed design method, genetic algorithm (GA) is embedded to realize automatic and inverse design. Aiming toward the different requirements of high-efficiency and broadband THz metasurface devices, different objective functions are set to optimize the design of different types of THz metasurface devices. Based on the rigorous electromagnetic simulation and genetic algorithm, the proposed design method can realize automatic and inverse design with high reliability, compared to the theoretical model based on catenary e-field theory. This study provides an important guiding role and an efficient method for designing and optimizing required metasurface devices with practical applied value.

Human-Computer Collaborative Visual Design Creation Assisted by Artificial Intelligence

With the support and promotion of big data and cloud computing, AI has penetrated into every field of people's lives more and more deeply, with its characteristics of sustainable work, extremely fast computing speed, and a certain intelligence. This is an effective way to solve the general lack of demand and productivity of visual design, and relieve the pressure off designers to deal with relatively low-quality and high-demand designs. Therefore, the combination of design and artificial intelligence technology is a necessity. Research on the application of artificial intelligence technology for visual design is also in full swing at home and abroad However, at present, teams at home and abroad are in the exploratory stage. This paper considers whether it is possible to build an intelligent visual design and creation system by using artificial intelligence technology to help graphic communication designers achieve high-quality, high-efficiency, and high-quantity design output. Additionally, this paperexplores how to combine artificial intelligence technology with designers' design workflow so as to form a complementary human-computer cooperation mode. We will explore how to integrate AI technology with designers' design workflow and then create a human-machine collaboration model with complementary advantages to achieve the high quality, high efficiency, and high quantity of design output required by the intelligent visual design creation system being built. Finally, a basic framework of a generative smart human-computer collaborative visual design creation system based on a subset of neural network expert systems in multiple domains and an aggregate of different modules supported by the system is formed, and the working principle and usage process of the system are further elaborated with the example of packaging design.

High-Valence Mo Doping and Oxygen Vacancy Engineering to Promote Morphological Evolution and Oxygen Evolution Reaction Activity.

The rational design of high-efficiency, low-cost electrocatalysts for electrochemical water oxidation in alkaline media remains a huge challenge. Herein, combined strategies of metal doping and vacancy engineering are employed to develop unique Mo-doped cobalt oxide nanosheet arrays. The Mo dopants exist in the form of high-valence Mo6+, and the doping amount has a significant effect on the structure morphology, which transforms from 1D nanowires/nanobelts to 2D nanosheets and finally 3D nanoflowers. In addition, the introduction of vast oxygen vacancies helps to modulate the electronic states and increase the electronic conductivity. The optimal catalyst MoCoO-3 exhibits greatly increased active sites and enhanced reaction kinetics. It gives a dramatically lower overpotential at 50 mA cm-2 (288 mV), much smaller than that of the undoped counterpart (418 mV) and comparable to those of the recently reported electrocatalysts. Density functional theory results further verify that the increased electronic conductivity and optimized adsorption energy toward oxygen evolution reaction intermediates are mainly responsible for the enhanced catalytic activity. Moreover, the assembled two-electrode electrolyzer (MoCoO-3||Pt/C) exhibits superior performance with the cell potential decreased by 233 mV to reach a current density of 50 mA cm-2 with respect to the benchmark counterpart catalysts (RuO2||Pt/C). This work might contribute to the rational design of effective, low-cost electrocatalyst materials by combining multiple strategies.

Logic Shrinkage: Learned Connectivity Sparsification for LUT-Based Neural Networks

