Experimental and analytical study on heat generation characteristics of a lithium-ion power battery

This document is the Accepted Manuscript version of the following article: Yongqi Xie, Shang Shi, Jincheng Tang, Hongwei Wu, and Jianzu Yu, ‘Experimental and analytical study on heat generation characteristics of a lithium-ion power battery’, International Journal of Heat and Mass Transfer, Vol. 122: 884-894, July 2018. Under embargo until 20 February 2019. The final, definitive version is available online via: https://doi.org/10.1016/j.ijheatmasstransfer.2018.02.038A combined experimental and analytical study has been performed to investigate the transient heat generation characteristics of a lithium-ion power battery in the present work. Experimental apparatus is newly built and the investigations on the charge/discharge characteristics and temperature rise behavior are carried out at ambient temperatures of 28 °C, 35 °C and 42 °C over the period of 1 C, 2 C, 3 C and 4 C rates. The thermal conductivity of a single battery cell is experimentally measured to be 5.22 W/(m K). A new transient model of heat generation rate based on the battery air cooling system is proposed. Comparison of the battery temperature between simulated results and experimental data is performed and good agreement is achieved. The impacts of the ambient temperature and charge/discharge rate on the heat generation rate are further analyzed. It is found that both ambient temperature and charge/discharge rate have significant influences on the voltage change and temperature rise as well as the heat generation rate. During charge/discharge process, the higher the current rate, the higher the heat generation rate. The effect of the ambient temperature on the heat generation demonstrates a remarkable difference at different charge states.Peer reviewe

Three-Dimensional Modelling and Simulation of the Ice Accretion Process on Aircraft Wings

© 2018 Chang S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.In this article, a new computational method for the three-dimensional (3D) ice accretion analysis on an aircraft wing is formulated and validated. The two-phase flow field is calculated based on Eulerian-Eulerian approach using standard dispersed turbulence model and second order upwind differencing with the aid of commercial software Fluent, and the corresponding local droplet collection efficiency, convective heat transfer coefficient, freezing fraction and surface temperature are obtained. The classical Messinger model is modified to be capable of describing 3D thermodynamic characteristics of ice accretion. Considering effects of runback water, which is along chordwise and spanwise direction, an extended Messinger method is employed for the prediction of the 3D ice accretion rates. Validation of the newly developed model is carried out through comparisons with available experimental ice shape and LEWICE codes over a GLC-305 wing under both rime and glaze icing conditions. Results show that good agreement is achieved between the current computational ice shapes and the compared results. Further calculations based on the proposed method over a M6 wing under different test conditions are numerically demonstrated.Peer reviewedFinal Published versio

Discovering explicit Reynolds-averaged turbulence closures for turbulent separated flows through deep learning-based symbolic regression with non-linear corrections

This work introduces a novel data-driven framework to formulate explicit algebraic Reynolds-averaged Navier-Stokes (RANS) turbulence closures. Recent years have witnessed a blossom in applying machine learning (ML) methods to revolutionize the paradigm of turbulence modeling. However, due to the black-box essence of most ML methods, it is currently hard to extract interpretable information and knowledge from data-driven models. To address this critical limitation, this work leverages deep learning with symbolic regression methods to discover hidden governing equations of Reynolds stress models. Specifically, the Reynolds stress tensor is decomposed into linear and non-linear parts. While the linear part is taken as the regular linear eddy viscosity model, a long short-term memory neural network is employed to generate symbolic terms on which tractable mathematical expressions for the non-linear counterpart are built. A novel reinforcement learning algorithm is employed to train the neural network to produce best-fitted symbolic expressions. Within the proposed framework, the Reynolds stress closure is explicitly expressed in algebraic forms, thus allowing for direct functional inference. On the other hand, the Galilean and rotational invariance are craftily respected by constructing the training feature space with independent invariants and tensor basis functions. The performance of the present methodology is validated through numerical simulations of three different canonical flows that deviate in geometrical configurations. The results demonstrate promising accuracy improvements over traditional RANS models, showing the generalization ability of the proposed method. Moreover, with the given explicit model equations, it can be easier to interpret the influence of input features on generated models

Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning

This paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL). More precisely, the proximal policy optimization (PPO) method is used to control the mass flow rate of four synthetic jets symmetrically located on the upper and lower sides of a cylinder immersed in a two-dimensional flow domain. The learning environment supports four flow configurations with Reynolds numbers 100, 200, 300 and 400, respectively. A new smoothing interpolation function is proposed to help the PPO algorithm to learn to set continuous actions, which is of great importance to effectively suppress problematic jumps in lift and allow a better convergence for the training process. It is shown that the DRL controller is able to significantly reduce the lift and drag fluctuations and to actively reduce the drag by approximately 5.7%, 21.6%, 32.7%, and 38.7%, at $Re$=100, 200, 300, and 400 respectively. More importantly, it can also effectively reduce drag for any previously unseen value of the Reynolds number between 60 and 400. This highlights the generalization ability of deep neural networks and is an important milestone to active flow control