Assessment of the LES-FGM framework for capturing stable and unstable modes in a hydrogen / methane fuelled premixed combustor

The main objective of this paper is to assess the capability of compressible Large Eddy Simulations (LES) to capture azimuthal combustion instability. The thickened flame model coupled with Flamelet Generated Manifold (FGM) tabulated chemistry is used as the combustion model. LES of an annular combustor is performed for five cases featuring stable and unstable combustion of hydrogen-methane mixtures. The unstable modes feature azimuthal instabilities and this annular combustor is used to test the LES-FGM framework. A consistent methodology is applied across all cases. It is found that LES predicts azimuthal modes for stable cases but these modes are weak and intermittent with pressure fluctuation amplitudes within the order of experimental noise. In addition, the unstable cases capture azimuthal modes that have approximately the same frequency as that of the experiment though the amplitudes of the modes are over-predicted. This suggests that the described LES-FGM framework is able to predict the onset of thermoacoustic instabilities and their qualitative changes with addition of hydrogen. © 2023 The Combustion InstituteAssessment of the LES-FGM framework for capturing stable and unstable modes in a hydrogen / methane fuelled premixed combustoracceptedVersio

A Conditional Moment Closure Study of Chemical Reaction Source Terms in SCCI Combustion

The objective of this study is to evaluate conditional moment closure (CMC) approaches to model chemical reaction rates in compositionally stratified, autoigniting mixtures, in thermochemical conditions relevant to stratified charge compression ignition (SCCI) engines. First-order closure, second-order closure and double conditioning are evaluated and contrasted as options in comparison to a series of direct numerical simulations (DNSs). The two-dimensional (2D) DNS cases simulate ignitions in SCCI-like thermochemical conditions with compositionally stratified n-heptane/air mixtures in a constant volume. The cases feature two different levels of stratification with three mean temperatures in the negative-temperature coefficient (NTC) regime of ignition delay times. The first-order closure approach for reaction rates is first assessed using hybrid DNS-CMC a posteriori tests when implemented in an open source computational fluid dynamics (CFD) package known as OpenFOAMⓇ. The hybrid DNS-CMC a posteriori tests are not a full CMC but a DNS-CMC hybrid in that they compute the scalar and velocity fields at the DNS resolution, thus isolating the first-order reaction rate closure model as the main source of modelling error (as opposed to turbulence model, scalar probability density function model, and scalar dissipation rate model). The hybrid DNS-CMC a posteriori test reveals an excellent agreement between the model and DNS for the cases with low levels of stratification, whereas deviations from the DNS are observed in cases which exhibit high level of stratifications. The a priori analysis reveals that the reason for disagreement is failure of the first-order closure hypothesis in the model due to the high level of conditional fluctuations. Second-order and double conditioning approaches are then evaluated in a priori tests to determine the most promising path forwards in addressing higher levels of stratification. The a priori tests use the DNS data to compute the model terms, thus directly evaluating the model assumptions. It is shown that in the cases with a high level of stratification, even the second-order estimation of the reaction rate source term cannot provide a reasonably accurate closure. Double conditioning using mixture-fraction and sensible enthalpy, however, provides an accurate first-order closure to the reaction rate source term

Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data

Analysis of compressible turbulent flows is essential for applications related to propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 TB network-of-datasets containing 744 full-domain samples from 34 high-fidelity direct numerical simulations, which addresses the current limited availability of 3D high-fidelity reacting and non-reacting compressible turbulent flow simulation data. With this data, we benchmark a total of 49 variations of five deep learning approaches for 3D super-resolution - which can be applied for improving scientific imaging, simulations, turbulence models, as well as in computer vision applications. We perform neural scaling analysis on these models to examine the performance of different machine learning (ML) approaches, including two scientific ML techniques. We demonstrate that (i) predictive performance can scale with model size and cost, (ii) architecture matters significantly, especially for smaller models, and (iii) the benefits of physics-based losses can persist with increasing model size. The outcomes of this benchmark study are anticipated to offer insights that can aid the design of 3D super-resolution models, especially for turbulence models, while this data is expected to foster ML methods for a broad range of flow physics applications. This data is publicly available with download links and browsing tools consolidated at https://blastnet.github.io.Comment: Accepted in Advances in Neural Information Processing Systems 36 (NeurIPS 2023). 55 pages, 21 figures. v2: Corrected co-author name. Keywords: Super-resolution, 3D, Neural Scaling, Physics-informed Loss, Computational Fluid Dynamics, Partial Differential Equations, Turbulent Reacting Flows, Direct Numerical Simulation, Fluid Mechanics, Combustio

Survey of both hepatitis B virus (HBsAg) and hepatitis C virus (HCV-Ab) coinfection among HIV positive patients

<p>Abstract</p> <p>Background</p> <p>HIV, HBVand HCV is major public health concerns. Because of shared routes of transmission, HIV-HCV coinfection and HIV-HBV coinfection are common. HIV-positive individuals are at risk of coinfection with HBV and HCV infections. The prevalence rates of coinfection with HBV and HCV in HIV-patients have been variable worldwide depending on the geographic regions, and the type of exposure.</p> <p>Aim</p> <p>This study aimed to examine HBV and HCV coinfection serologically and determine the shared and significant factors in the coinfection of HIV-positive patients.</p> <p>Methods</p> <p>This descriptive, cross-sectional study was carried out on 391 HIV-positive patients including 358 males and 33 females in Lorestan province, west Iran, to survey coinfection with HBsAg and anti-HCV. The retrospective demographic data of the subjects was collected and the patients' serums were analyzed by ELISA kits including HBsAg and anti-HCV. The collected data was analyzed with SPSS software (15) and Chi-square. Fisher's exact test with 5% error intervals was used to measure the correlation of variables and infection rates.</p> <p>Results</p> <p>The results of the study indicated that the prevalence of coinfection in HIV-positive patients with hepatitis viruses was 94.4% (370 in 391), out of whom 57 (14.5%) cases were HBsAg positive, 282 (72%) cases were anti-HCV positive, and 31 (7.9%) cases were both HBsAg and anti-HCV positive.</p> <p>Conclusion</p> <p>There was a significant correlation between coinfection with HCV and HBV and/or both among HIV-positive patients depending on different variables including sex, age, occupation, marital status, exposure to risk factors.(p < 0.001).</p

Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling

This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate-gradient, gradient descent and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from LM-NN and these results were then compared with those from PSO-based ANNs, including conventional PSO neural network (CPSONN) and improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. Our results show that the PSO-based ANNs performed better than LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing dataset for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 m) and LM-NN (0.205 m). For multi-parameter (rainfall and water level) inputs, the RMSE of the testing dataset for IPSONN was also the lowest (0.089 m) compared to those for CPSONN (0.105 m) and LM-NN (0.145 m). The results also indicate that the LM-NN model performed poorly in predicting the low and peak water levels, in comparison to the PSO-based ANNs. Moreover, the IPSONN model was superior to CPSONN in predicting extreme water levels. Lastly, IPSONN had a quicker convergence rate compared to CPSONN