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An Ultrasonic Synthetic Jet Piezo Pump for Air Delivery

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第5期72卷 5177-5186页

作者： Wang, Meng Liu, Zenghao Wang, Conghui Liu, Guojun Jilin Univ Sch Mech & Aerosp Engn Changchun 130025 Peoples R China

A combination of piezoelectric drive and synthetic jet technology is proposed for an ultrasonic synthetic jet piezo pump. The piezo vibrator operates silently and has a resonant frequency exceeding 20 kHz. The inlet channel of the proposed piezo pump is equipped with a special Tesla structure to increase the flow resistance difference and reduce flow loss. The results demonstrate that the proposed piezo pump has a significant advantage over a typical pump in flow rate. The optimized piezo pump achieves a flow rate of 1037 mL/min when subjected to a typical square wave signal excitation. The optimized waveform enhances the flow rate to 1343.9 mL/min, resulting in a 29.6% improvement. Additionally, this article also investigates the ultrasonic synthetic jet piezo pump in the form of a dual cavity. The two cavities are positioned on either side of the piezo vibrator and alternate between suction and discharge. When the two cavities were connected in parallel, the flow rate was 1912.68 mL/min. Finally, the proposed piezo pump was explored for impact cooling applications. The impact cooling effect on the heated ceramic is good at an impact distance and angle of 30 mm and 60 degrees, respectively.

关键词： Acoustics Cooling Voltage Resonant frequency Vibrations Ceramics Surface acoustic waves Plasmas Heat pumps Genetic algorithms Impact cooling optimization algorithm piezo pump synthetic jet ultrasonic

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A layered metastructure-based smart radiant regulator with enhanced emissivity modulation utilizing phase change material

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2025年 164卷

作者： Yao, Ting-Shuo Sui, Jun-Yang Du, Rui Zhang, Ting-Hao Zhang, Hai-Feng Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Flexible Elect Future Technol Nanjing 210023 Peoples R China

Based on thermal radiation, radiative cooling is a widely used technology, and plays an important role in many scenarios such as passive building cooling and infrared camouflage. Currently, dynamic control of thermal radiation devices is endowed with great expectations. However, a more efficient systematic strategy for designing smart radiant temperature regulators (SRTRs) is still urgently needed. By leveraging the phase change properties of vanadium dioxide, the proposed SRTR achieves dynamic modulation of thermal emissivity, demonstrating an average total directional emissivity exceeding 0.9 when the temperature is above the threshold temperature, and dropping below 0.1 when the threshold temperature is not been reached, particularly within the atmospheric transparent window band. The optimization results achieved by using Autonomous Particles Groups for Particle Swarm optimization demonstrate better performance metrics compared to those obtained using conventional methods. Notably, the absorption mechanism of layered metastructure is further investigated, and its high angle dependency is effectively addressed. The demonstrates excellent angular stability and polarization insensitivity, with up to 73 degrees in transverse electric mode and 60 degrees in transverse magnetic one. Without taking non-radiative heat transfer into account, the cooling power achieves 214.59 W.m(-2). In this paper, a more systematic strategy for designing SRTRs is exhibited, which offers extensive potential in fields such as surface radiative cooling, infrared camouflage, and spacecraft thermal management.

关键词： Radiative cooling Layered metastructure Phase change material Smart radiant temperature regulation optimization algorithm

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Task allocation algorithm and optimization model on edge collaboration

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JOURNAL OF SYSTEMS ARCHITECTURE 2020年 110卷 101778-101778页

作者： Deng, Xiaoheng Li, Jun Liu, Enlu Zhang, Honggang Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Univ Massachusetts Engn Dept Boston MA 02125 USA

