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 c...
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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.
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...
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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.
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 co...
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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.
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 m...
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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.
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...
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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.
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...
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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.
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 ...
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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.
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 f...
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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.
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 molte...
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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.
The use of optimization algorithms is essential to train neural networks effectively. The usage of a combination of two different optimizers is proposed in this method that, used together, can perform single optimizer...
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