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Linked challenge: Build an EnergyPlus Infiltration and Thermal Envelope Calibration Assistant

Format
Code-aware
Lines
65
Sections
11
Linked challenge
Build an EnergyPlus Infiltration and Thermal Envelope Calibration Assistant

Prompt source

Original prompt text with formatting preserved for inspection.

65 lines
11 sections
No variables
1 code block
## Build
Evaluate whole-building blower door air leakage test data against EnergyPlus epJSON ZoneInfiltration objects. Calibrate air flow coefficients and thermal properties to reconcile measured operational infiltration with simulation models.

## User
Building Energy Modeler

## Workflow
1. Parse baseline commercial building energy models formatted in EnergyPlus epJSON schema.
2. Ingest field blower door leakage rates and building envelope surface area measurements.
3. Adjust ZoneInfiltration:DesignFlowRate flow per exterior surface area parameters.
4. Generate calibrated epJSON building input objects and output a calibration variance summary.

## Interface
Interactive parameter tuning panel with air-tightness curve plots and JSON schema diff viewers.

## Data
Use example EnergyPlus epJSON files from the EnergyPlus repo test suites and mock blower-door test logs.
Starter examples below are synthetic. They are not downloaded records.

## Source
[EnergyPlus](https://raw.githubusercontent.com/NREL/EnergyPlus/develop/README.md)
Leverages EnergyPlus epJSON input schema and ZoneInfiltration input objects described in the repository documentation.

## Tools
optuna, pydantic, FastAPI.
Optuna optimizes multi-zone infiltration coefficients to minimize the error between simulated leakage and field test curves.
Fast instruction LLM for checking parameter boundaries and summarizing energy model adjustments.

## Deliverables
Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

## Acceptance
- Parses EnergyPlus epJSON ZoneInfiltration:DesignFlowRate objects accurately.
- Calculates calibrated design volume flow rates based on ACH50 field test measurements.
- Validates calibrated values against epJSON schema constraints and acceptable engineering boundaries.

## Starter Examples
```json
[
  {
    "input": {
      "zone_id": "Office_Floor_1",
      "exterior_surface_area_m2": 1200,
      "baseline_flow_per_zone_floor_area": 0.0003,
      "blower_door_ach50": 3.2,
      "zone_volume_m3": 3600
    },
    "expected_output": {
      "calibrated_design_flow_rate_m3_s": 0.576,
      "adjusted_flow_per_exterior_area": 0.00048,
      "variance_percentage": 60,
      "status": "calibrated"
    }
  },
  {
    "input": {
      "zone_id": "Server_Room",
      "exterior_surface_area_m2": 0,
      "baseline_flow_per_zone_floor_area": 0,
      "blower_door_ach50": 0,
      "zone_volume_m3": 400
    },
    "expected_output": {
      "calibrated_design_flow_rate_m3_s": 0,
      "adjusted_flow_per_exterior_area": 0,
      "variance_percentage": 0,
      "status": "interior_zone_skipped"
    }
  }
]
```

## Safety
Simulation inputs are validated against schema boundaries; no automated building equipment control overrides.
Use mock external writes. Require human review before any real deployment.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.