Related papers: Machine Learning for Electron-Scale Turbulence Mod…
This data set descriptor introduces a structured, high-resolution dataset of transient thermal simulations for a vertical axis of a machine tool test rig. The data set includes temperature and heat flux values recorded at 29 probe locations…
Fast and accurate approximations of gas cooling and heating functions are needed for hydrodynamic galaxy simulations. We use machine learning to analyze atomic gas cooling and heating functions computed by Cloudy in the presence of a…
Accurate particle shower simulation remains a critical computational bottleneck for high-energy physics. Traditional Monte Carlo methods, such as Geant4, are computationally prohibitive, while existing machine learning surrogates are tied…
The ability to perform ab initio molecular dynamics simulations using potential energies calculated on quantum computers would allow virtually exact dynamics for chemical and biochemical systems, with substantial impacts on the fields of…
One key challenge for efficiency and safety in fusion devices is the retention of tritium (T) in plasma-facing components. Tritium retention generates radioactive concerns and decreases the amount of fuel available to generate power. Hence,…
Simulations are performed to investigate turbulent properties of nonlinearly interacting two-dimensional (2D) magnetic electron drift vortex (MEDV) modes in a nonuniform unmagnetized plasma. The relevant nonlinear equations governing the…
Heatwaves are an important problem in cities, and climate change makes this problem more difficult. In this paper, we present a GPU-based deep learning framework for next-day prediction of urban thermal conditions and for heat risk…
Training data-driven turbulence models with high fidelity Reynolds stress can be impractical and recently such models have been trained with velocity and pressure measurements. For gradient-based optimization, such as training deep learning…
The disruption and runaway electron analysis model code was extended to include tungsten impurities in disruption simulations with the aim of studying the runaway electron (RE) generation. This study investigates RE current sensitivity on…
Reduced fluid models including electron inertia and ion finite Larmor radius corrections are derived asymptotically, both from fluid basic equations and from a gyrofluid model. They apply to collisionless plasmas with small ion-to-electron…
An understanding of how turbulent energy is partitioned between ions and electrons in weakly collisional plasmas is crucial for modelling many astrophysical systems. Using theory and simulations of a four-dimensional reduced model of…
Turbulence induced by the ion temperature gradient (ITG) is investigated in the helical and axisymmetric plasma states of a reversed field pinch device by means of gyrokinetic calculations. The two magnetic configurations are systematically…
Microturbulence in magnetic confined plasmas contributes to energy exchange between particles of different species as well as the particle and heat fluxes. Although the effect of turbulent energy exchange has not been considered significant…
The accurate prediction of the two-phase heat transfer coefficient (HTC) as a function of working fluids, channel geometries and process conditions is key to the optimal design and operation of compact heat exchangers. Advances in…
Ion temperature gradient(ITG) and trapped electron modes(TEM) driven turbulent transport in an ITER-like plasma is investigated by means of multi-species gyrokinetic Vlasov simulations with D, T, He, and real-mass kinetic electrons…
Accurate and efficient temperature prediction is critical for optimizing the preheating process of PET preforms in industrial microwave systems prior to blow molding. We propose a novel deep learning framework for generalized temperature…
State-of-the-art space science missions increasingly rely on automation due to spacecraft complexity and the costs of human oversight. The high volume of data, including scientific and telemetry data, makes manual inspection challenging.…
We introduce \textit{SeismoGPT}, a transformer-based model for forecasting three-component seismic waveforms in the context of future gravitational wave detectors like the Einstein Telescope. The model is trained in an autoregressive…
With the popularity of electric vehicles, the demand for lithium-ion batteries is increasing. Temperature significantly influences the performance and safety of batteries. Battery thermal management systems can effectively control the…
Outdoor thermal comfort is a critical determinant of urban livability, particularly in hot desert climates where extreme heat poses challenges to public health, energy consumption, and urban planning. Mean Radiant Temperature ($T_{mrt}$) is…