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optimization methodology of thermal energy storage systems for domestic water heating applications with different configurations

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JOURNAL OF ENERGY STORAGE 2022年 50卷

作者： Khoury, S. Maatouk, C. El Khoury, K. Khatounian, F. St Joseph Univ Beirut Fac Engn ESIB BP 1514 Riad El Solh Beirut 11072050 Lebanon Lebanese Univ Fac Engn POB 657314 Badaro Beirut Lebanon

This paper develops an optimization methodology for the Thermal Energy Storage (TES) tank embedded with Phase Change Materials (PCMs) for domestic water heating applications with respect to the design parameter constraints and tank size. It presents the optimum design conditions for two different TES configurations considering different control strategies for the conventional cascaded system and the multiple entry independent PCM modules. The optimum configurations are determined using adaptive simulated annealing algorithm aimed to enhance the energy content and compare the systems outputs, namely the available energy and the heat transfer fluid outlet temperature at the optimum conditions for the two systems. The present study is conducted during a complete melting and solidification processes. It simultaneously validates the effectiveness of the multiple entry independent PCM modules in satisfying the low and high user requirements of domestic hot water and highlights the reduction in the electric power consumption throughout the year compared to the conventional TES system.

关键词： Phase change material Control strategy optimization algorithm Energy Temperature

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Simulation optimization framework for dynamic probabilistic safety assessment

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年第0期220卷 108316-108316页

作者： Park, Jong Woo Lee, Seung Jun UNIST Dept Nucl Engn 50 UNIST Gil Ulsan 44919 South Korea

Probabilistic safety assessment (PSA) based on event trees and fault trees has been widely used in the risk assessment of nuclear power plants. A static approach by nature, PSA has limitations to consider dynamic scenarios with time-dependent sequences and interactions. In contrast to static-based PSA, dynamic PSA has been introduced as a complementary methodology that considers dynamic scenarios between the system and human operations by interfacing physical simulation with thermal-hydraulic models for risk assessment. However, the various research on dynamic PSA has a common challenge in that the number of dynamic scenarios to be simulated increases impractically. An approach is therefore necessary to manage the number of simulations for performing dynamic PSA efficiently. The objective of this paper is to propose a simulation optimization framework using an optimization algorithm to reduce, as much as reasonably achievable, the large number of dynamic scenarios to be evaluated. The optimization algorithm is proposed to optimize the large numbers of generated dynamic scenarios while maintaining accurate risk quantification in the performance of dynamic PSA. To demonstrate the application of the proposed framework to dynamic PSA, two case studies were conducted considering loss of coolant accidents.

关键词： Dynamic PSA Simulation optimization framework optimization algorithm TH simulation Loss of coolant accident

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Data-Driven Modeling Approach for Mistuned Cyclic Structures

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AIAA JOURNAL 2021年第7期59卷 2684-2696页

作者： Kelly, Sean T. Lupini, Andrea Epureanu, Bogdan, I Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

A data-driven approach to predict dynamic responses of mistuned cyclic dynamic structures is presented. Nominally cyclic structures such as turbomachinery bladed disks, or blisks, contain deviations from perfect symmetry, called mistuning, which can significantly increase the forced response of the structure subject to periodic excitations. The ability to accurately model the response is crucial for understanding a structure's dynamics during operation, and thus many reduced-order models have been developed. Data-driven approaches, however, are largely unexplored for predicting physical responses of cyclic structures, and are desirable due to their ability to leverage both computational and experimental data. In this paper, two feed-forward neural networks (FFNNs) are trained using physical responses and parameters from an individual sector of the cyclic structure. The desired physical responses of all sectors are predicted via a cyclic coupling procedure. By only using sector-level data, the number of simulations and/or experiments required to generate training data is substantially reduced, and the approach is not restricted to a specific projection basis unlike previous physics-based methods. A lumped mass model (LMM) of a blisk with stiffness mistuning is used to generate computational surrogate data for training and validation. Both FFNNs achieve high prediction accuracy on both training data and unseen test data. For a test case with an unseen mistuning pattern, this approach shows strong agreement with known actual responses. For this test case, prediction errors in the LMM blade tip amplitudes across all sectors are less than 2%, and the amplification factor error is less than 0.2%.

