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propeller_bemt_analysis

Analyze propeller performance using Blade Element Momentum Theory to calculate thrust, power, and efficiency based on geometry and operating conditions.

Instructions

Analyze propeller performance using Blade Element Momentum Theory.

Args: propeller_geometry: Propeller geometry (diameter_m, pitch_m, num_blades, etc.) operating_conditions: Operating conditions (rpm_list, velocity_ms, altitude_m) analysis_options: Optional analysis settings

Returns: Formatted string with propeller performance analysis

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
propeller_geometryYes
operating_conditionsYes
analysis_optionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler: processes input dicts, validates with PropellerGeometry pydantic model, delegates to core BEMT analysis, formats tabular and JSON output.
    def propeller_bemt_analysis(
        propeller_geometry: dict,
        operating_conditions: dict,
        analysis_options: dict | None = None,
    ) -> str:
        """Analyze propeller performance using Blade Element Momentum Theory.
    
        Args:
            propeller_geometry: Propeller geometry (diameter_m, pitch_m, num_blades, etc.)
            operating_conditions: Operating conditions (rpm_list, velocity_ms, altitude_m)
            analysis_options: Optional analysis settings
    
        Returns:
            Formatted string with propeller performance analysis
        """
        try:
            from ..integrations.propellers import (
                PropellerGeometry,
            )
            from ..integrations.propellers import (
                propeller_bemt_analysis as _propeller_analysis,
            )
    
            # Create geometry object
            geometry = PropellerGeometry(**propeller_geometry)
    
            rpm_list = operating_conditions.get("rpm_list", [2000, 2500, 3000])
            velocity_ms = operating_conditions.get("velocity_ms", 20.0)
            altitude_m = operating_conditions.get("altitude_m", 0.0)
    
            # Run analysis
            results = _propeller_analysis(geometry, rpm_list, velocity_ms, altitude_m)
    
            # Format response
            result_lines = [
                "Propeller BEMT Analysis",
                "=" * 60,
                f"Propeller: {geometry.diameter_m:.2f}m dia, {geometry.pitch_m:.2f}m pitch, {geometry.num_blades} blades",
                f"Conditions: {velocity_ms:.1f} m/s @ {altitude_m:.0f}m altitude",
                "",
                f"{'RPM':>6} {'Thrust (N)':>10} {'Power (W)':>9} {'Efficiency':>10} {'Adv Ratio':>10}",
            ]
            result_lines.append("-" * 60)
    
            for result in results:
                result_lines.append(
                    f"{result.rpm:6.0f} {result.thrust_n:10.1f} {result.power_w:9.0f} "
                    f"{result.efficiency:10.3f} {result.advance_ratio:10.3f}"
                )
    
            # Add JSON data
            json_data = json.dumps(
                [
                    {
                        "rpm": r.rpm,
                        "thrust_n": r.thrust_n,
                        "torque_nm": r.torque_nm,
                        "power_w": r.power_w,
                        "efficiency": r.efficiency,
                        "advance_ratio": r.advance_ratio,
                        "thrust_coefficient": r.thrust_coefficient,
                        "power_coefficient": r.power_coefficient,
                    }
                    for r in results
                ],
                indent=2,
            )
            result_lines.extend(["", "JSON Data:", json_data])
    
            return "\n".join(result_lines)
    
        except ImportError:
            return "Propeller analysis not available - install propulsion packages"
        except Exception as e:
            logger.error(f"Propeller analysis error: {str(e)}", exc_info=True)
            return f"Propeller analysis error: {str(e)}"
  • FastMCP tool registration: registers the propeller_bemt_analysis function as an MCP tool.
    mcp.tool(propeller_bemt_analysis)
  • Input schema: Pydantic BaseModel for propeller geometry parameters used for validation.
    class PropellerGeometry(BaseModel):
        """Propeller geometric parameters."""
    
        diameter_m: float = Field(..., gt=0, description="Propeller diameter in meters")
        pitch_m: float = Field(..., gt=0, description="Propeller pitch in meters")
        num_blades: int = Field(..., ge=2, le=6, description="Number of blades")
        hub_radius_m: float = Field(0.02, ge=0, description="Hub radius in meters")
        activity_factor: float = Field(
            100, ge=50, le=200, description="Propeller activity factor"
        )
        cl_design: float = Field(0.5, gt=0, le=1.5, description="Design lift coefficient")
        cd_design: float = Field(0.02, gt=0, le=0.1, description="Design drag coefficient")
  • Core analysis dispatcher: selects between AeroSandbox BEMT or simplified momentum theory based on availability.
    def propeller_bemt_analysis(
        geometry: PropellerGeometry,
        rpm_list: list[float],
        velocity_ms: float,
        altitude_m: float = 0,
    ) -> list[PropellerPerformancePoint]:
        """
        Blade Element Momentum Theory propeller analysis.
    
