首页|St. Petersburg University Researcher Adds New Findings in the Area of Machine Le arning (Simulation of Shock Waves in Methane: A Self-Consistent Continuum Approa ch Enhanced Using Machine Learning)
St. Petersburg University Researcher Adds New Findings in the Area of Machine Le arning (Simulation of Shock Waves in Methane: A Self-Consistent Continuum Approa ch Enhanced Using Machine Learning)
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Investigators publish new report on ar tificial intelligence. According to news originating from St. Petersburg, Russia , by NewsRx editors, the research stated, "This study presents a self-consistent one-temperature approach for modeling shock waves in single-component methane." Funders for this research include Ministry of Science And Higher Education of Th e Russian Federation. Our news correspondents obtained a quote from the research from St. Petersburg U niversity: "The rigorous mathematical model takes into account the complex struc ture of CH4 molecules with multiple vibrational modes and incorporates exact kin etic theory-based transport coefficients, including bulk viscosity. The effects of the bulk viscosity on gas-dynamic variables and transport terms are investiga ted in detail under varying degree of gas rarefaction. It is demonstrated that n eglecting bulk viscosity significantly alters the shock front width and peak val ues of normal stress and heat flux, with the effect being more evident in denser gases. The study also evaluates limitations in the use of a constant specific h eat ratio, revealing that this approach fails to accurately predict post-shock p arameters in polyatomic gases, even at moderate Mach numbers. To enhance computa tional efficiency, a simplified approach based on a reduced vibrational spectrum is assessed. The results indicate that considering only the ground state leads to substantial errors in the fluid-dynamic variables across the shock front."
St. Petersburg UniversitySt. Petersbur gRussiaEurasiaAlkanesCyborgsEmerging TechnologiesMachine LearningM ethane
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