关键词： Convolutional Neural Network Structural Dynamics Turbomachinery Bladed Disks Stiffness Matrices optimization algorithm Frequency Domain Boundary Value Problems Structural Health Monitoring Graphics Processing Unit Monte Carlo Simulation

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A Unified Electrothermal Behavior Modeling Method for Both SiC MOSFET and GaN HEMT

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第10期68卷 9366-9375页

作者： Long, Xiao Jun, Zhao Zhang, Botao Chen, Dongdong Liang, Wu Minnan Univ Sci & Technol Sch Elect & Elect Engn Quanzhou 362700 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Peoples R China

This article has presented a unified electrothermal behavior modeling method for both silicon carbide (SiC) metal oxide semiconductor field effect transistor (mosfet) and gallium nitride (GaN) high electron mobility transistor (HEMT). The electrothemal behavior of these two kind of devices in both the first and the third quadrant is accurately modeled with serval compact equations. A modeling data and optimization algorithm-based parameter extraction method have been proposed to replace the previous time consuming and labor intensive one. The proposed parameter fitting algorithm is a genetic algorithm and Levenberg-Marquardt combined optimization algorithm possessing the advantages of global optimization and fast convergence speed, and modeling data can come from both datasheets and measurement. The correctness and accuracy of the proposed modeling method has been verified by simulation and experiment result. Meanwhile, it has been proved that the proposed modeling method is applicable to several commercially available SiC mosfets and GaN HEMTs.

关键词： Mathematical model Semiconductor device modeling MOSFET Silicon carbide Gallium nitride HEMTs Logic gates Electrothermal model gallium nitride (GaN) high electron mobility transistor (HEMT) optimization algorithm parameter extraction silicon carbide metal oxide semiconductor field effect transistor (MOSFET)

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The development trend of China's natural gas consumption: A forecasting viewpoint based on grey forecasting model

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ENERGY REPORTS 2021年 7卷 4308-4324页

作者： Zhang, Jun Qin, Yanping Duo, Huaqiong Inner Mongolia Agr Univ Coll Sci Hohhot 010018 Peoples R China Inner Mongolia Agr Univ Coll Mat Sci & Art Design Hohhot 010018 Peoples R China

In the context of global emission reduction, as a type of clean and eco-friendly energy, natural gas has attracted wide attention. Accurate prediction of natural gas consumption can provide reference for relevant departments to formulate the corresponding development plans and strategies. At present, there are many forecasting tools for processing and analysing natural gas consumption, but most of them have the disadvantages of complex modelling or low precision. In order to solve this problem, inspired by the polynomial regression prediction model and the discrete grey prediction model, a novel polynomial discrete grey prediction model with fractional order accumulation (abbreviated as FPDGM(1,1) model) is established in this paper. In order to find the optimal parameters of the FPDGM(1,1) model, three most popular intelligent algorithms are used to solve the parameters of the model, and then the optimal parameters from the three intelligent algorithms are selected in this paper. In addition, the advantages of the FPDGM(1,1) model over other four forecasting models are confirmed through two parts in this paper. First, the natural gas consumptions of four municipalities in China are used as cases to verify the effectiveness of the FPDGM(1,1) model in this paper. Subsequently, the FPDGM(1,1) model and several kinds of frequently used forecasting models are applied to analyse and predict China's natural gas consumption, and the FPDGM(1,1) model is used to predict China's natural gas consumption in the next 5 years. The results of these two parts indicate that the FPDGM(1,1) model shows the best predictive performance in all cases. Lastly, based on the predicted results of the FPDGM(1,1) model, some reasonable suggestions are put forward for China's natural gas development plan. (C) 2021 Published by Elsevier Ltd.

