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Advanced neural network modeling with levenberg-marquardt algorithm for optimizing tri-hybrid nanofluid dynamics in solar HVAC systems

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CASE STUDIES IN THERMAL ENGINEERING 2025年 65卷

作者： Aziz, A. Shah, S. A. H. Bahaidarah, H. M. S. Zamir, T. Aziz, T. Natl Univ Sci & Technol Coll Elect & Mech Engn Rawalpindi Pakistan King Fahd Univ Petr & Minerals Dept Mech Engn Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Sustainable Energy Syst Dhahran Saudi Arabia COMSATS Univ Islamabad Dept Math Attock Campus Attock Pakistan King Fahd Univ Petr & Minerals Dept Math Sci DCC KFUPM Dhahran Saudi Arabia

The performance of photovoltaic (PV)-based heating, ventilation, and air conditioning (HVAC) systems is highly sensitive to operating temperature. To address this, we propose a nanofluidbased thermal cooling model and develop an advanced computational solver using an Artificial Neural Network (ANN) trained with the levenberg-marquardt algorithm (LMA-TNN). This model analyzes the magnetohydrodynamic (MHD) radiative flow of a rotating Sutterby trihybrid nanofluid, incorporating critical factors such as linear thermal radiation, boundary slip, and activation energy. The nonlinear differential equations derived from the physical model are solved using the three-step Lobatto IIIa method, ensuring precision and reliability. Reference data for the LMA-TNN solver are generated for various HVAC scenarios, with a focus on key parameters including Reynolds and Deborah numbers, radiation, temperature slip, and activation energy. The LMA-TNN model is rigorously trained, validated, and tested, achieving high accuracy in predicting numerical solutions for diverse HVAC operating conditions. The model's performance is evaluated using state transition (ST) index, error histogram (EH), mean squared error, and regression (R) analysis, demonstrating excellent agreement between predicted and reference solutions. The results show an error range of 10 -7 to 10 -11 , confirming the model's reliability and potential for optimizing PV-based HVAC systems.

关键词： Tri-hybrid nanofluids Thermal efficiency PV-solar HVAC levenberg marquardt algorithm Neural networks Sutterby model

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Artificial neural network model using levenberg marquardt algorithm to analyse transient flow and thermal characteristics of micropolar nanofluid in a microchannel

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Partial Differential Equations in Applied Mathematics 2025年 13卷

作者： Kumar, Pradeep Almeida, Felicita AR, Ajaykumar Al-Mdallal, Qasem Department of Mathematics School of Engineering Presidency University Rajanakunte Yelahanka Karnataka Bengaluru 560064 India Department of Mathematical Sciences UAE University P.O. Box 17551 Al-Ain United Arab Emirates Department of Mathematics JNN College of Engineering Karnataka Shivamogga India

The application of artificial neural network has kept the whole world amazed, as it has flourished its roots to analyse different domains of science and technology. The current article is modelled to bear witness to how the artificial neural network is administered to study heat transfer and fluid flow problems. The model constructed analyses the mixed convective and unsteady flow of micropolar fluid through the microchannel in the presence of activation energy and magnetic field using Buongiorno's model. No slip and convective boundary conditions are employed. The partial differential equation is solved using the finite difference method. The artificial neural network using the levenberg-marquardt algorithm with the feed-forward backpropagation method is constructed and trained. The results of the analysis show that the material parameter lowers the fluid's velocity. For higher magnetic effects, the micro-rotation profile maximises at left half and minimises at right half of microchannel. The temperature profile increases with increasing Eckert number and thermophoresis parameter. The reaction rate parameter is a depleting function, while the activation energy parameter is an enhancing function of the solutal profile. The results obtained from the artificial neural network for all 8 scenarios are highly reliable due to its high accuracy, which is pleasantly deliberated by the mean square error values, error histograms, training, and regression graphs of the neural network model. The absolute error analysis carried out is in the range of 10⁻⁴ to 10⁻⁵. The prominent conclusion from the analysis is that artificial neural network is sophisticated tool to predict the subsequent sequel of fluid flow and heat transport over a long period of time, reducing computational time to solve complicated fluid flow problems. © 2024 The Author(s)

关键词： Activation energy Artificial neural network Buongiorno model levenberg marquardt algorithm Micropolar nanofluid

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levenberg marquardt algorithm based economic load dispatch

Levenberg Marquardt algorithm based economic load dispatch

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IEEE Power India Conference 2006

作者： Chaturvedi, Krishna Teerth Srivastava, Laxmi Pandit, Manjaree MITS Dept Elect Engn Gwalior India

ISBN: (纸本)9780780395251

This paper presents levenberg marquardt algorithm based multi-layer feed-forward neural network for economic load dispatch, which is trained to provide the optimal value of lambda and economic generation allocation on all the committed generating units for a given power demand. Since the training of the ANN is extremely fast, it may be used for on-line implementation at energy control centre. Test results of two sample systems with 3 generating units and 6 generating units are given to illustrate the effectiveness of the proposed method.

