Artificial neural network based chemical mechanisms for computationally efficient modeling of hydrogen/carbon monoxide/kerosene combustion

To effectively simulate the combustion of hydrogen/hydrocarbon-fueled supersonic engines, such as scramjet and rocket-based combined cycle (RBCC) engines, a detailed mechanism for chemistry is usually required but computationally prohibitive. In order to accelerate chemistry calculation, an artificial neural network (ANN) based methodology was introduced in this study. This methodology consists of two different layers: self-organizing map (SOM) and back-propagation neural network (BPNN). The SOM is for clustering the dataset into subsets to reduce the nonlinearity, while the BPNN is for regression for each subset. Compared with previous studies, the chemical reaction mechanism involved in this study is more complex, therefore, the particle swarm optimization (PSO) method is employed for accelerating training process in this study. Then we were committed to constructing an ANN-based mechanism of hydrogen and kerosene for supersonic turbulent combustion and verifying it in a practical RBCC combustion chamber. The training data was generated by RANS simulations of the RBCC combustion chamber, and then fed into the SOM-BPNN with six different topologies (three different SOM topologies and two different BPNN topologies). Through LES simulation of the Rocket-Based Combined Cycle (RBCC) combustor, the six ANN-based mechanisms were verified. By comparing the predicted results of six cases with those of the conventional ODE solver, it is found that if the topology is properly designed, high-precision results in terms of ignition, quenching and mass fraction prediction can be achieved. As for efficiency, 8~20 times speedup of the chemical system integration was achieved, which will greatly improve the computational efficiency of combustion simulation of hydrogen/carbon monoxide/kerosene mixture

A Deep Learning Framework for Hydrogen-fueled Turbulent Combustion Simulation

The high cost of high-resolution computational fluid/flame dynamics (CFD) has hindered its application in combustion related design, research and optimization. In this study, we propose a new framework for turbulent combustion simulation based on the deep learning approach. An optimized deep convolutional neural network (CNN) inspired from a U-Net architecture and inception module is designed for constructing the framework of the deep learning solver, named CFDNN. CFDNN is then trained on the simulation results of hydrogen combustion in a cavity with different inlet velocities. After training, CFDNN can not only accurately predict the flow and combustion fields within the range of the training set, but also shows an extrapolation ability for prediction outside the training set. The results from CFDNN solver show excellent consistency with the conventional CFD results in terms of both predicted spatial distributions and temporal dynamics. Meanwhile, two orders of magnitude of acceleration is achieved by using CFDNN solver compared to the conventional CFD solver. The successful development of such a deep learning-based solver opens up new possibilities of low-cost, high-accuracy simulations, fast prototyping, design optimization and real-time control of combustion systems such as gas turbines and scramjets

CNN-based flow control device modelling on aerodynamic airfoils

Wind energy has become an important source of electricity generation, with the aim of achieving a cleaner and more sustainable energy model. However, wind turbine performance improvement is required to compete with conventional energy resources. To achieve this improvement, flow control devices are implemented on airfoils. Computational fluid dynamics (CFD) simulations are the most popular method for analyzing this kind of devices, but in recent years, with the growth of Artificial Intelligence, predicting flow characteristics using neural networks is becoming increasingly popular. In this work, 158 different CFD simulations of a DU91W(2)250 airfoil are conducted, with two different flow control devices, rotating microtabs and Gurney flaps, added on its Trailing Edge (TE). These flow control devices are implemented by using the cell-set meshing technique. These simulations are used to train and test a Convolutional Neural Network (CNN) for velocity and pressure field prediction and another CNN for aerodynamic coefficient prediction. The results show that the proposed CNN for field prediction is able to accurately predict the main characteristics of the flow around the flow control device, showing very slight errors. Regarding the aerodynamic coefficients, the proposed CNN is also capable to predict them reliably, being able to properly predict both the trend and the values. In comparison with CFD simulations, the use of the CNNs reduces the computational time in four orders of magnitude.The authors are thankful to the government of the Basque Country for the ELKARTEK21/10 KK-2021/00014 and ITSAS-REM IT1514-22 research programs, respectively

Evolutionary Multi-Objective Aerodynamic Design Optimization Using CFD Simulation Incorporating Deep Neural Network

An evolutionary multi-objective aerodynamic design optimization method using the computational fluid dynamics (CFD) simulations incorporating deep neural network (DNN) to reduce the required computational time is proposed. In this approach, the DNN infers the flow field from the grid data of a design and the CFD simulation starts from the inferred flow field to obtain the steady-state flow field with a smaller number of time integration steps. To show the effectiveness of the proposed method, a multi-objective aerodynamic airfoil design optimization is demonstrated. The results indicate that the computational time for design optimization is suppressed to 57.9% under 96 cores processor conditions

A Convolutional Neural Network Model based on Multiscale Structural Similarity for the Prediction of Flow Fields

We have seen the emerging applications of deep neural networks for flow field predictions in the past few years. Most of the efforts rely on the increased complexity of the model itself or take advantage of novel network architectures, such as convolutional neural networks (CNN). However, reaching low prediction error cannot guarantee the quality of the predicted flow fields in terms of the perceived visual quality. This work introduces the multi-scale structural similarity (MS-SSIM) index method for flow field prediction. First, we train CNN models using the commonly used root mean squared error (RMSE) loss function as the reference. Then we introduce the SSIM loss function to capture the high-level features. Furthermore, we investigate the effects of the MS-SSIM weights on the predictive performance. Our results show that while the pixel-wise prediction error of RMSE-based models is as low as 1.3141 x 10−2, the perceived visual quality of the predicted flow fields, such as contour-line smoothness, is poorly represented. In contrast, the MS-SSIM models significantly improve the perceived visual quality with an SSIM loss value as low as 7.370 x 10−3, although having a slightly higher prediction error of 1.3912x10−2 . These values are 41.7% lower in the SSIM loss and 5.9% higher in the RMSE than the best RMSE model. In particular, we report that a weight combination of 0.3 and 0.7 for the MS-SSIM loss function provides the best predictive performance in our case. Our study has pointed out a possible future endeavor to invent a quality metric based on structural similarity, which should excel in flow-field-related approximations