Utilizing Artificial Neural Network for Load Prediction Caused by Fluid Sloshing in Tanks

Chegini, Hossein Goudarzvand; Zarepour, Gholamreza

doi:10.1155/2021/3537542

Cited by 2 publications

(1 citation statement)

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“…The projected precipitation values were then utilized to forecast water levels at the same gauging station, with an accuracy of 85.3%, compared to just 71.1% for water level prediction with no estimates of missing precipitation data. The action of sloshing problems in a tank with fluid under harmonic stimulation was predicted using neural network models [42]. The sloshing behavior was initially simulated using the smooth particle hydrodynamics (SPH) approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Accurate Liquid Level Measurement with Minimal Error: A Chaotic Observer Approach

Shenoy,

Venkata

2024

Computation

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This paper delves into precisely measuring liquid levels using a specific methodology with diverse real-world applications such as process optimization, quality control, fault detection and diagnosis, etc. It demonstrates the process of liquid level measurement by employing a chaotic observer, which senses multiple variables within a system. A three-dimensional computational fluid dynamics (CFD) model is meticulously created using ANSYS to explore the laminar flow characteristics of liquids comprehensively. The methodology integrates the system identification technique to formulate a third-order state–space model that characterizes the system. Based on this mathematical model, we develop estimators inspired by Lorenz and Rossler’s principles to gauge the liquid level under specified liquid temperature, density, inlet velocity, and sensor placement conditions. The estimated results are compared with those of an artificial neural network (ANN) model. These ANN models learn and adapt to the patterns and features in data and catch non-linear relationships between input and output variables. The accuracy and error minimization of the developed model are confirmed through a thorough validation process. Experimental setups are employed to ensure the reliability and precision of the estimation results, thereby underscoring the robustness of our liquid-level measurement methodology. In summary, this study helps to estimate unmeasured states using the available measurements, which is essential for understanding and controlling the behavior of a system. It helps improve the performance and robustness of control systems, enhance fault detection capabilities, and contribute to dynamic systems’ overall efficiency and reliability.

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Section: Literature Reviewmentioning

confidence: 99%

Accurate Liquid Level Measurement with Minimal Error: A Chaotic Observer Approach

Shenoy,

Venkata

2024

Computation

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Numerical Study of Rectangular Tank with Sloshing Fluid and Simulation of the Model Using a Machine Learning Method

Chegini

Zarepour

2022

Geofluids

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This paper presents a fluid sloshing model using the artificial neural network method (ANN). Determining the fluid sloshing model in the tank is a challenging task due to its nonlinearity and complexity of behavior to its environmental and operational conditions. Due to the problems of laboratory modeling, the use of numerical modeling to analyze this phenomenon can be justified. In this paper, first, the fluid sloshing in the tank is simulated by the smooth particle hydrodynamics method (SPH). The input-output data for training the artificial neural network is based on the obtained results. Finally, the maximum force due to the fluid sloshing is obtained by changing different parameters.

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Utilizing Artificial Neural Network for Load Prediction Caused by Fluid Sloshing in Tanks

Cited by 2 publications

References 50 publications

Accurate Liquid Level Measurement with Minimal Error: A Chaotic Observer Approach

Accurate Liquid Level Measurement with Minimal Error: A Chaotic Observer Approach

Numerical Study of Rectangular Tank with Sloshing Fluid and Simulation of the Model Using a Machine Learning Method

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