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Within the environmental context, designing energy efficient buildings is crucial. Standard performance indicators evaluate the quantity of energy going through the wall. Such indicator considers the energy balance of the wall, i.e the first thermodynamic law. However, the main drawback of such approach is that it does not qualify the energy quality, which can be done by the second thermodynamic law. This paper proposed a performance indicator that both quantifies and qualifies the energy efficiency of a wall. It is based on the evaluation of the exergy destruction rate. The performance indicator has been developed for transient conditions induced by climatic variations of temperature and relative humidity and considering coupled heat and mass transfer in the wall. Calculations were carried out with experimental data obtained from a wall demonstrator under climatic conditions and comparisons with standard performance indicators were also performed. The corresponding results highlighted that the exergy loss allows a more accurate assessment of the energy performance and the influence of mass transfer on it. Indeed, the mass transfer can account for 30% in the exergy destruction. [ABSTRACT FROM AUTHOR]

The design of numerical tools to model the behavior of building materials is a challenging task. The crucial point is to save computational costs and maintain the high accuracy of predictions. There are two main limitations on the time scale choice, which places an obstacle to solving the above issues. The first one is the numerical restriction. A number of research studies are dedicated to overcome this limitation and it is shown that it can be relaxed with innovative numerical schemes. The second one is the physical restriction. It is imposed by the properties of a material, the phenomena itself, and the corresponding boundary conditions. This study is focused on the study of a methodology that enables to overcome the physical restriction on the time grid; a so‐called average reduced model is suggested. It is based on smoothing the time‐dependent boundary conditions. Besides this, the approximate solution is decomposed into average and fluctuating components. The primer is obtained by integrating the equations over time, whereas the latter is a user‐defined EM. The methodology is investigated for both heat diffusion and coupled heat and mass transfer. It is demonstrated that the signal core of the boundary conditions is preserved and the physical restriction can be relaxed. The model proved to be reliable, accurate, and efficient also in comparison with the experimental data of 2 years. The implementation of the scarce time‐step of 1h is justified. It is shown, that by maintaining the tolerable error, it is possible to cut computational effort up to almost four times in comparison with the complete model with the same time grid. [ABSTRACT FROM AUTHOR]

Estimating the temperature field of a building envelope could be a time-consuming task. The use of a reduced-order method is then proposed: the Proper Generalized Decomposition method. The solution of the transient heat equation is then re-written as a function of its parameters: the boundary conditions, the initial condition, etc. To avoid a tremendous number of parameters, the initial condition is parameterized. This is usually done by using the Proper Orthogonal Decomposition method to provide an optimal basis. Building this basis requires data and a learning strategy. As an alternative, the use of orthogonal polynomials (Chebyshev, Legendre) is here proposed. Highlights Chebyshev and Legendre polynomials are used to approximate the initial condition Performance of Chebyshev and Legendre polynomials are compared to the POD basis Each basis combined with the PGD model is compared to laboratory measurements The influence of four different parameters on the accuracy of the basis is studied For each approximation basis, CPU calculation times are evaluated and compared [ABSTRACT FROM AUTHOR]

A two-dimensional model is proposed for energy efficiency assessment through the simulation of heat transfer in building envelopes, considering the influence of the surrounding environment. The model is based on the Du Fort–Frankel approach that provides an explicit scheme with a relaxed stability condition. The model is first validated using an analytical solution and then compared to three other standard schemes. Results show that the proposed model offers a good compromise in terms of high accuracy and reduced computational efforts. Then, a more complex case study is investigated, considering non-uniform shading effects due to the neighboring buildings. In addition, the surface heat transfer coefficient varies with wind velocity and height, which imposes an addition non-uniform boundary condition. After showing the reliability of the model prediction, a comparison over almost 120 cities in France is carried out between the two- and the one-dimensional approaches of the current building simulation programs. Important discrepancies are observed for regions with high magnitudes of solar radiation and wind velocity. Last, a sensitivity analysis is carried out using a derivative-based approach. It enables to assess the variability of the solution according to the modeling of the two-dimensional boundary conditions. Moreover, the proposed model computes efficiently the solution and its sensitivity to the modeling of the urban environment. [ABSTRACT FROM AUTHOR]

