AI Research Projects
Measuring and advancing AI performance in industrial thermodynamics through open-source benchmarks and domain-specific models.
Entropy Hunter
Domain-Specific LLM for Exergy Analysis
A domain-specific LLM specialized in exergy analysis of industrial equipment via LoRA fine-tuning on Qwen3-8B. Achieved 92.7% benchmark score (Grade A-) with only $216 total cost on a single consumer GPU.
Key Findings
- 1JSON format acts as a reasoning scaffold — removing it drops score from 92.7% to 52.4%
- 2Frontier-model-level domain performance on a consumer GPU for $216
- 3Thinking mode disabled yields better results — conflicts with JSON scaffold
- 4EGM at 72% is the weakest area — abstract optimization reasoning is the limit of 8B
ThermoQA
Thermodynamic AI Benchmark
An open-source, multi-tier benchmark system measuring AI models' thermodynamic problem-solving competency. Tests property lookups, component analysis, and cycle analysis.
Tier 1: Property Lookups
110 questions, single fluid (water), steam table value reading with CoolProp 7.2.0 reference.
Tier 2: Component Analysis
101 questions, 7 components, 3 fluids. Multi-step thermodynamic reasoning including energy balance, isentropic efficiency, and exergy.
Tier 3: Cycle Analysis
82 questions, 9 cycle types, 4 fluids. Full Rankine, Brayton, VCR, and CCGT cycle analysis with weighted step-level scoring.
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