Being associated with natural ventilation, the pressure distribution on surfaces is relevant for energy consumption, thermal comfort, and air quality in buildings. The aim of this work is to present ...a simulation-based optimization methodology to recalibrate the closure coefficients of Reynolds-averaged Navier-Stokes (RANS) turbulence models in order to improve the prediction of wind surface-averaged pressure coefficients on a wide range of isolated low-rise buildings. To accomplish this, genetic algorithms and Computational Fluid Dynamics (CFD) simulations are dynamically coupled to find the closure coefficients set which minimize the CFD prediction error regarding wind-tunnel experimental data. The methodology is applied to two turbulence models, the renormalization group k-epsilon model (RNG) and the Spalart-Allmaras model (SA), considering as target cases buildings with different roof types (flat, gable and hip) and wind incidence angles. In order to show the strength of the novel optimal sets of closure coefficients obtained, an exhaustive validation is performed over other low-rise buildings (52 new cases) which were not calibrated against. Results validate using the optimal sets because the recalibrated RNG and SA models decrease the prediction error between 11-64% and 8–45%, respectively, regarding using the standard ones.
•A simulation-based optimization method to recalibrate RANS turbulence models is presented.•The optimization procedure dynamically couples genetic algorithms and CFD simulations.•Cp¯ prediction on a wide range of isolated low-rise buildings is considerably improved.•Recalibrated models are validated regarding wind-tunnel experimental data.•Recalibrated models decrease the error up to 64% regarding using the standard ones.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In most of the countries, buildings are often one of the major energy consumers, leading to the necessity of achieving sustainable building designs, and to the mandatory use of building performance ...simulation (BPS) tools in order to retrofit or design new energy efficient buildings. In the last years, the use of artificial neural networks (ANNs) metamodels has increased and gained confidence in BPS applications thanks to their favorable trade-off between accuracy and computational cost. This paper presents a comprehensive and in-depth systematic review of the up-to-date literature related to the application and characterization of ANN-based metamodels for BPS. First, a general insight into the methodology of metamodel generation and ANN theory is presented. The ANN metamodels are classified according to the type of building they are addressed to, screening them by their inputs (building design variables or indicators to take a certain decision) and outputs (energy consumption, comfort index, climatic condition, environment performance). Then, all the stages for the generation of ANN-based metamodels (sampling methods, data pre-processing, architectures, activations functions, the process of training and testing, and the platforms and frameworks for their implementation) are presented giving a brief theoretical introduction and making a critical review of the literature linked to each stage. For each of these analyzed stages, summary tables and graphs are presented showing the distributions of different alternatives and trends. Finally, the current limitations and areas for further investigation are discussed.
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•A novel method to predict the mean pressure coefficients on building surfaces is presented.•Data for flat-, gable- and hip-roofed low-rise buildings are processed from TPU database for the case ...studies.•Based on the experimental data, three artificial neural network models for the case studies are developed.•The method overcomes the accurate than other current ones to include mean Cp data in AFN or BES programs.
Knowing the pressure coefficient on building surfaces is important for the evaluation of wind loads and natural ventilation. The main objective of this paper is to present and to validate a computational modeling approach to accurately predict the mean wind pressure coefficient on the surfaces of flat-, gable- and hip-roofed rectangular buildings. This approach makes use of artificial neural network (ANN) to estimate the surface-average pressure coefficient for each wall and roof according to the building geometry and the wind angle. Three separate ANN models were developed, one for each roof type, and trained using an experimental database. Applied to a wide variety of buildings, the current ANN models were proved to be considerably more accurate than the commonly used parametric equations for the estimation of pressure coefficients. The proposed ANN-based methodology is as general and versatile as to be easily expanded to buildings with different shapes as well as to be coupled to building performance simulation and airflow network programs.
