TurbineSpool

class turbodesign.turbine_spool.TurbineSpool

Used with turbines

This class (formerly named Spool) encapsulates both the generic geometry/plotting utilities from the original base spool and the turbine-solving logic that lived in the turbine-specific spool implementation.

Notes on differences vs. the two-class design: - field(default_factory=…) was previously used on a non-dataclass attribute

(t_streamline). Here it’s handled in __init__ to avoid a silent bug.

• fluid defaults to Solution(‘air.yaml’) if not provided.

• All turbine-specific methods (initialize/solve/massflow balancing/etc.) are preserved here. If you ever add a CompressorSpool in the future, consider splitting turbine/compressor behaviors behind a strategy/solver object.

static __massflow_std__(blade_rows: List[BladeRow]) → float

Calculate massflow standard deviation across blade rows.

Computes the standard deviation of total massflow (without coolant) across all blade rows. Used as a convergence criterion for pressure balance and angle matching iterations. Warns if deviation exceeds 1.0 kg/s.

Parameters:

blade_rows – List of all blade rows (inlet, stators, rotors, outlet)

Returns:

Two times the standard deviation of massflow [kg/s]

Return type:

float

property blade_rows: List[BladeRow]

Backwards-compatible combined row list.

calculate_streamline_curvature(row: BladeRow, t_hub_shroud: List[float] | ndarray[tuple[Any, ...], dtype[_ScalarT]]) → None

Calculates the streamline curvature

Parameters:

• row (BladeRow) – current blade row

• t_radial (Union[List[float], npt.NDArray]) – percent along line from hub to shroud

export_properties(filename: str = 'turbine_spool.json') → None

Export turbine spool properties and blade row data to JSON file.

Exports comprehensive turbine design data including blade row properties, streamline coordinates, efficiency metrics, degree of reaction, stage loading, and power calculations for each stage. Useful for post-processing and result archiving.

Parameters:

filename – Output JSON file path (default: “turbine_spool.json”)

Returns:

None. Writes JSON file to specified path.

Example

>>> spool.export_properties("eee_hpt_results.json")

initialize() → None

Initialize massflow and thermodynamic state through rows (turbines).

initialize_streamlines() → None

Initialize streamline storage per row and compute curvature.

plot() → None

Plot hub/shroud and streamlines with improved labels and formatting.

plot_convergence(save_to_file: bool | str | None = None) → None

Plot convergence history showing massflow error vs iteration.

Displays a semi-log plot of the massflow standard deviation error across iterations. If convergence history is empty, warns user.

Parameters:

save_to_file – If True, saves to “convergence.png”. If string, saves to that filename. If None/False, displays plot without saving.

Returns:

None. Either displays plot or saves to file.

Example

>>> spool.solve()
>>> spool.plot_convergence()  # Display plot
>>> spool.plot_convergence(save_to_file=True)  # Save to convergence.png
>>> spool.plot_convergence(save_to_file="my_convergence.png")  # Save to custom file

plot_velocity_triangles() → None

Plot velocity triangles for each blade row with improved styling and annotations.

save_convergence_history(filename: str = 'convergence_history.jsonl') → None

Save convergence history to JSONL file.

Writes the convergence history collected during solve() to a JSON Lines file, where each line is a JSON object representing one iteration.

Parameters:

filename – Output JSONL file path (default: “convergence_history.jsonl”)

Returns:

None. Writes JSONL file to specified path.

Example

>>> spool.solve()
>>> spool.save_convergence_history("turbine_convergence.jsonl")

set_blade_row_exit_angles(radius: Dict[int, List[float]], beta: Dict[int, List[float]], IsSupersonic: bool = False) → None

Set intended exit flow angles for rows (useful when geometry is fixed).

solve() → None

Solve for exit angles/pressures to satisfy chosen massflow constraint.

solve_for_static_pressure(upstream: BladeRow, row: BladeRow)

Solve for static pressure at blade row exit using isentropic flow relations.

Uses massflow-area-Mach number relation to find static pressure from known total conditions. Attempts both subsonic and supersonic solutions and selects the subsonic solution.

Parameters:

• upstream – Upstream blade row providing inlet conditions

• row – Current blade row where static pressure is being solved

Returns:

None. Updates row.M, row.T, and row.P in-place.

solve_massflow_for_power(target_power: float, massflow_guess: float | None = None, tol_rel: float = 0.001, max_iter: int = 8, relax: float = 0.7, bounds: tuple[float, float] = (1e-06, 1000000000.0)) → tuple[float, float]

Power-driven closure: iterate inlet massflow to hit a target turbine power.

This uses a simple algebraic update (no additional nested optimizer):

mdot_next = mdot_current * (P_target / P_current)

The inner flow solution still uses the existing pressure-balance method to maintain a consistent massflow between rows for the current guess.

Parameters:

• target_power – Desired turbine power [W]. Use a positive value for power extracted.

• massflow_guess – Optional starting guess for inlet massflow [kg/s]. Defaults to self.massflow.

• tol_rel – Relative tolerance on power error.

• max_iter – Maximum outer iterations.

• relax – Under-relaxation factor (0–1) for massflow updates.

• bounds – (lower, upper) bounds for massflow during updates.

Returns:

Tuple of (achieved_massflow_kg_s, achieved_power_W).

total_power() → float

Return total turbine power extracted (sum over rotor rows).