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data-driven modeling for rapid prediction of aerodynamic noise directivity of flow over a cylinder

APPLIED MATHEMATICAL MODELLING 2025年 141卷

作者： Jin, Yao Liao, Fei Cai, Jinsheng Xian Univ Architecture & Technol Sch Bldg Serv Sci & Engn Xian 710055 Peoples R China Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Natl Key Lab Aircraft Configurat Design Xian 710072 Peoples R China

Conventional high-fidelity simulations are prohibitively expensive for aerodynamic noise optimization and control applications. Developing surrogate models of minimal computational costs provide efficient alternatives in noise estimation for various numerical conditions. In the present work, three data-driven acoustic directivity modeling strategies are proposed, including a purely data-driven POD model and two semi-analytical data-driven models: force-model and dipole- model. All the three models significantly accelerate the prediction of noise directivity, which is attributed to their independence of conventional numerical approaches, despite with minor discrepancies in accuracy among each other. The POD-model is constructed based on the singular vector decomposition and an interpolation method, enabling high accuracy but requiring a large amount of training data. A sensitivity study is conducted on four parametric aspects, such as mode number, interpolation method, inherent flow nonlinearity in wake region, and the distribution of interpolation states in training datasets. The force-model, derived from the Ffowcs Williams-Hawkings equation, requires force fluctuations on the body surfaces. Also the dipole-model, leveraging the analytical formulation of dipole velocity potential, requires a careful specification of potential coefficient. These required information in force-model and dipole-model are directly approximated by data regression methods. Compared to POD-model, the notable merit of force-model and dipole-model is that rapid predictions can be implemented with reduced data requirements.

关键词： Aerodynamic noise prediction data-driven modeling POD model Force model Dipole model Efficiency enhancement Cylinder flow

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data-driven modeling and control of oscillatory instabilities in Kolmogorov-like flow

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THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS 2025年第2期39卷 1-15页

作者： Conlin, Nicholas Tithof, Jeffrey Hemati, Maziar S. Univ Minnesota Aerosp Engn & Mech Minneapolis MN 55455 USA Princeton Univ Mech & Aerosp Engn Princeton NJ 08544 USA Univ Minnesota Mech Engn Minneapolis MN 55455 USA

We apply data-driven techniques to construct a nonlinear 3-mode model of a Kolmogorov-like flow transitioning from steady to periodic. data from direct numerical simulation that include features of experimental realizations of Kolmogorov-like flow are used to build the model. Our low-order modeling methodology does not require knowledge of the underlying governing equations. The 3-mode basis for the model is determined solely from data and the sparse identification of nonlinear dynamics framework (SINDy) is used to fit a dynamical system describing modal interactions. We impose constraints within the SINDy framework to ensure the resulting model will possess energy-preserving nonlinear terms that are consistent with the underlying flow physics. We use the low-order model to determine an appropriate equilibrium solution to stabilize, thereby avoiding searching for equilibrium solutions in the full-order system. The model is linearized about the identified equilibrium solution and subsequently used to design feedback controllers that successfully suppress an oscillatory instability when applied in direct numerical simulations-a testament to the model's ability to capture the underlying dynamics that are most relevant for flow control. Our results confirm that low-order models obtained in a purely data-driven framework can be implemented for flow control in experimentally-realizable Kolmogorov-like flow.

关键词： Kolmogorov flow Oscillatory instabilities Flow control data-driven modeling

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data-driven modeling of bifurcation systems by learning the bifurcation parameter generalization

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NONLINEAR DYNAMICS 2025年第2期113卷 1163-1174页

作者： Li, Shanwu Yang, Yongchao Michigan Technol Univ Dept Mech Engn Engn Mech Houghton MI 49931 USA

