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Exercise 600.3 — Turbofan design trade-offs

Pareto analysis: hydrogen vs kerosene

🧪 Script
Turbofan_pareto_NSGA_H2_vs_Ker.py

Learning objectives

By completing this exercise, you will learn to:

  • Interpret Pareto frontiers in a turbofan design context
  • Distinguish fuel effects from architectural constraints
  • Understand how hydrogen influences feasible design space, not just point designs
  • Analyze how design variables evolve along a Pareto frontier
  • Recognize when a new fuel does not fundamentally change trade-offs

How to run

From the script folder (chapters/600_hydrogen_combustion/scripts):

python Turbofan_pareto_NSGA_H2_vs_Ker.py

What the script does

The script performs a multi-objective optimization of a conventional turbofan engine using a genetic algorithm (NSGA-type approach).

Two fuels are considered:

  • kerosene,
  • hydrogen.

For each fuel, the algorithm explores a range of engine designs by varying parameters such as:

  • bypass ratio (BPR),
  • turbine inlet temperature \( T_4 \),
  • pressure ratios,
  • other cycle-related design variables.

The optimization seeks non-dominated solutions, forming a Pareto frontier.

Objectives and constraints (conceptual)

While details depend on the implementation, the Pareto optimization typically balances:

  • objective 1: a performance or efficiency-related metric (e.g. TSEC, TSFC, or fuel flow),
  • objective 2: a thermal or technological constraint proxy (e.g. specific thrust, or maximum temperature).

All designs share:

  • the same engine architecture,
  • the same cycle assumptions,
  • the same optimization bounds.

Only fuel properties differ between the two cases.

Key observation from the results

When comparing the Pareto frontiers:

  • The overall shape of the Pareto frontiers for hydrogen and kerosene is very similar.
  • Hydrogen designs exhibit a slightly lower maximum temperature \(T_\text{max}\) for comparable performance levels.
  • No dramatic expansion or contraction of the feasible design space is observed.

This indicates that:

For a conventional turbofan architecture, fuel choice alone does not fundamentally alter the core design trade-offs.

Guided questions

1) Interpreting similar Pareto frontiers

  • Why might hydrogen and kerosene lead to nearly overlapping Pareto frontiers?
  • Which aspects of the engine dominate the trade-off structure, regardless of fuel?

Discuss in terms of:

  • Brayton-cycle constraints,
  • fixed architecture,
  • unchanged aerodynamic assumptions.

2) Maximum temperature as a distinguishing feature

  • Why does hydrogen systematically allow for slightly lower \(T_\text{max}\)?

  • How does this relate to:

  • fuel–air ratio,

  • exhaust composition,
  • thermal efficiency trends observed in Exercise 600.2?

Is this difference likely to be technologically important?

For each fuel, examine how design parameters evolve along the Pareto front:

  • bypass ratio (BPR),
  • turbine inlet temperature \(T_4\),
  • pressure ratios.

Questions to address:

  • Do optimal designs move toward higher or lower BPR as performance improves?
  • Is \(T_4\) always pushed to its upper bound?
  • Are trends similar for hydrogen and kerosene?

4) Pareto dominance and engineering choice

  • Does hydrogen dominate kerosene anywhere on the Pareto front?
  • If not, what does this imply about “fuel-driven” optimization narratives?

Explain the difference between:

  • incremental advantage,
  • structural dominance.

Student tasks

Task 1 — Pareto front comparison (core)

Plot the Pareto frontiers for:

  • kerosene,
  • hydrogen,

on the same axes.

Identify:

  • regions of overlap,
  • any systematic offsets,
  • the role of \(T_\text{max}\) as a constraint.

Task 2 — Design-variable mapping

Select 3–5 representative points along each Pareto frontier (e.g. low-performance, mid-range, high-performance designs).

For each point, report:

  • BPR,
  • \(T_4\),
  • at least one additional design parameter.

Discuss how these parameters evolve along the frontier and whether trends differ between fuels.

Color the pareto front by values of BPR or \(T_4\) and explain the trends.

Task 3 — Engineering interpretation

In 10–12 lines, answer:

  • Why does hydrogen not dramatically reshape the Pareto frontier of a conventional turbofan?
  • Why is a small reduction in \(T_\text{max}\) still potentially meaningful?
  • What types of architectural changes would be required to obtain qualitatively different Pareto frontiers?

Limitations (important)

This Pareto analysis assumes:

  • a fixed turbofan architecture,
  • fixed component efficiency models,
  • no heat recovery or cryogenic integration,
  • no airframe–engine coupling.

As a result:

  • the optimization explores fuel substitution, not fuel-enabled redesign.

Key takeaway

In a conventional turbofan, architecture sets the Pareto structure.

Hydrogen can shift operating points slightly, but only architectural innovation can reshape the frontier itself.