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Reliability-based design optimization of offshore wind turbine support structures using RBF surrogatemodel

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 7,   Pages 1086-1099 doi: 10.1007/s11709-023-0976-8

Abstract: Reliability-based design optimization of offshore wind turbine support structures using RBF surrogatemodel

Keywords： RBF     surrogate model     turbine support structures    

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An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 277-291 doi: 10.1007/s11708-021-0731-6

Abstract: Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with thevalues of surrogate fuels.The BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel,Particularly, this model is one of the first attempts to predict the sooting tendencies of surrogateDuring surrogate formulation, the BMKL model can be used to shrink the surrogate candidate list in terms

Keywords： sooting tendency     yield sooting index     Bayesian multiple kernel learning     surrogate assessment     surrogate    

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A surrogate model for uncertainty quantification and global sensitivity analysis of nonlinear large-scale

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 12,   Pages 1813-1829 doi: 10.1007/s11709-023-0007-9

Abstract: To reduce the heavy computational burden, a surrogate model of a dome structure was constructed to solvemodel.The model considered the predominant sources of uncertainty that have a significant influence on theFinally, the effects of the sample size and correlation function on the accuracy of the surrogate modelThe results show that surrogate modeling has high computational efficiency and acceptable accuracy in

Keywords： large-scale dome structure     surrogate model     global sensitivity analysis     uncertainty quantification    

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Method for solving the nonlinear inverse problem in gas face seal diagnosis based on surrogate models

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0689-z

Abstract: The presented work develops a surrogate-model-assisted method for solving the nonlinear inverse problemin limited physical model evaluations.Latin hypercube design and then performs an iterative routine that benefits from the rapidity of the surrogatemodels and the reliability of the physical model.can effectively identify the parameters that induce the abnormal signal output with limited physical model

Keywords： surrogate model     gas face seal     fault diagnosis     nonlinear dynamics     tribology    

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A deep neural network based surrogate model for damage identification in full-scale structures with incomplete

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 3,   Pages 393-410 doi: 10.1007/s11709-024-1060-8

Abstract: The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogatestructures, which is addressed in this paper by generating data sets using a reduced finite element (FE) modelThe surrogate models are trained using response data obtained from the monitored structure through aThe proposed approach involves training a single surrogate model that can quickly predict the locationTo achieve the most generalized surrogate model, the study explores different types of layers and hyperparameters

Keywords： vibration-based damage detection     deep neural network     full-scale structures     finite element model updating    

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double-layer barrel vaults using genetic and pattern search algorithms and optimized neural network as surrogatemodel

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 3,   Pages 378-395 doi: 10.1007/s11709-022-0899-9

Abstract: The main idea is to utilize an optimized artificial neural network (OANN) as a surrogate model to reduce

Keywords： optimization     surrogate models     artificial neural network     SAP2000     genetic algorithm    

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approach for structural damage detection using meta-heuristic algorithms and group method of data handling surrogatemodel

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 4,   Pages 907-929 doi: 10.1007/s11709-020-0628-1

Abstract: performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogatemodel of the structure has been investigated.modal property change vector is evaluated using the group method of data handling (GMDH) network as a surrogateFinally, in order to achieve the most generalized neural network as a surrogate model, GMDH performancethan CFNN model.

Keywords： two-stage method     modal strain energy     surrogate model     GMDH     optimization damage detection    

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Synergistic optimization framework for the process synthesis and design of biorefineries

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 251-273 doi: 10.1007/s11705-021-2071-9

Abstract: In this context, several approaches for superstructure optimization based on different surrogate modelsThe results indicate that even though surrogate-based optimization approaches alleviate the underlyingThe development of appropriate surrogate models, comprising the selection of surrogate type, samplingThese findings invite for a critical assessment of surrogate-based optimization approaches and point

Keywords： biotechnology     surrogate modelling     superstructure optimization     simulation-based optimization     process    

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A surrogate-based optimization algorithm for network design problems Article

Meng LI, Xi LIN, Xi-qun CHEN

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1693-1704 doi: 10.1631/FITEE.1601403

Abstract: We adopt a surrogate-based optimization (SBO) framework to solve three featured categories of NDPs (continuous

Keywords： Network design problem     Surrogate-based optimization     Transportation planning     Heuristics    

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M-LFM: a multi-level fusion modeling method for shape−performance integrated digital twin of complex structure

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 4, doi: 10.1007/s11465-022-0708-0

Abstract: To leverage its capacity, the M-LFM method combines the advantages of different surrogate models and

Keywords： shape−performance integrated digital twin (SPI-DT)     multi-level fusion modeling (M-LFM)     surrogate model    

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Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design Perspective

Teng Zhou, Rafiqul Gani, Kai Sundmacher

Engineering 2021, Volume 7, Issue 9,   Pages 1231-1238 doi: 10.1016/j.eng.2020.12.022

Abstract:

The world’s increasing population requires the process industry to produce food, fuels, chemicals, and consumer products in a more efficient and sustainable way. Functional process materials lie at the heart of this challenge. Traditionally, new advanced materials are found empirically or through trial-and-error approaches. As theoretical methods and associated tools are being continuously improved and computer power has reached a high level, it is now efficient and popular to use computational methods to guide material selection and design. Due to the strong interaction between material selection and the operation of the process in which the material is used, it is essential to perform material and process design simultaneously. Despite this significant connection, the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required. Hybrid modeling provides a promising option to tackle such complex design problems. In hybrid modeling, the material properties, which are computationally expensive to obtain, are described by data-driven models, while the well-known process-related principles are represented by mechanistic models. This article highlights the significance of hybrid modeling in multiscale material and process design. The generic design methodology is first introduced. Six important application areas are then selected: four from the chemical engineering field and two from the energy systems engineering domain. For each selected area, state-ofthe- art work using hybrid modeling for multiscale material and process design is discussed. Concluding remarks are provided at the end, and current limitations and future opportunities are pointed out.