        Args:
            geometry: Propeller geometry parameters
            rpm_list: List of RPM values to analyze
            velocity_ms: Forward velocity in m/s
            altitude_m: Altitude for atmospheric conditions
    
        Returns:
            List of PropellerPerformancePoint objects
        """
        if AEROSANDBOX_AVAILABLE:
            try:
                return _aerosandbox_propeller_analysis(
                    geometry, rpm_list, velocity_ms, altitude_m
                )
            except Exception:
                # Fall back to simple method
                pass
    
        # Use simple momentum theory + basic blade element method
        return _simple_propeller_analysis(geometry, rpm_list, velocity_ms, altitude_m)
  • Fallback analysis: Implements simplified BEMT with momentum theory for static/forward flight conditions when AeroSandbox unavailable.
    def _simple_propeller_analysis(
        geometry: PropellerGeometry,
        rpm_list: list[float],
        velocity_ms: float,
        altitude_m: float = 0,
    ) -> list[PropellerPerformancePoint]:
        """
        Simple propeller analysis using momentum theory and basic blade element methods.
        Used as fallback when advanced libraries unavailable.
        """
        # Atmospheric conditions
        if altitude_m < 11000:
            temp = 288.15 - 0.0065 * altitude_m
            pressure = 101325 * (temp / 288.15) ** 5.256
        else:
            temp = 216.65
            pressure = 22632 * math.exp(-0.0001577 * (altitude_m - 11000))
    
        rho = pressure / (287.04 * temp)
    
        results = []
    
        for rpm in rpm_list:
            n = rpm / 60.0  # Revolutions per second
            D = geometry.diameter_m
    
            # Advance ratio
            J = velocity_ms / (n * D) if n > 0 else 0
    
            # Simple momentum theory for static thrust
            if J < 0.1:  # Static or near-static conditions
                # Simplified static thrust estimation
                CT_static = 0.12 * geometry.num_blades / 2  # Rough approximation
                thrust_n = CT_static * rho * n**2 * D**4
    
                # Power estimation from simplified BEMT
                CP_static = (
                    CT_static ** (3 / 2) / math.sqrt(2) * 1.2
                )  # Include profile power
                power_w = CP_static * rho * n**3 * D**5
    
                efficiency = 0.5 if power_w > 0 else 0  # Low efficiency in static
    
            else:
                # Forward flight conditions
                # Simplified propeller theory
                beta = math.atan(geometry.pitch_m / (math.pi * D))  # Geometric pitch angle
    
                # Thrust coefficient approximation
                alpha_eff = beta - math.atan(
                    J / (math.pi * 0.75)
                )  # Effective angle at 75% radius
    
                # Simplified lift and drag
                cl_eff = (
                    geometry.cl_design * math.sin(2 * alpha_eff)
                    if abs(alpha_eff) < math.pi / 4
                    else 0
                )
                cd_eff = geometry.cd_design + 0.01 * cl_eff**2
    
                # Thrust and power coefficients
                CT = (
                    0.5 * geometry.num_blades * cl_eff * (0.75**2) * (1 - 0.25)
                )  # Integrated over blade
                CP = 0.5 * geometry.num_blades * cd_eff * (0.75**2) * (
                    1 - 0.25
                ) + CT * J / (2 * math.pi)
    
                # Apply corrections for finite number of blades
                CT *= min(1.0, geometry.num_blades / 2)
                CP *= min(1.0, geometry.num_blades / 2)
    
                thrust_n = CT * rho * n**2 * D**4
                power_w = CP * rho * n**3 * D**5
    
                efficiency = J * CT / CP if CP > 0 else 0
    
            # Torque
            torque_nm = power_w / (2 * math.pi * n) if n > 0 else 0
    
            # Limit efficiency and ensure physical values
            efficiency = max(0, min(0.9, efficiency))
            thrust_n = max(0, thrust_n)
            power_w = max(1, power_w)  # Minimum power for losses
    
            results.append(
                PropellerPerformancePoint(
                    rpm=rpm,
                    thrust_n=thrust_n,
                    torque_nm=torque_nm,
                    power_w=power_w,
                    efficiency=efficiency,
                    advance_ratio=J,
                    thrust_coefficient=thrust_n / (rho * n**2 * D**4) if n > 0 else 0,
                    power_coefficient=power_w / (rho * n**3 * D**5) if n > 0 else 0,
                )
            )
    
        return results
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool performs analysis and returns a formatted string, but lacks critical details: computational intensity, potential runtime, error handling, or whether it's read-only or mutating. For a complex analysis tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and well-structured: a clear purpose statement followed by Args and Returns sections. Every sentence adds value, with no redundant information. However, the 'Args' and 'Returns' labels are slightly informal compared to standard MCP conventions, preventing a perfect score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (aerodynamic analysis with 3 parameters, nested objects) and no annotations, the description is minimally adequate. It covers purpose and parameters at a high level, and the presence of an output schema means return values don't need explanation. However, it lacks behavioral context and detailed parameter guidance, making it incomplete for safe, effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter details. The description lists parameter names and gives minimal examples (e.g., 'diameter_m, pitch_m' for geometry), but doesn't explain required units, valid ranges, or the structure of nested objects. With 3 parameters and low coverage, the description adds some value but doesn't fully compensate for the documentation gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Analyze propeller performance using Blade Element Momentum Theory.' It specifies the action ('Analyze'), resource ('propeller performance'), and method ('using Blade Element Momentum Theory'). However, it doesn't explicitly differentiate from sibling tools like 'get_propeller_database' or 'wing_vlm_analysis', which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_propeller_database' for data retrieval or 'wing_vlm_analysis' for different aerodynamic analyses. There's no context about prerequisites, such as needing propeller geometry data first, or exclusions for when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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