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Asia-Pacific International Symposium on Aerospace Technology (APISAT)

作者： Li, Bin Chen, Jiang Liu, Yi Xiang, Hang Beihang Univ Sch Energy & Power Engn Beijing 100191 Peoples R China

ISBN: (纸本)9789819740123;9789819740109;9789819740093

The full 3-dimensional (3D) aerodynamic optimization of axial flow compressors is a typical high-dimensional and expensive optimization problem, which has multiple variables and long evaluation time. To address this challenge, this study first establishes a novel aerodynamic optimization platform for multistage axial compressors, which reduces the time of single sample evaluation by a dimensionality reduction method which adoptsquasi-3-dimensional aerodynamic calculation instead of full 3D numerical simulation. The optimization platform also adopts radial basis function (RBF) parameterization technique. It can control the deformation of 3D geometry by fewer control points, and thus the control variables of single blade and the dimension of the optimization problem are reduced. Then the aerodynamic performance of a 3.5-stage highly loaded compressor is optimized based on the optimization platform. The results show that after optimization, within the basically unchanged total pressure ratio and surge margin, the efficiency of design point is increased by a maximum of 2.2% and the optimized characteristics curves are in good agreementwith the 3Dcalculation results. Compared to the full 3D aerodynamic optimization, the optimization platform in this article can save more than 10 times the time. Thus, the effectiveness of the optimization platform in compressor aerodynamic optimization is verified.

关键词： S2 throughflow analysis method Aerodynamic performance Parameterization method optimization algorithm

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Active and reactive power optimization control of photovoltaic and energy storage based on grid forming inverters 4

Active and reactive power optimization control of photovolta...

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4th Power System and Green Energy Conference (PSGEC)

作者： Wang, Qingshan Li, Yan Zhang, Qun Wang, Decheng State Grid Jiangsu Econ & Technol Res Inst Elect State Grid Jiangsu Elect Power Co Ltd Nanjing Peoples R China

ISBN: (纸本)9798350365573;9798350365580

Currently, grid forming inverters are used to support frequency and voltage in distribution networks. Hence, grid forming inverter is very important for active and reactive power optimization control. This paper first introduces the virtual synchronous generator control method. The Successive Quadratic Programming (SQP) algorithm and particle swarm optimization (PSO) algorithm are respectively used to optimize the active and reactive power control parameters. The simulation results validate that the optimization algorithm can improve the frequency and voltage dynamic performance of the system.

关键词： grid forming inverter frequency control voltage control parameter optimization optimization algorithm

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Approximate Jacobian Eigenanalysis For Unstructured Mesh optimization of Finite Volume Simulations

Approximate Jacobian Eigenanalysis For Unstructured Mesh Opt...

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AIAA SciTech Forum

作者： Zandsalimy, Mohammad Ollivier-Gooch, Carl Univ British Columbia Mech Engn Adv Numer Simulat Lab Vancouver BC V6T 1Z4 Canada

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This paper presents a novel method for approximate eigenanalysis of large linear systems, with a specific focus on unstable eigenvalues in the Lyapunov sense. The method utilizes residual vector analysis, principal component analysis, and dynamic mode decomposition to identify unstable solution modes efficiently. Outlier detection models are employed to find the problematic cells on the unstructured mesh in the case of residual vector and principal component analysis while the dynamic mode decomposition points directly to the control volumes of interest without such help. By extracting and assembling corresponding rows of the Jacobian matrix, the large system is projected on a new matrix with significantly fewer degrees of freedom, leading to improved computational efficiency. The eigenvalue problem is then solved on this smaller matrix, and the obtained results are utilized for mesh optimization, enhancing the local stability of the dynamic system. By addressing the challenges of computational complexity and automation faced by previous methods, this novel approach offers a comprehensive and automated solution for the eigenanalysis of large linear systems. The potential impact of this method extends to various fields, providing a more efficient eigenanalysis process and opening new avenues for exploring and optimizing complex systems.

关键词： Dynamic Mode Decomposition Anomaly Detection Finite Volume Method Linear Time Invariant System Finite Difference Method optimization algorithm Numerical Simulation Steepest Descent Method Computing Primitive Variable

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optimization of Multi-Mission Space Exploration Campaign Schedules Subject to Stochastic Launch Delay

Optimization of Multi-Mission Space Exploration Campaign Sch...

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AIAA SciTech Forum

作者： Gollins, Nicholas Ho, Koki Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Future plans for deep-space exploration campaigns, targeting locations such as the Moon and Mars, feature a multitude of interdependent missions. For example, a crewed mission may require robotic pre-cursor scouting missions, or supporting cargo delivery missions. As campaign complexity and mission inter-dependency increases, the potential knock-on effects and costs of launch delays also increase. Understanding the potential impacts of delays is an important part of architecture trade-offs in the early definition phase of a project, but quantifying those impacts for a large number of potential architecture solutions is difficult. This work aims to solve this issue by producing a method to both quantify the impact of launch delay and compare possible campaign launch schedules, in order to find robust and near-optimal solutions, by measuring the expected value of the optimization objective and expected probability of infeasibility due to the launch uncertainty. The method is applied specifically to potential lunar exploration architectures in a case study.