关键词： Natural gas consumption Polynomial regression prediction model Discrete grey prediction model optimization algorithm

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Brain tumor grade identification using deep Elman neural network with adaptive fuzzy clustering-based segmentation approach

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第16期80卷 25139-25169页

作者： Sankaran, K. Sakthidasan Thangapandian, M. Vasudevan, N. Hindustan Inst Technol & Sci Dept Elect & Commun Engn Chennai Tamil Nadu India

A brain tumor is a collection of anomalous evolution of harmful and unnecessary nerve cells in the brain. For advanced treatment, it is essential to categorize the tumors from the MRI (magnetic resonance imaging). In this paper, develop the adaptive fuzzy Tsallis entropy (FTE) clustering with the improved cuckoo search optimization (ICS) procedure, and the effective feature selection mechanism is proposed in this paper. Initially, the input MRI is pre-processed using the anisotropic diffusion filter and non-parametric region-based techniques to eliminate noise removal and skull stripping. After that, the tumor regions are segmented using the adaptive FTE clustering method with the ICS algorithm. The robust features are attained using the first-order statistical, discrete wavelet transform (DWT), the histogram of oriented gradients (HOG), and intensity histogram. After that, the set of optimal features are selected from the mined features using the black widow optimization (BWO) algorithm in the feature selection (FS) process. Finally, a deep Elman neural network (DENN) structure is utilized to categorize the grade of brain tumor. The suggested approach is simulated using the Matlab environment using the BRATS 2012, BRATS 2019 and BRATS 2020 datasets. The different performances are evaluated, and it is related to other existing classifiers and other approaches. The simulation results verified that the accuracy of developed approach is 99.8% for BRATS 2012, 98.7% for BRATS 2019 and 98% for BRATS 2020 as compared to other classifiers and other existing approaches.

关键词： Segmentation Feature selection Brain tumor identification MRI Classifiers optimization algorithm Classification

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Comparison of Electrified Aircraft Propulsion Drive Systems with Different Electric Motor Topologies

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JOURNAL OF PROPULSION AND POWER 2021年第5期37卷 733-747页

作者： Sirimanna, Samith Thanatheepan, Balachandran Lee, Dongsu Agrawal, Shivang Yu, Yangxue Wang, Yuyao Anderson, Aaron Banerjee, Arijit Haran, Kiruba Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Univ Illinois Urbana IL 61801 USA Hitet LLC Champaign IL USA

Y Electric aircraft propulsion is a growing research area that looks into achieving propulsion through fully electric or hybrid electric systems while achieving low CO2 emissions. The system-level benefit gained by different electric and hybrid-electric propulsion schemes depends heavily on the performance of system-level components in the electric drive-train, including the electric motor, gear box, motor drive, protection systems, as well as the thermal management system. When comparing motor topologies, it is important to understand performance measures such as efficiency and specific power on a drive system level. Many different motor types have been qualitatively compared and can be found in the literature. To guide appropriate component selection, this paper presents details of a quantitative study for a given electric propulsion drive system. A Pareto optimal front for a notional drive system of a 1.5 MW electrical propulsor with different motor types is generated and compared. An optimization algorithm coupled with an electromagnetic finite element analysis software tool was used to optimize the induction motor, switched reluctance motor, wound rotor synchronous motor, permanent magnet synchronous motor (PMSM), slotless PMSM, permanent-magnet-assisted synchronous reluctance motor, brushless DC motor, and brushless doubly fed reluctance motor types for efficiency and specific power. Overall advantages considering system-level efficiency, specific power, and a few other key metrics such as origin of losses, cooling complexity, manufacturing tolerance, and fault tolerance are discussed. This gives an indication of the relative performance of different motor types and confirms the overall advantage of PM motor topologies in aircraft propulsion.

关键词： Aircraft Propulsion System Switched Reluctance Motor Finite Element Analysis Electric Propulsion optimization algorithm Thermal Management System Fault Tolerance Software Tool Hybrid Electric Aircraft Torque Ripple

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An algorithm to optimize the optical sensor design for tip clearance and tip timing measurements 7

An algorithm to optimize the optical sensor design for tip c...