关键词： B-coefficients economic load dispatch Lambda iteration method levenberg marquardt algorithm Multi Layer Perceptron (MLP) networks

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Prediction of multiple characteristics of Friction-Stir welded joints by levenberg marquardt algorithm based artificial neural network

Prediction of multiple characteristics of Friction-Stir weld...

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International Conference on Recent Advances in Mechanical Engineering Research and Development (ICRAMERD)

作者： Senapati, N. Pallavi Panda, D. K. Bhoi, Rajat Kumar SOADU ITER Dept Mech Engg Bhubaneswar 751030 India DRDO Inst Technol Management Mussoorie 248179 India

The present paper investigates the influence of process parameters of friction stir welding (FSW) technique on the resulting microstructure and mechanical characteristics of the fabricated joints. The material considered for FSW is AA1100 aluminium rolled plates that are joined by using a square pin tool. In this research, parametric characterization of surface properties has been carried out by simulation using an artificial neural network (ANN) that is designed with multi layer, multi neuron and logistic sigmoid activation function. levenberg-marquardt algorithm including second-order error optimization has been selected to train the ANN. The ANN simulation has been done to estimate the tensile and yield strength, elongation, flexure stress and grain size with respect to tool rotational speed (RS), travel speed (TS) and plunge depth (PD), which are the process parameters of FSW process and also find an optimum condition which is essential to save energy and resources. This process finds its application in various automotive industries as there is a wide use of aluminium in the automobiles and aircrafts. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Artificial neural network Average grain size Friction stir welding Tensile strength levenberg marquardt algorithm

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Ferromagnetic Mass Localization in Check Point Configuration Using a levenberg marquardt algorithm

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SENSORS 2009年第11期9卷 8852-8862页

作者： Alimi, Roger Geron, Nir Weiss, Eyal Ram-Cohen, Tsuriel Soreq Nucl Res Ctr Prop Phys Div IL-81800 Yavne Israel

A detection and tracking algorithm for ferromagnetic objects based on a two stage levenberg marquardt algorithm (LMA) is presented. The procedure is applied to localization and magnetic moment estimation of ferromagnetic objects moving in the vicinity of an array of two to four 3-axis magnetometers arranged as a check point configuration. The algorithms first stage provides an estimation of the target trajectory and moment that are further refined using a second iteration where only the position vector is taken as unknown. The whole procedure is fast enough to provide satisfactory results within a few seconds after the target has been detected. Tests were conducted in Soreq NRC assessing various check point scenarios and targets. The results obtained from this experiment show good localization performance and good convivial with "noisy" environment. Small targets can be localized with good accuracy using either a vertical "doorway" two to four sensors configuration or ground level two to four sensors configuration. The calculated trajectory was not affected by nearby magnetic interference such as moving vehicles or a combat soldier inspecting the gateway.

关键词： magnetic moment localization magnetic sensors levenberg marquardt algorithm

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Prediction of multiple characteristics of Friction-Stir welded joints by levenberg marquardt algorithm based artificial neural network

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Materials Today: Proceedings 2021年 41卷 391-396页

作者： N. Pallavi Senapati D.K. Panda Rajat Kumar Bhoi Dept of Mechanical Engg ITER SOADU Bhubaneswar 751030 India Institute of Technology Management DRDO Mussoorie 248179 India

关键词： Artificial neural network Average grain size Friction stir welding Tensile strength levenberg marquardt algorithm

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Identification of an Industrial Proportional Valve Using levenberg-marquardt algorithm 3

Identification of an Industrial Proportional Valve Using Lev...

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3rd International Conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Belguidoum, Fawzi Sari, Bilal Jiajia, Jia Electrical Engineering Department University of Setif 1 Algeria DAC Laboratory Algeria LMETR Laboratory University of Setif 1 Algeria School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China

ISBN: (纸本)9781643685649

This paper presents the dynamic modeling and parameter identification of an industrial actuator known as a voice coil proportional valve. This actuator is crucial for precise position control in industrial hydraulic cylinders, particularly when high accuracy, smooth motion, high speed, and acceleration are required. Accurate parametric modeling of the system is essential for effective control and monitoring. However, identifying the physical parameters of this model is challenging due to its nonlinear parameter dependencies, rendering traditional least-squares methods unsuitable. To address this, the levenberg-marquardt algorithm is proposed as a solution for nonlinear parameter identification. Both simulations and experimental data are employed to validate the proposed continuous parametric model. Despite the presence of noisy data, the results demonstrate the effectiveness of the proposed approach. © 2024 The Authors.

