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IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Afram, Abdul Janabi-Sharifi, Farrokh Giorgio, Giuseppe Ryerson Univ Dept Mech & Ind Engn Toronto ON Canada Aalborg Univ Dept Elect Syst Aalborg Denmark

ISBN: (纸本)9781479930999

In this paper data-driven model of thermal energy storage tank also known as buffer tank is developed using the measured data from Toronto and Region Conservation Authority (TRCA) Archetype House. Radiant floor heating (RFH) system uses hot water stored in the tank to supply heat to a zone during the heating season. The tank temperature is maintained by a ground source heat pump (GSHP). During the charging mode, GSHP supplies hot water to the buffer tank which raises the temperature of the water inside the tank. During the discharging mode, the hot water is supplied to the RFH system and cold water is returned to the buffer tank which lowers its temperature. The purpose of modeling is to determine the buffer tank temperature during the charging, simultaneous charging-discharging and discharging modes. First order linear models were developed by decoupling these modes. A good fit was found between the measured and predicted tank temperature.

关键词： data-driven modeling validation radiant floor heating thermal storage tank buffer tank ground source heat pump

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modeling spatial patterns in a moving crowd of people using data-driven approach-A concept of Interplay Floor Field

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SAFETY SCIENCE 2023年第1期167卷

作者： Porzycki, Jakub Was, Jaroslaw Ailleron SA Krakow Poland AGH Univ Sci & Technol Krakow Poland

On the basis of data-driven methodology, especially velocity correlations of pedestrians moving in a crowd, we have proposed a new model of pedestrian dynamics with an easy-adjustable space discretization. The model is based on Cellular Automata (CA) with an adaptive lattice and takes into account proxemics patterns among pedestrians. The proposed model uses agents located on the CA lattice, constructed using the concept of Floor Field (FF), namely, a set of gradient potential fields influencing the movement of agents. In the model, we have proposed three kinds of such fields: Static FF-responsible for navigation to agents' Points of Interest (POIs), Wall FF-a repulsive influence with obstacles, and Interplay FF-to model agents' volume and proxemics effects. The third field, which models mutual relations between pedestrians taking into account the rules of proxemics is an added value in the area of crowd modeling and *** the creation of the proposed model, we have based on a set of experimental data provided by Julich Supercomputing Centre and our previous analysis of spatial patterns and velocity correlations. Thus, the proposed model includes, among other things the most probable positions of the nearest neighbors in a crowd-including the shape of "the forbidden zone"around a pedestrian and the finding of rules explaining observed mutual spatial relations between neighbors in a crowd. In the described model the cell size is an adjustable parameter, with available values from 1 cm to 50 cm. It is possible with the usage of embodied agents which always occupy only one cell of Cellular Automata, actual pedestrian volume is modeled with the Interplay FF. An interesting property of the model is that changing the cell size does not involve changing the rules of the model, e.g. its transition function.

关键词： Crowd modeling Social distances Cellular Automata Floor Field data-driven modeling Proxemics

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data-driven Distributed Spectrum Estimation for Linear Time-Invariant Systems

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 1125-1136页

作者： Liu, Shenyu Cortes, Jorge Martinez, Sonia Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Univ Calif San Diego Dept Aerosp & Mech Engn San Diego CA 92093 USA

This article tackles spectrum estimation of a linear time-invariant system by a multiagent network using data. We consider a group of agents that communicate over a strongly connected, aperiodic graph and do not have any knowledge of the system dynamics. Each agent only measures some signals that are linear functions of the system states or inputs, and does not know the functional form of this dependence. The proposed distributed algorithm consists of two steps that rely on the collected data: first, the identification of an unforced trajectory of the system, and second, the estimation of the coefficients of the characteristic polynomial of the system matrix using this unforced trajectory. We show that each step can be formulated as a problem of finding a common solution to a set of linear algebraic equations, which are amenable to distributed algorithmic solutions. We prove that under mild assumptions on the collected data, when the initial condition of the system is random, the proposed distributed algorithm accurately estimates the spectrum with probability 1.

