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A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation

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ENGINEERING WITH COMPUTERS 2023年第6期39卷 3895-3926页

作者： Fu, Jinlong Wang, Min Chen, Bin Wang, Jinsheng Xiao, Dunhui Luo, Min Evans, Ben Swansea Univ Zienkiewicz Inst Modelling Data & AI Fac Sci & Engn Swansea SA1 Wales Los Alamos Natl Lab Theoret Div Fluid Dynam & Solid Mech Grp Los Alamos NM 87545 USA Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Southwest Jiaotong Univ Sch Civil Engn Chengdu 610031 Peoples R China Tongji Univ Sch Math Sci Shanghai 200092 Peoples R China Zhejiang Univ Ocean Coll Zhoushan 316021 Zhejiang Peoples R China

Understanding the microstructure-property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure-property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure-permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. High-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are

关键词： Porous rocks Permeability prediction Microstructural characterization Lattice Boltzmann simulation Feature selection data-driven modeling

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Water quality prediction using data-driven models case study: Ardabil plain, Iran

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SOFT COMPUTING 2023年第11期27卷 7439-7448页

作者： Kashani, Mahsa Hasanpour Nikpour, Mohammad Reza Jalali, Reza Univ Mohaghegh Ardabili Fac Agr & Nat Resources Water Management Res Ctr Dept Water Engn Ardebil Iran Univ Mohaghegh Ardabili Fac Agr & Nat Resources Dept Water Engn Ardebil Iran

This research investigates the capabilities of four data-driven models, namely artificial neural network, wavelet-based artificial neural network (WANN), gene expression programming, and support vector machine, to predict water quality parameter, i.e., sodium absorption ratio (SAR) of the groundwater of the Ardabil plain, Ardabil Province, Iran. A combination of data sets including electrical conductivity, total dissolved solids, and sodium was considered as input data of the models. The models' performances were compared with the classical multiple linear regression model. The models were calibrated and validated using the measured quality data for the period of year 2005 to 2015. The performance of the models was evaluated using various statistical criteria, scatter, and PDF plots. Results showed that the data-driven models are more capable in simulating the SAR data than the classical multiple linear regression model. The WANN model provided reliable predictions of the SAR values rather than the other models.

关键词： data-driven modeling Classical model Water quality parameters Groundwater Iran

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A data-driven digital twin framework for key performance indicators in CNC machining processes

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2023年第12期36卷 1823-1841页

作者： Vishnu, V. S. Varghese, Kiran George Gurumoorthy, B. Indian Inst Sci Ctr Prod Design & Mfg Bangalore India Hindustan Aeronaut Ltd Rotary Wing Res & Design Ctr Bangalore India Indian Inst Sci Ctr Prod Design & Mfg Bangalore 560012 Karnataka India

This paper presents a data-driven digital twin (DT) framework that predicts key performance indicators (KPIs) in a CNC machining environment. The decision-makers can use these predicted KPIs in the CNC machining process flow to better choose cutting parameters to accomplish the required KPIs. Those beneficiaries would be the process planner in the process planning stage and the machine operator in the machining stage. The cutting parameters affect major performance KPIs such as machining time, quality, and energy consumption. So, correctly selected cutting parameters can improve KPIs in CNC machining operations. In this paper, the two KPIs considered for building predictive models, and their application in the proposed DT with experimental data are energy and surface roughness. The data for building the predictive models for a CNC milling process are obtained through experiments. This work also illustrates the choice of predictive modelling methods in both the stages of CNC machining and its outcomes.

关键词： Digital-twin data-driven modeling CNC machining

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data-driven Dynamic Motion Planning for Practical FES-Controlled Reaching Motions in Spinal Cord Injury

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 2246-2256页

作者： Wolf, Derek N. N. van den Bogert, Antonie J. J. Schearer, Eric M. M. Vanderbilt Univ Dept Mech Engn Nashville TN 37240 USA Cleveland State Univ Ctr Human Machine Syst Cleveland OH 44115 USA Metrohlth Med Ctr Dept Phys Med & Rehabil Cleveland OH 44109 USA

Functional electrical stimulation (FES) is a promising technology for restoring reaching motions to individuals with upper-limb paralysis caused by a spinal cord injury (SCI). However, the limited muscle capabilities of an individual with SCI have made achieving FES-driven reaching difficult. We developed a novel trajectory optimization method that used experimentally measured muscle capability data to find feasible reaching trajectories. In a simulation based on a real-life individual with SCI, we compared our method to attempting to follow naive direct-to-target paths. We tested our trajectory planner with three control structures that are commonly used in applied FES: feedback, feedforward-feedback, and model predictive control. Overall, trajectory optimization improved the ability to reach targets and improved the accuracy for the feedforward-feedback and model predictive controllers (p < 0.001). The trajectory optimization method should be practically implemented to improve the FES-driven reaching performance.

