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Recent developments in deep learning have brought many inspirations for the scientific computing community and it is perceived as a promising method in accelerating the computationally demanding reacting flow simulations. In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows. We combine the individual strengths of the computational fluid dynamics library OpenFOAM, machine learning framework Torch, and chemical kinetics program Cantera. The complexity of cross-library function and data interfacing (the core of DeepFlame) is minimised to achieve a simple and clear workflow for code maintenance, extension and upgrading. As a demonstration, we apply our recent work on deep learning for predicting chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to highlight the potential of machine learning in accelerating reacting flow simulation. A thorough code validation is conducted via a broad range of canonical cases to assess its accuracy and efficiency. The results demonstrate that the convection-diffusion-reaction algorithms implemented in DeepFlame are robust and accurate for both steady-state and transient processes. In addition, a number of methods aiming to further improve the computational efficiency, e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their performances are also evaluated and reported. With the deep learning method implemented in this work, a speed-up of two orders of magnitude is achieved in a simple hydrogen ignition case when performed on a medium-end graphics processing unit (GPU). Further gain in computational efficiency is expected for hydrocarbon and other complex fuels. A similar level of acceleration is obtained on an AI-specific chip -deep computing unit (DCU), highlighting the potential of DeepFlame in leveraging the next-generation computing architecture and hardware.

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Road traffic noise control in urban green space is a big concern for urban designers and public managers. The introduction of water sounds into noisy environment has been proven effective based on the soundscape approach. To extend more effective and applicable strategies for water sound informational masking, the exploration of the spatial settings of virtual water sound playbacks in urban parks were conducted both in the laboratory and field settings. Three different spatial water-sound sequences were added into the virtual noisy environment through an immersive spatial audio system and the real urban green park through the digital audio programming of bluetooth loudspeakers. The mental activities and subjective feelings of two group subjects were evaluated by a portable electroencephalogram (EEG) measurement with a post-doc questionnaire. The better masking effects introduced by the spatial settings of water sounds had been confirmed from the results of more positive emotional feedbacks and more relaxed mental state revealed by the spectral power of alpha band across two experimental conditions. Especially, the two-position switching water sounds brought more attentional network activations. Moreover, more sensory accumulation effects reflected by the mental network activations were observed from the brain activities in the in situ measurement compared to laboratory-setting.

Regarding prevention and control measures related to the COVID-19 pandemic, if healthcare workers are on the frontlines of the battlefield, there is also another, invisible battlefield on which the virus must be prevented from prevailing – the environmentally sound disposal of related medical waste. The charging system for medical waste disposal varies around the world, and the typical case chosen for this research is China’s medical waste disposal. In contrast to many Western countries that chose to adopt co-existent strategies for combating the COVID-19 pandemic, China chose to adopt a more centralized policy, including PCR testing on a community basis, a home-based quarantine method, and government-managed quarantine centers, which led to producing much more medical waste than ever before. However, China does not apply a national standard for medical waste disposal charges, and each local government has different charge systems and management regulations. There are differences in charging methods, management systems, and the nature of medical institutions in the implementation of the charging system. Given the actual situation, the paper puts forward feasible suggestions for government policy at the hospital and technology levels.