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GP-AE-0XGE00XAerospaceReady

Sustainable Aviation Fuel and Contrail-Mitigation Trade-Offs for Short-Haul Aviation

A reproducible aerospace climate study comparing six fuel evidence positions and four contrail-avoidance policies across six short-haul route archetypes and two decision horizons.

Sustainable Aviation Fuel and Contrail-Mitigation Trade-Offs for Short-Haul Aviation project visual
GP-AE-0XGE00X · Aerospace
  • Python 3.12
  • NumPy
  • Pandas
  • Matplotlib

Project definition

Problem statement

Sustainable aviation fuel can reduce lifecycle carbon and soot, while targeted trajectory changes can avoid climate-sensitive contrail formation at the cost of additional fuel.

The engineering problem is to compare those interacting choices without treating lifecycle carbon dioxide and short-lived contrail effects as physically interchangeable.

Project objectives

  • Define six transparent fuel evidence positions and four contrail-avoidance policies.
  • Compare every combination across six short-haul route archetypes.
  • Evaluate separate 20-year and 100-year decision horizons.
  • Measure lifecycle, contrail, fuel, cost, readiness and operational-complexity effects.
  • Test uncertainty with 25,000 seeded samples per central strategy.
  • Identify non-dominated strategies and the evidence needed before operational use.

Project structure

Project components

01

Fuel evidence model

Stores lifecycle, contrail-factor, price and readiness positions with bounded uncertainty for six fuel cases.

02

Avoidance policy model

Defines no avoidance, targeted, moderate and aggressive policies with fuel penalties, effectiveness and complexity.

03

Mission archetypes

Provides six short-haul distance, baseline-fuel and contrail-opportunity cases for controlled comparison.

04

Decision-horizon model

Keeps near-term and longer-term lifecycle and contrail priorities explicit without calling the result CO2e.

05

Uncertainty analysis

Runs triangular bounded sampling for lifecycle, fuel soot response, avoidance effectiveness and fuel penalty.

06

Evidence pipeline

Exports complete CSV and JSON results, twelve figures, annotated references and repository checks.

Methodology

Project workflow

  1. 01
    Load evidence positions

    Read the retained fuel, policy, route and horizon assumptions.

  2. 02
    Build the full matrix

    Calculate lifecycle, contrail, climate-priority, cost, readiness and complexity values for all 288 cases.

  3. 03
    Test uncertainty

    Run 25,000 seeded samples for each fuel-policy-horizon combination on the central route.

  4. 04
    Compare decisions

    Review rankings, horizon sensitivity, uncertainty intervals and the Pareto frontier.

  5. 05
    Apply engineering gates

    Keep certification, safety, weather confidence, operational authority and claim integrity outside the summary index.

Demonstration scenario

For the 1,000-kilometre central mission, the declared PtL and aggressive-avoidance case gives the lowest central H20 climate-priority index at about 15.41, but its relative cost index is about 3.264. The student explains why the mathematical minimum is not an automatic near-term operational recommendation.

Engineering

Tools and method

Tools
The project uses Python 3.12, NumPy, Pandas, Matplotlib for subject analysis, simulation, and results.
Analysis
Python, NumPy and Pandas calculate the deterministic and uncertainty evidence.
Visualisation
Matplotlib produces twelve labelled figures from the retained result tables.
Evidence
Frozen metadata, 60 annotated references and two sourced research-flight images support the literature review.
Verification
Automated tests, Ruff, repository validation, page-by-page document rendering and a clean Docker run support the release.

Testing

Evaluation

Evaluation measures

  • Lifecycle reduction and relative contrail-opportunity reduction
  • H20 and H100 climate-priority indices
  • Relative fuel-cost index and readiness
  • Operational complexity and fuel penalty
  • P05, median and P95 uncertainty intervals
  • Probability below the conventional baseline and Pareto position

Project boundaries

  • The climate-priority index is dimensionless and is not a physical CO2e conversion.
  • Route values are mission archetypes, not aircraft performance data or flight plans.
  • Fuel values are pathway evidence positions, not product or batch certificates.
  • The contrail opportunity model is relative and does not resolve weather fields or radiative transfer.
  • Operational use requires qualified fuel, aircraft and route data, weather validation, air traffic coordination, safety approval and compliant accounting.

Included

  1. 01Complete Python source code and command-line workflow
  2. 02Six fuel positions, four avoidance policies, six route archetypes and two horizons
  3. 03Complete 288-case matrix and 25,000-sample uncertainty analysis per central strategy
  4. 04Seven CSV result files, one JSON summary and twelve generated figures
  5. 0578-page project report in PDF and editable Word formats
  6. 0616-page setup and usage guide in PDF and editable Word formats
  7. 0760 annotated references and two sourced NASA and DLR literature images
  8. 08Automated tests, repository validation and Docker workflow

Project record

No information is collected on this page.

Permanent project ID
GP-AE-0XGE00X
Catalogued
21 Aug 2026
Completed
30 Aug 2026
Verified
30 Aug 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.