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Source range detector response modeling using VERA 14

Source range detector response modeling using VERA

14th International Nuclear Fuel Cycle Conference, GLOBAL 2019 and Light Water reactor Fuel Performance Conference, TOP FUEL 2019

作者： Gentry, Cole Godfrey, Andrew Davidson, Eva Ilas, Germina Collins, Benjamin Hart, Shane Kim, Kang Seog Pandya, Tara Royston, Katherine Davidson, Gregory Johnson, Seth Evans, Thomas Wolfram, Gary Palmtag, Scott Oak Ridge National Laboratory Reactor Physics Groups Oak RidgeTN United States Oak Ridge National Laboratory Radiation Transport Group Oak RidgeTN United States Pacific Northwest National Laboratory Signatures Science and Technology Division Richland Washington United States North Carolina State University Department of Nuclear Engineering RaleighNC United States

The CASL reactor simulation package VERA has been adapted to provide high-fidelity simulation capabilities for modeling source range detector response during subcritical reactor configurations. New features include the activation and shuffling of secondary source assemblies, transfer of burned fuel neutron emission data from the ORIGEN depletion solver to the MPACT deterministic neutron transport solver, allowance of user-defined sources in MPACT based on material composition, ability to solve the subcritical source-driven system with neutron multiplication using the MPACT diffusion solver, and transfer of the calculated fission source from MPACT to the continuous energy Monte Carlo solver Shift for final detector response evaluation. These new capabilities were benchmarked against Watts Bar Unit 1 plant operating data and found to be in very good agreement. Copyright © GLOBAL 2019 - International Nuclear Fuel Cycle Conference and TOP FUEL 2019 - Light Water reactor Fuel Performance *** rights reserved.

关键词： Light water reactors

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A Three-Dimensional Prediction Model of Surrounding Rock Classification of Tunnel Based on Machine Learning Algorithms

SSRN

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SSRN 2023年

作者： Ouyang, Jinwu Zhang, Guijin Duan, Jihong Li, Wen Liu, Jie Zhou, Cuiying School of Hydraulic and Environmental Engineering Changsha University of Science & Technology Hunan Changsha410114 China Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province Hunan Changsha410114 China Honghe Water Conservancy and Hydropower Engineering of Geological Drilling Team Mengzi661400 China Yunnan Honghe Hydropower Survey and Design Institute Mengzi661199 China Sun Yat-sen University Guangzhou510275 China

Surrounding rock classification of tunnel is a major factor that has a very significant impact on underground engineering design and construction. In this study, Back Propagation (BP) neural network was used to classify surrounding rock classification and build the three-dimensional (3D) prediction model of surrounding rock of tunnel, and particle swarm optimization (PSO) was used to optimize the input weight matrix and the hidden layer bias in BP network. Five quantitative surrounding rock parameters were used as input for the PSO-BP network, including the elastic longitudinal wave velocity of rock mass and rock, the influence of groundwater, the attitude influence of the main weak structural plane, uniaxial saturation compression of rock. The network was used to learn from a database of 48 collected surrounding rock cases in the research area, on which the PSO-BP surrounding rock classification prediction model was established and verified using eight validation samples. The PSO-BP model was then tested on a tunnel surrounding rock in the research area, and the 3D prediction model of surrounding rock of the tunnel was built according to the prediction results. The results showed that the 3D prediction model of tunnel surrounding rock based on the PSO-BP algorithm performed well in surrounding rock classification prediction. In addition, the model showed superior performance compared with BP model unoptimized, which underscores its utility in future surrounding rock classification prediction and 3D visualized modelling. © 2023, The Authors. All rights reserved.

关键词： Backpropagation

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Deep reinforcement learning in fluid mechanics:A promising method for both active flow control and shape optimization

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Journal of Hydrodynamics 2020年第2期32卷 234-246页

作者： Jean Rabault Feng Ren Wei Zhang Hui Tang Hui Xu Department of Mathematics University of OsloOsloNorway Research Center of Fluid Structure Interactions Department of Mechanical Engineering Hong Kong Polytechnical UniversityHong KongChina Science and Technology on Water Jet Propulsion Laboratory Marine Design and Research Institute of ChinaShanghai200011China School of Aeronautic and Astronautic Shanghai Jiao Tong UniversityShanghai200011China Department of Aeronautics Imperial College LondonLondonUK

In recent years,artificial neural networks(ANNs)and deep learning have become increasingly popular across a wide range of scientific and technical fields,including fluid *** it will take time to fully grasp the potentialities as well as the limitations of these methods,evidence is starting to accumulate that point to their potential in helping solve problems for which no theoretically optimal solution method is *** is particularly true in fluid mechanics,where problems involving optimal control and optimal design are ***,such problems are famously difficult to solve effectively with traditional methods due to the combination of non linearity,non convexity,and high dimensionality they *** contrast,deep reinforcement learning(DRL),a method of optimization based on teaching empirical strategies to an ANN through trial and error,is well adapted to solving such *** this short review,we offer an insight into the current state of the art of the use of DRL within fluid mechanics,focusing on control and optimal design problems.