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Natural ventilation (NV) is a key passive strategy to design energy-efficient buildings and improve indoor air quality. Therefore, accurate modeling of the NV effects is a basic requirement to ...include this technique during the building design process. However, there is an important lack of wind pressure coefficients (
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) data, essential input parameters for NV models. Besides this, there are no simple but still reliable tools to predict
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data on buildings with arbitrary shapes and surrounding conditions, which means a significant limitation to NV modeling in real applications. For this reason, the present contribution proposes a novel cloud-based platform to predict wind pressure coefficients on buildings. The platform comprises a set of tools for performing fully unattended computational fluid dynamics (CFD) simulations of the atmospheric boundary layer and getting reliable
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data for actual scenarios. CFD-expert decisions throughout the entire workflow are implemented to automatize the generation of the computational domain, the meshing procedure, the solution stage, and the post-processing of the results. To evaluate the performance of the platform, an exhaustive validation against wind tunnel experimental data is carried out for a wide range of case studies. These include buildings with openings, balconies, irregular floor-plans, and surrounding urban environments. The
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results are in close agreement with experimental data, reducing 60%–77% the prediction error on the openings regarding the EnergyPlus software. The platform introduced shows being a reliable and practical
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p
data source for NV modeling in real building design scenarios.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Thermal energy storage using phase change materials (PCMs) is a promising technology for improving the thermal performance of buildings and reducing their energy consumption. However, the ...effectiveness of passive PCMs in buildings depends on their optimal design regarding the building typology and typical climate conditions. Within this context, the present contribution introduces a novel multiobjective computational method to optimize the thermophysical properties of cementitious building panels enhanced with a microencapsulated PCM (MPCM). To achieve this, a parametric model for PCM-based cementitious composites is developed in EnergyPlus, considering as design variables the melting temperature of PCMs and the thickness and thermal conductivity of the panel. A multiobjective genetic algorithm is dynamically coupled with the building energy model to find the best trade-off between annual heating and cooling loads. The optimization results obtained for a case study building in Sofia (Bulgaria-EU) reveal that the annual heating and cooling loads have contradictory performances regarding the thermophysical properties studied. A thick MPCM-enhanced panel with a melting temperature of 22 °C is needed to reduce the heating loads, while a thin panel with a melting temperature of 27 °C is required to mitigate the cooling loads. Using these designs, the annual heating and cooling loads decrease by 23% and 3%, respectively. Moreover, up to 12.4% cooling load reduction is reached if the thermal conductivity of the panels is increased. Therefore, it is also concluded that the thermal conductivity of the cement-based panels can significantly influence the effectiveness of MPCMs in buildings.
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The present contribution describes an optimization-based design technique of elastic isotropic periodic microarchitectures with crystal symmetries aiming at the realization of composites with extreme ...properties. To achieve this goal, three consecutive procedures are followed: (i) a series of inverse homogenization problems with symmetry constraints, (ii) a correlation analysis between symmetries and effective elastic properties of the attained microarchitectures, and (iii) the pattern resemblance recognition of these topologies and their redesign, by adopting microstructures with two length-scales, through optimized parametric geometries. This paper is devoted to assessing the third procedure because the first two procedures have been evaluated in previous works of the authors, and here they are only summarized. By applying the methodology, two plane group symmetries are assessed to define two families of 2D periodic parameterized microarchitecture. Once the parameters have been optimized, the resulting composites achieve elastic isotropic properties close to the whole range of the theoretically estimated bounds. Particularly, an unprecedented microstructure attaining the theoretical maximum stiffness is reported. Starting from these parameterized topologies, simple, one-length scale, and easily manufacturable geometries are defined. One of the so-designed microarchitectures has been manufactured and tested, displaying an effective Poisson’s ratio of − 0.90 simultaneously with a high shear modulus.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This work proposes a numerical procedure to simulate and optimize the thermal response of a multilayered wallboard system for building envelopes, where each layer can be possibly made of Phase Change ...Materials (PCM)-based composites to take advantage of their Thermal-Energy Storage (TES) capacity. The simulation step consists in solving the transient heat conduction equation across the whole wallboard using the enthalpy-based finite element method. The weather is described in detail by the Typical Meteorological Year (TMY) of the building location. Taking the TMY as well as the wall azimuth as inputs, EnergyPlusTM is used to define the convective boundary conditions at the external surface of the wall. For each layer, the material is chosen from a predefined vade mecum, including several PCM-based composites developed at the Institut für Werkstoffe im Bauwesen of TU Darmstadt together with standard insulating materials (i.e., EPS or Rockwool). Finally, the optimization step consists in using genetic algorithms to determine the stacking sequence of materials across the wallboard to minimize the undesired heat loads. The current simulation-based optimization procedure is applied to the design of envelopes for minimal undesired heat losses and gains in two locations with considerably different weather conditions, viz. Sauce Viejo in Argentina and Frankfurt in Germany. In general, for each location and all the considered orientations (north, east, south and west), optimal results consist of EPS walls containing a thin layer made of the PCM-based composite with highest TES capacity, placed near the middle of the wall and closer to the internal surface.