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Conference on Optical Sensing and Detection VII Part of SPIE Photonics Europe Conference

作者： Zubia, Gorka Amorebieta, Josu Zubia, Joseba Durana, Gaizka Univ Basque Country Dept Commun Engn Plaza Ingeniero Torres Quevedo 1 E-48013 Bilbao Basque Country Spain

ISBN: (纸本)9781510651555;9781510651548

In this work, an algorithm for the optimization of the design of an optical fiber bundle displacement sensor for Tip Clearance and Tip Timing measurements is presented. The former is based on the gaussian beam mathematical approach, and implements a fiber arrangement comprised of a transmitting single-mode fiber and two concentric rings of receiving multimode fibers. The software includes a graphical user interface that allows modifying relevant geometrical parameters of the sensor and fiber characteristics, and enables monitoring in real time the effects of such modifications. Results indicate that in order to achieve the most sensitive design with the largest operating range, the key parameters are the gain configuration, the distance between the receiving fiber rings and the radii of receiving fibers, and that the best fitting sensor configuration is a tradeoff among the aforementioned characteristics as it is heavily dependent on the measurement constraints. The results obtained by the simulation tool were validated in good agreement with the calibration curve of the manufactured optical fiber bundle displacement sensor in a scaled rotor.

关键词： optical fiber sensor optimization algorithm tip clearance tip timing aircraft turbines displacement measurement

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Aeroacoustic and aerodynamic optimization of propeller blades

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Chinese Journal of Aeronautics 2020年第3期33卷 826-839页

作者： Peixun YU Jiahui PENG Junqiang BAI Xiao HAN Xiang SONG School of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China Unmanned System Research Institute Northwestern Polytechnical UniversityXi’an 710072China

It is of great significance to develop a high-efficiency and low-noise propeller optimization method for new-generation propeller aircraft *** with free form deformation method,dynamic mesh interpolation technology,optimization algorithm,surrogate model,aerodynamic calculation and aeroacoustic prediction model module,the integrated aerodynamic and aeroacoustic design method of propeller is *** optimization design for the six-blade propeller is carried *** non-reduction in efficiency,thrust coefficient and the minimum of aerodynamic noise is treated as the optimization design *** spatial vorticity distribution of the propeller before and after the design is also analyzed by using unsteady computational fluid dynamics *** results show that the optimized propeller can effectively reduce the aerodynamic noise *** maximum total sound pressure level can be reduced by 5 dB without reducing its aerodynamic *** developed method has good application potential in low-noise optimization design of propeller and other rotating machinery.

关键词： Aeroacoustic Aerodynamic Dynamic mesh interpolation technology Free form deformation method optimization algorithm Propeller Sound pressure level Surrogate model

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A multivariable hybrid prediction system of wind power based on outlier test and innovative multi-objective optimization

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ENERGY 2022年第PartE期239卷 122333-122333页

作者： Guo, Honggang Wang, Jianzhou Li, Zhiwu Jin, Yu Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa Macau Peoples R China

Wind power forecasting is critical to the safe running of the power grid. However, due to the strong intermittence and instability of wind, reliable forecast of wind power remains a significant difficulty. In this study, a novel multivariable machine learning hybrid prediction system that incorporates data preprocessing, prediction, and multi-objective system optimization is designed to quantify the certainty and uncertainty of wind power. To increase the quality of data input, the data preparation module performs outlier tests based on the correlation between wind power and wind speed, as well as feature extraction, on the original data. In the prediction process, this paper offers an incremental kernel extreme learning machine (IK-elm), the parameters of which are set synchronously by an enhanced multi-objective optimization technique (MOCEHHO) developed in this paper. It overcomes the restrictions of duplicated hidden layer nodes and low learning efficiency caused by classic ELM and successfully maximizes the model's prediction capabilities. The simulation results on four datasets from Turkish wind farms show that the hybrid forecasting system outperforms the benchmark and may be utilized as a useful tool for wind power forecasting. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Wind power prediction Outlier test Multivariable machine learning Multi-objective optimization algorithm

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