关键词： Machine learning deep reinforcement learning(DRL) flow control

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Recent development of chemically complex metallic glasses: from accelerated compositional design, additive manufacturing to novel applications

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Materials Futures 2022年第1期1卷 9-31页

作者： J Y Zhang Z Q Zhou Z B Zhang M H Park Q Yu Z Li J Ma A D Wang H G Huang M Song B S Guo Q Wang Y Yang Department of Mechanical Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongPeople’s Republic of China Shenzhen Key Laboratory of High Performance Nontraditional Manufacturing College of Mechatronics and Control EngineeringShenzhen UniversityShenzhenGuangdong 518060People’s Republic of China School of Materials Science and Engineering Dongguan University of TechnologyDongguanGuangdong 523808People’s Republic of China Institute of Materials China Academy of Engineering PhysicsJiangyouSichuan 621907People’s Republic of China State Key Laboratory of Powder Metallurgy Central South UniversityChangshaHunan 410083People’s Republic of China Institute of Advanced Wear&Corrosion Resistant and Functional Materials Jinan UniversityGuangzhouGuangdong 510632People’s Republic of China Institute of Materials Laboratory for MicrostructuresShanghai UniversityShanghai 200444People’s Republic of China Department of Materials Science and Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongPeople’s Republic of China Department of Advanced Design and System Engineering College of EngineeringCity University of Hong KongKowloon TongKowloonHong KongPeople’s Republic of China

Metallic glasses(MGs)or amorphous alloys are an important engineering material that has a history of research of about 80–90 *** different fast cooling methods were developed for multi-component MGs between 1960s and 1980s,1990s witnessed a surge of research interest in the development of bulk metallic glasses(BGMs).Since then,one central theme of research in the metallic-glass community has been compositional design that aims to search for MGs with a better glass forming ability,a larger size and/or more interesting properties,which can hence meet the demands from more important *** this review article,we focus on the recent development of chemically complex MGs,such as high entropy MGs,with new tools that were not available or mature yet until recently,such as the state-of-the-art additive manufacturing technologies,high throughput materials design techniques and the methods for big data analyses(*** learning and artificial intelligence).We also discuss the recent use of MGs in a variety of novel and important applications,from personal healthcare,electric energy transfer to nuclear energy that plays a pivotal role in the battle against global warming.

关键词： metallic glasses amorphous alloys alloy design additive manufacturing machine learning high throughput materials design

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Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data

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Green Chemical Engineering 2025年第2期 233-248页

作者： Zhiqiang Wu Yunquan Chen Bingjian Zhang Jingzheng Ren Qinglin Chen Huan Wang Chang He School of Materials Science and Engineering Sun Yat-Sen University Guangdong Engineering Center for Petrochemical Energy Conservation Department of Industrial and Systems Engineering The Hong Kong Polytechnic University School of Chemical Engineering and Technology Sun Yat-sen University State Key Laboratory of Heavy Oil Processing China University of Petroleum-Beijing CNNC No.7 Research and Design Institute Co.Ltd.

Pressure swing adsorption(PSA) modeling remains a challenging task since it exhibits strong dynamic and cyclic behavior. This study presents a systematic physics-informed machine learning method that integrates transfer learning and labeled data to construct a spatiotemporal model of the PSA process. To approximate the latent solutions of partial differential equations(PDEs) in the specific steps of pressurization, adsorption, heavy reflux,counter-current depressurization, and light reflux, the system's network representation is decomposed into five lightweight sub-networks. On this basis, we propose a parameter-based transfer learning(TL) combined with domain decomposition to address the long-term integration of periodic PDEs and expedite the network training process. Moreover, to tackle challenges related to sharp adsorption fronts, our method allows for the inclusion of a specified amount of labeled data at the boundaries and/or within the system in the loss function. The results show that the proposed method closely matches the outcomes achieved through the conventional numerical method,effectively simulating all steps and cyclic behavior within the PSA processes.