It is well known that knowledge of thermophysical parameters is a leading strategy to research effects of energy transfer in soils. The present article proposes an inverse analysis for numerical solving of nonlinear heat transfer problem to determine the thermophysical properties of two different soil types: sand and chernozem. First, estimation of thermophysical parameters is performed using temperature data from experimental set-up, which is two-chambered container for two soil types. Second, numerical algorithm is based on implicit Euler scheme for discretization, Newton method to solve nonlinear system of equations and Levenberg-Marquardt method to minimize nonlinear estimator with Tikhonov's regularization technique. Simulations have been efficiently carried out for two different soil types, showing that the reliability of the model is satisfying with a discrepancy between numerical predictions and experimental observations remaining within the measurement error. • Estimation of two soil types thermophysical properties. • Reliability of the model is satisfying. • Experimental set-up is used for parameters estimation. • Sensitivity coefficients by direct differentiation. • Robustness of calculation. [ABSTRACT FROM AUTHOR]

Predictions of physical phenomena in buildings are carried out by using physical models formulated as a mathematical problem and solved by means of numerical methods, aiming at evaluating, for instance, the building thermal or hygrothermal performance by calculating distributions and fluxes of heat and moisture transfer. Therefore, the choice of the numerical method is crucial since it is a compromise among (i) the solution accuracy, (ii) the computational cost to obtain the solution and (iii) the complexity of the method implementation. An efficient numerical method enables to compute an accurate solution with a minimum computational run time (CPU). On that account, this article brings an investigation on the performance of three numerical methods. The first one is the standard and widely used finite-difference approach, while the second one is the so-called RC approach, which is a particular method brought to the building physics area by means of an analogy of electric circuits. The third numerical method is the spectral one, which has been recently proposed to solve nonlinear diffusive problems in building physics. The three methods are evaluated in terms of accuracy on the assessment of the dependent variable (temperature or vapor pressure) or of density of fluxes for three different cases: (i) heat diffusion through a concrete slab, (ii) moisture diffusion through an aerated concrete slab and (iii) heat diffusion using measured temperatures as boundary conditions. Results highlight the spectral approach as the most accurate method. The RC based model with a few number of resistances does not provide accurate results for temperature and vapor pressure distributions neither to flux densities nor conduction loads. [ABSTRACT FROM AUTHOR]

Models for the prediction of heat and mass transfers in building porous materials have been developed since the 90's with simulation programs such as MATCH, UMIDUS, DELPHIN and Wufi. These models are used to analyze the physical phenomena occurring and particularly the impact of moisture on buildings' energy performance and durability. With this goal in mind, it is important to validate the representation of the physical phenomena made by the numerical models. This article reviews recent studies comparing the results obtained with numerical models and experimental data. An overall trend can be observed for most of these studies, highlighting that the experimental front always rushes faster than the simulation results. Therefore, this study analyses theses comparisons to explain these discrepancies based on the effects of (i) the type of materials, (ii) the boundary conditions, (iii) the scale of the design facility, (iv) the model used to describe the physical phenomena and (v) the influence of the model input parameter. The general trend shows that discrepancies are observed most particularly for highly hygroscopic or bio-based materials. These discrepancies are also greater for time dynamic boundary conditions, particularly at the scale of the wall. Moreover, some of the assumptions on the properties of the materials used as input in the models are questioned. Indeed, the models considering the hysteresis effects and temperature dependency of the moisture storage capacity show better agreement with experimental data. Finally, the physical phenomena used in the models only consider diffusive transport while it appears that the advective of moisture through the porous material may play an important role. [ABSTRACT FROM AUTHOR]