关键词： Spectral analysis Distributed algorithms Linear systems Estimation System identification Signal processing algorithms Trajectory data-driven modeling discrete-time systems distributed processing system identification time invariant systems

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data-driven RANS Turbulence Closures for Forced Convection Flow in Reactor Downcomer Geometry

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NUCLEAR TECHNOLOGY 2024年第7期210卷 1167-1184页

作者： Iskhakov, Arsen S. Tai, Cheng-Kai Bolotnov, Igor A. Nguyen, Tri Merzari, Elia Shaver, Dillon R. Dinh, Nam T. North Carolina State Univ Dept Nucl Engn Raleigh NC 27607 USA Penn State Univ Dept Nucl Engn State Coll PA USA Argonne Natl Lab Nucl Sci & Engn Div Lemont IL USA

Recent progress in data-driven turbulence modeling has shown its potential to enhance or replace traditional equation-based Reynolds-averaged Navier-Stokes (RANS) turbulence models. This work utilizes invariant neural network (NN) architectures to model Reynolds stresses and turbulent heat fluxes in forced convection flows (when the models can be decoupled). As the considered flow is statistically one dimensional, the invariant NN architecture for the Reynolds stress model reduces to the linear eddy viscosity model. To develop the data-driven models, direct numerical and RANS simulations in vertical planar channel geometry mimicking a part of the reactor downcomer are performed. Different conditions and fluids relevant to advanced reactors (sodium, lead, unitary-Prandtl number fluid, and molten salt) constitute the training database. The models enabled accurate predictions of velocity and temperature, and compared to the baseline k -tau turbulence model with the simple gradient diffusion hypothesis, do not require tuning of the turbulent Prandtl number. The data-driven framework is implemented in the open-source graphics processing unit-accelerated spectral element solver nekRS and has shown the potential for future developments and consideration of more complex mixed convection flows.

关键词： Machine learning turbulence modeling forced convection low- and high-Prandtl fluids data-driven modeling

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data-driven Event-Triggered Sliding Mode Secure Control for Autonomous Vehicles Under Actuator Attacks

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第1期55卷 436-446页

作者： Sun, Hong-Tao Chen, Xinran Zhang, Zhengqiang Ge, Xiaohua Peng, Chen Qufu Normal Univ Coll Engn Rizhao 276826 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia Shanghai Univ Sch Mechatron Engn & Automat Dept Automat Shanghai 200444 Peoples R China Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic Australia

This article investigates a comprehensive data-driven event-triggered secure lateral control of autonomous vehicles under actuator attacks. We consider stabilization issues of autonomous vehicles subject to modeling difficulties, limited communication resources, and actuator attacks. The dynamic model decomposition (DMD) from data is exploited to characterize the inherent lateral dynamics model of autonomous vehicles, the event-triggered transmission scheme is utilized to alleviate communication burden for limited bandwidth network, and the sliding mode control scheme is designed to ensure the security of autonomous vehicles under actuator attacks. The stability analysis and the stabilization method as well as its algorithm are presented. The proposed secure control scheme can actively counteract the malicious effects caused by actuator attacks and integrates the advantages of both data-driven modeling and model-based control design. Finally, several comparative case studies show the effectiveness of the proposed secure control scheme.

关键词： Autonomous vehicles data-driven modeling event-triggered scheme secure control

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Sampling Performance of Periodic Event-Triggered Control Systems: A data-driven Approach

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 800-811页

作者： Peruffo, Andrea Mazo, Manuel Delft Univ Technol Fac Mech Engn Delft Netherlands

In this article, we employ the scenario optimization theory to compute a traffic abstraction, with probability guarantees of correctness, of a periodic event-triggered control (PETC) system with unknown dynamics from a finite number of samples. To this end, we extend the scenario optimization approach to multiclass support vector machines in order to compute a map between the concrete state space and the intersample times of the PETC. This map allows the construction of a traffic abstraction, through an $\ell$-complete relation, that provides upper and lower bounds on the sampling performance of the concrete system. We further propose an alternative path to build such abstraction;first, we identify the model and then apply a model-based procedure. Numerical benchmarks show the practical applicability of our methods for noiseless and noisy samples.