This paper investigates a mobile edge computing environment for video analysis tasks where edge nodes provide their computation capacities to process the computation intensive tasks submitted by end users. First, we introduce a Cloudlet Assisted Cooperative Task Assignment (CACTA) system that organizes edge nodes that are geographically close to an end user into a cluster to collaboratively work on the user's tasks. It is challenging for the system to find an optimal strategy that assigns workload to edge nodes to meet the user's optimization goal. To address the challenge, this paper proposes multiple algorithms for different situations. Firstly, considering the situation that historical data cannot be obtained, a multi-round allocation algorithm based on EMA prediction is proposed, and the experimental results prove the efficiency and necessity of multiple rounds of transmission. To address the second case of obtaining historical data, this paper introduces a prediction-based dynamic task assignment algorithm that assigns workload to edge nodes in each time slot based on the prediction of their capacities/costs and an empirical optimal allocation strategy which is learned from an offline optimal solution from historical data. Experimental results demonstrate that our proposed algorithm achieves significantly higher performance than several other algorithms, and especially its performance is very close to that of an offline optimal solution. Finally, we propose an online task assignment algorithm based on Q-learning, which uses the model-free Q-learning algorithm to actively learn the allocation strategy of the system, and the experimental results verify the superiority and effectiveness of this algorithm.

关键词： Edge computing Task assignment Prediction Q-Learning optimization algorithm

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Sustainable optimization of Construction Project Portfolio Management Through a Data-Driven Approach 2nd

Sustainable Optimization of Construction Project Portfolio M...

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2nd Decision Science Alliance International Summer Conference-DSA ISC

作者： Lostumbo, Marisa A. Bara Iniesta, Marc Calvet, Laura Univ Oberta Catalunya Dept Comp Sci Barcelona Spain EAE Business Sch Barcelona Spain Univ Autonoma Barcelona Telecommun & Syst Engn Dept Sabadell Spain

ISBN: (纸本)9783031782374;9783031782381

Effective management of construction project portfolios demands informed decisions driven by data and mathematical models, aiming to enhance decision-making and address complex decision problems. This article introduces an innovative approach that prioritizes sustainability criteria selection, utilizing data-driven insights and mathematical optimization to optimize project portfolio management. Through multi-objective optimization, our study targets improved resource efficiency, time reduction, and minimized environmental impact, offering portfolio managers a powerful tool for decision-making.

关键词： Project Portfolio Management Decision-Making Data-Driven Sustainability optimization algorithm

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Global Multiobjective Aerodynamic optimization of Wingtip Design for Micro Aerial Vehicle

Global Multiobjective Aerodynamic Optimization of Wingtip De...

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AIAA SciTech Forum

作者： Nimura, Naruhiko Oyama, Akira Univ Tokyo Dept Aeronaut & Astronaut 3-1-1 Yoshinodai Sagamihara Kanagawa 2525210 Japan JAXA Inst Space & Astronaut Sci Dept Space Flight Syst 3-1-1 Yoshinodai Sagamihara Kanagawa 2525210 Japan

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

A global multiobjective design optimization method is introduced for optimizing three-dimensional geometries based on topology optimization. To validate the method's effectiveness in aerodynamic design optimization problems, design optimization of wingtip design for micro aerial vehicles is conducted The proposed method employs a technique for compressing three-dimensional images using octree encoding to reduce the number of design variables and increase optimization efficiency. The octree structure is optimized through genetic programming. It is confirmed that the proposed method can reproduce a variety of geometry. Compared to the baseline SD7003 rectangular wing, the obtained non-dominated solutions show improvements of up to about 50% in lift coefficient and a reduction of up to about 2% in drag coefficient. The geometry, aerodynamic performance, and surface pressure distribution of the obtained non-dominated solutions revealed that volume, surface area, and positions of cant-up and cant-down geometry play a significant role in designing the wingtip geometry.

关键词： Unmanned Aerial Vehicle Aerodynamic Design optimization Aerodynamic Performance Rectangular Wing Design optimization Topology optimization Wing Planforms Reynolds Averaged Navier Stokes Winglets optimization algorithm

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GridFL: A 3D-Grid-based Federated Learning framework

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2025年 236卷

作者： Wu, Jiagao Jiang, Yudong Fan, Zhouli Liu, Linfeng Nanjing Univ Posts & Telecommun Sch Comp Nanjing 210023 Peoples R China Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing 210023 Peoples R China