Field-programmable gate array (FPGA)–specific deep neural network (DNN) architectures using native lookup tables (LUTs) as independently trainable inference operators have been shown to achieve favorable area-accuracy and energy-accuracy trade-offs. The first work in this area, LUTNet, exhibited state-of-the-art performance for standard DNN benchmarks. In this article, we propose the learned optimization of such LUT-based topologies, resulting in higher-efficiency designs than via the direct use of off-the-shelf, hand-designed networks. Existing implementations of this class of architecture require the manual specification of the number of inputs per LUT, K . Choosing appropriate K a priori is challenging. Doing so at even high granularity, for example, per layer, is a time-consuming and error-prone process that leaves FPGAs’ spatial flexibility underexploited. Furthermore, prior works see LUT inputs connected randomly, which does not guarantee a good choice of network topology. To address these issues, we propose logic shrinkage , a fine-grained netlist pruning methodology enabling K to be automatically learned for every LUT in a neural network targeted for FPGA inference. By removing LUT inputs determined to be of low importance, our method increases the efficiency of the resultant accelerators. Our GPU-friendly solution to LUT input removal is capable of processing large topologies during their training with negligible slowdown. With logic shrinkage, we improve the area and energy efficiency of the best-performing LUTNet implementation of the CNV network classifying CIFAR-10 by 1.54× and 1.31×, respectively, while matching its accuracy. This implementation also reaches 2.71× the area efficiency of an equally accurate, heavily pruned binary neural network (BNN). On ImageNet, with the Bi-Real Net architecture, employment of logic shrinkage results in a post-synthesis area reduction of 2.67× vs. LUTNet, allowing for implementation that was previously impossible on today’s largest FPGAs. We validate the benefits of logic shrinkage in the context of real application deployment by implementing a face mask detection DNN using a BNN, LUTNet, and logic-shrunk layers. Our results show that logic shrinkage results in area gains versus LUTNet (up to 1.20×) and equally pruned BNNs (up to 1.08×), along with accuracy improvements.

Research on High-Performance Multiphase DC–DC Converter Applied to Distributed Electric Propulsion Aircraft

The performance of power converter of electric propulsion aircraft (EPA), which should have high efficiency, high power density and high reliability, affects the volume, weight and efficiency of the whole electric aircraft (EA). This paper proposes a novel DC-DC converter with gallium nitride (GaN) transistors. Firstly, the working modes and main operation waveforms of the converter are analyzed. The new converter can achieve ZVS and ZCS through auxiliary soft switch branches. Compared with the traditional Buck-Boost converter, the soft switching capability of the proposed converter allows operation at very high frequency and has a higher efficiency. The multiphase interleaving magnetic integration technology is adopted to realize the modular design, which not only meets the requirements of high power and low ripple, but also eliminates right half-plane zero in boost mode, achieves high dynamic response and improves the power density. A novel parallel multiphase inverse coupled inductor (ICI) and a symmetrical array ICI are proposed, to allow a high-density and high-efficiency bidirectional DC-DC converter design for hybrid energy storage system of EPA. Finally, the proposed ICI prototype was compared with the state-of-the-art commercial ICI product, and the comprehensive performance of the converter was verified through steady-state, transient, efficiency and thermal experiments.

Efficient photoelectrochemical water splitting and photocatalytic performance for graphene-bridged MoS2–Fe2O3 nanocomposite under visible light active: Insights into photocatalysis mechanism

In this work, we aim to design a new nanocomposite with an improved photocatalytic performance by the synergistic formation of a Z-scheme photocatalytic system with graphene-bridged molybdenum disulfide (MoS2) and iron oxide (Fe2O3). We fabricated graphene-bridged MoS2–Fe2O3 nanocomposites by a simple hydrothermal method for enhanced photoelectrochemical (PEC) performance and higher degradation of TC. This nanocomposite can increase carrier separation efficiency. Moreover, the photocatalysts exhibit excellent oxidation and reduction rates capabilities assessed by photocatalytic and photoelectrochemical experiments under visible light illumination. The photocatalyst showed 97% removal capability of tetracycline and photocurrent density is five times higher than of bare Fe2O3. The active specie of .O2− and ⋅OH radicals was detected by the ESR instruments during photocatalytic activity improvement that could confirm the constructed Z-scheme MoS2–Fe2O3-Graphene nanocomposite showed that two folds photocatalytic degradation greater than those of pristine MoS2 and Fe2O3. According to the results shows UV–Visible spectroscopy, photoluminescence spectroscopy, Electron impendence spectroscopy, and photocurrent response, the photo-response electrons of transfer from the conduction band of Fe2O3 could fast transport in the valence band of MoS2. The graphene bridge served as a charge-migrate channel between two materials, and the electrons in protonated MoS2 and Fe2O3 achieved a charge balance. This work provides a new strategy for further design of high-efficiency of MFG nanocomposite with graphene as the electronic mediator for environmental remediation.