Keywords： Data-driven     Surrogate model     Machine learning     Hybrid modeling     Material design     Process optimization    

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An experimental study on spray auto-ignition of RP-3 jet fuel and its surrogates

Yaozong DUAN, Wang LIU, Zhen HUANG, Dong HAN

Frontiers in Energy 2021, Volume 15, Issue 2,   Pages 396-404 doi: 10.1007/s11708-020-0715-y

Abstract: Surrogate fuel is usually used for fundamental combustion study due to the complex composition of practicalAs for jet fuels, two-component or three-component surrogate is usually selected to emulate practicalSurrogate 1 and Surrogate 2 possess the same components, but their blending percentages are different, as the two surrogates were designed to capture the H/C ratio (Surrogate 1) and DCN (Surrogate 2) ofSurrogate 3 could emulate more physiochemical properties of RP-3 jet fuel, including molecular weight

Keywords： RP-3 jet fuel     surrogate     spray auto-ignition     constant volume combustion chamber    

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Tall Buildings with Dynamic Facade Under Winds Article

Fei Ding, Ahsan Kareem

Engineering 2020, Volume 6, Issue 12,   Pages 1443-1453 doi: 10.1016/j.eng.2020.07.020

Abstract:

Burgeoning growth of tall buildings in urban areas around the world is placing new demands on their performance under winds. This involves selection of the building form that minimizes wind loads and structural topologies that efficiently transfer loads. Current practice is to search for optimal shapes, but this limits buildings with static or fixed form. Aerodynamic shape tailoring that consists of modifying the external form of the building has shown great promise in reducing wind loads and associated structural motions as reflected in the design of Taipei 101 and Burj Khalifa. In these buildings, corner modifications of the cross-section and tapering along the height are introduced. An appealing alternative is to design a building that can adapt its form to the changing complex wind environment in urban areas with clusters of tall buildings, i.e., by implementing a dynamic facade. To leap beyond the static shape optimization, autonomous dynamic morphing of the building shape is advanced in this study, which is implemented through a cyber–physical system that fuses together sensing, computing, actuating and engineering informatics. This approach will permit a building to intelligently morph its profile to minimize the source of dynamic wind load excitation, and holds the promise of revolutionizing tall buildings from conventional static to dynamic facades by taking advantage of the burgeoning advances in computational design.

Keywords： buildings     Aerodynamic shape tailoring     Autonomous morphing     Cyber–physical system     Computational design     Surrogate    

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Standard model of knowledge representation

Wensheng YIN

Frontiers of Mechanical Engineering 2016, Volume 11, Issue 3,   Pages 275-288 doi: 10.1007/s11465-016-0372-3

Abstract: methods include predicate logic, semantic network, computer programming language, database, mathematical modelintrinsic link between various knowledge representation methods, a unified knowledge representation modelAccording to ontology, system theory, and control theory, a standard model of knowledge representationThe model is composed of input, processing, and output.In addition, the standard model of knowledge representation provides a way to solve problems of non-precision

Keywords： knowledge representation     standard model     ontology     system theory     control theory     multidimensional representation    

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Title Author Date Type Operation

Reliability-based design optimization of offshore wind turbine support structures using RBF surrogatemodel

Journal Article

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Journal Article

A surrogate model for uncertainty quantification and global sensitivity analysis of nonlinear large-scale

Journal Article

Method for solving the nonlinear inverse problem in gas face seal diagnosis based on surrogate models

Journal Article

A deep neural network based surrogate model for damage identification in full-scale structures with incomplete

Journal Article

double-layer barrel vaults using genetic and pattern search algorithms and optimized neural network as surrogatemodel

Journal Article

approach for structural damage detection using meta-heuristic algorithms and group method of data handling surrogatemodel

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

Journal Article

Synergistic optimization framework for the process synthesis and design of biorefineries

Journal Article

A surrogate-based optimization algorithm for network design problems

Meng LI, Xi LIN, Xi-qun CHEN

Journal Article

M-LFM: a multi-level fusion modeling method for shape−performance integrated digital twin of complex structure

Journal Article

Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

Teng Zhou, Rafiqul Gani, Kai Sundmacher

Journal Article

An experimental study on spray auto-ignition of RP-3 jet fuel and its surrogates

Yaozong DUAN, Wang LIU, Zhen HUANG, Dong HAN

Journal Article

Tall Buildings with Dynamic Facade Under Winds

Fei Ding, Ahsan Kareem

Journal Article

Standard model of knowledge representation

Wensheng YIN

Journal Article

Zhang Zhengyan: Pre Training Language Model Integrating Knowledge (2020-4-3)

18 Apr 2022

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