L1 ⚛ L1-456 ⊙ Testnet

Rotor Dynamics Aeroelastic Inversion

Robotics · Aerial robotics

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⬇ L1-456.md

# ⚛ L1 Principle — Rotor Dynamics Aeroelastic Inversion **ID:** `L1-456` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Robotics — *Aerial robotics* > **🎯 Problem class:** parameter estimation · **🧮 Solution space:** rotor parameter vector > **📡 Carrier:** N/A · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41555279 --- ## 🧠 1. Introduction **Rotor Dynamics Aeroelastic Inversion** is a **parameter-estimation problem** whose unknown lives in **rotor parameter vector** space, within the **Aerial robotics** sub-domain of **Robotics**. Measurements consist of N/A via a **rotor thrust torque model** sensing mechanism. The forward operator applies, in order: O · composite method operator; S · bvp · flapping equation operator; O · chi2 · thrust torque operator. Observations are corrupted by additive Gaussian noise. Existence of the recovered rotor_parameter_vector is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 500); dynamic_stall_effect dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Gaussian sets the irreducible data-fidelity floor. ## ⚙ 2. Forward Model Physical chain: **x** → O · composite method → S · bvp · flapping equation → O · chi2 · thrust torque → **y** (detector). ``` y = `O.chi2.thrust_torque` `S.bvp.flapping_equation` `O.composite_method` x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `O.composite_method` | O · composite method operator | | `S.bvp.flapping_equation` | S · bvp · flapping equation operator | | `O.chi2.thrust_torque` | O · chi2 · thrust torque operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Robotics | | Sub domain | Aerial robotics | | Carrier | N/A | | Problem class | parameter_estimation | | Solution space | rotor_parameter_vector | | Noise model | gaussian | | Integration axis | azimuth_angle | | Difficulty delta | 5 | | L dag | 4 | ## 📡 4. Measurement Model Existence of the recovered rotor_parameter_vector is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 500); dynamic_stall_effect dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Gaussian sets the irreducible data-fidelity floor. | Metric | Value | |---|---| | Metric | thrust_prediction_RMSE_N | | Secondary | vibration_mode_error_Hz | ## 📏 5. Operating Range (Ω) **Center problem class:** `parameter_estimation` · **Forward operator:** `rotor_thrust_torque_model` **Center point:** | Parameter | Unit | Value | |---|---|---| | Rotor rpm | — | 3000 | | Altitude m | m | 0 | | Advance ratio mu | — | 0 | | Collective pitch deg | deg | 8 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | Rotor rpm | — | 500 – 10000 | | Altitude m | m | 0 – 5000 | | Advance ratio mu | — | 0.0 – 0.4 | | Collective pitch deg | deg | 0 – 20.0 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 5 thrust_prediction_RMSE_N | Metric | Range | |---|---| | Thrust prediction rmse n | 0.5 – 50 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **thrust_prediction_RMSE_N**, with κ = `10000.0` and δ = `5`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x28410d58466e8760437dad2358b166756a20da07497965653a3e7400be55c875` - **Chain tx hash:** `0x2e808b4eafd884e77a97e05f7c6327b6e85bfc5c687686701e01b26ad306c66d` - **Chain block:** `41555279` --- ## File Mapping This bundle consists of: `L1-456.md`, `L1-456.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-456.md` | Source of truth — edit this | Human or LLM | | `L1-456.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 `L1-456`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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Read the attached Markdown (L1-456.md).
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📋 JSON metadata

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  "artifact_id": "L1-456",
  "chain_block": 41555279,
  "chain_hash": "0x28410d58466e8760437dad2358b166756a20da07497965653a3e7400be55c875",
  "chain_tx_hash": "0x2e808b4eafd884e77a97e05f7c6327b6e85bfc5c687686701e01b26ad306c66d",
  "domain": "Robotics",
  "hardness_fn": {
    "delta": 5,
    "kappa": 10000.0,
    "metric": "thrust_prediction_RMSE_N",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "thrust_prediction_RMSE_N",
    "regime": "Existence of the recovered rotor_parameter_vector is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 500); dynamic_stall_effect dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Gaussian sets the irreducible data-fidelity floor.",
    "secondary": "vibration_mode_error_Hz"
  },
  "physics_fingerprint": {
    "L_DAG": 4.0,
    "carrier": "N/A",
    "difficulty_delta": 5,
    "domain": "Robotics",
    "integration_axis": "azimuth_angle",
    "noise_model": "gaussian",
    "primitives": [
      "O.composite_method",
      "S.bvp.flapping_equation",
      "O.chi2.thrust_torque"
    ],
    "problem_class": "parameter_estimation",
    "sensing_mechanism": "rotor_thrust_torque_model",
    "solution_space": "rotor_parameter_vector",
    "sub_domain": "Aerial robotics",
    "title": "Rotor Dynamics Aeroelastic Inversion"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "rotor_thrust_torque_model"
    ],
    "allowed_omega_dimensions": [
      "rotor_RPM",
      "advance_ratio_mu",
      "collective_pitch_deg",
      "altitude_m"
    ],
    "allowed_problem_classes": [
      "parameter_estimation"
    ],
    "center_spec": {
      "epsilon_fn_center": "5 thrust_prediction_RMSE_N",
      "forward_operator": "rotor_thrust_torque_model",
      "input_format": "measurement_only",
      "omega": {
        "advance_ratio_mu": 0.0,
        "altitude_m": 0,
        "collective_pitch_deg": 8.0,
        "rotor_RPM": 3000
      },
      "problem_class": "parameter_estimation"
    },
    "epsilon_bounds": {
      "thrust_prediction_RMSE_N": [
        0.5,
        50
      ]
    },
    "omega_bounds": {
      "advance_ratio_mu": [
        0.0,
        0.4
      ],
      "altitude_m": [
        0,
        5000
      ],
      "collective_pitch_deg": [
        0,
        20.0
      ],
      "rotor_RPM": [
        500,
        10000
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Aerial robotics",
  "title": "Rotor Dynamics Aeroelastic Inversion"
}

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