TNO Intern

Commit 1f9e131c authored by Hen Brett's avatar Hen Brett 🐔
Browse files

Merge branch '85-incorporate-new-thermogis-jar' into 'main' 

Resolve "incorporate new thermogis jar"

Closes #85

See merge request !103
parents cec4b020 4ef8be42

docs/changelog.md

+4 −0
Original line number Diff line number Diff line
@@ -18,3 +18,7 @@ Another bug was fixed, this occured when sampling for transmissivity with high o 
Additional Functionality:

    - two functions have been added to print out the environment variables THERMOGIS_JAR and JAVA_HOME. This enables users to debug if their are issues with their installation.



## v1.2.3 (27-11-2025)

Updating the Java jar which has a fixed implementation of the heat pump. Before the heat pump cop was not being correctly calculated. There is a test added to test_pythermogis_doublet_benchmark.py to showcase how the new heatpump mode 

should be used.

pyproject.toml

+1 −1
Original line number Diff line number Diff line
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" 


[project]

name = "pythermogis"

version = "1.2.1"

version = "1.2.2"

description = "This repository is used as a python API for the ThermoGIS Doublet simulations"

authors = [

    { name = "Hen Brett", email = "hen.brett@tno.nl" },

tests/test_pyThermoGIS_Doublet_Benchmark.py→tests/test_pythermogis_doublet_benchmark.py

+244 −0
Original line number Diff line number Diff line
@@ -2,6 +2,7 @@ from unittest import TestCase 


from pythermogis import *





class ThermoGISDoubletBenchmark(TestCase):

    """

    This is a series of tests which produce the same results found in the Java code and in test_ThermoGISDoublet_Benchmark, but using the ntended pythermogis implementation
@@ -9,12 +10,15 @@ class ThermoGISDoubletBenchmark(TestCase): 


    def test_calculateDoubletPerformanceTest(self):

        viscosity_mode = get_viscosity_mode("kestin")

        utc_properties = (instantiate_utc_properties_builder()

        utc_properties = (

            instantiate_utc_properties_builder()

            .setDhReturnTemp(40)

            .setViscosityMode(viscosity_mode)

                          .build())

            .build()

        )



        input_data = xr.Dataset({

        input_data = xr.Dataset(

            {

                "thickness_mean": ((), 100),

                "thickness_sd": ((), 0.0),

                "ntg": ((), 1.0),
@@ -23,9 +27,12 @@ class ThermoGISDoubletBenchmark(TestCase): 
                "temperature": ((), 76),

                "ln_permeability_mean": ((), np.log(175)),

                "ln_permeability_sd": ((), 0.0),

        })

            }

        )



        results = calculate_doublet_performance_stochastic(input_data, utc_properties=utc_properties, rng_seed=0)

        results = calculate_doublet_performance_stochastic(

            input_data, utc_properties=utc_properties, rng_seed=0

        )



        # Assert

        self.assertTrue(np.isclose(17500, results.transmissivity.data[0], 0.001))
@@ -43,9 +50,12 @@ class ThermoGISDoubletBenchmark(TestCase): 
        """

        # Arrange

        # Instantiate the UTC properties class

        utc_properties = self.setup_template_utc_properties_builder().setOpexBase(0).build()

        utc_properties = (

            self.setup_template_utc_properties_builder().setOpexBase(0).build()

        )



        input_data = xr.Dataset({

        input_data = xr.Dataset(

            {

                "thickness_mean": ((), 100),

                "thickness_sd": ((), 0.0),

                "ntg": ((), 1.0),
@@ -54,11 +64,13 @@ class ThermoGISDoubletBenchmark(TestCase): 
                "temperature": ((), 76),

                "ln_permeability_mean": ((), np.log(175)),

                "ln_permeability_sd": ((), 0.0),

        })

            }

        )



        # Act

        results = calculate_doublet_performance_stochastic(input_data, utc_properties=utc_properties, rng_seed=0)



        results = calculate_doublet_performance_stochastic(

            input_data, utc_properties=utc_properties, rng_seed=0

        )



        # Assert

        self.assertTrue(np.isclose(17500, results.transmissivity.data[0], 0.001))
@@ -76,13 +88,18 @@ class ThermoGISDoubletBenchmark(TestCase): 
        """

        # Arrange

        # Instantiate the UTC properties class

        utc_properties = (self.setup_template_utc_properties_builder().setCapexConst(0.5)

        utc_properties = (

            self.setup_template_utc_properties_builder()

            .setCapexConst(0.5)

            .setCapexVariable(1100)

            .setHeatExchangerEfficiency(0.4)

            .setDhReturnTemp(60)

                          .setHeatExchangerParasitic(0.1).build())

            .setHeatExchangerParasitic(0.1)

            .build()

        )



        input_data = xr.Dataset({

        input_data = xr.Dataset(

            {

                "thickness_mean": ((), 100),

                "thickness_sd": ((), 0.0),

                "ntg": ((), 1.0),
@@ -91,10 +108,13 @@ class ThermoGISDoubletBenchmark(TestCase): 
                "temperature": ((), 76),

