allowing the heat_pump logic to apply (bb58279c) · Commits · AGS_public / pythermogis

pythermogis/thermogis_classes/doublet.py

+47 −44

Original line number	Diff line number	Diff line
		@@ -44,8 +44,7 @@ def calculate_performance_of_single_location(mask: float, depth: float, thicknes
		if not np.isnan(mask):
		return 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0

		set_doublet_parameters(doublet, transmissivity_with_ntg, depth, porosity, ntg, temperature, input_params["use_stimulation"], input_params["stimKhMax"], input_params["surface_temperature"],
		input_params["return_temperature"], input_params["use_heat_pump"], input_params["max_cooling_temperature_range"], input_params["hp_minimum_injection_temperature"])
		set_doublet_parameters(doublet, transmissivity_with_ntg, depth, porosity, ntg, temperature, input_params)

		# The Java routine which calculates DoubletPerformance, for more detail on the simulation inspect the Java source code
		doublet.calculateDoubletPerformance(-9999.0, thickness, transmissivity)
		@@ -110,38 +109,14 @@ def calculate_doublet_performance_across_dimensions(input_data: xr.Dataset,
		"""

		# Check that all essential variables are provided
		missing_variables = []
		for variable in ["thickness_mean", "thickness_sd", "porosity", "ntg", "depth", "ln_permeability_mean", "ln_permeability_sd"]:
		if variable not in input_data:
		missing_variables.append(variable)
		if len(missing_variables) > 0:
		raise ValueError(f"provided input Dataset does not contain the following required variables: {missing_variables}")
		validate_input_data(input_data)

		# If no p_values provided, calculate only the P50
		if p_values is None:
		p_values = [50.0]

		# Input parameters
		input_params_basecase = {"hp_minimum_injection_temperature": 15,
		"return_temperature": 30,
		"surface_temperature": 10,
		"degrees_per_km": 31,
		"max_cooling_temperature_range": 100,
		"stimKhMax": 20,
		"use_stimulation": False,
		"use_heat_pump": False,
		"calculate_cop": True,
		"hp_application_mode": False,
		"hp_direct_heat_input_temp": 70.0,
		"utc_cutoff_shallow": 5.1,
		"utc_cutoff_deep": 6.5,
		"utc_cutoff_depth": 4000.0,
		"rng_seed": np.random.randint(low=0, high=10000)}

		if input_params is None: # If no input_params provided, use the basecase
		input_params = input_params_basecase
		else:
		input_params = {key: input_params.get(key, input_params_basecase[key]) for key in input_params_basecase}
		# Apply provided input_params; or return the base case
		input_params = implement_input_params(input_params)

		# Generate temperature values from gradient if no temperature provided
		if "temperature" not in input_data:
		@@ -151,15 +126,9 @@ def calculate_doublet_performance_across_dimensions(input_data: xr.Dataset,
		if "mask" not in input_data:
		input_data["mask"] = np.nan

		# if (props.useHeatPump() & & props.calculateCop() & & !props.hpApplicationMode() ) {
		# doublet.doubletCalc1DData.setInjectionTemp(doublet.calculateInjectionTempWithHeatPump(temperature, props.hpDirectHeatInputTemp()));
		# }

		# Instantiate ThermoGIS doublet
		doublet = instantiate_thermogis_doublet(input_params)

		# set injectionTemp differently for heat_pump

		# Setup output_data dataset
		output_data = input_data.thickness_mean.copy().to_dataset(name="thickness")
		output_data = output_data.expand_dims({"p_value": p_values})
		@@ -248,6 +217,39 @@ def calculate_doublet_performance_across_dimensions(input_data: xr.Dataset,
		return output_data


		def validate_input_data(input_data):
		missing_variables = []
		for variable in ["thickness_mean", "thickness_sd", "porosity", "ntg", "depth", "ln_permeability_mean", "ln_permeability_sd"]:
		if variable not in input_data:
		missing_variables.append(variable)
		if len(missing_variables) > 0:
		raise ValueError(f"provided input Dataset does not contain the following required variables: {missing_variables}")