Federated Learning (FL) is an emerging distributed machine learning framework that enables a large number of devices to train machine learning models collaboratively without sharing local data. Despite the extensive potential of FL, in practical scenarios, different characteristics of clients lead to the presence of different heterogeneity in resources, data distribution, and data quantity, which poses a challenge for the training of FL. To address this problem, in this paper, we first conduct an exhaustive experimental study on all three kinds of heterogeneity in FL and provide insights into the specific impact of heterogeneity on training performance. Subsequently, we propose GridFL, a 3D-grid-based FL framework, where the three kinds of heterogeneity are defined as three dimensions (i.e., dimensions of training speed, data distribution, and data quantity) independently, and all clients in FL training are assigned to corresponding cells of the 3D grid by a gridding algorithm based on K-means clustering. In addition, we propose a grid scheduling algorithm with a dynamic selection strategy, which can select an optimal subset of clients to participate in FL training per round by adopting different strategies for different dimensions and cells. The simulation experiments show that GridFL exhibits superior performance in heterogeneous environments and outperforms several related state-of-the-art FL algorithms. Thus, the effectiveness of the proposed algorithms and strategies in GridFL are verified.

关键词： Federated learning Heterogeneity Client selection optimization algorithm Distributed machine learning

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Enhancing electromagnetic tracking: a hybrid pose calculation algorithm and a novel transmitting coil calibration approach

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PHYSICA SCRIPTA 2025年第4期100卷 046001-046001页

作者： Wen, Shihan Feng, Yuke Lai, Jiacheng Shi, Jianqiao Yang, Rongqian South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Peoples R China Guangdong Hong Kong Macao Joint Lab Millimeter Wav Guangdong Prov Key Lab Millimeter Wave & Terahertz Guangzhou 510641 Peoples R China South China Univ Technol Sch Microelect Guangzhou 510641 Peoples R China Guangzhou Aimooe Technol Co Ltd Guangzhou 510641 Peoples R China

Medical electromagnetic tracking technology offers significant benefits in puncture and interventional surgeries by effectively mitigating obstacles. Traditional optimization-based algorithms for pose estimation in electromagnetic localization often converge to local optima and exhibit slow iterative convergence, which limits their accuracy and efficiency. To address these issues, we propose an enhanced pose estimation algorithm that integrates advanced optimization techniques for receiver sensors. The algorithm successfully converged to the global optimal solution in all test cases and no local optimal solution problem occurred in certain volume, which enables rapid real-time tracking of multiple coils simultaneously, outperforming traditional methods. Furthermore, we have developed a novel calibration method for transmission coils that corrects manufacturing-induced errors in size, position, and orientation. These innovations achieved system positional accuracy of 2.64 mm and directional accuracy of 1.33 degrees within a tracking volume of 350 x 350 x 350 mm3.

关键词： electromagnetic tracking magnetic dipole model optimization algorithm system calibration

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Prediction of compressive strength and porosity of vegetated concrete based on hybrid BP neural networks

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MATERIALS TODAY COMMUNICATIONS 2025年 44卷

作者： Li, Qiong Su, Rui Qiao, Hongxia Su, Li Wang, Penghui Gong, Linhan Lanzhou Univ Technol Sch Civil Engn Lanzhou Gansu Peoples R China Gansu Adv Civil Engn Mat Engn Res Ctr Lanzhou 730050 Gansu Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Guangdong Prov Key Lab Durabil Marine Civil Engn Shenzhen 518060 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

To quickly and accurately obtain the proportions of various raw materials for Vegetated Concrete (VC) based on performance requirements, this study employs a neural network model for forward performance prediction and reverse proportion design. A backpropagation neural network model with a topology of 13-19-2 was designed, and optimized using Ant Colony optimization (ACO), Genetic algorithm (GA), and Bat algorithm (BAT). The input layer includes VC mixing ratio parameters as the amount of aggregate, ceramic granules, pebbles, cement, fly ash, silica fume, nano-SiO2, Anhydrous fluorgypsum, water, water reducer, binder, air-entraining agent, and straw powder, and the output layer includes compressive strength and porosity. The model dataset comprises 182 data points, and a 7:1.5:1.5 data partitioning was used for model development and validation. Correlation analysis was conducted to determine the relationships between raw materials and the mechanical and permeability properties of VC, and a comparative analysis of VC performance evaluation indicators was performed. Principal component analysis(PCA) was adopted to compare the importance and overall scores of different mix proportions. The results showed that the highest correlation coefficients of nano-SiO2 admixture with compressive strength and porosity of vegetated concrete were 0.7 and -0.5, respectively. The ACO-BPNN model demonstrates the highest accuracy in predicting the relationship between the mix proportions, mechanical properties and water permeability of VC, with specific indicators as follows: T1: R2= 0.85, MAE= 0.18, MAPE= 0.02, and RMSE= 0.34;T2: R2= 0.97, MAE= 0.37, MAPE= 0.01, and RMSE= 0.79. Additionally, the 34th mix proportion exhibited the highest performance with a total score of 3.49. Therefore, the ACO-BPNN model can provide scientific guidance for the mix proportion design of VC.