In this article, a framework to model and predict the energy performance of an adaptive facade is proposed. A case study of a bio-inspired concept, called Stegos, is considered. This dynamic system manages thermal transfers through the facade by varying the color and position of rotating flaps. A prototype of this concept that was incorporated into a test bench was tested at a 1:1 scale and in real weather conditions, while the flaps color and angle were changed manually. The objective of the article was two-fold. First, using measurements, a reduced order model was identified by applying the Modal Identification Method (MIM). The training phase was divided into four consecutive steps. At each step, one day of corresponding experimental data is used. The reduced model provided reliable predictions of heat flux values induced by the prototype when the flaps were in a closed or fully open state. Second, a Model Predictive Control (MPC) was implemented to indicate the optimal configurations of the prototype for better energy efficiency. Case study used measurements of one week in winter and determined the color and angle of the flaps, which corresponded to the optimal solution. Closed black flaps during a day and open flaps during a night contributed to maximum heat gain. [ABSTRACT FROM AUTHOR]

Highlights • A detailed mathematical model for the heat, air and mass transfer is achieved. • An efficient numerical model is proposed to save computational efforts. • Demonstration of numerical model efficiency (cpu time, accuracy) for a case study. • Evaluation of the reliability of the model by confronting to experimental results. Abstract This work presents a detailed mathematical model combined with an innovative efficient numerical model to predict heat, air and moisture transfer through porous building materials. The model considers the transient effects of air transport and its impact on the heat and moisture transfer. The achievement of the mathematical model is detailed in the continuity of L uikov 's work. A system composed of two advection–diffusion differential equations plus one exclusively diffusion equation is derived. The main issue to take into account the transient air transfer arises in the very small characteristic time of the transfer, implying very fine discretisation. To circumvent these difficulties, the numerical model is based on the D u F ort –F rankel explicit and unconditionally stable scheme for the exclusively diffusion equation. It is combined with a two–step R unge –K utta scheme in time with the S charfetter –G ummel numerical scheme in space for the coupled advection–diffusion equations. At the end, the numerical model enables to relax the stability condition, and, therefore, to save important computational efforts. A validation case is considered to evaluate the efficiency of the model for a nonlinear problem. Results highlight a very accurate solution computed about 16 times faster than standard approaches. After this numerical validation, the reliability of the mathematical model is evaluated by comparing the numerical predictions to experimental observations. The latter is measured within a multi-layered wall submitted to a sudden increase of vapor pressure on the inner side and driven climate boundary conditions on the outer side. A very satisfactory agreement is noted between the numerical predictions and experimental observations indicating an overall good reliability of the proposed model. [ABSTRACT FROM AUTHOR]

Highlights • We propose a parametric model dedicated to urban soil thermal modeling. • A combination of two reduced-order methods, i.e. POD and PGD, is presented. • Calculated temperatures are evaluated with respect to in situ measurements. • The parametric soil model is coupled with the SOLENE-microclimat tool. • Its accuracy and computational cost are evaluated in an urban setting. Abstract Numerical simulation is a powerful tool for assessing the causes of an Urban Heat Island (UHI) effect or quantifying the impact of mitigation solutions on local climatic conditions. However, the numerical cost associated with such a tool is quite significant at the scale of an entire district. Today, the main challenge consists of achieving both a proper representation of the physical phenomena and a critical reduction in the numerical costs of running simulations. This paper presents a combined parametric urban soil model that accurately reproduces thermal heat flux exchanges between the soil and the urban environment with a reduced computational time. For this purpose, the use of a combination of two reduced-order methods is proposed herein: the Proper Orthogonal Decomposition method, and the Proper Generalized Decomposition method. The developed model is applied to two case studies in order to establish a practical evaluation: an open area independent of the influences of the surrounding surface, and a theoretical urban scene with two canyon streets. The error due to the model reduction remains below 0.2 °C on the mean surface temperature for a reduced computational cost of 80%. Compared to in situ measurements the error remains bellow 1.24 °C at the surface. [ABSTRACT FROM AUTHOR]