关键词：

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Metamodelling the Hot Deformation Behaviour of Titanium Alloys Using a Mean-Field Approach

SSRN

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SSRN 2023年

作者： Branco Ferraz, Franz Miller Buzolin, Ricardo Henrique Sztangret, Lukasz Carazo, Fernando Wang, Peng Szeliga, Danuta dos Santos Effertz, Pedro Maciol, Piotr Krumphals, Alfred Poletti, Maria Cecilia Institute of Materials Science Joining and Forming Graz University of Technology Kopernikusgasse 24/I Graz8010 Austria Christian Doppler Laboratory for Design of High-Performance Alloys by Thermomechanical Processing Kopernikusgasse 24 Graz8010 Austria Faculty of Metals Engineering and Industrial Computer Science AGH University of Science and Technology Al. A. Mickiewicza 30 Krakow30-059 Poland voestalpine BÖHLER Edelstahl GmbH & Co KG Mariazellerstraße 25 Kapfenberg8605 Austria San Juan1290 Argentina Consejo Nacional de Investigaciones Científicas y Técnicas CONICET Godoy Cruz 2290 Buenos Aires 1425 Argentina Philip Morris International Quai Jeanrenaud 3 Neuchâtel2000 Switzerland

During the thermomechanical processing of titanium alloys in the β-domain, the β-phase undergoes restoration phenomena. In this work, they are described by a mean-field physical model that correlates the flow stress with the microstructural evolution. To reduce the computational time of process simulations, metamodels are developed for specific outputs of the mean-field physical model using Artificial Neural Network (ANN) and Decision Tree Regression (DTR). The performance of the obtained metamodels is evaluated in terms of the coefficient of determination (R²), the root-mean-square error (RMSE), and the mean relative error (MRE). No significant difference was observed between R2training and R2testing, meaning that all the metamodels correctly generalise the overall behaviour of the outputs for a wide range of inputs. The evolution of the metamodel outputs is compared with the model predictions in two different situations: 1) at a constant strain rate and temperature, and 2) during Finite Element (FE) simulations of the hot deformation of a hat-shaped sample, where temperature and effective strain rate vary at each element during deformation. The evolution of the outputs at constant and non-constant strain rate and temperature demonstrated the robustness of the metamodels in predicting the heterogeneous deformation within a workpiece. The computational time required by the metamodels to calculate selected outputs can be more than 100 times less than that of the model itself at constant strain rate using MATLAB®, and up to 19% less when coupled with FE simulations. The simulation results combined with microstructural analysis are used to visualise the different restoration mechanisms occurring in different regions of the hat-shaped sample as a function of the local thermomechanical history. The changes in strain rate and temperature during deformation influence the evolution of the wall dislocation density and the immobilisation rate of mobile dislocations at subgrai

关键词： Strain rate

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Intercalation-Deposition Mechanism Induced by Aligned Carbon Fiber Toward Dendrite-Free Metallic Potassium Batteries

SSRN

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SSRN 2022年

作者： Liu, Jilei Xiao, Kuikui Wu, Jian-Fang Yan, Hanghang Mo, Ying Zhou, Wang Peng, Yufan Chen, Shi Cui, Xiangyang Chen, Lei Xu, Chaohe College of Materials Science and Engineering Hunan Joint International Laboratory of Advanced Materials and Technology of Clean Energy Hunan Province Key Laboratory for Advanced Carbon Materials and Applied Technology Hunan University Hunan Changsha410082 China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Hunan 410082 China Joint Key Laboratory of the Ministry of Education Institute of Applied Physics and Materials Engineering University of Macau Avenida da Universidade Taipa 999078 China Department of Mechanical Engineering University of Michigan-Dearborn DearbornMI48128-1491 United States College of Aerospace Engineering Chongqing University Chongqing400044 China

Metallic potassium (K) anode is highly desirable for high energy density batteries. However, challenges lie in uncontrollable dendrite growth and/or inevitable dead K generation. Here a composite anode, covering a layer of aligned carbon fiber on the surface of a K metal anode (K-ACF), is constructed. Symmetrical K-ACF//K-ACF cells can work stably for over 600 h without short circuit, which is quite longer than cells with unaligned carbon fiber modified K metal (K-UCF) and bare K metal. Comprehensive characterizations reveal a step-wise intercalation-deposition process occurs on the K-ACF anode at the initial state of the K plating process, in which the intercalation is proved to be necessary for the uniform K+ flux due to the improved potassiophility. Besides, the aligned structure of ACF plays a key role in offering the uniform current density and K+ over the surface of the anode, successfully promoting the stable K stripping/plating. Consequently, the K-ACF//Prussian blue (PB) cell exhibits a high reversible capacity of 83.6 mAh g-1 (267.4 Wh kg-1 based on the mass of PB) under a charge/discharge rate of 200 mA g-1 with a capacity loss rate of 0.044% per cycle. This work opens new avenue toward high energy and dendrite-free potassium batteries. © 2022, The Authors. All rights reserved.

