Thermal-Fluid Research Workflow Plugin
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A domain-rigor layer for thermal-fluid mechanical engineering research with AI agents.
Generic research agents can summarize papers and draft prose. This plugin helps them do the harder mechanical-engineering work: check heat-transfer and fluid-flow assumptions, catch invalid correlation use, question CFD validation, protect uncertainty analysis, explain mechanisms, and turn evidence into decision-ready research artifacts.
Use it when a thermal-fluid answer needs to be physically defensible, not just well written.
If this helps your research workflow, please star the repo so other mechanical-engineering researchers can find it.
Want the fastest path? Start with QUICKSTART.md.
Two-Minute Demo
Paste a prompt like this after installing the skill:
Use the mechanical-engineering-research skill to review this claim:
"The CFD model proves that the new microchannel heat sink is optimal because
the average Nusselt number is 40% higher than the baseline. The simulation used
k-epsilon turbulence, a coarse wall mesh, constant water properties, and three
flow rates between Re = 350 and 900. Pressure drop is not discussed."
Expected style of response:
Bottom line: The claim is overconfident. The result may indicate better heat
transfer in the tested cases, but it does not prove optimality.
Key issues:
- Re = 350-900 may be laminar or transitional for the stated geometry, so a
k-epsilon turbulence model needs justification.
- A coarse wall mesh weakens confidence in wall heat flux and Nusselt number.
- Constant water properties may be acceptable only over a small temperature
range; the temperature span must be stated.
- A 40% Nusselt increase is incomplete without pressure drop, pumping power,
and thermal resistance.
- "Optimal" requires a design space, objective function, constraints, and
uncertainty or grid-independence evidence.
Next checks: compare against laminar correlations or a conjugate heat-transfer
baseline, report y+ or wall treatment, run mesh independence, include pressure
drop and pumping power, and rewrite the claim as evidence from a limited CFD
study rather than proof of global optimality.
Workflow
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flowchart TB
A["Research Request"]:::input
B["Plugin Router"]:::core
C["Academic Scaffold"]:::scaffold
D["ME Judgment Layer"]:::core
A --> B
C -. "process" .-> B
B --> D
D --> E{"Mode"}:::gate
E --> F1["Literature Map"]:::lane
E --> F2["Analysis + DOE"]:::lane
E --> F3["CFD + Tests"]:::lane
E --> F4["Writing + Proposals"]:::lane
E --> F5["Code + AI/ML"]:::lane
E --> F6["Slides + IP"]:::lane
F1 --> G
F2 --> G
F3 --> G
F4 --> G
F5 --> G
F6 --> G
G["Rigor Gate"]:::gate
H["Decision Output"]:::output
I["Reusable Artifact"]:::artifact
G --> H --> I
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classDef core fill:#ccfbf1,stroke:#0f766e,stroke-width:3px,color:#0f172a;
classDef scaffold fill:#f8fafc,stroke:#64748b,stroke-width:2px,color:#0f172a;
classDef lane fill:#fff7ed,stroke:#f97316,stroke-width:2px,color:#0f172a;
classDef gate fill:#fef3c7,stroke:#d97706,stroke-width:3px,color:#0f172a;
classDef output fill:#ecfdf5,stroke:#16a34a,stroke-width:3px,color:#0f172a;
classDef artifact fill:#f5f3ff,stroke:#7c3aed,stroke-width:2px,color:#0f172a;
Editable Mermaid source: assets/workflow.mmd.
What It Catches