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The goal of this research is to improve and validate a Reynolds Averaged Navier–Stokes (RANS) turbulence model to perform accurate Computational Fluid Dynamics (CFD) simulations of the urban wind ...flow. The k-ω SST model is selected for calibration since its blended formulation holds remarkable optimization potential and has increased relevancy in recent studies in the field. A simulation-based optimization approach recalibrates the model closure constants by minimizing the prediction error of wind pressure coefficients on an isolated cubical building because this scenario contains many salient features observed in the flow in actual urban areas. The optimization procedure ensures both the coherence of calibrated model constants involved in the wall function formulations and the relationship between them to satisfy the flow horizontal homogeneity of the atmospheric boundary layer. The tuned closure coefficients increase momentum diffusion in the wake, resulting in shorter and more accurate predictions of the reattachment lengths. Validation case studies with wind tunnel measurement data from various urban scenarios were addressed to comprehensively assess the adaptability of the optimal set of coefficients reached. The results confirm that CFD predictions with the optimized model are consistently in closer agreement with experimental data than the standard version of k-ω SST. The root mean square errors are reduced by about 75% in pressure, 40% in velocity, and 20% in turbulent kinetic energy.
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•An enhanced k-ω SST model to predict urban airflow is developed.•The RANS closure coefficients are optimized to fit wind tunnel-measured data.•The flow in the wake is better predicted due to an increased momentum diffusion.•Validation case studies use wind tunnel data from various urban configurations.•The new model reduces the prediction error up to 75% (pressures) & 40% (velocities).
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Recognizing the urgent need for mitigating global warming, natural ventilation presents a potential strategy to reduce cooling energy demands, enhance thermal comfort, and contribute to indoor air ...quality. H-shaped buildings are prevalent worldwide, and they constitute the majority of the social housing construction in Brazil. Research suggests that the inadequate design of these buildings can result in poor ventilation; however, investigations about their natural ventilation performance are limited. Thus, the present contribution aims to determine the impact of the geometric characteristics of H-shaped buildings on the pressure distribution through wind tunnel experiments. Three models were tested in the wind tunnel experiments, representing different proportions. Their scales were configured to comply with the 5% obstruction limit allowed for wind tunnel testing, which was performed for 20 wind attack angles. Moreover, a scour test was carried out to allow a better understanding of the wind flow. Python scripting was developed to automate data processing, which is openly available in this paper. The results indicate that the proportion of the model influences the pressure distribution on roofs and leeward walls. Additionally, the depth of the recessed cavity affects its side surfaces and can result in a mirrored behavior on the frontal face of deep cavities (i.e., the wind direction is 45°). The model height influences the windward surfaces in its lower portion, since taller models present a recirculation vortex that modifies the pressure near the ground.
•A method for the multi-objective optimization of residential buildings, taking advantage of high performance computing, was introduced.•An actual single-family, two-story house in the Argentine ...Littoral region was the case study.•The normalized degree-hours and the energy consumption in a separate way for winter and summer were the objective functions.•The thermal and energy performance of the case study was drastically improved.
In the last years, multi-objective optimization techniques became into one of the main challenges of the building energy efficiency area. The objective of this paper is to develop and validate a computational code for multi-objective building performance optimization by linking an evolutionary algorithm and a building simulation software in a powerful cluster. A sophisticated version of the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was implemented in Python code to determine the optimal building design, which allows working with categorical and discrete variables, and the objectives were evaluated using the building energy simulation software EnergyPlus. NSGA-II was implemented to run in a high-performance cluster for the parallel computing of the fitness of each population (set of possible designs). In this work, the strengths of the proposed method were demonstrated by its application to the optimal design of a typical single-family house, located in the Argentine Littoral region. This house has some rooms conditioned only by natural ventilation, and other rooms with natural ventilation supplemented by mechanical air-conditioning (hybrid ventilation). The most influential design variables like roof types, external and internal wall types, solar orientation, solar absorptance, size, type, and windows shading of this house among others were studied in two complex cases of 108 and 1016 possibilities to obtain the best trade-off (Pareto front) between heating and cooling performance. Finally, a decision-making method was applied to select one configuration of the Pareto front. Optimal simulation results for the study cases indicated that is possible to improve up to 95% the thermal comfort in naturally ventilated rooms and up to 82% energy performance in air-conditioned rooms of the building with respect to the original configuration by using a design that takes simultaneous advantage of passive strategies like thermal inertia and natural ventilation. The methodology was proved to give a robust and powerful tool to design efficient dwellings reducing the optimization time from almost 12 days to 4.4h.
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