L1 ⚛ L1-270 ⊙ Testnet

Aeroacoustics — Lighthill analogy (jet/airframe noise)

Acoustics · Lighthill source-term aeroacoustic prediction

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⬇ L1-270.md

# ⚛ L1 Principle — Aeroacoustics — Lighthill analogy (jet/airframe noise) **ID:** `L1-270` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Acoustics — *Lighthill source-term aeroacoustic prediction* > **🎯 Problem class:** nonlinear inverse · **🧮 Solution space:** far field spl spectrum > **📡 Carrier:** acoustic · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 10 · **⛓ Block:** 41554050 --- ## 🧠 1. Introduction **Aeroacoustics — Lighthill analogy (jet/airframe noise)** is a **nonlinear inverse problem** whose unknown lives in **far field spl spectrum** space, within the **Lighthill source-term aeroacoustic prediction** sub-domain of **Acoustics**. Measurements consist of acoustic pressure waves recorded by transducers via a **far field mic** sensing mechanism. The forward operator applies, in order: L · les source operator; L · lighthill integral operator; L · green function operator; detector accumulates flux over the exposure window. Observations are corrupted by additive Gaussian noise. Conditional stability; mismatch parameters dominate at Omega bounds. ## ⚙ 2. Forward Model Physical chain: **x** → L · les source → L · lighthill integral → L · green function → Temporal integration → **y** (detector). ``` y = ∫_t dt `L.green_function` `L.lighthill_integral` `L.les_source` x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.les_source` | L · les source operator | | `L.lighthill_integral` | L · lighthill integral operator | | `L.green_function` | L · green function operator | | `int.temporal` | Detector accumulates flux over the exposure window | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Acoustics | | Sub domain | Lighthill source-term aeroacoustic prediction | | Carrier | acoustic | | Problem class | nonlinear_inverse | | Solution space | far_field_spl_spectrum | | Noise model | gaussian | | Integration axis | temporal | | Difficulty delta | 10 | | L dag | 3.8 | ## 📡 4. Measurement Model Conditional stability; mismatch parameters dominate at Omega bounds. | Metric | Value | |---|---| | Metric | SPL_error_dB | | Secondary | SSIM | ## 📏 5. Operating Range (Ω) **Center problem class:** `lighthill_aeroacoustic` · **Forward operator:** `lighthill_aeroacoustic_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | M jet | — | 0.8 | | N les | — | 10000000 | | N mic | — | 32 | | Snr db | dB | 30 | | Freq hz max | — | 10000 | | Refraction error | — | 0 | | Observer pos error | — | 0 | | Les resolution error | — | 0.05 | | Source region trunc error | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | M jet | — | 0.1 – 2.0 | | N les | — | 1000000 – 10000000000 | | N mic | — | 4 – 256 | | Snr db | dB | 0 – 40 | | Freq hz max | — | 100 – 50000 | | Refraction error | — | 0.0 – 0.2 | | Observer pos error | — | 0.0 – 0.05 | | Les resolution error | — | 0.01 – 0.3 | | Source region trunc error | — | 0.0 – 0.3 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 3.0 | Metric | Range | |---|---| | Spl db | 0.5 – 30.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **SPL_error_dB**, with κ = `1600` and δ = `10`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x060827f466c50a201e63d9cf576060fd9014fa2a3358e697ca798d0b1b8cee3b` - **Chain tx hash:** `0x72caca7d75d010cdefd81e08ba9bafa5b26890eed4ffcf0fbf99ae2fd356f80a` - **Chain block:** `41554050` --- ## File Mapping This bundle consists of: `L1-270.md`, `L1-270.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-270.md` | Source of truth — edit this | Human or LLM | | `L1-270.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-270`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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Read the attached Markdown (L1-270.md).
Regenerate L1-270.json so every field matches the Markdown.
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L2-270-001 Aeroacoustics — Lighthill analogy (jet/airframe noise) Mismatch-Only Spec

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📋 JSON metadata

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  "artifact_id": "L1-270",
  "chain_block": 41554050,
  "chain_hash": "0x060827f466c50a201e63d9cf576060fd9014fa2a3358e697ca798d0b1b8cee3b",
  "chain_tx_hash": "0x72caca7d75d010cdefd81e08ba9bafa5b26890eed4ffcf0fbf99ae2fd356f80a",
  "domain": "Acoustics",
  "hardness_fn": {
    "delta": 10,
    "kappa": 1600,
    "metric": "SPL_error_dB",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
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      "license_hash": null,
      "name": "primary",
      "weight": 1.0
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  "layer": "L1",
  "observable_profile": {
    "metric": "SPL_error_dB",
    "regime": "Conditional stability; mismatch parameters dominate at Omega bounds.",
    "secondary": "SSIM"
  },
  "physics_fingerprint": {
    "L_DAG": 3.8,
    "carrier": "acoustic",
    "difficulty_delta": 10,
    "domain": "Acoustics",
    "integration_axis": "temporal",
    "noise_model": "gaussian",
    "primitives": [
      "L.les_source",
      "L.lighthill_integral",
      "L.green_function",
      "int.temporal"
    ],
    "problem_class": "nonlinear_inverse",
    "sensing_mechanism": "far_field_mic",
    "solution_space": "far_field_spl_spectrum",
    "sub_domain": "Lighthill source-term aeroacoustic prediction",
    "title": "Aeroacoustics \u2014 Lighthill analogy (jet/airframe noise)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "lighthill_aeroacoustic_forward"
    ],
    "allowed_omega_dimensions": [
      "N_les",
      "N_mic",
      "M_jet",
      "freq_Hz_max",
      "SNR_dB",
      "les_resolution_error",
      "source_region_trunc_error",
      "refraction_error",
      "observer_pos_error",
      "les_resolution_error",
      "source_region_trunc_error",
      "refraction_error",
      "observer_pos_error"
    ],
    "allowed_problem_classes": [
      "lighthill_aeroacoustic"
    ],
    "center_spec": {
      "epsilon_fn_center": "3.0",
      "forward_operator": "lighthill_aeroacoustic_forward",
      "input_format": "measurement_only",
      "omega": {
        "M_jet": 0.8,
        "N_les": 10000000,
        "N_mic": 32,
        "SNR_dB": 30,
        "freq_Hz_max": 10000,
        "les_resolution_error": 0.05,
        "observer_pos_error": 0.0,
        "refraction_error": 0.0,
        "source_region_trunc_error": 0.0
      },
      "problem_class": "lighthill_aeroacoustic"
    },
    "epsilon_bounds": {
      "SPL_dB": [
        0.5,
        30.0
      ]
    },
    "omega_bounds": {
      "M_jet": [
        0.1,
        2.0
      ],
      "N_les": [
        1000000,
        10000000000
      ],
      "N_mic": [
        4,
        256
      ],
      "SNR_dB": [
        0,
        40
      ],
      "freq_Hz_max": [
        100,
        50000
      ],
      "les_resolution_error": [
        0.01,
        0.3
      ],
      "observer_pos_error": [
        0.0,
        0.05
      ],
      "refraction_error": [
        0.0,
        0.2
      ],
      "source_region_trunc_error": [
        0.0,
        0.3
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Lighthill source-term aeroacoustic prediction",
  "title": "Aeroacoustics \u2014 Lighthill analogy (jet/airframe noise)"
}

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