Numerical and experimental verification of the single neural adaptive PID real-time inverse method for solving inverse heat conduction problems

Wan, Shibin; Wang, Kun; Xu, Peng; Huang, Yajin

doi:10.1016/j.ijheatmasstransfer.2022.122657

Cited by 18 publications

(8 citation statements)

References 27 publications

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“…Acquiring the temperature field using a numerical model implies no noise, measurement errors, or uncertainties once the computations are exact. However, experimental data could be used as a baseline for estimating materials thermal properties through the QOM, and possible temperature measurement errors may be enhanced in the estimation result, resulting in the method's instability [23]. In this case, evaluating if the algorithm would return comparable results for noisy data is necessary.…”

Section: Effect Of Measurement Errors On the Estimated Resultsmentioning

confidence: 99%

Numerical Estimation of Nonlinear Thermal Conductivity of SAE 1020 Steel

de Oliveira,

Magalhães,

Zilnyk

et al. 2024

Computation

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Thermally characterizing high-thermal conductivity materials is challenging, especially considering high temperatures. However, the modeling of heat transfer processes requires specific material information. The present study addresses an inverse approach to estimate the thermal conductivity of SAE 1020 relative to temperature during an autogenous LASER Beam Welding (LBW) experiment. The temperature profile during LBW is computed with the aid of an in-house CUDA-C algorithm. Here, the governing three-dimensional heat diffusion equation is discretized through the Finite Volume Method (FVM) and solved using the Successive Over-Relaxation (SOR) parallelized iterative solver. With temperature information, one may employ a minimization procedure to assess thermal properties or process parameters. In this work, the Quadrilateral Optimization Method (QOM) is applied to perform estimations because it allows for the simultaneous optimization of variables with no quantity restriction and renders the assessment of parameters in unsteady states valid, thereby preventing the requirement for steady-state experiments. We extended QOM’s prior applicability to account for more parameters concurrently. In Case I, the optimization of the three parameters that compose the second-degree polynomial function model of thermal conductivity is performed. In Case II, the heat distribution model’s gross heat rate (Ω) is also estimated in addition to the previous parameters. Ω [W] quantifies the power the sample receives and is related to the process’s efficiency. The method’s suitability for estimating the parameters was confirmed by investigating the reduced sensitivity coefficients, while the method’s stability was corroborated by performing the estimates with noisy data. There is a good agreement between the reference and estimated values. Hence, this study introduces a proper methodology for estimating a temperature-dependent thermal property and an LBW parameter. As the performance of the present algorithm is increased using parallel computation, a pondered solution between estimation reliability and computational cost is achieved.

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Section: Effect Of Measurement Errors On the Estimated Resultsmentioning

confidence: 99%

Numerical Estimation of Nonlinear Thermal Conductivity of SAE 1020 Steel

de Oliveira,

Magalhães,

Zilnyk

et al. 2024

Computation

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“…The self-learning capability of the neuron endows the SNPID algorithm with powerful adaptability when dealing with nonlinear, uncertain, and time-varying systems. This not only maintains the simplicity and high reliability of the traditional PID algorithm design, but also significantly improves its control performance in complex dynamic systems [43,44]. Compared to traditional PID, the SNPID algorithm exhibits stronger adaptability and real-time performance, making it particularly suitable for handling systems with complex nonlinearity and time-varying characteristics, such as greenhouse environment control.…”

Section: Design Of Snpid Controllermentioning

confidence: 99%

Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm

Huang,

Xiang,

Leng

et al. 2024

Electronics

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In order to meet the demands of autonomy and control optimization in solar greenhouse control systems, this paper developed an intelligent temperature and humidity control system for greenhouses based on the Single Neuron Proportional Integral Derivative (SNPID) algorithm. The system is centered around the Huada HC32F460 Micro-Controller Unit (MCU) and the RT-Thread operating system, integrated with the SNPID control algorithm. Through comprehensive simulation, model construction, and comparative experiments, this system was thoroughly evaluated in comparison with traditional PID control systems (cPID) that rely on overseas software and hardwsbuare. Simulation results show that our new system significantly outperforms traditional PID (Proportional Integral Derivative) systems in terms of temperature control stability and accuracy. Experimental data further confirm that, while ensuring cost-effectiveness, the new system achieves a remarkable 50.2% improvement in temperature and humidity control precision compared to traditional systems. The temperature Root Mean Square Error (RMSE) in the experimental greenhouse is 0.734 compared to 1.594 in the comparison greenhouse, indicating better stable temperature control capability. The vents in the experimental greenhouse have a maximum opening of 67 cm and a minimum of 5 cm, showing a quick response property to high temperatures. In contrast, the control greenhouse has a maximum vent opening of 55 cm, remaining unchanged during the test period, which reflects its slower response to temperature fluctuations. These results demonstrate the significant advantages of the designed solar greenhouse temperature and humidity control system in terms of autonomy and control optimization, providing an efficient and economical solution for solar greenhouse environmental management. This system shows significant practical application perspective in promoting intelligent agriculture and sustainable agricultural production, highlighting its broad impact and potential significance.

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“…Wan [31] combined the PID algorithm with a single neuron to automatically adjust the PID tuning parameters. This method can achieve online prediction of heat flux in a onedimensional heat conduction problem.…”

Section: Physical Modelmentioning

confidence: 99%

“…Lohner [30] applied the ANN to achieve the inversion of nonlinear heat transfer problems and compared the results with those from interpolation algorithms, finding that the ANN method provided more accurate results. Wan [31] combined the PID algorithm with a single neuron to automatically adjust the PID tuning parameters. This method can achieve online prediction of heat flux in a one-dimensional Energies 2023, 16, 7819 3 of 14 heat conduction problem.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network-Based Method for Real-Time Inversion of Nonlinear Heat Transfer Problems

Chen,

Pan

2023

Energies

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Inverse heat transfer problems are important in numerous scientific research and engineering applications. This paper proposes a network-based method utilizing the nonlinear autoregressive with exogenous inputs (NARX) neural network, which can achieve real-time identification of thermal boundary conditions for nonlinear transient heat transfer processes. With the introduction of the NARX neural network, the proposed method offers two key advantages: (1) The proposed method can obtain inversion results using only surface temperature time series. (2) The heat flux can be estimated even when the state equation of the system is unknown. The NARX neural network is trained using the Bayesian regularization algorithm with a dataset comprising exact surface temperature and heat flux data. The neural network takes current and historical surface temperature measurements as inputs to calculate the heat flux at the current time. The capability of the NARX method has been verified through numerical simulation experiments. Experimental results demonstrate that the NARX method has high precision, strong noise resistance, and broad applicability. The composition of the input data, the surface temperature measurement noise, and the boundary heat flux shape have been studied in detail for their impact on the inversion results. The NARX method is a highly competitive solution to inverse heat transfer problems.

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Numerical and experimental verification of the single neural adaptive PID real-time inverse method for solving inverse heat conduction problems

Cited by 18 publications

References 27 publications

Numerical Estimation of Nonlinear Thermal Conductivity of SAE 1020 Steel

Numerical Estimation of Nonlinear Thermal Conductivity of SAE 1020 Steel

Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm

A Neural Network-Based Method for Real-Time Inversion of Nonlinear Heat Transfer Problems

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