A data-driven artificial neural network model for predicting wind load of buildings using GSM-CFD solver

Sang, Jianbing; Pan, Xingda; Lin, Tao; Liang, Weida; Liu, G.R.

doi:10.1016/j.euromechflu.2021.01.007

Cited by 12 publications

(3 citation statements)

References 20 publications

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“…In the learning phase, however, the corresponding weights change so long as to establish such a correlation dependence between the input and output data that give results with as few errors as possible. 29 This is followed by the process of testing the ANN architecture made. In the validation process, the quality of the ANN architecture is assessed.…”

Section: Creation and Validation Of Various Ann-1 Unit Architecturesmentioning

confidence: 99%

Integration of machine learning to increase steam turbine condenser vacuum and efficiency through gasket resealing and higher heat extraction into the atmosphere

Strušnik

2021

Intl J of Energy Research

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In large thermal power facilities, thermal machines mainly fitted with steam turbines are used in thermodynamic process of converting thermal energy into mechanical work. In order to improve the efficiency of heat energy conversion into mechanical work, the increase of vacuum level in the condenser of a steam turbine was analyzed using the machine learning method. The vacuum in the steam turbine condenser increases after replacing the labyrinth gaskets with honeycomb ones, resealing the vacuum system and increasing the amount of cooling water through the condenser. The latter is controlled by algorithm, which is integrated into the steam turbine distribution control system. As a result of the increased vacuum in the steam turbine condenser, the enthalpy difference of the steam turbine low-pressure cylinder increases, and the steam turbine performs more mechanical work resulting in higher conversion efficiency. The drawback of the increased vacuum in the turbine condenser is that the flow of cooling water through the steam turbine condenser increases as well as heat dissipation from the condenser into the environment to be replaced by regenerative condensate heating. The results of the analysis show that the overall balance of the vacuum increase in the steam turbine condenser is positive, the steam turbine generates more power, and consequently the process efficiency increases by as much as 2.48 %.

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Section: Creation and Validation Of Various Ann-1 Unit Architecturesmentioning

confidence: 99%

Integration of machine learning to increase steam turbine condenser vacuum and efficiency through gasket resealing and higher heat extraction into the atmosphere

Strušnik

2021

Intl J of Energy Research

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“…The remaining data are used for testing. The testing dataset does not participate in the training and is only used to evaluate the accuracy of the network to predict the true dataset after training [5]. A typical ANN structure consists of an input layer, hidden layers, and an output layer, which are connected to each other by neurons.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network to Predict Pressure Drops in Heat Sinks

Mengesha¹,

Shaeri²,

Sarabi³

2022

International Conference on Fluid Flow, Heat and Mass Transfer

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In this study, pressure drop ( ) across air-cooled heat sinks (HSs) are predicted using an artificial neural network (ANN). A multilayer feed-forward ANN architecture with two hidden layers is developed. Backpropagation algorithm is used for training the network, and the accuracy of the network is evaluated by the root mean square error. The input data for training the neural network is prepared through three-dimensional simulation of air inside the channels of heat sinks using a computational fluid dynamics (CFD) approach. The developed ANN-based model in this study predicts with a high accuracy and within of the CFD-based data. The present study suggests that developing an ANN-based model with a high level of accuracy overcomes the limitations of physics-based correlations that their accuracy strongly depends on identifying and implementing key variables that affect the physics of a thermo-fluid phenomenon.

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“…This alternative is the use of neural networks-based predictive models that could work as a source of data when a reasonable accuracy has been reached. A wide range of these kinds of models can be found in the literature with application in different fields of science [52][53][54][55][56][57][58][59][60][61][62].…”

Section: Introductionmentioning

confidence: 99%

A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE

et al. 2022

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The capability analysis of a process against requirements is often an instrument of change. The traditional and fuzzy process capability approaches are the most useful statistical techniques for determining the intrinsic spread of a controlled process for establishing realistic specifications and use for comparative processes. In the industry, the traditional approach is the most commonly used instrument to assess the impact of continuous improvement projects. However, these methods used to evaluate process capability indices could give misleading results because the dataset employed corresponds to the final product/service measures. This paper reviews an alternative procedure to assess the fuzzy process capability indices based on the statistical methodology involved in the modeling and design of experiments. Firstly, a model with reasonable accuracy is developed using a neural network approach. This model is embedded in a graphic user interface (GUI). Using the GUI, an experimental design is carried out, first to know the membership function of the process variability and then include this variability in the model. Again, an experimental design identifies the improved operating conditions for the significative independent variables. A new dataset is generated with these operating conditions, including the minimum error reached for each independent variable. Finally, the GUI is used to get a new prediction for the response variable. The fuzzy process capability indices are determined using the triangular membership function and the predicted response values. The feasibility of the proposed method was validated using a random data set corresponding to the basis weight of a papermaking process. The results indicate that the proposed method provides a better overview of the process performance, showing its true potential. The proposed method can be considered non-invasive.

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A data-driven artificial neural network model for predicting wind load of buildings using GSM-CFD solver

Cited by 12 publications

References 20 publications

Integration of machine learning to increase steam turbine condenser vacuum and efficiency through gasket resealing and higher heat extraction into the atmosphere

Integration of machine learning to increase steam turbine condenser vacuum and efficiency through gasket resealing and higher heat extraction into the atmosphere

Artificial Neural Network to Predict Pressure Drops in Heat Sinks

A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE

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