关键词： Deep Space Exploration Earth Lunar Exploration Moon optimization algorithm Evolutionary algorithm Space Logistics optimization Propellant Lunar Orbit NASA

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Efficient Hybrid Energy optimization Method in Location Aware Unmanned WSN

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Intelligent Automation & Soft Computing 2023年第1期35卷 705-725页

作者： M.Suresh Kumar G.A.Sathish Kumar Department of CSE Sri Venkateswara College of EngineeringSriperumbudur602117India Department of ECE Sri Venkateswara College of EngineeringSriperumbudur602117India

The growth of Wireless Sensor Networks(WSNs)has revolutionized thefield of technology and it is used in different application *** edges and other critical locations can be monitored using the navigation sensor *** WSN required low energy consumption to provide a high network and guarantee the ultimate *** main objective of this work is to propose hybrid energy optimization in local aware *** hybrid proposed work consists of clustering,optimization,direct and indirect communication and *** aim of this research work is to provide and framework for reduced energy and trusted communication with the shortest path to reach source to destination in WSN and an extending lifetime of wireless *** proposed Artificial Fish Swarm optimization algorithm is used for energy optimization in military applications which is simulated using Network Simulator(NS)*** work optimizes the energy level and the same is compared with various genetic algorithms(GA)and also the cluster selection process was compared with thefission-fusion(FF)selection *** results of the proposed work show,improvement in energy optimization,throughput and time delay.

关键词： Wireless sensor networks(WSNs) optimization algorithm routing algorithm military applications

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optimization algorithms for spectral coarse-graining of complex networks

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2019年 514卷 925-935页

作者： Jia, Zhen Zeng, Lang Wang, Ying-Ying Wang, Pei Guilin Univ Technol Coll Sci Guilin 541004 Peoples R China Henan Univ Sch Math & Stat Inst Appl Math Lab Data Anal Technol Kaifeng 475004 Peoples R China

Coarse-graining techniques of large-scale complex networks have been an important approach to reduce network size, which merge nodes who share the same or similar properties while preserving some significant functions or properties of the original networks. However, reducing network size is often at the cost of worsening network performance. Thus, there is a trade-off between the coarse-grained network sizes and network performance. To find the balance between the two sides and based on the spectral coarse-graining approach (SCG), we propose two optimization algorithms, which are called variable step size optimization algorithm (VSSOA) and variable scale optimization algorithm (VSOA). The two algorithms can calculate the optimal coarse-grained step size and the optimal scale to reduce the share size of the network. The two algorithms are applied to the coarse-graining of several typical networks. And the feasibility and validity of the proposed algorithms are further verified by phase synchronization of coupled Kuramoto oscillators on typical networks. The related investigation provides a deep insight to the coarse-graining of larg-escale complex networks. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Complex network Spectral coarse-graining optimization algorithm Synchronization

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Static Task Scheduling for Parallel Execution of Large-Scale Multidisciplinary Design optimization Models

Static Task Scheduling for Parallel Execution of Large-Scale...

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AIAA SciTech Forum

作者： Sperry, Mark Z. Kondap, Kavish Hwang, John T. Univ Calif San Diego Dept Mech & Aerosp Engn 9500 Gilman Dr La Jolla CA 92093 USA Francis Parker Sch 6501 Linda Vista Rd San Diego CA 92111 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Multidisciplinary design optimization (MDO) models are built by assembling sub-models that represent varying disciplines and design conditions. Consequently, such models are often computationally expensive, requiring their evaluation in a parallel computing environment to decrease solution time. MDO modeling frameworks, which are software tools to allow construction of MDO models, generally require manual user input to specify where to parallelize their code. However, this process can be tedious if the model is large or complex. In this paper, we propose a method heavily inspired by scheduling theory that aims to fully automate parallelization of MDO models. We use static task scheduling to partition mathematical operations in the model to different processors prior to model execution. Additionally, our approach supports adjoint-based derivative computation to enable the use of gradient-based optimization algorithms. Our results show a significant performance improvement when applying our method to several MDO applications. We believe our approach enables MDO modelers to solve computationally expensive problems in a more efficient manner.

关键词： Multidisciplinary Design and optimization Parallel Computing Environment optimization algorithm Software Tool Uncertainty Quantification Computing Vortex Lattice Method Blade Element Momentum Theory Asynchronous Communications Dynamic Scheduling algorithm

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optimization of typical industrial load forecasting based on CNN-BILSTM-Attention-NGO 9

Optimization of typical industrial load forecasting based on...

