forked from EduCraft/curriculum-project-hub
hongjr03
9927d38c18
ADR-0029 — nested outline manifest, supersedes ADR-0008's flat [[parts]]:
- cph-model: recursive loader over manifest.toml containers / element.toml
leaves; Lesson.parts (pure elements, DFS order) + Lesson.outline (elements
interleaved with section headings at their DFS-open position); rejects
ambiguous/incomplete folders and root-vs-container table misplacement
- cph-diag: new DiagCode::ManifestMalformed for carrier-document structure
errors (discharges an existing TODO)
- cph-typst: augmented manifest now serializes the outline (element/section
entries) instead of a flat parts array
- render/lib.typ: render-lesson renders section headings at their depth
- examples/TH-141 migrated to 5 nested section containers + 3 root segments,
byte-identical element order; smoke-verified via cph check/build + pdftotext
ADR-0030 — batch & combined export, extends ADR-0009/0011:
- cph build with no --target batches every declared target (repeatable
--target for an explicit subset); any target failure => non-zero exit,
per-target ledger, independent per-target execution
- cph-model: bundle.toml loader (directory + [info]/[targets.*]/ordered
lessons with per-lesson target overrides)
- cph-typst: augmented bundle manifest (path-prefixed member outlines),
Engine::{compile_check_bundle,build_bundle_pdf}
- render/lib.typ: render-bundle assembles member lessons under per-lesson
headings, depth-shifts their own section headings, resets example/lemma
counters at each lesson boundary by default
- cph-cli: `cph bundle <path> --target <name>` subcommand, same batching
contract as `cph build`
- new bundle fixtures/tests (cph-model unit + cph-typst through-template PDF
compile), smoke-verified via a real 2-lesson merged PDF
Verification: cargo fmt/clippy/test clean across the workspace (68 tests);
real cph check/build/bundle runs against TH-141 and a bundle fixture, PDF
content inspected via pdftotext.
17 lines
1.0 KiB
Typst
17 lines
1.0 KiB
Typst
把本章四种模型对常温水液气界面 $sigma_(L G)$ 的预测整理如下：
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#figure(
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table(
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columns: (auto, auto, auto),
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align: (left, left, left),
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table.header[*模型*][*$sigma$ 预测（$"N/m"$）*][*相对实测 $0.072$*],
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[量纲分析], [$tilde.op 0.1$], [量级正确],
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[Stefan ($zeta = 1\/2$)], [$approx 0.38$], [偏大约 5 倍],
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[缺键模型 ($zeta = 3\/4$，FCC (111))], [$approx 0.13$], [偏大约 2 倍],
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[Eötvös 规则（外推）], [$approx 0.072$], [量级与具体值都接近],
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),
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caption: [本章各模型对常温水 $sigma_(L G)$ 的预测]
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) <模型对照表>
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从表上读出的事实有两条：其一，所有模型都能给出对的量级；其二，量纲分析与 Stefan 这类"几乎不假设"的模型反而偏离最大，缺键模型代入具体晶面 $zeta$ 后精度提升一档，而完全唯象的 Eötvös 规则反而最接近实测。最简单的微观模型并不是最准的——粗略的微观模型给出量级，唯象的拟合规则给出具体值。
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