Nonlinear dynamical systems in such applications as design and control often depend on a set of parameters, resulting in parameterized dynamical systems. Establishing mathematical models of such parameterized systems is essential for numerical simulations, where first-principle models are often not affordable although they are desired whenever possible. data-driven modeling is an important alternative with a trade-off among modeling difficulty, model complexity, and computational efficiency. However, data-driven modeling of such parameterized systems is challenging because not only nonlinear dynamics but also their parametric dependence need to be identified from data;especially for bifurcation systems where small changes in the parameters may cause drastic, qualitative changes of dynamical behaviors. Thus, data-driven modeling of bifurcation systems can be intractable. This work presents a novel method for data-driven modeling of bifurcation systems by transforming the intractable modeling problem into two tractable steps: first, learning an intermediate meta-model that is general for a wide range of bifurcation parameter values;subsequently, using this meta-model to perform efficient adaptation to target/new bifurcation values. Particularly, we leverage the meta-learning to guide the intermediate model to learn to generalize over the bifurcation parameter values, yielding a meta-model which allows a fast and data-efficient adaptation (generalization) to any new bifurcation parameter values. We conduct numerical experiments to validate the presented method on three classic bifurcation systems. It is observed that the adaptation (generalization) to new parameter values enabled by the obtained meta-model is faster than the direct modeling for the new parameter values from scratch. Furthermore, the meta-model-based adaptation yields more accurate models that allow long-term future-state prediction. Finally, we discuss the limitations of this work and potential future

关键词： Parameterized nonlinear systems Bifurcation data-driven modeling Meta-learning Deep neural network

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data-driven modeling of Grid-Forming Inverter Dynamics Using Power Hardware-in-the-Loop Experimentation

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IEEE ACCESS 2024年 12卷 52267-52281页

作者： Guruwacharya, Nischal Chakraborty, Soham Saraswat, Govind Bryce, Richard 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 Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Enphase Energy Austin TX 78758 USA Tech Univ Munich Dept Elect Power Transmiss & Distribut D-80333 Munich Germany

Recently, there is rapid integration of power electronic converter (PECs) into the power grid. Most of these PECs are grid-following inverters, where weak grid operation becomes an issue. Research is now shifting focus to grid-forming (GFM) inverters, resembling synchronous generators. The shift towards converter-based generation necessitates accurate PEC models for assessing system dynamics that were previously ignored in conventional power systems. data-driven modeling (DDM) techniques are becoming valuable tools for capturing the dynamic behavior of advanced control strategies for PECs. This paper proposes using power hardware-in-the-loop experiments to capture dynamic GFM data in the application of DDM techniques. Furthermore, the paper derives an analytical approach to obtaining a mathematical model of GFM inverter dynamics and compares it with the DDM. A square-chirp probing signal was employed to perturb the active and reactive power of the load inside an Opal-RT model. The dynamic response of the GFM inverter, including changes in frequency and voltage, was recorded. This data was then used in a system identification algorithm to derive the GFM DDMs. The effectiveness of DDM is cross-validated with an analytical approach through experimental simulation studies, and the goodness-of-fit for both approaches is compared. Both approaches show more than 85% accuracy in capturing the dynamic response of GFM inverters under different loading conditions.

关键词： data-driven modeling grid-forming inverter power hardware-in-the-loop power system dynamics real-time digital simulator system identification

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data-driven modeling of Microgrid Transient Dynamics Through Modularized Sparse Identification

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2024年第1期15卷 109-122页

作者： Nandakumar, Apoorva Li, Yan Zheng, Honghao Zhao, Junhui Zhao, Dongbo Zhang, Yichen Hong, Tianqi Chen, Bo Penn State Univ Dept Elect Engn University Pk PA 16802 USA Commonwealth Edison Co Chicago IL 60197 USA Eversource Energy Adv Engn Analyt Berlin CT 06037 USA Eaton Corp Global Technol Golden CO 80401 USA Univ Texas Arlington Dept Elect Engn Arlington TX 76010 USA Argonne Natl Lab Lemont IL 60439 USA

Modularized sparse identification (M-SINDy) is developed in this paper for effective data-driven modeling of the nonlinear transient dynamics of microgrid systems. The high penetration of power-electronic interfaces makes microgrids highly susceptible to disturbances, causing severe transients, especially in the islanded mode. The M-SINDy method realizes distributed discovery of nonlinear dynamics by partitioning a higher-order microgrid system into multiple subsystems and introducing pseudo-states to represent the impact of neighboring subsystems. This specific property of the proposed algorithm is found to be very useful while working with re-configurable and scalable microgrids. The governing equations of the subsystems are identified through regression by mapping the nonlinear system's data to a linear system in a large functional space. The effectiveness of the M-SINDy method is tested and validated through numerical examples and comparisons with other existing identification models on a typical islanded microgrid. The method can simultaneously compute the governing equations of different subsystems and is examined to be robust to measurement noises and partial observations. This paper highlights the advances of data science in providing a potent tool for modeling and analyzing higher-order nonlinear microgrid systems. Dynamic discovery of system transients from measurements can be beneficial for designing control strategies that improve the overall microgrid stability and reliability.

