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cantera

K-Dense-AI
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정보

이 기술은 폐쇄형 단열 균질 반응기의 점화 지연 시간과 온도 이력을 계산하기 위해 Cantera 시뮬레이션을 실행합니다. 상세한 운동학적 메커니즘을 갖춘 이상 기체 혼합물에 대해 정적 점화와 정압 점화를 모두 지원합니다. 주요 기능으로는 메커니즘 출처 추적, 보존 법칙 검증, 그리고 신뢰할 수 있는 연소 동역학 분석을 위한 수치 정밀화가 포함됩니다.

빠른 설치

Claude Code

추천
기본
npx skills add K-Dense-AI/claude-scientific-skills -a claude-code
플러그인 명령대체
/plugin add https://github.com/K-Dense-AI/claude-scientific-skills
Git 클론대체
git clone https://github.com/K-Dense-AI/claude-scientific-skills.git ~/.claude/skills/cantera

Claude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요

문서

Cantera: homogeneous ignition calculations

When to use

Use for a closed, adiabatic, homogeneous ideal-gas reactor with a known kinetic mechanism, initial temperature, pressure, and mole composition. The bundled helper runs both constant volume and constant pressure cases and reports a precisely defined temperature-based delay. It is not a flame solver or a general reactor-network builder.

A calculation completing successfully establishes numerical execution, not mechanism validity for the fuel, pressure, temperature, diluent, or measured ignition observable. Read references/interpretation.md when choosing a mechanism, comparing experiments, or interpreting unresolved/two-stage ignition.

Workflow

  1. Identify the mechanism and its validated condition range. Preserve its source, version, citation, and any modifications. Check that its phase is ideal-gas and that every reactant, diluent, and tracked species exists. For custom YAML with imports, retain the original dependency files as well as the generated phase snapshot. Custom Python rate extensions additionally need their original code and environment for replay.
  2. Choose constant volume or constant pressure from the physical experiment. Supply K, Pa, seconds, and mole amounts explicitly. mole_amounts is normalized to mole fractions; it is not a mass-fraction mapping. The report includes the normalized initial composition.
  3. Copy assets/hydrogen-ignition.json and change its conditions. The supplied H2/O2/Ar case uses Cantera's bundled h2o2.yaml for an executable numerical example; it is not a recommendation for every hydrogen experiment.
  4. Set a time horizon long enough to observe the temperature rise and the decline of the heating-rate peak. Choose output spacing fine enough to locate that peak. Set a minimum temperature rise to distinguish ignition from negligible heating or numerical noise.
  5. Run the helper and inspect all four histories and the report. Refine again if the delay changes materially, if the maximum approaches a time boundary, or if conservation fails. Compare the temperature and tracked-species histories with the actual ignition definition.
  6. Report the condition set, mechanism hash, reactor constraint, delay definition, output spacing, numerical changes, and scientific limits together with the delay.

Execute the tested example

From the collection root:

uv run --no-project --python 3.12 --with cantera==3.2.0 --with numpy==2.5.3 \
  python skills/cantera/scripts/ignition_delay.py \
  skills/cantera/assets/hydrogen-ignition.json hydrogen-result

Tested on Python 3.12, Cantera 3.2.0, and NumPy 2.5.3. No external solver executable or credentials are needed. Local relative mechanism paths resolve against the configuration file directory before Cantera's built-in data search. Use a new output directory each run.

The 1000 K, 101325 Pa, H2:O2:Ar = 2:1:7 constant-volume example gives about 0.313 ms using the stated max(dT/dt) definition. At 3 ms its temperature is approximately 2920.67 K and agrees with a separate UV equilibrium calculation. These are package regression values, not experimental validation data.

Exact delay and refinement contract

Delay is the time of the global maximum of numpy.gradient(T, time, edge_order=2) on a uniform output grid. It is reported only if the maximum temperature rise reaches minimum_temperature_rise_k and the maximum is at least two sample indices from each boundary. Otherwise delay_s is null and a status explains why. No delay beyond the simulation horizon is extrapolated.

