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GP-ME-14WFRA7MechanicalReady

Passive Radiative-Cooling Materials for Industrial Thermal Management

A heat-transfer study of passive radiative cooling for outdoor industrial equipment, connecting material spectra, equipment load, thermal contact and sky access.

Passive Radiative-Cooling Materials for Industrial Thermal Management project visual
GP-ME-14WFRA7 · Mechanical
  • Python 3.12
  • NumPy
  • Matplotlib

Project definition

Problem statement

A surface that cools below ambient without a heat load does not necessarily provide the best operating temperature for equipment.

The engineering question is how material response, atmospheric radiation, equipment load and thermal contact combine under a stated installation boundary.

Project objectives

  • Check published optical spectra and treatment data against traceable source extracts.
  • Compare selective and broadband thermal surfaces under the same assumptions.
  • Quantify equipment temperature and contact-resistance effects at prescribed loads.
  • Calculate available heat-rejection capacity at a specified equipment-temperature limit.
  • Identify dry-model limits and the measurements required for independent validation.

Project structure

Project components

01

Source reanalysis

Integrates published spectra with reference solar irradiance and thermal-window weighting, retaining exclusions and discrepancies.

02

Thermal balance

Combines emitted and incoming longwave radiation, solar absorption, atmospheric transmission and signed nonradiative transfer.

03

Equipment model

Connects the radiator to a specified equipment heat load through area-normalised contact resistance.

04

Capacity calculation

Solves the heat rejection supported by a specified equipment-temperature limit and checks it against the forward model.

05

Verification

Checks analytic limits, independent integration, finite perturbations, source hashes and reproduced result files.

Methodology

Project workflow

  1. 01
    Review the sources

    Read the evidence matrix and distinguish published measurements from hypothetical thermal scenarios.

  2. 02
    Declare the boundary

    Set irradiance, ambient conditions, surface family, sky access, load and contact resistance.

  3. 03
    Run the study

    Reproduce spectral integrals, equipment equilibria, selected contrasts, derivatives and inverse capacities.

  4. 04
    Interpret the results

    Compare temperature and heat-rejection results while retaining the failed dry-surface screens.

  5. 05
    Plan an extension

    Identify suitable measured boundaries, transient loads, spatial conduction or moisture modelling for independent work.

Demonstration scenario

For the reference assumptions, the selective radiator is colder without load. At 150 W/m2, the broadband radiator instead gives the lower equipment temperature. Increasing contact resistance from 0.005 to 0.05 m2 K/W raises equipment temperature by 6.75 K at that fixed load.

Engineering

Tools and method

Tools
The project uses Python 3.12, NumPy, Matplotlib for subject analysis, simulation, and results.
Radiation physics
Two spectral bands, Planck integration and reciprocal angular exchange define an explicit idealised model.
Numerical analysis
NumPy evaluates the deterministic study and checks finite, physically admissible input values.
Evidence outputs
CSV and JSON retain numerical outputs; labelled figures and editable presentation charts explain the results.
Reproducibility
A pinned Python container regenerates all eight result files without network access.

Testing

Evaluation

Evaluation measures

  • Weighted solar reflectivity and thermal-window emissivity
  • Equipment and radiator temperature relative to ambient
  • Net heat rejection at ambient and at equipment-temperature limits
  • Sensitivity to contact, solar absorption and sky boundaries
  • Analytic limits, inverse round trips and independent numerical checks
  • Source provenance, document readability and reproducibility

Project boundaries

  • The equipment scenarios use hypothetical material families and environmental inputs, not calibrated coatings or measured site weather.
  • Thirty cases fail the assumed dry-surface screen. The model does not calculate condensation.
  • The study omits transient storage, lateral gradients and parallel equipment heat-loss paths.
  • Published experiments belong to their cited researchers. This project does not claim new physical experiments, field qualification or annual energy savings.

Included

  1. 01Complete Python source code and offline numerical reproduction
  2. 02Fourteen traceable published-data extracts with source hashes
  3. 032,304 equipment cases and twelve separate temperature-limit calculations
  4. 04Eight CSV and JSON result files with automated comparison checks
  5. 05Five scientific plots and two credited literature figures
  6. 0672-page project documentation in PDF and editable Word formats
  7. 0710-page setup and usage guide in PDF and editable Word formats
  8. 0819-slide editable presentation with charts, tables and source notes
  9. 0918 annotated references and an evidence source matrix
  10. 10143 automated tests, document validation and Docker reproduction

Project record

No information is collected on this page.

Permanent project ID
GP-ME-14WFRA7
Catalogued
21 Aug 2026
Completed
05 Sept 2026
Verified
05 Sept 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.