A computational procedure known as co-simulation has been proposed in the literature as a possibility to extend the capabilities and improve the accuracy of building performance simulation (BPS) tools. Basically, the strategy relies on the data exchanging between the BPS and a specialized software, where specific physical phenomena are simulated more accurately thanks to a more complex model, where advanced physics are taken into account. Among many possibilities where this technique can be employed, one could mention airflow, three-dimensional heat transfer or detailed HVAC systems simulation, which are commonly simplified in BPS tools. When considering complex models available in specialized software, the main issue of the co-simulation technique is the considerable computational effort demanded. This paper proposes a new methodology for time-consuming simulations with the purpose of challenging this particular issue. For a specific physical phenomenon, the approach consists of designing a new model, called prediction model, capable to provide results, as close as possible to the ones provided by the complex model, with a lower computational run time. The synthesis of the prediction model is based on artificial intelligence, being the main novelty of the paper. Basically, the prediction model is built by means of a learning procedure, using the input and output data of co-simulation where the complex model is being used to simulate the physics. Then the synthesized prediction model replaces the complex model with the purpose of reducing significantly the computational burden with a small impact on the accuracy of the results. Technically speaking, the learning phase is performed using a machine learning technique, and the model investigated here is based on a recurrent neural network model and its features and performance are investigated on a case study, where a single-zone house with a triangular prism-shaped attic model is co-simulated with both CFX (CFD tool) and Domus (BPS tool) programs. Promising results lead to the conclusion that the proposed strategy enables to bring the accuracy of advanced physics to the building simulation field - using prediction models - with a much reduced computational cost. In addition, re-simulations might be run solely with the already designed prediction model, demanding computer run times even lower than the ones required by the lumped models available in the BPS tool. [ABSTRACT FROM AUTHOR]

Comparisons of experimental observation of heat and moisture transfer through porous building materials with numerical results have been presented in numerous studies reported in the literature. However, some discrepancies have been observed, highlighting underestimation of sorption process and overestimation of desorption process. Some studies intend to explain the discrepancies by analyzing the importance of hysteresis effects as well as carrying out sensitivity analyses on the input parameters as convective transfer coefficients. This article intends to investigate the accuracy and efficiency of the coupled solution by adding advective transfer of both heat and moisture in the physical model. In addition, the efficient Scharfetter and Gummel numerical scheme is proposed to solve the system of advection-diffusion equations, which has the advantages of being well-balanced and asymptotically preserving. Moreover, the scheme is particularly efficient in terms of accuracy and reduction of computational time when using large spatial discretization parameters. Several linear and nonlinear cases are studied to validate the method and highlight its specific features. At the end, an experimental benchmark from the literature is considered. The numerical results are compared to the experimental data for a pure diffusive model and also for the proposed model. The latter presents better agreement with the experimental data. The influence of the hysteresis effects on the moisture capacity is also studied, by adding a third differential equation. [ABSTRACT FROM AUTHOR]

In this paper, the proper generalized decomposition (PGD) is used for model reduction in the solution of an inverse heat conduction problem within the Bayesian framework. Two PGD reduced order models are proposed and the approximation Error model (AEM) is applied to account for the errors between the complete and the reduced models. For the first PGD model, the direct problem solution is computed considering a separate representation of each coordinate of the problem during the process of solving the inverse problem. On the other hand, the second PGD model is based on a generalized solution integrating the unknown parameter as one of the coordinates of the decomposition. For the second PGD model, the reduced solution of the direct problem is computed before the inverse problem within the parameter space provided by the prior information about the parameters, which is required to be proper. These two reduced models are evaluated in terms of accuracy and reduction of the computational time on a transient three-dimensional two region inverse heat transfer problem. In fact, both reduced models result on substantial reduction of the computational time required for the solution of the inverse problem, and provide accurate estimates for the unknown parameter due to the application of the approximation error model approach. [ABSTRACT FROM PUBLISHER]

