相关论文: A Rapid Compression Expansion Machine (RCEM) for s…
The ignition delay of a fuel/air mixture is an important quantity in designing combustion devices, and these data are also used to validate chemical kinetic models for combustion. One of the typical experimental devices used to measure the…
This paper presents RDCSim, an interactive simulator for reaction--diffusion chemistry (RDC) research, being developed as part of an ongoing project studying how humans interact with unconventional computing systems. While much research…
Rapid compression machines (RCMs) have been extensively used to quantify fuel autoignition chemistry and validate chemical kinetic models. Historically, the analyses of experimental and modeling RCM autoignition data have been conducted…
$\textit{In situ}$ environmental transmission electron microscopy (ETEM) is a powerful tool for observing structural modifications taking place in heterogeneous catalysts under reaction conditions. However, to strengthen the link between…
Recent developments of the Cascade-Exciton Model (CEM) of nuclear reactions are described. The improved cascade-exciton model as implemented in the code CEM97 differs from the CEM95 version by incorporating new approximations for the…
Increasing complexity of scientific simulations and HPC architectures are driving the need for adaptive workflows, where the composition and execution of computational and data manipulation steps dynamically depend on the evolutionary state…
In many scientific fields, there is an interest in understanding the way in which complex chemical networks evolve. The chemical networks which researchers focus upon, have become increasingly complex and this has motivated the development…
This paper introduces a new platform to accelerate the modeling of complex aerothermochemical interactions in new turbomachines, turbo-reactors, to decarbonise chemical processes. While previous work has aerothermally demonstrated the…
New experimental data are collected for methyl-cyclohexane (MCH) autoignition in a heated rapid compression machine (RCM). Three mixtures of MCH/O2/N2/Ar at equivalence ratios of $\phi$=0.5, 1.0, and 1.5 are studied and the ignition delays…
Rapid compression machines (RCMs) have been widely used in the combustion literature to study the low-to-intermediate temperature ignition of many fuels. In a typical RCM, the pressure during and after the compression stroke is measured.…
The chemical master equation (CME) is the exact mathematical formulation of chemical reactions occurring in a dilute and well-mixed volume. The reaction-diffusion master equation (RDME) is a stochastic description of reaction-diffusion…
Recent developments of the Cascade-Exciton Model (CEM) of nuclear reactions to describe high energy particle induced fission are briefly described. The increased accuracy and predictive power of the CEM are shown by several examples.…
An environmental cell high resolution electron microscope (EHREM) has been developed for in situ studies of dynamic chemical reactions on the atomic scale. It allows access to metastable intermediate phases of catalysts and to sequences of…
Fuel efficient Homogeneous Charge Compression Ignition (HCCI) engine combustion timing predictions must contend with non-linear chemistry, non-linear physics, period doubling bifurcation(s), turbulent mixing, model parameters that can drift…
To deploy machine learning models on-device, practitioners use compression algorithms to shrink and speed up models while maintaining their high-quality output. A critical aspect of compression in practice is model comparison, including…
Laser flash melting and revitrification experiments have recently improved the time resolution of cryo-electron microscopy (cryo-EM) to the microsecond timescale, making it fast enough to observe many of the protein motions that are…
Extreme Learning Machines (ELM) provide a fast alternative to traditional gradient-based learning in neural networks, offering rapid training and robust generalization capabilities. Its theoretical basis shows its universal approximation…
Many applications require that we learn the parameters of a model from data. EM is a method used to learn the parameters of probabilistic models for which the data for some of the variables in the models is either missing or hidden. There…
In order to understand the mechanisms involved in the forming step of LCM processes and provide validation date to numerical models, a specific experimental device has been designed in collaboration between PRISME Institute and EADS. The…
High-resolution climate simulations are valuable for understanding climate change impacts. This has motivated use of regional convection-permitting climate models (CPMs), but these are very computationally expensive. We present a…