HTS Coil Optimization Algorithms

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:

• "Computational Analysis of High-Temperature Superconducting Coils for High-Beta Plasma Confinement" (https://github.com/DawsonInstitute/hts-coils/blob/main/papers/warp/hts_plasma_confinement.tex)
• "Validation Framework for Lentz Soliton Formation in High-Beta Plasma" (https://github.com/DawsonInstitute/hts-coils/blob/main/papers/warp/soliton_validation.tex)

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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.