Calibration of Hygrothermal Simulations by the Help of a Generic Optimization Tool

Freudenberg, Peggy; Ruisinger, Ulrich; Stöcker, Eric

doi:10.1016/j.egypro.2017.09.645

Cited by 12 publications

(9 citation statements)

References 1 publication

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“…The authors pointed out several "biases" from within the experimental setup, measurement of boundary conditions, the simulation program, and material properties that are often forgotten or neglected when creating the simulation model, which could potentially close the gap between measured and simulated results. In contrast to these studies, Freudenberg et al [60] obtained a good match between experimental measurements and simulation results using the Generic Optimization tool "GenOpt" for the model calibration. An alternative explanation for the discrepancies between the calibrated models and the measured data could be the measurement uncertainties of the digital sensors during periods with near 100% RH.…”

Section: Model Calibrationmentioning

confidence: 94%

Hygrothermal assessment of four insulation systems for interior retrofitting of solid masonry walls through calibrated numerical simulations

Jensen

Bjarløv

Rode

et al. 2020

Building and Environment

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Section: Model Calibrationmentioning

confidence: 94%

Hygrothermal assessment of four insulation systems for interior retrofitting of solid masonry walls through calibrated numerical simulations

Jensen

Bjarløv

Rode

et al. 2020

Building and Environment

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“…With the help of simulations of the undisturbed, 1D wall structures, it is possible to identify simulation settings such as thermal transition conditions and to revise material properties. As described in an earlier work [20], the software GenOpt helps with semi-automatised simulations to achieve quicker results. In the case of the perlite insulation board a higher design thermal conductivity of 0.047 instead of 0.045 W/(m•K) was found out.…”

Section: Comparison Of Measurements With 2d and 3d Simulation Resultsmentioning

confidence: 99%

Comparison of hygrothermal 2D- and 3D-simulation results with measurements from a test house

Ruisinger

Kautsch

2020

E3S Web Conf.

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This article examines whether transient two-dimensional simulations are sufficient or three-dimensional simulations are necessary for the hygrothermal analysis of the three-dimensional detail of a beam support. Detailed measurement series from a test house with various interior insulation systems available for the investigation allow the results of two- and three-dimensional simulations to be verified with the measured data. In both the cases, a very good agreement between measurement and simulations was found. With the thermal simulations, agreement with the measured data is similar in both two- and three-dimensional calculations. However, the hygric measurements agreed slightly better with the three-dimensional calculations. For planning real tasks, two-dimensional simulations should be sufficient, provided that the simulation settings are selected within the secure margins. The actual comparison is initiated by the validation of the software DELPHIN 6 by means of a three-dimensional, stationary test case from EN 10211.

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“…For the envelope, it is necessary to know how the stratigraphy is composed, in particular the type of materials, their properties, and their thickness within the structure. Typically, if the material is not included in the materials' database, material properties for the numerical model are chosen from technical standards or a commercial data-sheet, leading to significant uncertainties in the evaluation [10]. In the case of ancient components, this operation is even more complex.…”

Section: Introductionmentioning

confidence: 99%

Thermal Modeling of a Historical Building Wall: Using Long-Term Monitoring Data to Understand the Reliability and the Robustness of Numerical Simulations

et al. 2022

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Thermal modeling of building components plays a crucial role in designing energy efficiency measures, assessing living comfort, and preventing building damages. The accuracy of the modeling process strongly depends on the reliability of the physical models and the correct selection of input parameters, especially for historic buildings where uncertainties on wall composition and material properties are higher. This work evaluates the reliability of building thermal modeling and identifies the input parameters that most affect the simulation results. A monitoring system is applied to a historic building wall to measure the temperature profile. The long-term dataset is compared with the result of a simulation model. A sensitivity analysis is applied for the determination of the influential input parameters. A two-step optimization is performed to calibrate the numerical model: the first optimization step is based on an optimized selection of the database materials, while the second optimization step uses a particle swarm algorithm. The results indicate that the output of the simulation model is largely influenced by the coefficients describing the coupling with the boundary conditions and by the thermal conductivities of the materials. Very good results are obtained already after the first optimization step (RMSE=0.75 °C) while the second optimization step improves further the agreement (RMSE=0.48 °C). The parameter values reported in the datasheets do not match those found through optimization. Even with extensive optimization using an algorithm, starting with monitoring data is insufficient to identify material parameter values.

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Calibration of Hygrothermal Simulations by the Help of a Generic Optimization Tool

Cited by 12 publications

References 1 publication

Hygrothermal assessment of four insulation systems for interior retrofitting of solid masonry walls through calibrated numerical simulations

Hygrothermal assessment of four insulation systems for interior retrofitting of solid masonry walls through calibrated numerical simulations

Comparison of hygrothermal 2D- and 3D-simulation results with measurements from a test house

Thermal Modeling of a Historical Building Wall: Using Long-Term Monitoring Data to Understand the Reliability and the Robustness of Numerical Simulations

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