Skip to content

README.md

Latest commit

 

History

History
103 lines (70 loc) · 4.78 KB

README.md

File metadata and controls

103 lines (70 loc) · 4.78 KB
Raw

HTS Coil Optimization Algorithms

Python 3.8+ License: MIT

Overview

Computational optimization algorithms for superconducting magnet design and plasma physics applications.

This repository provides multi-objective optimization algorithms used by the hts-coils framework for:

  • High-beta plasma confinement parameter optimization
  • REBCO superconducting magnet design
  • Multi-objective electromagnetic field optimization
  • Computational validation and uncertainty quantification

The algorithms are integrated with HTS coil simulations through the src/warp/ directory in the hts-coils repository, supporting peer-reviewed preprints on plasma physics and superconducting magnet applications.

Primary Integration: HTS Coil Optimization

Key Applications in HTS Framework:

  • Multi-objective optimization for magnetic field uniformity and thermal stability
  • JAX-accelerated electromagnetic field calculations
  • Monte Carlo uncertainty quantification for manufacturing tolerances
  • Validation framework for REBCO paper reproduction

Related Publications:

Experimental Research Components

Note: This repository also contains experimental warp field research code not currently integrated with Dawson Institute preprints. See EXPERIMENTAL_COMPONENTS.md for complete documentation of advanced optimization algorithms, theoretical frameworks, and simulation capabilities.

Installation & Usage

# Install dependencies
pip install -r requirements.txt

# Install package in development mode
pip install -e .

# Run tests
pytest

Contributing

We welcome collaboration on hardware scaling and theoretical integration:

  • Hardware: laser–coil synchronization, plasma chamber design, envelope tracking
  • Theory: LQG corrections with sinc(πμ), Natário zero-expansion, positive-energy soliton profiles

Start here:

  • Read CONTRIBUTING.md
  • Fork the repo, create a feature branch, add tests (pytest), and open a PR

Focus areas that need help now:

  • Device facades and experiment plans for 2035–2050 prototypes
  • UQ extensions and CI artifact dashboards
  • Cross-repo schema consumers for mission/perf analytics

Testing and CI

  • Local quick run: use the lightweight tests and filters already configured in pytest.ini and conftest.py.
    • Site-packages and heavy script-style tests are excluded from collection.
    • Focused runs: pytest -k vector_impulse -q or pytest tests/test_vnv_vector_impulse.py -q.
    • Markers: use -m "not slow" to skip slow tests.
  • Dependencies: install requirements-test.txt for a consistent environment.
  • CI: GitHub Actions workflow runs the test suite on Ubuntu with Python 3.11 using the pinned test requirements.

Mission timeline logging (--timeline-log)

The mission CLI supports an optional --timeline-log argument to record planning and execution milestones. The log can be written as CSV (default) or JSONL (if the path ends with .jsonl).

  • CSV header: iso_time,t_rel_s,event,segment_id,planned_value,actual_value
  • JSONL fields: { iso_time, t_rel_s, event, segment_id, planned_value, actual_value }

Events currently emitted:

  • plan_created (t_rel_s = 0, planned_value = total planned energy J)
  • rehearsal_complete or dry_run_abort_complete (non-executing paths)
  • mission_complete (actual_value = total energy used J)

Example usage:

PYTHONPATH=src python -m impulse.mission_cli \
    --waypoints examples/waypoints/simple.json \
    --export mission.json \
    --timeline-log mission_timeline.csv \
    --rehearsal --hybrid simulate-first --seed 123

Scope, Validation & Limitations

  • Scope: The materials and numeric outputs in this repository are research-stage examples and depend on implementation choices, parameter settings, and numerical tolerances.
  • Validation: Reproducibility artifacts (scripts, raw outputs, seeds, and environment details) are provided in docs/ or examples/ where available; reproduce analyses with parameter sweeps and independent environments to assess robustness.
  • Limitations: Results are sensitive to modeling choices and discretization. Independent verification, sensitivity analyses, and peer review are recommended before using these results for engineering or policy decisions.