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1. Supply Air Temperature Prediction in an Air-Handling Unit Using Artificial Neural Network

   https://doi.org/10.1115/IMECE2018-88507  

   Sukthankar, Nikita; Walekar, Abhishek; Agonafer, Dereje (November 2018, ASME 2018 International Mechanical Engineering Congress and Exposition)

   Continuous provision of quality supply air to data center’s IT pod room is a key parameter in ensuring effective data center operation without any down time. Due to number of possible operating conditions and non-linear relations between operating parameters make the working mechanism of data center difficult to optimize energy use. At present industries are using computational fluid dynamics (CFD) to simulate thermal behaviour for all types of operating conditions. The focus of this study is to predict Supply Air Temperature using Artificial Neural Network (ANN) which can overcome limitations of CFD such as high cost, need of an expertise and large computation time. For developing ANN, input parameters, number of neurons and hidden layers, activation function and the period of training data set were studied. A commercial CFD software package 6sigma room is used to develop a modular data center consisting of an IT pod room and an air-handling unit. CFD analysis is carried out for different outside air conditions. Historical weather data of 1 year was considered as an input for CFD analysis. The ANN model is “trained” using data generated from these CFD results. The predictions of ANN model and the results of CFD analysis for a set of example scenarios were compared to measure the agreement between the two. The results show that the prediction of ANN model is much faster than full computational fluid dynamics simulations with good prediction accuracy. This demonstrates that ANN is an effective way for predicting the performance of an air handling unit. 

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2. Machine learning assisted inverse heat transfer problem to find heat flux in ablative materials

   https://doi.org/10.1016/j.mtcomm.2025.112337  

   Islam, Md Shariful; Dutta, Prashanta (April 2025, Materials Today Communications)

   Thermal ablation of materials is a complex phenomenon that involves physical and chemical processes for the thermal protection of systems. However, due to the extreme thermal conditions and moving boundaries, predicting temperature and heat flux at the ablative material is quite challenging. A physics-informed neural network is a promising technique for many such inverse problems, including the prediction of unsteady heat flux. However, traditional physics-informed machine learning algorithms struggle with heat flux predictions in thermal ablation problems due to moving boundary conditions and lack of temperature data in the inaccessible domain. This study presents a hybrid approach, where an artificial neural network (ANN) is used for the accessible domain of the material and a physics-based numerical solution (PNS) technique is used in the inaccessible domain of the material, to find heat flux at the ablative surface. Temperature data at the accessible sensor points are used to train the ANN model. The heat flux at the ablative boundary was iteratively obtained from the numerical solution of the energy equation in the inaccessible domain by matching the ANN-predicted temperature at the last accessible sensor point. Our results indicate that this hybrid methodology significantly outperforms traditional physics-informed machine learning techniques, achieving excellent accuracy in predicting the temperature profiles and heat fluxes under complex conditions for both constant and variable heat flux and properties. By addressing the limitations of conventional physics-informed machine learning methods, our approach provides a robust and reliable solution for modeling the intricate dynamics of ablative processes. 

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3. A novel hybrid deep learning model for real-time monitoring of water pollution using sensor data

   https://doi.org/10.1016/j.jwpe.2024.106595  

   Bagheri, Majid; Bagheri, Karim; Farshforoush, Nakisa; Velazquez, Antonio; Liu, Ying (December 2024, Journal of Water Process Engineering)

   This study developed a hybrid model for predicting dissolved oxygen (DO) using real-time sensor data for thirteen parameters. This novel hybrid model integrated one-dimensional convolutional neural networks (CNN) and long short-term memory (LSTM) to improve the accuracy of prediction for DO in water. The hybrid CNNLSTM model predicted DO concentration in water using soft sensor data. The primary input parameters to the model were temperature, pH, specific conductivity, salinity, density, chlorophyll, and blue-green algae. The model used 38,681 water quality data for training and testing the hybrid deep learning network. The training procedure for the model was successful. The training and test losses were both nearly zero and within a similar range. With a coefficient of determination (R2) of 0.94 and a mean squared error (MSE) of 0.12, the hybrid model indicated higher performance compared to the classical models. The normal distribution of residual errors confirmed the reliability of the DO predictions by the hybrid CNN-LSTM model. Feature importance analysis indicated pH as the most significant predictor and temperature as the second important predictor. The feature importance scores based on extreme gradient boosting (XGBoost) for the pH and temperature were 0.76 and 0.12, respectively. This study indicated that the hybrid model can outperform the classical machine learning models in the real-time prediction of DO concentration. 