关键词： Identification industrial hydraulic actuator levenberg marquardt algorithm parameter estimation Proportional valve

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Maximum likelihood LM identification based on particle filtering for scarce measurement-data MIMO Hammerstein Box-Jenkins systems

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MATHEMATICS AND COMPUTERS IN SIMULATION 2025年 230卷 241-255页

作者： Zong, Tiancheng Li, Junhong Lu, Guoping Nantong Univ Sch Elect Engn & Automat Nantong 226019 Peoples R China

The scarce measurement-data system means that the input or output of one system are sampled at scarce time series. Thus, the sampled data are incomplete in scarce measurement-data systems. In this paper, the parameter estimation of scarce measurement-data multiple input multiple output Hammerstein Box-Jenkins (S-MIMO-H-BJ) systems is studied. To make full use of the system data without adding unknown parameters, the particle filtering method is applied to obtain unknown states and variables in scarce measurement-data systems. Thus, the maximum likelihood levenberg marquardt (ML-LM) iterative method based on particle filtering (ML-LM-I-PF) is derived. To verify the superiority of the proposed algorithm, the ML- LM iterative method based on auxiliary model (ML-LM-I-AM) is also derived. Finally, using these two algorithms, unknown parameters in the S-MIMO-H-BJ numerical example and the two-tank level system are identified. Simulations prove that these two methods can all estimate S-MIMO-H-BJ models effectively, but the ML-LM-I-PF method behaves better because it has smaller calculation amount and more accurate parameter estimation.

关键词： System identification Scarce measurement-data system MIMO Hammerstein Box-Jenkins model Maximum likelihood method levenberg marquardt algorithm Particle filtering

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Artificial neural network-based approach for simulating influenza dynamics: A nonlinear SVEIR model with spatial diffusion

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ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS 2025年 176卷

作者： Zarin, Rahat King Mongkuts Univ Technol Fac Sci Dept Math Thonburi KMUTT Bangkok 10140 Thailand

Artificial Neural Networks (ANNs) have revolutionized machine learning by enabling systems to learn from data and generalize to new, unseen examples. As biologically inspired models, ANNs consist of interconnected neurons organized in layers, mimicking the human brain's functioning. Their ability to model complex, nonlinear processes makes them powerful tools in various domains. In this study, the author apply ANNs to simulate the dynamics of a nonlinear Influenza transmission model with spatial diffusion. The model comprises five compartments: Susceptible (S(x,t)), Vaccinated (V(x,t)), Exposed (E(x,t)), Infected (I(x,t)), and Recovered (R(x, t)), governed by a system of partial differential equations (PDEs). We employ the levenberg-marquardt backpropagation algorithm to train the ANN, utilizing reference datasets generated through meshless and finite difference methods in MATLAB. The performance of the ANN is validated through mean square error (MSE) metrics, achieving a mean square error as low as 10(-12). Regression and state transition plots illustrate the training, testing, and validation processes. Furthermore, absolute error analyses across various components of the system confirm the robustness and accuracy of the proposed approach. The data were split into 81% for training, with 9% each for testing and validation.

关键词： Spatial diffusion Influenza Epidemic modeling Artificial neural networks levenberg marquardt algorithm

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Intelligent back-propagated neural networks to study nonlinear heat transfer in tangent-hyperbolic fluids

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CASE STUDIES IN THERMAL ENGINEERING 2025年 65卷

作者： Raja, Muhammad Asif Zahoor Tayyab, Huma Malik, Aamna Muskan Hassan, Qazi Mahmood Ul Nisar, Kottakkaran Sooppy Shoaib, Muhammad Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Yunlin 64002 Taiwan Univ Wah Dept Math Wahcantt 47010 Pakistan Prince Sattam bin Abdulaziz Univ Coll Sci & Humanities Dept Math Al Kharj Saudi Arabia SIMATS Saveetha Sch Engn Chennai India Yuan Ze Univ AI Ctr Taoyuan 320 Taiwan

This paper presents a novel way to analyze unsteady flow and heat transfer in Magnetohydrodynamic Tangent-Hyperbolic Fluid (MHD-THF) flow over a stretching sheet that uses Recurrent Neural Networks (RNNs) optimized with the levenberg-marquardt algorithm (LMA). The controlling PDEs are similarly converted into nonlinear ordinary differential equations (ODEs), and reference data is generated using the Adam-Bashforth numerical solver. The reference dataset, divided into 70 % training, 15 % testing, and 15 % validation sets, which was separated into three distinct cases for each scenario, was trained, tested, and validated using MATLAB's RNN-LMA technique. The absolute error for each scenario of model instance is approximately 10- 06, 10-05, 10-07, 10-05, and 10-08. The numerical solutions for the nonlinear fluid dynamics system were considered to reduce mean square error. The stochastic technique's reliability and competency are demonstrated using comparison configurations of MSE, error histograms, correlation, and regression. A graphical assessment of the model shows that power-law index, unsteadiness, Hartmann number, and Weissenberg number decrease velocity profiles. Meanwhile, the temperature profile reveals an increase with the Eckert number and unsteadiness, whereas the Prandtl number has the reverse impact. This study illustrates the potential of RNN-LMA techniques to handle complicated fluid dynamics problems with more accuracy and efficiency.

关键词： Tangent hyperbolic fluid Magnetohydrodynamic Nanofluids Recurrent neural networks levenberg marquardt algorithm Adams numerical solver

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