The helper explicitly uses Cantera 3.2's clone=True and reads evolving properties from reactor.phase. The original Solution retains the initial state; do not read it as the reactor's final state. ReactorNet.advance(t) requests an absolute time, and no advance limits are configured, so the output grid remains uniform.

It runs four independent fresh reactors:

RunChange from configured conditions
baselineOriginal settings
finer_outputHalf output spacing, same horizon and solver controls
tighter_solverBoth solver tolerances divided by ten; maximum internal time step halved
longer_horizonTwice the horizon with the original output spacing

numerically_resolved requires all runs to yield delays, relative delay changes within delay_relative_tolerance, and all conservation checks to pass. Agreement on a discrete grid is not a statistical error bar: also report the output spacing. The baseline samples must be between 11 and 50000, leaving room for refinement. Runtime grows with mechanism size, stiffness, and the chosen horizon; integration failures retain Cantera's error text.

Outputs and checks

  • report.json: all input settings, package versions, configuration and mechanism hashes, normalized starting composition, four delay estimates, numerical changes, conservation, and mechanism thermodynamic temperature bounds.
  • baseline.csv, finer_output.csv, tighter_solver.csv, longer_horizon.csv: time, temperature, pressure, volume, mass, total internal energy, total enthalpy, and requested species mole fractions.
  • mechanism.yaml: a Cantera-written snapshot of the loaded phase, species, and reactions. The helper requests write_yaml(precision=17) and saves the exact UTF-8 bytes it hashes, without platform newline conversion. The report also hashes the located original mechanism file. Imported source dependencies are not separately hashed; the snapshot captures the loaded model. Its generated header includes a date, so the snapshot hash identifies the saved artifact and need not match between otherwise identical reruns.

Closed reactors conserve mass and elemental mass fractions. The constant-volume case checks total internal energy; the constant-pressure case checks total enthalpy. Energy error is divided by max(abs(initial_energy_J), 1 J). Diagnostic tolerances are mass relative drift <1e-8, elemental absolute drift <1e-8, energy scaled drift <1e-6, species mass-fraction sum error <1e-8, and species mass fractions >-1e-10. These checks expose numerical issues and do not measure kinetic-model uncertainty.

Check within_thermo_temperature_range separately: it checks saved output states, not every internal integration state. Numerical resolution does not mean species thermodynamic fits stayed within their temperature bounds. The helper cannot assess pressure-dependent kinetic validity from these bounds.

Scope and upstream references

The suite covers both reactor constraints, conservation, final-state agreement with independent Cantera equilibrium, nonigniting conditions, unresolved boundary maxima, refinement, snapshot replay, and invalid composition/conditions. It does not validate shock-tube heat loss, real-gas effects, surfaces, flow devices, flames, or multistage experimental ignition definitions. Build those models only with the necessary physics and their own checks; do not relabel this helper's result as one of them.

GitHub 저장소

K-Dense-AI/claude-scientific-skills
경로: skills/cantera
0
agent-skillsai-scientistbioinformaticschemoinformaticsclaudeclaude-skills
FAQ

자주 묻는 질문

cantera Skill이란 무엇인가요?

cantera은(는) K-Dense-AI이(가) 만든 Claude Skill입니다. Skill은 Claude가 필요할 때 불러오는 지침과 리소스를 묶어 추가 프롬프트 없이 cantera 관련 작업을 수행할 수 있게 합니다.

cantera은(는) 어떻게 설치하나요?

이 페이지의 설치 명령을 사용하세요. cantera을(를) Claude Code 플러그인으로 추가하거나 저장소를 skills 디렉터리에 복제한 다음 Claude를 다시 시작해 Skill을 불러옵니다.

cantera은(는) 어떤 카테고리에 속하나요?

cantera은(는) 기타 카테고리에 속합니다.

cantera은(는) 무료로 사용할 수 있나요?

네. cantera은(는) AIMCP에 등록되어 있으며 무료로 설치할 수 있습니다.

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