                "ln_permeability_mean": ((), np.log(175)),

                "ln_permeability_sd": ((), 0.0),

        })

            }

        )



        # Act

        results = calculate_doublet_performance_stochastic(input_data, utc_properties=utc_properties, rng_seed=0)

        results = calculate_doublet_performance_stochastic(

            input_data, utc_properties=utc_properties, rng_seed=0

        )



        # Assert

        self.assertTrue(np.isclose(17500, results.transmissivity.data[0], 0.001))
@@ -113,16 +133,19 @@ class ThermoGISDoubletBenchmark(TestCase): 
        """

        # Arrange

        # Instantiate the UTC properties class

        utc_properties = (self.setup_template_utc_properties_builder()

        utc_properties = (

            self.setup_template_utc_properties_builder()

            .setCapexConst(0)

            .setCapexVariable(2300)

            .setHeatExchangerEfficiency(0.6)

            .setUseORC(True)

            .setHeatExchangerBasetemp(20)

            .setDhReturnTemp(60)

                          .build())

            .build()

        )



        input_data = xr.Dataset({

        input_data = xr.Dataset(

            {

                "thickness_mean": ((), 100),

                "thickness_sd": ((), 0.0),

                "ntg": ((), 1.0),
@@ -131,10 +154,13 @@ class ThermoGISDoubletBenchmark(TestCase): 
                "temperature": ((), 100),

                "ln_permeability_mean": ((), np.log(175)),

                "ln_permeability_sd": ((), 0.0),

        })

            }

        )



        # Act

        results = calculate_doublet_performance_stochastic(input_data, utc_properties=utc_properties, rng_seed=0)

        results = calculate_doublet_performance_stochastic(

            input_data, utc_properties=utc_properties, rng_seed=0

        )



        # Assert

        self.assertTrue(np.isclose(17500, results.transmissivity.data[0], 0.001))
@@ -146,10 +172,62 @@ class ThermoGISDoubletBenchmark(TestCase): 
        self.assertTrue(np.isclose(0.06459403120103477, results.cop.data[0], 0.001))

        self.assertTrue(np.isclose(12.44409167482118, results.capex.data[0], 0.001))



    def test_calculateDoubletPerformance_directHeat_and_heatpump(self):

        """

        This is a copy of a test from the Java ThermoGisDoubletTest.java script; to validate that this python implementation of the ThermoGIS Doublet

        returns the same values.

        """

        # Arrange

        # Instantiate the UTC properties class

        utc_properties = (

            self.setup_template_utc_properties_builder()

            .setOpexPerPower(100)

            .setOpexBase(0)

            .setHpDirectHeatInputTemp(70)

            .setUseHeatPump(True)

            .setDhReturnTemp(35)

            .build()

        )



        input_data = xr.Dataset(

            {

                "thickness_mean": ((), 100),

                "thickness_sd": ((), 0.0),

                "ntg": ((), 1.0),

                "porosity": ((), 0.0),

                "depth": ((), 2000),

                "temperature": ((), 50),

                "ln_permeability_mean": ((), np.log(175)),

                "ln_permeability_sd": ((), 0.0),

            }

        )



        # Act

        results = calculate_doublet_performance_stochastic(

            input_data, utc_properties=utc_properties, rng_seed=0

        )

        power_hpelec = results.heat_pump_power / (results.cophp - 1)

        power_ratio = results.power / (results.power + power_hpelec)





        # Assert

        self.assertTrue(np.isclose(5.856159806251526, results.power + power_hpelec, 0.1))

        self.assertTrue(np.isclose(8.83718197747828, results.utc * power_ratio, 0.1))

        self.assertTrue(np.isclose(17499.99940, 17500, 0.001))

        self.assertTrue(np.isclose(163.99771118164062, results.flow_rate, 0.001))

        self.assertTrue(np.isclose(60, results.pres, 0.001))

        self.assertTrue(np.isclose(4.97566556930542, results.power, 0.001))

        self.assertTrue(np.isclose(989.2578125, results.welld, 0.001))

        self.assertTrue(np.isclose(4.383212200392486, results.cop, 0.001))

        self.assertTrue(np.isclose(3.798196792602539, results.cophp, 0.001))

        self.assertTrue(np.isclose(10.401010755009017, results.utc, 0.001))

        self.assertTrue(np.isclose(16.50359210062243, results.capex, 0.001))



    def setup_template_utc_properties_builder(self):

        viscosity_mode = get_viscosity_mode("kestin")



        return (instantiate_utc_properties_builder()

        return (

            instantiate_utc_properties_builder()

            .setSalinityGradient(47.0)

            .setDrillingTime(1)

            .setTaxRate(20)
@@ -162,4 +240,5 @@ class ThermoGISDoubletBenchmark(TestCase): 
            .setWellCostZ(1050)

            .setWellCostZ2(0.3)

            .setDhReturnTemp(40)

         .setViscosityMode(viscosity_mode))
 
 No newline at end of file
 
            .setViscosityMode(viscosity_mode)

        )
 
 No newline at end of file