关键词： Muscles Shoulder Mathematical models Robots Torque Trajectory optimization Iron neuroprostheses neuromuscular stimulation data-driven modeling

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data-driven Short-Term Forecasting of Residential Building Energy Demand: A Case Study 9th

Data-Driven Short-Term Forecasting of Residential Building E...

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9th International Building Physics Conference

作者： Zygmunt, Marcin Gawin, Dariusz Lodz Univ Technol Dept Bldg Mat Phys & Sustainable Design Politech 6 PL-90924 Lodz Poland Katholieke Univ Leuven Dept Civil Engn Bldg Phys & Sustainable Design Kasteelpk Arenberg 40 B-3001 Leuven Belgium

ISBN: (纸本)9789819783113;9789819783090;9789819783083

The energy consumption from buildings in Poland, as well as the corresponding CO2 emissions, are high. A significant potential for energy-efficiency improvement for buildings can be achieved either by long- or short-term strategies. Long-term actions, in particular building refurbishments, are effective approaches to obtain significant energy savings;yet a huge financial contribution is required. On the other hand, short-term methods can be considered as less expensive strategies for energy efficiency and sustainability improvement of buildings. Energy demand control and optimization, and constant energy consumption monitoring stood among other easy-to-apply strategies. The data-driven short-term forecasting of energy demand for heating and cooling for a case-study residential single-family house in Poland is presented. The analyzed buildingwas examined on-site to evaluate its performance and characteristics. The performed measurements were used as input data for building energy modeling (BEM) by means of the Energy Plus software. The white-box (WB) method was applied to generate a time-series database, further used for predictions of future energy consumption. This paper presents an approach for short-term forecasts based on the autoregressive (AR) model. The predicted time-series of operation loads for the examined single-family house assumes that the quality of the indoor climate is not compromised. Additionally, this study skipped the unpredictable human-related factors: residents' behavior is assumed as a pattern. Various climate data for the selected localizations of Poland are examined as the critical factor affecting building energy consumption. The examination of the forecasting errors is carried out, compared with the outputs from theWB model. This work aims to evaluate the accuracy of predictions regarding the energy response of the building for different climate conditions.

关键词： Building Energy modeling data-driven modeling Short-Term Forecasting Energy Efficiency

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A Novel data-driven Approach to Analysis and Optimal Design of Forced Periodic Operation of Chemical Reactions

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第8期70卷 8365-8376页

作者： Dong, Yuhan Lang, Zi-Qiang Zhao, Jun Wang, Wei Lan, Zhong Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England East China Univ Sci & Technol Shanghai 200231 Peoples R China Dalian Univ Technol Inst Chem Engn Dalian 116024 Peoples R China

Forced periodic operation is a technique that periodically changes the manipulating variable of a chemical reaction system in order to exploit nonlinear dynamics to improve reactant conversion rate. However, the analysis and design of a periodically operated chemical process is a significant challenge. To resolve this problem, recently, nonlinear frequency response (NFR) based methods have been proposed. However, because of the need to derive the NFR from a first principle model, existing NFR methods can only perform qualitative analysis to simple processes and are often difficult to be applied in engineering practice. This article proposes a novel data driven approach to the analysis and optimal design of forced periodic operation of chemical reactions. From the data generated numerically using the first principle model or experimentally from experimental tests, the approach produces a data-driven NFR model that can readily be used for both quantitative study and optimal design of forced periodic operation of any complexities. This can fundamentally address the challenges faced by the existing NFR methods, and provides an effective approach that can potentially be applied in engineering practice. Simulation studies and experimental works are carried out on the application of the new method to an isothermal continuous stirred tank reactor system and a laboratory-scale carbon dioxide absorption process, respectively. The results verify the effectiveness and advantage of the newly proposed data driven approach and demonstrate the potential of the new approach in engineering applications.

关键词： Chemicals Chemical reactions Steady-state Analytical models Chemical processes Production Mathematical models Analysis and optimal design data-driven modeling forced periodic operation nonlinear chemical system

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On the Certainty-Equivalence Approach to Direct data-driven LQR Design

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第12期68卷 7989-7996页

作者： Dorfler, Florian Tesi, Pietro De Persis, Claudio Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland Univ Florence Dept Informat Engn I-50139 Florence Italy Univ Groningen Engn & Technol Inst Groningen NL-8092 Groningen Netherlands

The linear quadratic regulator (LQR) problem is a cornerstone of automatic control, and it has been widely studied in the data-driven setting. The various data-driven approaches can be classified as indirect (i.e., based on an identified model) versus direct or as robust (i.e., taking uncertainty into account) versus certainty-equivalence. Here, we show how to bridge these different formulations and propose a novel, direct, and regularized formulation. We start from indirect certainty-equivalence LQR, i.e., least-square identification of state-space matrices followed by a nominal model-based design, formalized as a bilevel program. We show how to transform this problem into a single-level, regularized, and direct data-driven control formulation, where the regularizer accounts for the least-square data fitting criterion. For this novel formulation, we carry out a robustness and performance analysis in presence of noisy data. In a numerical case study, we compare regularizers promoting either robustness or certainty-equivalence, and we demonstrate the remarkable performance when blending both of them.