关键词： Vegetated concrete Neural network model optimization algorithm Correlation analysis Principal component analysis

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HEMLAB algorithm Applied to the High-Lift Japan Aerospace Exploration Agency Standard Model

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JOURNAL OF AIRCRAFT 2025年第1期62卷 207-218页

作者： Sukas, Hulya Sahin, Mehmet Istanbul Tech Univ Fac Aeronaut & Astronaut TR-34469 Istanbul Turkiye

The high-lift Japan Aerospace Exploration Agency (JAXA) standard model (HL-JSM) has been numerically analyzed in order to further validate the HEMLAB code for realistic aircraft configurations. The numerical algorithm is based on highly efficient edge-based data structure for a vertex-based finite volume algorithm on hybrid meshes. The data pattern is arranged to meet the requirements of a vertex-based finite volume algorithm by considering data access patterns and cache efficiency. A fully implicit version of the numerical algorithm has also been implemented based on the Portable, Extensible Toolkit for Scientific Computation (PETSc) library in order to improve the robustness of the algorithm. The resulting algebraic equations, including the one-equation Spalart-Allmaras, are solved in a monolithic manner using the restricted additive Schwarz preconditioner combined with the flexible generalized minimal residual method [FGMRES(m)] Krylov subspace algorithm. The numerical method is also combined with the metric-based anisotropic mesh refinement library Python Adaptive Mesh Geometry Suite-INRIA (pyAMG) from National Institute for Research in Digital Science and Technology in order to improve the numerical accuracy. The numerical algorithm is initially applied to the two-dimensional L1T2 (National High Lift Programme) high-lift system, and then the calculations around the HL-JSM are carried out at relatively high angles of attack. The numerical results with the anisotropic mesh refinement library pyAMG indicate significant improvements in numerical accuracy.

关键词： algorithms and Data Structures optimization algorithm Aircraft Components and Structure Aerodynamic Force Unsteady Reynolds Averaged Navier Stokes Adaptive Mesh Refinement Computational Fluid Dynamics High Performance Computing Fluid Flow Properties

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Enhance chaotic gravitational search algorithm (CGSA) by balance adjustment mechanism and sine randomness function for continuous optimization problems

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 537卷 122621-000页

作者： Jiang, Jianhua Yang, Xi Meng, Xianqiu Li, Keqin Jilin Univ Finance & Econ Dept Data Sci Changchun 130117 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Jilin Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

The gravitational search algorithm (GSA) is a population-based meta-heuristic optimization algorithm which finds the optimal solution by the law of gravity and attraction between objects. However, as the number of iterations increases, the increase of the quality of the agents makes GSA fall into the local optimal solution more easily, which greatly reduces the exploration capability of the algorithm. Although the chaotic gravitational search algorithm (CGSA) uses chaotic maps for improving diversity to solve this problem, it still has problems with the balance of exploration and exploitation. This paper proposes the balance adjustment based chaotic gravitational search algorithm (BA-CGSA), which introduces the sine randomness function and the balance mechanism to solve the above problem. 30 benchmark functions of IEEE CEC 2014 are adopted to evaluate the performance of the proposed algorithm in terms of exploration and exploitation. Meanwhile, a real engineering design problem is used to illustrate the ability of the algorithm to solve practical application problems. The experimental results demonstrate its good performance in continuous optimization problems. (C) 2019 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Gravitational search algorithm Sine cosine algorithm Balance adjustment mechanism

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