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9th International Conference on Electronic Technology and Information Science (ICETIS)

作者： He, Fulong Li, Bin Wang, Fengqiao Wu, Dan Tian, Shiming Xu, Yuting North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China State Grid Shanghai Municipal Elect Power Co Shanghai Peoples R China China Elect Power Res Inst Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350388350;9798350388343

The randomness and fluctuation of short-term power load are strong, and the traditional load forecasting method is difficult to grasp the law of short-term load change. In order to improve the accuracy of power load forecasting, a CNN-BILSTM-Attention-NGO typical industrial load forecasting optimization method was proposed based on the Northern Goshawk optimization (NGO) algorithm and the traditional forecasting model. After feature extraction by CNN, the traditional joint prediction model uses BiLSTM to effectively capture the long-term dependence between sequences, and then uses the Attention algorithm to ensure that some important feature information of the original model is not ignored. In order to solve the problem of complex data of power system, this paper introduces the NGO optimization algorithm, adopts the gradient decline of adaptive learning rate, and establishes a new optimization framework through the normalization and balance of weights, and constructs a new activation function and forgetting gate mechanism to improve the performance of the model. The results before and after optimization are compared and analyzed by practical examples. The simulation results show that the proposed method can accurately grasp the law of load change, achieve more accurate load prediction, and the fitting effect is better.

关键词： Demand response Load forecasting NGO optimization optimization algorithm

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An Improved Fading Factor-Based Adaptive Robust Filtering algorithm for SINS/GNSS Integration with Dynamic Disturbance Suppression

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REMOTE SENSING 2025年第8期17卷 1449-1449页

作者： Chen, Zhaohao Liu, Yixu Liu, Shangguo Wang, Shengli Yang, Lei Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Peoples R China Natl Deep Sea Base Management Ctr Qingdao 266237 Peoples R China Shandong Univ Sci & Technol Coll Ocean Sci & Engn Qingdao 266590 Peoples R China

Aiming at the problem of nonlinear observation model mismatch and insufficient anti-interference ability of SINS/GNSS integrated navigation system in complex dynamic environment, this paper proposes an adaptive robust filtering algorithm with improved fading factor. Aiming at the problem that the traditional Kalman filter is easy to diverge in severe heave motion and abnormal observation, a multi-source information fusion framework integrating satellite positioning geometric accuracy factor (PDOP), solution quality factor (Q value), effective satellite observation number (Satnum), and residual vector is constructed. The dynamic weight adjustment mechanism is designed to realize the real-time optimization of the fading factor. Through the collaborative optimization of robust estimation theory and adaptive filtering, a dual robust mechanism is constructed by combining the sequential update strategy. In the measurement update stage, the observation weight is dynamically adjusted according to the innovation covariance, and the fading memory factor is introduced in the time update stage to suppress the error accumulation of the model. The experimental results show that compared with EKF, Sage-Husa adaptive filtering and robust filtering algorithms, the three-dimensional positioning accuracy is improved by 47.12%, 35.26%, and 9.58%, respectively, in the vehicle strong maneuvering scene. In the scene of ship-borne heave motion, the corresponding increase is 19.44%, 10.47%, and 8.28%. The research results provide an effective anti-interference solution for navigation systems in high dynamic and complex environments.

关键词： SINS/GNSS integrated navigation robust adaptive filtering kalman filtering optimization algorithm dynamic disturbances positioning accuracy

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High-precision machine learning for predicting latent heat in diverse multicomponent molten salts

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SOLAR ENERGY MATERIALS AND SOLAR CELLS 2025年 281卷

作者： Wang, Xue-meng Tao, Yi-dan Dong, Guan-chen Wang, Shuai-yu Miao, Qi Ding, Hong-liang Lv, Jing Wu, Qiong Jin, Yi Tan, Ling-hua Nanjing Univ Sci & Technol Sch Chem & Chem Engn Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Natl Special Superfine Powder Engn Res Ctr Nanjing 210014 Peoples R China Nanjing Jinhe Energy Mat Co Ltd Nanjing 210047 Jiangsu Peoples R China Nanjing Inst Technol Sch Mat Sci & Engn Jiangsu Key Lab Adv Struct Mat & Applicat Technol Nanjing 211167 Peoples R China

Molten salts in phase change materials offer significant advantages, including high thermal storage density, a wide operational temperature range, and low cost. However, the development of novel high-latent-heat molten salts remains largely empirical. Machine learning offers the potential to expedite theoretical advancements and enable precise, cost-efficient performance predictions. Nonetheless, the diversity of molten salt s complicates the accuracy and generalizability of machine learning models. This study proposes a novel latent heat prediction methodology that integrates data analysis and machine learning. A comprehensive dataset encompassing various inorganic salts was systematically analyzed to extract key features influencing latent heat. Subsequently, a predictive model was constructed by combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO). The PSO-BPNN model demonstrated high predictive accuracy, achieving R2 values of 0.9389 and 0.9413 for binary and ternary molten salts, respectively, with experimental validation indicating prediction errors within 10 %. This approach establishes a high-precision, scalable framework for predicting the latent heat of multicomponent molten salts, thereby advancing the design of salts with tailored thermal properties and offering a valuable reference for predicting other thermophysical characteristics.

关键词： Phase change materials Molten salts Artificial neural network optimization algorithm Latent heat Wide-range prediction

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