Thermodynamics powered by Machine Learning
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Updated
May 7, 2021 - Python
Thermodynamics powered by Machine Learning
A bilingual (EN/ZH) 4-stage research workflow skill for Claude Code and Codex CLI — topic refinement, urgency assessment, route evaluation, and experiment planning with citation verification
Materials that are dependent on conditions
Study of molecular motion of Glycerol using NMR modeling and simulations
PressureX is an engineering evaluation package for a passive layered structural mitigation concept using shear-thickening fluid behavior to broaden impulsive loads and reduce peak transmitted response in high-vibration aerospace structures. Targets are design-intent until validated.
Integrated thermal, kinetic & scheduling framework that cuts a steelmaking Ladle Furnace cycle 70 → 37.6 min — coupling queueing theory, transient heat transfer, dephosphorisation kinetics & Monte Carlo. IIT Madras × Amalgam Steel.
Public-source systems-engineering commentary on Starship's stainless-steel route, reuse governance, and safety-boundary thinking.
Thermo-metallurgical FEM (Abaqus) predicting phase transformation in a quenched S5140 steel gear - JMAK + Koistinen-Marburger kinetics, parametric cooling-rate study. IIT Madras.
Microstructure vision-based porosity analysis
A small-scale Extract-Transform-Load framework focused on materials characterization
Metal LPBF process documentation from my Uniformity Labs Metal 3D Print Specialist role — AlSi10Mg, Ti-6Al-4V, 316/304 SS, and Inconel 625 + 718, printed on SLM Solutions SLM 125 and SLM 280 machines.
Open-source Arabic assistant for ceramic manufacturing defect diagnosis, quality control, and OpenAlex-powered research summaries.
Brass tensile test analysis using MATLAB: stress–strain visualization, elastic-region fitting, yield determination, and mechanical properties extraction.
Material property database for Grade 91 (9Cr-1Mo-V-Nb) steel. Larson-Miller creep rupture, Norton power law, tensile correlations, and Coffin-Manson strain-life from published NIMS/ORNL sources.
Python-based coating robustness and machining severity decision-support framework
[11] You don't choose Ridge or Lasso - you let the data decide.
Python-based GUI application for Dynamic Mechanical Analysis (DMA) data processing, visualization and automated reporting.
Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world.
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