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Highlights • An inverse heat transfer model linked to Bayesian statistics is presented. • This model is used to identify the conductivity and volumetric heat capacity. • In situ measurements are used by the inverse model. • The proposed model can use shorter time series than the standard ISO 9869. • The proposed model produces more accurate and robust results compared to the standard. Abstract Non-destructive thermal diagnosis is necessary for identification and quantification of structural defects and verification of construction performances. In this paper, we test different inverse heat transfer models to identify the thermal conductivity of a building's wall in its environment. The approaches proposed and used in this paper rely only on non-intrusive measurements: inside and outside wall surface temperatures and heat flow through the wall. First, a Bayesian statistical dynamic inference method, which has the advantage to quantify the unknown parameter and its credible interval, is presented. This method considers the uncertainties of the measured temperature and heat flow data and of the unknown thermal properties. Markov chain Monte Carlo (MCMC) algorithm is used to explore the posterior distribution. Then, the average and the dynamic procedures ISO 9869 (I. 9869-1, 2014) are introduced. Finally, the probabilistic distributions of the unknown parameters are presented and compared to the standard results. The impact of experimental conditions (average indoor-to-outdoor temperature) and the measurements length on the accuracy of the results are discussed. The relationship between the number of iterations of the MCMC , time series length, shape of the prior distribution and accuracy are studied as well as the simulation time to run the inverse models. The Bayesian approach gives the most accurate results and has the advantage of considering several unknowns (conductivity and volumetric heat capacity), which is not the case for the studied standard. The Bayesian method needs much shorter time series than the ISO standard and produces robust results at all times of year, including when the average indoor-to-outdoor temperature difference was low. [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]

In this paper, the use of Bayesian inference is explored for estimating both the thermal conductivity and the internal convective heat transfer coefficient of an old historic building wall. The room air temperature, as well as the temperatures at the surface and within the wall have been monitored during one year and then used to solve the identification problem. With Bayesian inference, the posterior distributions of the unknown parameters are explored based on their prior distributions and on the likelihood function that models the measurement errors. In this work, the Markov Chain Monte Carlo method is used to explore the posterior distribution. The error of the inadequacy of mathematical model are considered using the approximation error model. The distribution of the estimated parameters have a small standard deviation, which illustrates the accuracy of the method. The parameters have been compared to the standard values from the French thermal regulations. The heat flux at the internal surface has been calculated with the estimated parameters and the standard values. It is shown that the standard values underestimate the heat flux of an order by 10%. This study also illustrates the importance of the preliminary diagnosis of a building with the estimation of the thermal properties of the wall for model calibration. [ABSTRACT FROM AUTHOR]

Urban morphology relates to the form, structure, physical characteristics, and arrangement of buildings affecting the urban microclimate. As the morphological characteristics vary across the city, small units such as urban blocks are analysed for microclimate estimation. However, microclimatic analysis of all the blocks in a city is computationally challenging and time-consuming. Therefore, it is vital to identify representative blocks in a city to obtain a general overview of the microclimate. Urban morphological archetypes are the representative units of a homogenous group of blocks based on morphological parameters. Here, we propose a systematic approach for identifying urban morphological archetypes suited for microclimatic analysis. Specifically, we employ a well-defined, PCA-based k-means clustering approach supported by validation using external criterion analysis. We use urban morphological parameters based on form, shape, arrangement, and variations within a block in Liege, Belgium. We use the cubic clustering criterion and pseudo F statistic to identify nine distinct homogenous clusters. Then, we propose a validation approach in the absence of existing typologies using ANOVA analysis on the external criterion of land surface temperature, a proxy for measuring microclimate. The validation suggests that the clusters are significantly different, indicating successful clustering. We also compare our classification to the existing local climate zone (LCZ) classification. We identify relevant sub-classes within the broader LCZ classes essential for capturing microclimatic variation. Finally, the study provides realistic archetypes for performing microclimatic simulations at a city scale. The proposed approach can be effectively applied to other cities for urban microclimate studies. • We propose systematic PCA-based k-means clustering approach to find urban archetypes. • Validation - ANOVA with land surface temperature in absence of existing typologies. • Our clusters are compared with WUDAPT's local climate zones (LCZs). • Our approach provides essential sub-classes to the existing LCZs. • We identify 9 urban morphological archetypes defining the morphology of Liege city. [ABSTRACT FROM AUTHOR]

Expected Global warming and heatwaves coupled with the urban heat island effect (UHI) can overheat indoor environments of free-floating buildings in temperate climate regions. Overheating assessment requires practitioners to use appropriate climate data and suitable measurement indices. The aim of this article is first, to propose a practical approach to generate yearly and typical ready-to-use future typical weather datasets (FTWY) using high-resolution Regional Climate Model (RCM) data from Coordinated Regional Climate Downscaling Experiment (CORDEX), and second, investigate the potential of FTWYs in the assessment of indoor overheating, considering UHI effect. To achieve these objectives, three dynamically downscaled (DDS) FTWYs generated from RCMs (IPSL-SMHI, CNRM-ALADIN, MPI-REMO) were compared with one statistically downscaled (ESD) FTWY from Meteonorm, and observed heatwave weather data of 2003. Comparative analysis was performed in two stages: comparison of monthly statistical distribution of climate variables, and analysis of heatwave presence. Urban weather generator (UWG) was used to project UHI effect on two weather files for two buildings, and three overheating measurement indices were used to exploit results. Comparative analysis of weather files show that temperature in a FTWY in the medium future (2040–2070) is likely not as intense as the heatwave of 2003 for Nantes. Results also confirm that it is better to use two weather files, and at least two overheating indices to obtain reliable outputs. This study also revealed that indoor overheating is not limited to densely built areas where impact of UHI is highest; buildings located in sparsely built neighbourhoods are also at risk. [Display omitted] • A practical workflow to construct yearly and typical weather files from EURO-CORDEX is presented. • Contrary to assumptions, medium future typical years are not as intense as 2003 heatwave year in Nantes. • Buildings located in sparsely built locations that are less affected by UHI are also at risk of overheating. [ABSTRACT FROM AUTHOR]

The urban context is often simplified or neglected in Building Energy Models (BEMs) due to the difficulties of taking accurately into account all the heat fluxes emanating from the environment. Oversimplifying the urban context can impact the accuracy of the BEM predictions. Nevertheless, several approaches can be used to allow for the impact of the urban environment on the dynamic behavior of a building, its heating and cooling demands, and thermal comfort. This state of the art review provides a critical overview of the different methods currently used to take into account the urban microclimate in building design simulations. First, both the microclimate and building models are presented, focusing on their assumptions and capabilities. Second, a few examples of coupling, performed between both modeling scales are analyzed. Last, the discussion highlights the differences obtained between simulations that take the urban context into consideration and those that simplify or neglect urban heat fluxes. The remaining scientific obstacles to a more effective consideration of the urban context impacting the BEMs are indicated. • The local data produced by a selection of urban climate models (UCMs) are presented. • An analysis of how the urban context is taken into account in building energy models (BEMs) is given. • Several chaining or coupling strategies to link UCMs and BEMs are analyzed and compared. • Using local climate data leads to a noticeable impact on the thermal behavior of buildings. • Recommendations for better consideration of the urban context in BEMs are formulated. [ABSTRACT FROM AUTHOR]