Sn-doping facilitating electronic modulation and surface reconstruction of 3D hierarchical nanoflowers assembled by Fe(PO3)2/Ni2P nanosheets to trigger oxidation evolution reaction

Transition metal phosphides (TMPs) are considered as potential alternative for noble-metal oxides in oxygen evolution reaction (OER), but the limited intrinsic activity hinders its practical application. Herein, high-valence Sn doped 3D hierarchical nanoflowers assembled by Fe(PO3)2/Ni2P nanosheets (Sn-Fe(PO3)2/Ni2P) were fabricated by hydrothermal and thermal phosphating process. The XPS characterization and in-situ UV–vis spectroscopy unveil that Sn doping can modulate the electronic structure of Ni2P, stabilize Fe2+, and further facilitate the surface reconstruction of Sn-Fe(PO3)2/Ni2P to generate Ni/FeOOH as active phases. And as a result, the optimal Sn-Fe(PO3)2/Ni2P displays a low overpotential of 229 mV at 10 mA cm−2 with small Tafel slope (43 mV dec−1) and high stability at 100 mA cm−2. Beyond, the overall alkaline water splitting with Sn-Fe(PO3)2/Ni2P/NF and Pt/C/NF as anode and cathode exhibits small cell voltage of 1.63 V at 100 mA cm−2, and excellent stability for 80 h at 100 mA cm−2. The superior performance of Sn-Fe(PO3)2/Ni2P is ascribed to the rich mesopore for exposure more active sites, rapid deprotonation of OH- and the increased charge transfer after high-valance Sn4+ doping. This work provides a new avenue of accelerating surface reconstruction and optimizing electronic structure by high-valence metal doping for design high-efficiency electrocatalysts.

Extremely Sparse Networks via Binary Augmented Pruning for Fast Image Classification.

Network pruning and binarization have been demonstrated to be effective in neural network accelerator design for high speed and energy efficiency. However, most existing pruning approaches achieve a poor tradeoff between accuracy and efficiency, which on the other hand, has limited the progress of neural network accelerators. At the same time, binary networks are highly efficient, however, a large accuracy gap exists between binary networks and their full-precision counterparts. In this article, we investigate the merits of extremely sparse networks with binary connections for image classification through software-hardware codesign. More specifically, we first propose a binary augmented extremely pruning method that can achieve ~98% sparsity with small accuracy degradation. Then we design the hardware architecture based on the resulting sparse and binary networks, which extensively explores the benefits of extreme sparsity with negligible resource consumption introduced by binary branch. Experiments on large-scale ImageNet classification and field-programmable gate array (FPGA) demonstrate that the proposed software-hardware architecture can achieve a prominent tradeoff between accuracy and efficiency.

GPU-HADVPPM V1.0: a high-efficiency parallel GPU design of the piecewise parabolic method (PPM) for horizontal advection in an air quality model (CAMx V6.10)

Abstract. With semiconductor technology gradually approaching its physical and thermal limits, graphics processing units (GPUs) are becoming an attractive solution for many scientific applications due to their high performance. This paper presents an application of GPU accelerators in an air quality model. We demonstrate an approach that runs a piecewise parabolic method (PPM) solver of horizontal advection (HADVPPM) for the air quality model CAMx on GPU clusters. Specifically, we first convert the HADVPPM to a new Compute Unified Device Architecture C (CUDA C) code to make it computable on the GPU (GPU-HADVPPM). Then, a series of optimization measures are taken, including reducing the CPU–GPU communication frequency, increasing the data size computation on the GPU, optimizing the GPU memory access, and using thread and block indices to improve the overall computing performance of the CAMx model coupled with GPU-HADVPPM (named the CAMx-CUDA model). Finally, a heterogeneous, hybrid programming paradigm is presented and utilized with GPU-HADVPPM on the GPU clusters with a message passing interface (MPI) and CUDA. The offline experimental results show that running GPU-HADVPPM on one NVIDIA Tesla K40m and an NVIDIA Tesla V100 GPU can achieve up to a 845.4× and 1113.6× acceleration. By implementing a series of optimization schemes, the CAMx-CUDA model results in a 29.0× and 128.4× improvement in computational efficiency by using a GPU accelerator card on a K40m and V100 cluster, respectively. In terms of the single-module computational efficiency of GPU-HADVPPM, it can achieve 1.3× and 18.8× speedup on an NVIDIA Tesla K40m GPU and NVIDIA Tesla V100 GPU, respectively. The multi-GPU acceleration algorithm enables a 4.5× speedup with eight CPU cores and eight GPU accelerators on a V100 cluster.