Numerical Methods for Constrained Coulomb Gases, Theoretical Insights and Computational Applications
Implementing Didactic Reconstruction to Enhance Physics Education for Non-Physics Majors: A Case Study in Nutritional Sciences
Experimental Validation of Fluctuation Theorems under Thermal and Mechanical Driving using Levitated Nanoparticles
Mixed Convection in Turbulent Channel Flow a DNS Based Analysis of Thermal Scale Interactions with Pressure-Driven Turbulence
Spectral Energy Transfer and Turbulence Modulation in Viscoelastic Channel Flows with High Schmidt Number Polymers
Investigation of Temperature Sensitive Electrical Properties of Manganese-Zinc Ferrites
Effect of TiO2 Modifier Oxide on a B2O3 Glass System
Synthesis, Structural Characterization and DC Conductivity Study of (PMMA+PEG) Polymer Blend Films
Erbium Rare-Earth Metal Schottky Contact to P-Type Si and its Temperature-Dependent Current-Voltage Characteristics
Study of Moderate Temperature Plasma Nitriding of Inconel 601 Alloy
Study of Moderate Temperature Plasma Nitriding of Inconel 601 Alloy
Exact Solution of an Unsteady Buoyancy Force Effects on MHD Free Convective Boundary Layer Flow of Non-Newtonian Jeffrey Fluid
Numerical Methods for Constrained Coulomb Gases, Theoretical Insights and Computational Applications
Synthesis and Characterization of SnO2 Nanoparticles
Mapping and Forecasting the Land Surface Temperature in Response to the Land Use and Land Cover Changes using Machine Learning Over the Southernmost Municipal Corporation of Tamilnadu, India
Constrained Coulomb gases arise in various domains of physics and mathematics, notably in statistical mechanics and random matrix theory, where particle interactions are governed by singular repulsive potentials under external confinement and additional constraints. Simulating such systems poses significant challenges due to the interplay between singular interactions and constraint conditions. In this work, an efficient numerical framework based on adapted Hamiltonian Monte Carlo methods to sample constrained Coulomb gases was developed. A theoretical foundation using the Gibbs conditioning principle, providing rigorous insight into the behavior of these systems under linear and quadratic constraints. Numerical experiments in one and two dimensions validate the theoretical prediction that was established. Demonstrating the effectiveness of the approach in capturing equilibrium measures and fluctuation properties. This methodology opens avenues for precise simulation of conditioned particle systems, with potential applications in physics, probability, and computational mathematics.
This study presents the redesign of physics practical's for students of nutritional sciences at the University of Vienna, addressing long-standing challenges in motivation, relevance, and learning outcomes. Traditional physics exercises, originally developed for physics majors, often lack contextual alignment with the needs and backgrounds of non-physics students. To bridge this gap, the method of Didactic Reconstruction was applied, emphasizing learner-centered design, contextual relevance, and cognitive accessibility. Through a structured process involving expert consultation, learner perspective analysis, and iterative development, a new set of practical exercises was created. These exercises integrate interdisciplinary contexts, interactive digital tools, and adapted instructional strategies. Evaluation results, based on surveys and performance assessments, demonstrate improved student engagement, conceptual understanding, and perceived relevance to nutritional sciences. This paper outlines the methodological framework, implementation process, and key outcomes, offering a scalable model for similar interdisciplinary educational settings.
An experimental investigation was done on fluctuation theorems in thermally and mechanically driven systems using optically levitated nanoparticles. Using programmable feedback cooling in a hollow-core photonic crystal fiber trap allows for accurate control of the mechanical potential and the thermal conditions around a single trapped particle. This setup enables fast driving protocols far from equilibrium, where classical linear response theories fail. The Williams- Searles-Evans (WSE) equality and the generalized Jarzynski equality were experimentally tested and validated under thermal and simultaneous thermal-mechanical driving conditions, respectively. Tested results confirm the applicability of these fluctuation theorems up to two orders of magnitude beyond the quasi-static regime. The derived free energy differences were then benchmarked against equilibrium and linear response expectations, revealing considerable deviations under quick protocols and emphasizing the need for the complete statistical treatment. This work establishes levitated nanoparticles as a versatile platform for probing stochastic thermodynamics in extreme regimes.
This study presents a direct numerical simulation (DNS) investigation into the interactions between pressure-driven and thermally driven turbulence in mixed convection flows. By systematically varying Reynolds and Prandtl numbers, it is examined how weakly diffusive thermal fluctuations influence energy transfer and turbulence production across scales in a periodic channel configuration. The results reveal that while large-scale convection rolls dominate global flow dynamics, their coupling with pressure-driven turbulence is highly dependent on thermal diffusivity. Lower thermal diffusivity (higher Prandtl number) confines thermal activity to smaller scales, reducing its influence on pressure-driven mechanisms and altering the spectral energy distribution. Detailed energy budgets show that multi-physics interactions in mixed convection do not create sub-Kolmogorov turbulence but significantly reshape turbulence production pathways. The spectral analysis further confirms that thermal effects remain confined to larger structures, and the Batchelor scaling holds for temperature fields. These findings suggest that Reynolds-averaged Navier-Stokes (RANS) or large-eddy simulation (LES) models require tailored treatment of the two-way thermal momentum coupling for accurate prediction in stratified flow environments.
This study presents a comprehensive Direct Numerical Simulation (DNS) analysis of viscoelastic turbulence in channel flow, focusing on how dilute polymers with high Schmidt numbers (Sc) modify classical energy transfer mechanisms. The addition of polymers introduces nonlinear coupling between momentum and elastic stresses, altering the turbulence cascade by enabling energy exchanges at sub-Kolmogorov scales. As Sc increases, polymer concentration fluctuations become confined to finer scales yet retain a significant influence on the overall turbulence dynamics. Scale-resolved spectral analyses reveal a redistribution of energy from inertial to elastic scales, particularly in the streamwise direction, driven by polymer-induced stresses. Elastic instabilities in the buffer and logarithmic layers amplify turbulence regeneration processes and give rise to non-Kolmogorov scaling behavior in the energy spectrum. These findings demonstrate that traditional turbulence models fail to capture the nonlocal and multiphysics energy interactions characteristic of viscoelastic flows, underscoring the need for advanced subgrid-scale modeling strategies in LES and RANS frameworks to accurately simulate such complex systems.