关键词： Control systems Picture archiving and communication systems Numerical models Network systems Computational modeling Optimization Mathematical models data-driven modeling discrete-event systems system verification

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A data-driven framework for learning governing equations of Li-ion batteries and co-estimating voltage and state-of-charge

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JOURNAL OF ENERGY STORAGE 2024年 84卷

作者： Ahmadzadeh, Omidreza Wang, Yan Soudbakhsh, Damoon Temple Univ Mech Engn Dept Philadelphia PA 19122 USA Ford Motor Co Dearborn MI USA

This paper presents a reduced -order nonlinear model for Lithium -ion batteries (LiBs). Unlike mechanistic models, data -driven models offer accurate representations of system dynamics without relying on in -situ measurements and proprietary information. However, these models may perform poorly in unseen scenarios due to overfitting training data, which is typical. We propose a physics -inspired, data -driven approach to determine LiBs governing equations based on their electrochemistry rather than generic terms. We employ a sparse identification method achieved through sequentially thresholding ridge regression to construct a nonlinear model from electrical current (excitation input) and measured voltage. We formulate the problem to optimize the sparsification parameters as hyperparameters and minimize a cost function comprised of training and validation sets and the number of terms as a measure of complexity. We augment the model with a joint unscented Kalman filter to handle noisy experimental data, enabling a more accurate estimate of the state of charge (SOC) and voltage. Model performance in unseen scenarios is evaluated with urban dynamometer driving schedule (UDDS) data, where the identified model achieves a root mean square error of 1.26e-2 for SOC and voltage prediction.

关键词： Energy storage Sparse modeling SINDyC Joint unscented Kalman filter SOC prediction data-driven modeling

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Experimentation in Exploring Photovoltaic Inverter Dynamics Under Different Irradiance Levels Through a data-driven Approach

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IEEE ACCESS 2024年 12卷 164137-164150页

作者： Poudel, Bidur Guruwacharya, Nischal Subedi, Sunil Tamrakar, Ujjwol Wilches-Bernal, Felipe Rekabdarkolaee, Hossein Moradi Hansen, Timothy M. Tonkoski, Reinaldo South Dakota State Univ Dept Elect Engn & Comp Sci Brookings SD 57007 USA Natl Renewable Energy Lab Golden CO 80401 USA Oak Ridge Natl Lab Oak Ridge TN 37830 USA Sandia Natl Labs Albuquerque NM 87123 USA South Dakota State Univ Dept Math & Stat Brookings SD 57007 USA Tech Univ Munich Elect Power Transmiss & Distribut D-80333 Munich Germany

As conventional direct connections of synchronous generators are being phased out, inverter-based resources (IBRs) with grid support functions are increasingly being integrated into power systems. This transition requires the development of accurate dynamic models for IBRs to predict how power systems will adapt to varying levels of IBRs penetration, establish grid code requirements, and ensure compliance. This study introduces an active probing signal-based data-driven modeling technique to accurately derive the dynamics model of a smart photovoltaic inverter operating in Volt-Watt and Freq-Watt modes, in compliance with the IEEE 1547-2018 standard. The paper focuses on investigating how the dynamics of the PV inverter model respond to fluctuations in solar irradiance, utilizing real-time digital simulator experimentation. The experimental analysis demonstrates that the amplitude of dynamics fluctuates with changes in irradiance across both operational modes and confirms the active power's dependence on irradiance levels. Furthermore, the nature of inverter dynamics varies distinctly between the different modes of activation. Critically, our findings indicate that dynamic models require DC-gain adjustments to accommodate contrasting irradiance levels, highlighting a negative gradient linear relationship between the DC-gain of each model and the irradiance.

