# ParaPowerPython Python adaptation of the (open-source) thermal solver [ParaPower](https://github.com/USArmyResearchLab/ParaPower) (originally written in MATLAB) for rapid evaluation of 3D power module thermal behavior using a lumped thermal resistance network.

## Overview ParaPowerPython builds a discretized 3D representation (features: substrates, layers, components, bond wires, etc.) and computes temperature distribution for (currently) a single static thermal step. Internally it: 1. Loads a user JSON description (geometry, materials, boundary conditions, parameters). 2. Executes a static thermal solution (`main(..., mode="thermal", type_="static")`). 3. Returns either global maximum temperatures or per‑feature maxima. ## Installation Requirements: * Python ≥ 3.8 * `numpy` Clone or copy this repository and ensure `materials.npy` stays in the root directory. ## Input JSON Structure Top‑level keys expected by `main()`: ```jsonc { "ExternalConditions": { /* convection & ambient temps */ }, "Features": [ /* geometric feature list */ ], "Params": { /* simulation parameters */ } } ``` ### 1. ExternalConditions Boundary convection coefficients (h) and ambient temperatures (Ta) are face‑labeled. Also include process / internal reference temperature `Tproc` if applicable. | JSON Key | Meaning | Example | |-------------|------------------------------------|---------| | h_Left | Convection coeff at -X face | 0 | | h_Right | Convection coeff at +X face | 0 | | h_Front | Convection coeff at +Y face | 0 | | h_Back | Convection coeff at -Y face | 0 | | h_Bottom | Convection coeff at -Z face | 150.0 | | h_Top | Convection coeff at +Z face | 0 | | Ta_Left | Ambient temp at -X face (°C) | 27.0 | | Ta_Right | Ambient temp at +X face (°C) | 27.0 | | Ta_Front | Ambient temp at +Y face (°C) | 27.0 | | Ta_Back | Ambient temp at -Y face (°C) | 27.0 | | Ta_Bottom | Ambient temp at -Z face (°C) | 27.0 | | Ta_Top | Ambient temp at +Z face (°C) | 27.0 | | Tproc | Internal process / junction temp | 230.0 | Example: ```json "ExternalConditions": { "h_Left": 0, "h_Right": 0, "h_Front": 0, "h_Back": 0, "h_Bottom": 150.0, "h_Top": 0, "Ta_Left": 27.0, "Ta_Right": 27.0, "Ta_Front": 27.0, "Ta_Back": 27.0, "Ta_Bottom": 27.0, "Ta_Top": 27.0, "Tproc": 230.0 } ``` ### 2. Params General simulation parameters (current implementation uses a single static step): | Key | Meaning | Example | |----------|-----------------------------------------|---------| | Tsteps | (Reserved) list of time steps | [] | | DeltaT | Time increment between steps (s) | 1 | | Tinit | Initial temperature (°C) | 27.0 | Example: ```json "Params": { "Tsteps": [], "DeltaT": 1, "Tinit": 27.0 } ``` ### 3. Features Each feature describes a rectangular prism region (layer/component/etc.). Coordinates are given in micrometers (µm) and converted internally to meters. Mesh resolution is controlled by `dx`, `dy`, `dz` (number of divisions per axis segment). | Key | Meaning | |------|---------------------------------------------------| | name | Feature descriptor (will map to `Desc`) | | x | `[x_start, x_end]` in µm | | y | `[y_start, y_end]` in µm | | z | `[z_start, z_end]` in µm | | dx | Divisions along X (larger => finer) | | dy | Divisions along Y | | dz | Divisions along Z | | Q | Volumetric heat generation term (units TBD) | | Matl | Material name (mapped to internal library) | Example feature list (simplified): ```json "Features": [ { "name": "base_plate", "x": [0, 10000], "y": [0, 10000], "z": [0, 500], "dx": 2, "dy": 2, "dz": 2, "Q": 0, "Matl": "Copper" }, { "name": "mosfet_A", "x": [2000, 4000], "y": [2000, 4000], "z": [500, 800], "dx": 2, "dy": 2, "dz": 2, "Q": 10, "Matl": "SiC" } ] ``` Material names from external sources are auto‑translated (e.g. `copper` → `Cu`, `Pb-Sn Solder Alloy` → `SAC405`, `Air` → `AIR`). If an unknown name is supplied it is passed through unchanged. ## Full Minimal JSON Example ```json { "ExternalConditions": { "h_Left": 0, "h_Right": 0, "h_Front": 0, "h_Back": 0, "h_Bottom": 150.0, "h_Top": 0, "Ta_Left": 27.0, "Ta_Right": 27.0, "Ta_Front": 27.0, "Ta_Back": 27.0, "Ta_Bottom": 27.0, "Ta_Top": 27.0, "Tproc": 230.0 }, "Params": { "Tsteps": [], "DeltaT": 1, "Tinit": 27.0 }, "Features": [ {"name": "base_plate", "x": [0, 10000], "y": [0, 10000], "z": [0, 500], "dx": 10, "dy": 10, "dz": 2, "Q": 0, "Matl": "Copper"}, {"name": "mosfet_A", "x": [2000, 4000], "y": [2000, 4000], "z": [500, 800], "dx": 4, "dy": 4, "dz": 2, "Q": 10, "Matl": "SiC"} ] } ``` ## Usage ```python from main import main result = main("model.json", mode="thermal", type_="static", results_type="global") print(result) ``` Arguments: | Parameter | Allowed Values | Description | |---------------|-------------------------|-------------| | json_file | Path to JSON model | Input geometry & conditions | | mode | "thermal" | (Future: other physics) | | type_ | "static" | Static thermal step (no transient loop yet) | | results_type | "global" / "individual" | Max temperature overall or per feature | Return value: * `global` → array of length 2 (initial and computed max temperatures). * `individual` → dict: `{feature_name: [T_init, T_final], ...}`. ## Mesh & Performance Notes * Larger `dx/dy/dz` values increase resolution but also runtime. * All coordinates in the JSON are in micrometers (µm); they are automatically converted to meters.