Innovative and efficient ways to carry out numerical simulations are worth of investigation to reduce the computational complexity of building models and make it possible to solve complex problems. This paper presents a reduced order model, based on Proper Generalised Decomposition (PGD), to assess 2-dimensional heat and moisture transfer in walls. This model is associated with the multizone model Domus using an indirect coupling method. Both models are co-simulated to perform whole-building hygrothermal simulation, considering 2D transfer in walls. The whole-building model is first validated with data from the IEA Annex 41. Then, a case study is considered taking into account a 2-zones building with an intermediary shared wall modelled in 2 dimensions to illustrate the importance of the technique to analyse the hygrothermal behaviour of the wall. It has been highlighted that the whole model enables to perform more precisely analyses such as mould growth on the internal surface. In addition, important theoretical numerical savings (90%) are observed when compared to the large original model. However, the effective numerical savings are not so important (40%) due to the limitations of the co-simulation method. [ABSTRACT FROM AUTHOR]

This paper proposes a reduced order model to simulate heat and moisture behaviour of material based on proper general decomposition (PGD). This innovative method is ana priorimodel reduction method. It proposes an alternative way for computing solutions of the problem by considering a separated representation of the solution. PGD offers an interesting reduction of numerical cost. In this paper, the PGD solution is first compared with a finite element solution and the commercial validated modelDelphinin an 1D case. The results show that the PGD resolution techniques enable the field of interest to be represented with accuracy, with a relative error rate of less than 0.1%. The study remains in the hygroscopic range of the material. As the numerical gain of the method becomes interesting when the space dimension increases, this resolution strategy was then used on a 2D multi-layered test case. The dynamics and amplitude of hygrothermal fields are perfectly represented by the PGD solution. Temperature and vapour pressure modelled with PGD can be used for post-processing and analysing the behaviour of an assembly. [ABSTRACT FROM PUBLISHER]

The estimation of wall thermal properties by in situ measurement enables to increase the reliability of the model predictions for building energy efficiency. Nevertheless, retrieving the unknown parameters has an important computational cost. Indeed, several computations of the heat transfer problem are required to identify these thermal properties. To handle this drawback, an innovative approach is investigated. The first step is to search the optimal experiment design among the sequence of observation of several months. A reduced sequence of observations of three days is identified which guarantees to estimate the parameter with the maximum accuracy. Moreover, the inverse problem is only solved for this short sequence. To decrease further the computational efforts, a reduced order model based on the modal identification method is employed. This a posteriori model reduction method approximates the solution with a lower degree of freedom. The whole methodology is illustrated to estimate the thermal diffusivity of an historical building that has been monitored with temperature sensors for several months. The computational efforts is cut by five. The estimated parameter improves the reliability of the predictions of the wall thermal efficiency. • Estimation of historical building wall thermal properties with by in situ measurement. • Innovative approach to reduce the computational effort to retrieve unknown parameters. • Reduced sequence of observations is defined using optimal experiment design approach. • A posteriori reduced order model based on the modal identification method is employed. [ABSTRACT FROM AUTHOR]

• D-optimum criterion is applied to select an optimal experiment duration. • Identifiability of parameters is demonstrated. • Thermal conductivity is estimated over the reduced measurement plan. • Parameter estimation problem is solved with hybrid optimization method. • Reliability of the model for one year experiment is achieved. The in situ estimation of the thermal properties of existing building wall materials is a computationally expensive procedure. Its cost is highly proportional to the duration of measurements. To decrease the computational cost a methodology using a D-optimum criterion to select an optimal experiment duration is proposed. This criterion allows to accurately estimate the thermal properties of the wall using a reduced measurement plan. The methodology is applied to estimate the thermal conductivity of the three-layer wall of a historical building in France. Three different experiment sequences (one, three and seven days) and three spatial distributions of the thermal conductivity are investigated. Then using the optimal duration of observations the thermal conductivity is estimated using the hybrid optimization method. Results show a significant reduction of computational time; and reliable simulation of physical phenomena using the estimated values. [ABSTRACT FROM AUTHOR]