关键词： data-driven modeling distributed energy resources (DERs) microgrids modularized design psedo-states sparse identification transient dynamics

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data-driven modeling for electro-active liquid crystal polymer networks

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DISCOVER APPLIED SCIENCES 2025年第1期7卷 1-15页

作者： Amiri, Anahita Shakib, Mohammad Fahim Arteaga, Ines Lopez van de Wouw, Nathan Eindhoven Univ Technol Dept Mech Engn NL-5600 MB Eindhoven Netherlands Imperial Coll London Dept Elect & Elect Engn London England Eindhoven Univ Technol Inst Complex Mol Syst NL-5600 MB Eindhoven Netherlands KTH Royal Inst Technol Dept Engn Mech Marcus Wallenberg Lab Sound & Vibrat MWL S-10044 Stockholm Sweden

In this paper, we propose a data-driven nonlinear modeling approach to describe the dynamics of smart surfaces composed of electroactive liquid crystal networks (LCNs). LCNs are among the top candidates for materials to be employed in smart surfaces such as haptic displays. To realize such applications, the ability to predict an accurate LCN surface response as a function of the input signal is crucial. In this paper, we propose a data-driven modeling approach to identify the parameters of a dynamic model based on experimental data. The resulting model is used for feedforward control to compute the appropriate excitation parameters that ensure a certain desired surface deformation. This feedforward control approach is validated in a simulation study.

关键词： Liquid Crystal Networks (LCNs) Nonlinear system identification data-driven modeling Dynamic models Nonlinear feedforward control Electro-active coatings

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data-driven modeling and fast adjustment for digital coded metasurfaces database: Application in adaptive electromagnetic energy harvesting

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APPLIED ENERGY 2024年 365卷

作者： Liu, Cheng Wang, Wei Wang, Zhixia Ding, Bei Wu, Zhiqiang Feng, Jingjing Tianjin Univ Sch Mech Engn Tianjin 300350 Peoples R China Tianjin Key Lab Nonlinear Dynam & Control Tianjin 300350 Peoples R China Tianjin Univ Technol Tianjin Key Lab Adv Mechatron Syst Design & Intell Tianjin 300384 Peoples R China

Metasurfaces (MSs) show great promise in efficient electromagnetic energy harvesting (EMEH) due to their compactness, high efficiency, and long -distance transmission capabilities. Nonetheless, the conventional iterative and time-consuming solving process of MSs significantly escalates computational demands. Furthermore, once processed, the MS shape remains fixed and cannot be adapted to changing requirements. Accordingly, a critical challenge is the development of a new efficient solver for MS real -time tuning. Here, we introduce a class of digital coded MS databases including multiple pre-defined resonant frequency MS. The combination of multiple MS base functions from the database enables swift resonance frequency adjustments to adapt to changing environmental conditions. A topology optimization method based on data-driven modeling is employed to rapidly acquire the optimal digital coding for the corresponding MS at various operating frequencies, facilitating the construction of a database. This approach integrates a convolutional neural network and genetic algorithm (CNNGA). It not only enables more accurate and expedited forward prediction of MSs' electromagnetic (EM) response but also facilitates inverse design based on specified requirements. We employ this method to design a MS that achieves perfect energy harvesting (EH) over a broad range of incident angles and polarization directions. In addition, a data -driven modeling is used to establish an EH efficiency predictive model corresponding to MS combination. This model serves as a guide for real-time MS adjustments as per changing requirements. Compared to previously designed MSs, this model achieves rapid design and adaptive adjustment capabilities. Through the incorporation of various functional MS base functions into the database, this method can be universally applied to MS combinations tailored to specific functions, including EM cloaking, ultra -thin flat lenses, and computational MSs.