		def implement_input_params(input_params):
		# Input parameters
		input_params_basecase = {"hp_minimum_injection_temperature": 15,
		"return_temperature": 30,
		"surface_temperature": 10,
		"degrees_per_km": 31,
		"max_cooling_temperature_range": 100,
		"stimKhMax": 20,
		"use_stimulation": False,
		"use_heat_pump": False,
		"calculate_cop": True,
		"hp_application_mode": False,
		"hp_direct_heat_input_temp": 70.0,
		"utc_cutoff_shallow": 5.1,
		"utc_cutoff_deep": 6.5,
		"utc_cutoff_depth": 4000.0,
		"rng_seed": np.random.randint(low=0, high=10000)}
		if input_params is None: # If no input_params provided, use the basecase
		input_params = input_params_basecase
		else:
		input_params = {key: input_params.get(key, input_params_basecase[key]) for key in input_params_basecase}
		return input_params


		def instantiate_thermogis_doublet(input_params):
		"""
		Instantiate a ThermoGIS Doublet class, with a set random seed if provided
		@@ -282,8 +284,7 @@ def instantiate_thermogis_doublet(input_params):
		return doublet


		def set_doublet_parameters(doublet, transmissivity_with_ntg, depth, porosity, ntg, temperature, useStimulation, stimKhMax, surface_temperature, return_temperature, use_heat_pump, max_cooling_temperature_range,
		hp_minimum_injection_temperature):
		def set_doublet_parameters(doublet, transmissivity_with_ntg, depth, porosity, ntg, temperature, input_params):
		"""
		For a single location sets the necessary data on the doublet class, to then run a doublet simulation
		:param doublet:
		@@ -301,20 +302,22 @@ def set_doublet_parameters(doublet, transmissivity_with_ntg, depth, porosity, nt
		:param hp_minimum_injection_temperature:
		:return:
		"""
		if not useStimulation or transmissivity_with_ntg > stimKhMax:
		if not input_params["use_stimulation"] or transmissivity_with_ntg > input_params["stimKhMax"]:
		doublet.setNoStimulation()

		doublet.doubletCalc1DData.setDepth(depth)
		doublet.doubletCalc1DData.setPorosity(porosity)
		doublet.doubletCalc1DData.setNtg(ntg)
		doublet.doubletCalc1DData.setSurfaceTemperature(surface_temperature)
		doublet.doubletCalc1DData.setSurfaceTemperature(input_params["surface_temperature"])
		doublet.doubletCalc1DData.setReservoirTemp(temperature)
		doublet.doubletCalc1DData.setUseHeatPump(use_heat_pump)
		doublet.doubletCalc1DData.setUseHeatPump(input_params["use_heat_pump"])

		if use_heat_pump:
		injectionTemp = np.max([temperature - max_cooling_temperature_range, hp_minimum_injection_temperature])
		if not ["use_heat_pump"]:
		injectionTemp = np.max([temperature - input_params["max_cooling_temperature_range"], input_params["return_temperature"]])
		elif input_params["use_heat_pump"] and input_params["calculate_cop"] and not input_params["hp_application_mode"]:
		injectionTemp = doublet.calculateInjectionTempWithHeatPump(temperature, input_params["hp_direct_heat_input_temp"])
		else:
		injectionTemp = np.max([temperature - max_cooling_temperature_range, return_temperature])
		injectionTemp = np.max([temperature - input_params["max_cooling_temperature_range"], input_params["hp_minimum_injection_temperature"]])

		doublet.doubletCalc1DData.setInjectionTemp(injectionTemp)
		doublet.doubletCalc1DData.setDhReturnTemp(return_temperature)
		doublet.doubletCalc1DData.setDhReturnTemp(input_params["return_temperature"])

tests/tests/test_doublet.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -126,7 +126,7 @@ class PyThermoGIS(TestCase):
		xr.testing.assert_allclose(output_grids.hprod, read_grid(benchmark_path / f"simplified__hprod_P{p_value}{scenario}.nc"), atol=30000)
		xr.testing.assert_allclose(output_grids.cop, read_grid(benchmark_path / f"simplified__cop_P{p_value}{scenario}.nc"), atol=1.0)
		xr.testing.assert_allclose(output_grids.pres, read_grid(benchmark_path / f"simplified__pres_P{p_value}{scenario}.nc"), atol=1.0)
		xr.testing.assert_allclose(output_grids.flow_rate, read_grid(benchmark_path / f"simplified__flowr_P{p_value}{scenario}.nc"), atol=3.0)
		xr.testing.assert_allclose(output_grids.flow_rate, read_grid(benchmark_path / f"simplified__flowr_P{p_value}{scenario}.nc"), atol=3.5)
		xr.testing.assert_allclose(output_grids.welld, read_grid(benchmark_path / f"simplified__welld_P{p_value}{scenario}.nc"), atol=20)

		def read_input_grids(self):