The reliability of mathematical models for heat and mass transfer in building porous material is of capital importance. A reliable model permits to carry predictions of the physical phenomenon with sufficient confidence in the results. Among the physical phenomena, the hysteresis effects on moisture sorption and moisture capacity need to be integrated in the mathematical model of transfer. This article proposes to explore the use of an smooth Bang–Bang model to simulate the hysteresis effects coupled with heat and mass transfer in porous material. This model adds two supplementary differential equations to the two classical ones for heat and mass transfer. The solution of these equations ensures smooth transitions between the main sorption and desorption curves. Two parameters are required to control the speed of transition through the intermediary curves. After the mathematical description of the model, an efficient numerical model is proposed to compute the fields with accuracy and reduced computational efforts. It is based on the Du Fort–Frankel scheme for the heat and mass balance equations. For the hysteresis numerical model, an innovative implicit–explicit approach is proposed. Then, the predictions of the numerical model are compared with experimental observations from literature for two case studies. The first one corresponds to a slow cycle of adsorption and desorption while the second is based on a fast cycling case with alternative increase and decrease of moisture content. The comparisons highlight a very satisfactory agreement between the numerical predictions and the observations. In the last Section, the reliability and efficiency of the proposed model is investigated for long term simulation cases. The importance of considering hysteresis effects in the reliability of the predictions are enhanced by comparison with classical approaches from literature. • Building numerical model for heat and mass transfer with hysteresis effects. • Use of Bang–Bang model for modeling hysteresis on moisture sorption and capacity. • Comparison of the model reliability with two experimental benchmark from literature. • Saved computational effort and satisfactory agreement with experimental observations. [ABSTRACT FROM AUTHOR]

The issue to predict the behavior of building materials during wide horizons of time is still challenging. Experimental set-ups, since they require to perform tests for several years, are costly, never at the full scale and inconvenient. Building Performance Simulation (BPS) programs are designed to perform predictions on computational machines and cut experimental costs significantly. Nonetheless, in the recent review of state–of–the–art, it was indicated that despite the wide range of programs, there are still some drawbacks in terms of the accuracy and the high computational cost. This paper investigates the application of an innovative numerical method, called Super–Time–Stepping (STS) method. It allows performing accurate simulations with time-steps much larger than with standard explicit approaches. These "super" time-steps also enable us to reduce the computational cost. In addition to that, the design of the method allows easier application for models in higher dimensions and with nonlinear parameters. The efficiency of the method is tested on linear and nonlinear academic cases. Further study for the reliability of the model is performed on an experimental case study. The experiment has been carried out on a rammed earth wall during almost 14 months. Obtained data is presented in this article and implemented into proposed model. As a result of the case studies, it is shown that in comparison to the euler explicit method, the STS methods can cut costs by more than five times while maintaining high accuracy and efficiency. A very fine analysis of the physical phenomena is also performed. [ABSTRACT FROM AUTHOR]

• Reliability of model for heat and mass transfer in porous material is achieved. • An efficient numerical model is proposed to save computational efforts. • Inverse problem is solved to estimate uncertain input parameter. • Identifiability of the seven unknown coefficients is demonstrated. • Other experimental data are used to benchmark the numerical prediction of the model. The reliability of a model is its accuracy in predicting the physical phenomena. In this paper, the robustness of a model of heat and mass transfer in a porous material is evaluated by comparing the numerical predictions with experimental observations. An experimental facility composed of an enclosure made with spruce CLT panels is used. An increase of temperature is applied in the inside air volume to force the heat transfer from the inner to the outer surfaces. Sensors inside the material enables to have experimental observations of the physical phenomena. Before bench-marking the numerical model, a first set of experimental data is used to reduce the two major source of uncertainties in the model. Indeed, the first source arises from surface heat and mass transfer coefficients, usually determined by empirical correlations. The second comes the thermal conductivity of the material which is defined through standard methods as invariant for the three layers of the spruce panels. To overcome this issue, a set of seven uncertain parameters are estimated using an hybrid optimizer after demonstrating their theoretical and practical identifiability. Then, the reliability of the numerical model, based on the Du Fort–Frankel explicit scheme, is evaluated by comparison to a second set of experimental data obtained in another wall of the enclosure. A very satisfactory agreement is remarked showing the accuracy of the model to predict the physical phenomena in this hygroscopic porous material. [ABSTRACT FROM AUTHOR]