关键词： Digital coded metasurface database data-driven modeling Rapid design Adaptive adjustment

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data-driven modeling With Experimental Augmentation for the Modulation Strategy of the Dual-Active-Bridge Converter

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第3期71卷 2626-2637页

作者： Li, Xinze Pou, Josep Dong, Jiaxin Lin, Fanfan Wen, Changyun Mukherjee, Suvajit Zhang, Xin Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Nanyang Technol Univ Interdisciplinary Grad Sch Singapore 639798 Singapore Rolls Royce NTU Corp Lab Singapore 797565 Singapore Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

For the performance modeling of power converters, the mainstream approaches are essentially knowledge-based, suffering from heavy manpower burden and low modeling accuracy. Recent emerging data-driven techniques greatly relieve human reliance by automatic modeling from simulation data. However, model discrepancy may occur due to unmodeled parasitics, deficient thermal and magnetic models, unpredictable ambient conditions, etc. These inaccurate data-driven models based on pure simulation cannot represent the practical performance in physical world, hindering their applications in power converter modeling. To alleviate model discrepancy and improve accuracy in practice, this article proposes a novel data-driven modeling with experimental augmentation (D(2)EA), leveraging both simulation data and experimental data. In D(2)EA, simulation data aims to establish basic functional landscape, and experimental data focuses on matching actual performance in real world. The D(2)EA approach is instantiated for the efficiency optimization of a hybrid modulation for neutral-point-clamped dual-active-bridge (NPC-DAB) converter. The proposed D(2)EA approach realizes 99.92% efficiency modeling accuracy, and its feasibility is comprehensively validated in 2-kW hardware experiments, where the peak efficiency of 98.45% is attained. Overall, D(2)EA is data-light and can achieve highly accurate and highly practical data-driven models in one shot, and it is scalable to other applications, effortlessly.

关键词： Artificial intelligence data-driven modeling dual-active-bridge (DAB) converter experimental augmentation modulation design neutral-point-clamped converter

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data-driven modeling and integrated optimization of machining quality and energy consumption for internal gear power honing process

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2025年 93卷

作者： Zhang, You Li, Congbo Tang, Ying Cao, Huajun Tao, Guibao Chongqing Univ State Key Lab Mech Transmiss Adv Equipment Chongqing 400044 Peoples R China Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA

The internal gear power honing process is increasingly used in the gear machining of electric vehicles due to superior tooth surface quality. Most of the existing work only investigates the quality improvement of gear machining processes, and focuses little attention on energy saving. However, the total rated power of multi-axis motion for gear honing process reaches 60 kW, which has great energy-saving potential. To this end, this article proposes a data-driven modeling and integrated optimization method of machining quality and energy consumption for internal gear power honing process. The machining quality formation mechanism and energy consumption characteristics of gear honing process are first analyzed. A gradient-enhanced Kriging (GEK) method is then used to establish data-driven tooth profile form deviation model and energy consumption model. Furthermore, an integrated honing process optimization model considering tooth profile form deviation and energy consumption is constructed. An improved multi-objective coati optimization algorithm (IMOCOA) is used to solve the optimization problem. The experimental results show that the R-square of the GEK model reaches 0.99, which has superior modeling accuracy compared with other methods. The optimization results demonstrate that compared with the empirical scheme, the proposed integrated optimization model reduces the tooth profile form deviation and energy consumption by 38.46 % and 10.26 %, respectively. Moreover, the developed IMOCOA also presents competitive algorithm performance. The proposed integrated optimization scheme significantly balances honing machining quality and energy consumption.

关键词： Machining quality Energy consumption data-driven modeling Integrated optimization Internal gear power honing

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data-driven modeling of droop controlled parallel inverters with generalization performance

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JOURNAL OF POWER ELECTRONICS 2024年 1-8页

作者： Wang, Yuzong Tian, Jiangbin Zeng, Guohui Zhao, Jinbin Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai Peoples R China Shanghai Univ Elect Power Engn Res Ctr Offshore Wind Technol Minist Educ Shanghai Peoples R China

In a microgrid inverter parallel operation system, droop control requires less communication between inverters. It has the ability of system self-regulation to maintain voltage and frequency stability. When the system load suddenly becomes large, using the traditional droop control method causes a huge drop in the system output frequency. In this paper, with the assistance of a long short-term memory neural network (LSTM), a data-driven model of a three-phase inverter shunt system is established based on droop control. Experimental data under different operating conditions are used as the training set in the training process to improve the generalization performance of the data-driven model. The improved data-driven model can accurately predict the output power of a system following the moment on the basis of the current electrical quantity of the system. It can accomplish this even under brand new operating conditions. By compensating the predicted quadratic function term of active power in the active power sag control equation, when the load power suddenly increases, the system compensates the output frequency in advance, which reduces the frequency deviation during the sag control process, and keeps the output frequency stable. This in turn, improves the power quality. Experimental results show that the data-driven model established in this paper has good generalization performance and improves droop control.

关键词： Droop control data-driven modeling Long short-term memory Generalization performance

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