关键词： Inverters Mathematical models Computational modeling Power system dynamics Chirp Voltage measurement Standards Adaptation models Power system stability data models data-driven modeling IEEE 1547-2018 PV inverter dynamics real-time digital simulator solar irradiance

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data-driven machine learning models for predicting deliverability of underground natural gas storage in aquifer and depleted reservoirs

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ENERGY 2025年 319卷

作者： Hussain, Altaf Pan, Peng-Zhi Hussain, Javid Feng, Yujie Zheng, Qingsong Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Safety Wuhan 430071 Peoples R China Univ Chinese Acad Sci Beijing 100043 Peoples R China China Pakistan Joint Res Ctr Earth Sci Islamabad 45320 Pakistan Hubei Key Lab Geoenvironm Engn Wuhan 430071 Peoples R China

Subsurface parameters, like geological formations and fluid dynamics, are vital for evaluating connectivity in aquifers and depleted reservoirs. Accurate models predicting Underground Natural Gas Storage (UNGS) deliverability are crucial for stakeholders due to demand-supply inconsistencies but remain complex and underexplored. Therefore, in this study, machine learning (ML) models were employed, particularly Artificial Neural Networks (ANNs) optimized with;the Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG), to predict the deliverability of UNGS in the aquifer and depleted reservoirs. The prediction capacities were assessed using 2084 field storage data points from 78 operational reservoirs in the United States. Performance evaluation based on root mean square error (RMSE) and coefficient of determination (R) showcases the superior predictive precision of the BR algorithm, accomplishing RMSE values between (0.010-0.0496) and R values of (0.909-0.997). Moreover, the Regression Error Characteristic (REC) curve and Taylor diagram both indicate that the BR model significantly outperforms the LM and SCG models in terms of accuracy and reliability. Furthermore, validation has been performed using datasets from active aquifers and depleted reservoirs in 2024, which showed good accuracy and reliability. Hence, the proposed ML models effectively forecast UNGS deliverability and provide clear insights for informed decision-making.

关键词： Aquifer and depleted reservoirs data-driven modeling Deliverability ANN Underground natural gas storage

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Framework for data-driven propeller and rudder modeling for ship maneuvering

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OCEAN ENGINEERING 2022年 263卷

作者： Knight, Bradford G. Maki, Kevin J. Univ Michigan 2600 Draper Dr Ann Arbor MI 48109 USA

The maneuvering characteristics of a vessel must be understood for safe and efficient operation in the ocean environment. During a maneuver, the propeller and rudder can operate in off-design conditions. Computational Fluid Dynamics is a useful tool to capture flow effects like viscous separation, but it is too costly to perform a large number of maneuvering simulations with a discretized propeller. The propeller has much smaller time and length scales compared to the maneuver, which drives up the computational cost when the propeller is discretized. Body-force propeller models provide a less expensive means of modeling a maneuvering vessel with CFD, but many propeller models can be inaccurate when the propeller operates off-design. This work develops a framework for a data-driven propeller and rudder model which is trained with high-fidelity simulations of the propeller and deflected rudder operating in the wake of the hull. The dimensionless velocity and oblique flow angle are used as features for the propeller and rudder model. The uncertainty of the data-driven model is dependent upon the error inherent in the training and validation data as well as the uncertainty induced by using a surrogate model to determine the response surface. The underlying uncertainties are examined. The calculated propeller and rudder forces from the data-driven model are compared to the experimental forces. The turning circle characteristics of the KRISO Container ship at model scale are examined with the implemented data-driven propeller and rudder model. The accuracy of the method used to train the model is maintained and the computational cost of modeling a maneuvering vessel is dramatically reduced compared to using a discretized propeller.

关键词： Computational Fluid Dynamics data-driven modeling Propeller model Rudder model Ship maneuvering

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