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4. Machine-Learning-Based Thermal Conductivity Prediction for Additively Manufactured Alloys

   https://doi.org/10.3390/jmmp7050160  

   Bhandari, Uttam; Chen, Yehong; Ding, Huan; Zeng, Congyuan; Emanet, Selami; Gradl, Paul R.; Guo, Shengmin (October 2023, Journal of Manufacturing and Materials Processing)

   Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures, thermal processing (heat treatment) history and the composition of alloys. Due to computational costs and lengthy experimental procedures, obtaining the thermal conductivity for novel alloys, particularly parts made with additive manufacturing, is difficult and it is almost impossible to optimize the compositional space for an absolute targeted value of thermal conductivity. To address these difficulties, a machine learning method is explored to predict the TC of additive manufactured alloys. To accomplish this, an extensive thermal conductivity dataset for additively manufactured alloys was generated for several AM alloy families (nickel, copper, iron, cobalt-based) over various temperatures (300–1273 K). This unique dataset was used in training and validating machine learning models. Among the five different regression machine learning models trained with the dataset, extreme gradient boosting performs the best as compared with other models with an R2 score of 0.99. Furthermore, the accuracy of this model was tested using Inconel 718 and GRCop-42 fabricated with laser powder bed fusion-based additive manufacture, which have never been observed by the extreme gradient boosting model, and a good match between the experimental results and machine learning prediction was observed. The average mean error in predicting the thermal conductivity of Inconel 718 and GRCop-42 at different temperatures was 3.9% and 2.08%, respectively. This paper demonstrates that the thermal conductivity of novel AM alloys could be predicted quickly based on the dataset and the ML model. 

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5. Fabrication of a Two-Step Thermoresponsive Ultrafiltration Membrane Via Polymerization of a Lyotropic Liquid Crystal

   https://doi.org/10.2139/ssrn.4065618  

   Saadat, Younes; Kim, Kyungtae; Foudazi, Reza (March 2022, SSRN Electronic Journal)

   In this work, we present the fabrication of a two-step thermoresponsive ultrafiltration (UF) membrane through polymerization of a lyotropic liquid crystal (LLC). A mixture of commercially available Pluronic F127 block copolymer, water (containing ammonium persulfate as the initiator), and polymerizable oil (n-butyl acrylate/ethylene glycol dimethacrylate) is used to create an LLC with lamellar structure, as characterized by cross-polarized light microscopy and atomic force microscopy. Differential scanning calorimetry is employed to evaluate the thermoresponsive behavior of the polymerized LLC (polyLLC). Two-step thermoresponsiveness (~35 °C and ~50 °C) of the polyLLC is observed due to the lower critical solution temperature (LCST) of F127 and melting of the crystalline structure of the polyethylene oxide (PEO) chains of the F127 surfactant. In the next step, the obtained mesophase is cast on a nonwoven polyester support sheet followed by thermal polymerization. The hydration capacity, water flux, water flux recovery after fouling, and molecular weight cut-off (MWCO) of the obtained membrane are evaluated at different temperatures to examine its thermoresponsiveness. The experimental results reveal that the UF membrane has a reversible thermoresponsive behavior at the LCST and PEO melting of polyLLC. Additionally, cleaning efficiency of the fouled membrane can be enhanced by using its thermoresponsive behavior, resulting in an extended lifetime of the product. Furthermore, the MWCO of the membrane can be altered with temperature due to the pore size change with temperature stimulus. 

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