关键词： data-driven modeling linear feedback control systems optimal control

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data-driven prediction of temperature variations in an open cathode proton exchange membrane fuel cell stack using Koopman operator

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Energy and AI 2023年第4期14卷 439-450页

作者： Da Huo Carrie M.Hall Mechanical Materialsand Aerospace Engineering DepartmentIllinois Institute of Technology10 West 35th StreetChicago60616ILUnited States

In this study,a novel application of the Koopman operator for control-oriented modeling of proton exchange membrane fuel cell(PEMFC)stacks is *** primary contributions of this paper are:(1)the design of Koopman-based models for a fuel cell stack,incorporating K-fold cross-validation,varying lifted dimensions,radial basis functions(RBFs),and prediction horizons;and(2)comparison of the performance of Koopman-based approach with a more traditional physics-based *** results demonstrate the high accuracy of the Koopman-based model in predicting fuel cell stack behavior,with an error of less than 3%.The proposed approach offers several advantages,including enhanced computational efficiency,reduced computational burden,and improved *** study demonstrates the suitability of the Koopman operator for the modeling and control of PEMFCs and provides valuable insights into a novel control-oriented modeling approach that enables accurate and efficient predictions for fuel cell stacks.

关键词： Proton exchange membrane fuel cell(PEMFC) data-driven modeling Koopman operator Dynamic modeling Control-oriented modeling Physics-based modeling

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Optimization of dome shape for filament wound pressure vessels using data-driven evolutionary algorithms

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MATERIALS AND MANUFACTURING PROCESSES 2023年第15期38卷 1899-1910页

作者： Vondracek, Dominik Padovec, Zdenek Mares, Tomas Chakraborti, Nirupam Czech Tech Univ Fac Mech Engn Dept Mech Biomech & Mechatron Prague Czech Republic

Filament wound glass/epoxy and carbon/epoxy pressure vessels were designed using a data-driven evolutionary approach along with a classical mechanics-based analysis. Analytical solutions for three known end domes (a spherical shell, a geodesic-isotensoid shell and a shell based on minimizing of the Tsai-Hill's criterion) and end dome based on Hoffman's criterion were used to create surrogate models through two different evolutionary optimization algorithms: Evolutionary Neural Net (EvoNN) and Bi-objective Genetic Programming (BioGP) and bi-objective evolutionary optimization studies were carried out using them. Composite pressure vessels are manufactured by means of filament (helical) winding. The stresses in the end domes were evaluated analytically and compared with each other. Hoffman strength criterion was chosen for the evaluation of the critical areas of the shells. Moreover, the best dome shape for given material and polar hole/equator ratio was selected for various types of failure.

关键词： Pressure vessels end dome helical winding composite material evolutionary computation genetic algorithms data-driven modeling multi-objective optimization EvoNN BioGP

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Comparison of data-driven methods in the prediction of hydro-socioeconomic parameters

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AQUA-WATER INFRASTRUCTURE ECOSYSTEMS AND SOCIETY 2023年第4期72卷 438-455页

作者： Rezaee, Alireza Bozorg-Haddad, Omid Chu, Xuefeng Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Karaj Alborz Iran North Dakota State Univ Dept Civil Construct & Environm Engn Dept 2470 Fargo ND 58108 USA

One of the weaknesses of water resources management is the neglect of the nonstructural aspects that involve the most important relationships between water resources and socioeconomic parameters. Particularly, socioeconomic evaluation for different regions is crucial before implementing water resources management policies. To address this issue, 14 countries in the world that have continuous increasing trends of using renewable water per capita (RWPC) during 1998-2017 were used for the estimation of eight socioeconomic parameters associated with four key indicators (i.e., economy, demographics, technology communication, and health sanitation) by using four different data-driven methods, including artificial neural networks, support vector machines (SVMs), gene expression programming (GEP), and wavelet-gene expression programming (WGEP). The performances of the models were evaluated by using correlation coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE). It was found that the WGEP model had the best performance in estimating all parameters. The mathematical expressions for these socioeconomic parameters were explored and their potential to be expanded in different spatial and temporal dimensions was assessed. The derived equations provide a quantitative means for the future estimation of the socioeconomic parameters in the studied countries.

关键词： artificial neural networks data-driven modeling renewable water resources socioeconomic parameters water availability wavelet-GEP models

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