BITS Faculty Publications

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    Application of New Artificial Neural Network to Predict Heat Transfer and Thermal Performance of a Solar Air-Heater Tube
    (MDPI, 2021-07) Bhattacharyya, Suvanjan
    In the present study, the heat transfer and thermal performance of a helical corrugation with perforated circular disc solar air-heater tubes are predicted using a machine learning regression technique. This paper describes a statistical analysis of heat transfer by developing an artificial neural network-based machine learning model. The effects of variation in the corrugation angle (θ), perforation ratio (k), corrugation pitch ratio (y), perforated disc pitch ratio (s), and Reynolds number have been analyzed. An artificial neural network model is used for regression analysis to predict the heat transfer in terms of Nusselt number and thermohydraulic efficiency, and the results showed high prediction accuracies. The artificial neural network model is robust and precise, and can be used by thermal system design engineers for predicting output variables. Two different models are trained based on the features of experimental data, which provide an estimation of experimental output based on user-defined input parameters. The models are evaluated to have an accuracy of 97.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce
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    Efficacy of ANN and ANFIS as an AI Technique for the Prediction of COF at Finger Pad Interface in Manipulative Tasks
    (Springer, 2023-03) Rathore, Jitendra S.; Srivastava, Sharad
    Current work intends to compare the modelling ability of two popular artificial intelligence (AI) techniques, namely artificial neural network (ANN) and adaptive-neuro fuzzy inference system (ANFIS). Outcome of study is useful in prediction and further optimization of the coefficient of friction in the design of assistive devices for an ergonomics and comfort of the user. Experiments were conducted using Taguchi L16 design of experiments (DOE). Total of 16 experimental runs were conducted. Two extrinsic factors normal load (2, 4,6, & 8 N) and sliding velocity (4, 6, 8 & 10 cm/s) that affect the finger pad friction are taken as input variables, while coefficient of friction (COF) between finger pad and the stainless steel (SS) probe is the output variable. ANN with 2 inputs, 10 hidden, and 1 output layer is trained by three algorithms, viz. Levenberg–Marquardt (R2 = 0.96), Bayesian Regularization (R2 = 0.93), and Scaled Conjugate Gradient (R2 = 0.98) based on the correlation coefficient. Although, both the techniques highlight significant predictability and accuracy, ANFIS results shows overfitting of the data. Hence, ANN technique is relatively better than ANFIS.
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    Parametric analysis and optimization of CO2 trans-critical cycle for chiller application in a warm climate
    (Elsevier, 2019-03) Dasgupta, Mani Sankar
    This article presents parametric analysis and subsequent optimization of a CO2 trans-critical chiller system operating in a warm climate. High side pressure and gas cooler face velocities are two controllable parameters investigated. COP of the system is analyzed and optimized using a developed and validated mathematical model. The mean relative error of prediction in COP is found to be within ±10% for physics-based model and within ±1% for Artificial Neural Network (ANN) based model of the experimental findings. The validated mathematical model is utilized to predict optimal high side pressure as well as gas cooler face velocity for the varying ambient and evaporation conditions to achieve best possible COP. A possibility of 5.31% improvement in COP is found based on the optimization of parameters. The proposed methodology is deemed suitable for design and testing of control system for maximization of energy efficiency.
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    Earthquake Prediction Using Deep Neural Networks
    (IEEE, 2022) Pasari, Sumanta
    Reliable prediction of earthquakes has numerous societal and engineering benefits. In recent years, the exponentially rising volume of seismic data has led to the development of several automatic earthquake detection algorithms through machine learning approaches. In this study, we propose a fully functional and efficient earthquake detector cum forecaster based on deep neural networks of long-short-term memory (LSTM) units. The model captures inherent temporal characteristics of earthquake data. For illustration, we consider an earthquake catalog from the Himalaya and its neighboring regions. The proposed LSTM model shows satisfactory performance for small to medium-sized earthquakes. We also implement a baseline artificial neural network (ANN) model to perform a suitable comparison. It is observed that both ANN and LSTM models fail to produce desired result for large events.
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    To predict the impact of passive architecture on the temperature conditions inside a building using ANN
    (IEEE, 2016) Gupta, Anu; Gupta, Rajiv
    The environmental impact of the building industry is significant. The construction industry constitutes a major part of the world's total energy consumption. As a result, building designers have constantly been urged to pay attention to the energy economics of buildings. Green building practices like passive solar building design, advanced construction, and building operation practices have been evolved over the years. Passive solar architecture basically refers the usage of structural and non-structural elements of the building for the comfort conditions with no additional operational costs. It makes the best possible use of the local geographic and climatic conditions. Such measures help in decreasing the operational costs of a building and increasing the thermal comfort of its occupants often leading to enhanced productivity. These energy efficiency measures also improve building marketability as the cost of operation is reduced. The temperature inside a building is one of the factors to identify the comfort level up to a great extent. Hence, it necessary to predict the internal air temperatures of any building. With this study, an attempt has been made at developing mathematical models that could predict the air temperature inside any building. The works attempts the development of a neural network regression tool which can predict the temperature inside a simple construction when fed with various building specifications as input. The results of this Artificial Neural Network were found to be in close agreement with the actual on-site recorded data. This is the first time that ANN is being used for such application. For the purpose of validation and testing, the data recorded for four rooms varying in architectural aspects over the past year have been taken. These recordings were taken on an hourly basis.
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    To predict the impact of passive architecture conditions inside a building using ANN
    (IEEE, 2016) Gupta, Rajiv
    The environmental impact of the building industry is significant. The construction industry constitutes a major part of the world's total energy consumption. As a result, building designers have constantly been urged to pay attention to the energy economics of buildings. Green building practices like passive solar building design, advanced construction, and building operation practices have been evolved over the years. Passive solar architecture basically refers the usage of structural and non-structural elements of the building for the comfort conditions with no additional operational costs. It makes the best possible use of the local geographic and climatic conditions. Such measures help in decreasing the operational costs of a building and increasing the thermal comfort of its occupants often leading to enhanced productivity. These energy efficiency measures also improve building marketability as the cost of operation is reduced. The temperature inside a building is one of the factors to identify the comfort level up to a great extent. Hence, it necessary to predict the internal air temperatures of any building. With this study, an attempt has been made at developing mathematical models that could predict the air temperature inside any building. The works attempts the development of a neural network regression tool which can predict the temperature inside a simple construction when fed with various building specifications as input. The results of this Artificial Neural Network were found to be in close agreement with the actual on-site recorded data. This is the first time that ANN is being used for such application. For the purpose of validation and testing, the data recorded for four rooms varying in architectural aspects over the past year have been taken. These recordings were taken on an hourly basis.