tespy.models package

tespy.models.template module

class tespy.models.template.ModelTemplate

Bases: object

get_objectives(objective_list: list) → list

get_parameter(parameter: str) → float

get_results(labels)

optimize(algorithm, termination, variables: dict, constraints: dict = None, objective: list = None, minimize_flags: list = None, kpi: list = None) → tuple

Run a pymoo optimization and return the full evaluation log and the pymoo result.

Parameters:

• algorithm – A pymoo algorithm instance, e.g. PSO(pop_size=20).

• termination – Pymoo termination criterion, e.g. ("n_gen", 50).

• variables (dict) – Decision variables mapping parameter name to {"min": ..., "max": ...}.

• constraints (dict, optional) – Inequality constraints mapping parameter name to a bound, e.g. {"p_extraction_1": "p_extraction_2"} enforces p_extraction_1 > p_extraction_2.

• objective (list, optional) – Names of model parameters to use as objectives.

• minimize_flags (list, optional) – One bool per objective; True minimizes, False maximizes. Defaults to minimization for all.

• kpi (list, optional) – Additional parameter names to record in the log alongside the objectives.

Returns:

tuple – A (log, result) tuple where log is a pandas.DataFrame of every evaluated individual (including infeasible ones) and result is the pymoo result object. When constraints are active, filter log for feasibility before selecting the optimum. result.X and result.F already contain only feasible solutions.

plot_QT_diagram_matplotlib(heatexchanger_label, ax=None, save_dir=None, figsize=None)

plot_Ts_diagram_matplotlib(connection_label, ax=None, save_dir=None, figsize=None, xlim=None, ylim=None)

plot_logph_diagram_matplotlib(connection_label, ax=None, save_dir=None, figsize=None, xlim=None, ylim=None)

run_exergy_analysis(Tamb, pamb, E_F, E_P, E_L=None)

Run an exergy analysis via exerpy and cache the result.

Parameters:

• Tamb (float) – Ambient temperature in °C.

• pamb (float) – Ambient pressure in bar.

• E_F (dict) – Fuel exergy definition, e.g. {'inputs': [...], 'outputs': [...]}.

• E_P (dict) – Product exergy definition.

• E_L (dict, optional) – Loss exergy definition.

Returns:

exerpy.ExergyAnalysis

save_design(path=None) → None

Save current network state as the design reference.

sensitivity_analysis(param_dict=None, result_param_list=None, mode='design', postproc_func=None) → DataFrame

Parameters:

• param_dict (dict) – A dictionary of parameter names and lists of values to be used in the sensitivity analysis. All lists must have the same length, which determines the number of simulations to be run.

• result_param_list (list, optional) – Names of model parameters (from _parameter_lookup()) to record after each simulation step.

• mode (str, optional) – 'design' or 'offdesign'. Default is 'design'.

• postproc_func (callable, optional) – A function postproc_func(model) -> dict | None called after each successful solve. Use it to run any postprocessing - e.g. an exergy analysis, custom KPI calculation, or result export. The returned dict (if any) is merged into the result row as additional columns alongside result_param_list. If the function returns None no extra columns are added.

  Example - running an exergy analysis after each offdesign solve:

  def run_exergy(model):
      model.run_exergy_analysis(Tamb, pamb, E_F, E_P)
  
  hp.sensitivity_analysis(
      param_dict={"T_geo": [8, 9, 10, 11]},
      result_param_list=["epsilon"],
      mode="offdesign",
      postproc_func=run_exergy,
  )
  

Returns:

pandas.core.frame.DataFrame – DataFrame with input parameter columns and result columns.

set_parameters(**kwargs) → None

solve_model(**kwargs) → None

solve_model_design(**kwargs) → None

solve_model_offdesign(**kwargs) → None