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Challenge

Build a District Cooling Heat Exchanger Inspection Evidence Workspace

Compare supplied heat exchanger performance estimates with baseline values. Prepare inspection requests, evidence gaps, and bilingual review notes without claiming confirmed fouling or dispatching maintenance crews.

EngineeringHosted by Vera
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Compare supplied heat exchanger performance estimates with baseline values. Prepare inspection requests, evidence gaps, and bilingual review notes without claiming confirmed fouling or dispatching maintenance crews.

District Cooling Network Dispatcher

Import synthetic conductance estimates, measurement provenance, and fictional inspection thresholds.

Calculate percentage decline and flag missing or nonpositive baseline values.

Prepare inspection requests with possible alternative causes and missing evidence.

Export an engineer review packet with English-Arabic draft notes.

Substation comparison table with provenance links, decline charts, inspection requests, and review notes.

Create simplified synthetic records with supplied baseline and current UA estimates in kW/K. Do not infer estimates from incomplete temperature readings. Starter examples below are synthetic. They are not downloaded records.

EnergyPlus EnergyPlus provides a simulation integration reference. This application compares supplied estimates. DistrictCooling objects do not diagnose fouling.

ollama, pandas, fastapi. Ollama drafts local review notes. Deterministic code calculates decline and retains measurement provenance. Use a currently available local open-weight model for English-Arabic notes. Keep calculations deterministic.

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.

Computes decline as 100 times (baseline minus current) divided by baseline, rounded to one decimal place.

Flags decline strictly above the supplied fictional inspection threshold.

Rejects missing or nonpositive baseline values.

Never confirms fouling or dispatches cleaning.

```json [ { "input": { "substation_id": "SUB-01", "baseline_ua_kw_k": 350, "current_ua_kw_k": 280, "fictional_threshold_pct": 15, "provenance": "synthetic estimates" }, "expected_output": { "decline_percent": 20, "inspection_proposed": true, "fouling_status": "unverified", "approval_required": true } }, { "input": { "substation_id": "SUB-02", "baseline_ua_kw_k": 350, "current_ua_kw_k": 345, "fictional_threshold_pct": 15, "provenance": "synthetic estimates" }, "expected_output": { "decline_percent": 1.4, "inspection_proposed": false, "fouling_status": "unverified", "approval_required": true } } ] ```

Use synthetic estimates and proposed inspections. No confirmed diagnosis, chemical flushing advice, valve control, or crew dispatch. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Use synthetic estimates and proposed inspections. No confirmed diagnosis, chemical flushing advice, valve control, or crew dispatch. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Computes decline as 100 times (baseline minus current) divided by baseline, rounded to one decimal place. Flags decline strictly above the supplied fictional inspection threshold. Rejects missing or nonpositive baseline values. Never confirms fouling or dispatches cleaning.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

Complete workflow

Import synthetic conductance estimates, measurement provenance, and fictional inspection thresholds. Calculate percentage decline and flag missing or nonpositive baseline values. Prepare inspection requests with possible alternative causes and missing evidence. Export an engineer review packet with English-Arabic draft notes.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

Correctness and evidence

Computes decline as 100 times (baseline minus current) divided by baseline, rounded to one decimal place. Flags decline strictly above the supplied fictional inspection threshold. Rejects missing or nonpositive baseline values. Never confirms fouling or dispatches cleaning.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

Substation comparison table with provenance links, decline charts, inspection requests, and review notes.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

Use synthetic estimates and proposed inspections. No confirmed diagnosis, chemical flushing advice, valve control, or crew dispatch.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Import synthetic conductance estimates, measurement provenance, and fictional inspection thresholds.

  • Calculate percentage decline and flag missing or nonpositive baseline values.

  • Prepare inspection requests with possible alternative causes and missing evidence.

  • Export an engineer review packet with English-Arabic draft notes.

Resources and assets

Reference links and supporting material

Dataset notes

Create simplified synthetic records with supplied baseline and current UA estimates in kW/K. Do not infer estimates from incomplete temperature readings. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

Ollama

Local LLM runtime for offline inference.

Evaluated output

No output checks are configured.

Evaluation checks
  • Import synthetic conductance estimates, measurement provenance, and fictional inspection thresholds. Calcul...
  • Computes decline as 100 times (baseline minus current) divided by baseline, rounded to one decimal place. F...
  • Substation comparison table with provenance links, decline charts, inspection requests, and review notes.
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 1 configured tool

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Policy Serving
  • Ollama · Optional

Evaluation contract

  • Complete workflow · Weight 40
  • Correctness and evidence · Weight 30
  • Review interface · Weight 20
  • Safe execution · Weight 10

Reference cases

  • 2 public reference cases

Workflow tasks

No workflow tasks are configured.

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build a District Cooling Heat Exchanger Inspection Evidence Workspace