关键词： Anodes

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A class of optimal structures for node computations in message passing algorithms

arXiv

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arXiv 2020年

作者： He, Xuan Cai, Kui Zhou, Liang The School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China Cluster Singapore University of Technology and Design Singapore487372 Singapore The National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China

Consider the computations at a node in a message passing algorithm. Assume that the node has incoming and outgoing messages x = (x1, x2,..., xn) and y = (y1,y2,..., yn), respectively. In this paper, we investigate a class of structures that can be adopted by the node for computing y from x, where each yj, j = 1, 2,..., n is computed via a binary tree with leaves x excluding xj. We make three main contributions regarding this class of structures. First, we prove that the minimum complexity of such a structure is 3n − 6, and if a structure has such complexity, its minimum latency is δ + ⌈log(n − 2δ)⌉ with δ = ⌊log(n/2)⌋, where the logarithm always takes base two. Second, we prove that the minimum latency of such a structure is ⌈log(n − 1)⌉, and if a structure has such latency, its minimum complexity is n log(n − 1) when n − 1 is a power of two. Third, given (n, τ) with τ ≥ ⌈log(n − 1)⌉, we propose a construction for a structure which we conjecture to have the minimum complexity among structures with latencies at most τ. Our construction method runs in O(n3 log2(n)) time, and the obtained structure has complexity at most (generally much smaller than) n⌈log(n)⌉ − 2. Copyright © 2020, The Authors. All rights reserved.

关键词： Binary trees

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Evidence of In-Memory Computing in a Ferrofluid

arXiv

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arXiv 2022年

作者： Crepaldi, Marco Mohan, Charanraj Garofalo, Erik Adamatzky, Andrew Szacilowski, Konrad Chiolerio, Alessandro Electronic Design Laboratory Istituto Italiano di Tecnologia Genova16163 Italy Bioinspired Soft Robotics Istituto Italiano di Tecnologia Genova16163 Italy Unconventional Computing Laboratory University of West England BristolBS16 1QY United Kingdom Academic Centre for Materials and Nanotechnology AGH University of Science and Technology Kraków30-059 Poland

Magnetic fluids are excellent candidates for important research fields including energy harvesting, biomedical applications, soft robotics and exploration. However, notwithstanding relevant advancements such as shape reconfigurability, that have been demonstrated, there is no evidence for their computation capability, including the emulation of synaptic functions. Here, we experimentally demonstrate that a Fe3O4 water-based Ferrofluid (FF) can perform electrical analog computing and be programmed using quasi DC signals and read at Radio Frequency (RF) mode. We have observed features in all respects attributable to a memristive behavior, featuring both short and long-term information storage capacity and plasticity. The colloid was capable of classifying digits of a 8 × 8 pixel dataset using a custom in-memory signal processing scheme, and through Physical Reservoir Computing (PRC) by training a readout layer. © 2022, CC BY-NC-ND.

关键词： Classification (of information)

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Microstructure and mechanical properties of Al-Fe meshing bonding interfaces manufactured by explosive welding

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Transactions of Nonferrous Metals Society of China 2019年第4期29卷 680-691页

作者： Ming YANG Hong-hao MA Zhao-wu SHEN Dai-guo CHEN Yong-xin DENG CAS Key Laboratory of Mechanical Behavior and Design of Materials University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Fire Science University of Science and Technology of ChinaHefei 230026China

In order to improve the mechanical properties of *** transition joints manufactured by explosive welding,meshing bonding interfaces were obtained by prefabricating dovetail grooves in base *** microstructure and mechanical properties of the meshing interfaces were systematically *** microstructure observation showed that metallurgical bonding without pores was created in the form of direct bonding and melting zone bonding at the *** on tensile specimens showed cleavage fracture on the steel side and ductile fracture on the aluminum side near the *** tensile shear test results indicated that the shear strength of the meshing interface 0°and 90°was increased by 11%and 14%,respectively,when being compared to that of the ordinary *** transition *** values of microhardness decreased as the distance from the interface *** three-point bending,cracks were observed at the bonding interface for some specimens due to the existence of brittle *** compounds.

关键词： explosive welding meshing interface aluminum stainless steel

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