Validation of a Climatic CFD Model to Predict the Surface Temperature of Building Integrated Photovoltaics

AbstractThe current market of the photovoltaic (PV) industry is dominated by silicon-based modules, which are malfunctioned and degraded in higher temperatures mainly above 25°C. Consequently, one of the challenges for such modules is finding a more efficient way in their integration into the buildings in order to reduce the mentioned temperature. The present work is a part of a comprehensive framework toward the investigation of the lifetime durability of the BIPV modules. Therefore, this paper explain the development and validation of a computational fluid dynamics (CFD) model to be later utilized to evaluate the temperature distribution of BIPV's surfaces under different arrangements and climate loadings. For this purpose, a high resolution 3D CFD model is firstly developed by generation of about 3 million cells. Then, the model is validated with a velocimetry experimental dataset from the same model tested in a wind tunnel experiment by [6]. Furthermore, the solar radiation is added into simulation to model the non-isothermal condition of the BIPV module. The non-isothermal case is further validated with a thermography observation conducted by [5] where a solar simulator is installed inside the tunnel. The simulation results show that the developed model can accurately simulate the impact of 3D flow over/underneath the PV modules

MoGA: Searching Beyond MobileNetV3

The evolution of MobileNets has laid a solid foundation for neural network applications on mobile end. With the latest MobileNetV3, neural architecture search again claimed its supremacy in network design. Unfortunately, till today all mobile methods mainly focus on CPU latencies instead of GPU, the latter, however, is much preferred in practice for it has faster speed, lower overhead and less interference. Bearing the target hardware in mind, we propose the first Mobile GPU-Aware (MoGA) neural architecture search in order to be precisely tailored for real-world applications. Further, the ultimate objective to devise a mobile network lies in achieving better performance by maximizing the utilization of bounded resources. Urging higher capability while restraining time consumption is not reconcilable. We alleviate the tension by weighted evolution techniques. Moreover, we encourage increasing the number of parameters for higher representational power. With 200x fewer GPU days than MnasNet, we obtain a series of models that outperform MobileNetV3 under the similar latency constraints, i.e., MoGA-A achieves 75.9% top-1 accuracy on ImageNet, MoGA-B meets 75.5% which costs only 0.5 ms more on mobile GPU. MoGA-C best attests GPU-awareness by reaching 75.3% and being slower on CPU but faster on GPU.The models and test code is made available here https://github.com/xiaomi-automl/MoGA.Comment: Accepted by ICASSP202

An IT Professional Talents Training Model in Colleges Based on Animal Cell Structure

Under the current period background of big data and cloud computing, there is a huge demand for professionals in related fields such as information technology (IT). To solve this problem, this paper puts forward an IT professional talents training model based on animal cell structure by comparing the structures of animal cells and its efficient operation principle with IT professional training model system. According to the efficient-working principle of ‘Nucleus-Cytoplasm- Environment’, this model is built as a ‘Class (The Core)-College (Internal Environment)-Enterprise (External Environment)’ training model for IT-majored students. The motivation is to cultivate students’ abilities in these four aspects: structure, application, analysis and innovation, namely, regarding theory teaching as the core, college practice training as the pulling force and enterprise project resources as the pushing force. The reliability and validation of this model have been demonstrated by simulation results in Wuhan University of Science and Technology

Modelling of neighbourhood effect in cities by coupling computational fluid dynamics and building energy simulation techniques

Building energy simulation (BES) is widely applied to assess indoor comfort and building energy demand, which is reported to encompass a one-third share of the world’s energy demand in 2021. There is a range of worldwide accepted building energy simulation packages in hand, such as EnergyPlus ©, Revit©, DOE-2©. These tools comprise series of subroutines to predict the behaviour of systems within the buildings. The calculations in these programs are based on the combination of well-defined laws (i.e., energy and mass balance) and empirical algorithms (e.g., convective heat transfer coefficient). In particular, despite numerous updates over the empirical algorithms of convective heat transfer coefficient (CHTC), the current packages are still considered not proper in representing the local CHTC values in many urban occasions as they simplify the surrounding airflows with homogeneous patterns. Furthermore, it has been reported that the inadequate understanding of outdoor airflow can lead to up to 20 – 40 % error in building energy predictions. This weakness, thus, initiated a subject of the research in the past decades to couple BES with computational fluid dynamics (CFD) tools, which are known for their strengths in airflow modelling, especially in representing the neighbourhood effects in an urban area. Dynamic coupling of BES to computational fluid dynamics (CFD) techniques are a common strategy to improve the simulation performance. The precedent research of CFD-BES coupling mainly focuses on the indoor environment, but only a few consider the outdoor microclimate conditions. This research aims to investigate the neighbourhood effect on the convection of buildings' exterior surfaces and enhance their presence in the local convective heat transfer coefficient format in building energy modelling. Among the dynamic coupling strategies, the fully dynamic coupling is understood as computationally intensive and impractical in medium-to-long-term modelling or even short-term (hourly, daily or weekly) modelling of naturally ventilated scenarios on a neighbourhood scale. Therefore, though it provides a more accurate assessment than the quasi-dynamic approach, it is less popular than the latter one. In this study, frameworks of fully dynamic coupling and virtual dynamic coupling are proposed for short-term and medium-to-long-term (monthly, seasonally or annually) modelling, respectively. Three case studies are performed for 1) short-term modelling of scenarios with all buildings sealed from outdoors without natural ventilation (sealed scenarios); 2) short-term modelling of scenarios with all buildings under the natural ventilation during the night-time (ventilated scenarios); 3) medium-to-long-term modelling of sealed scenarios. where ‘sealed’ here means the rooms are sealed from outdoors with the windows closed all the time. The first case study proves the feasibility of the developed benchmark coupling framework. After that, the second case study expands the framework for application in scenarios of natural ventilation with fast calculations. The last case study provides the virtual dynamic coupling of the CFD and BES with artificial neural network for medium-to-long-term prediction of CHTCs. All case studies are performed in typical hot weather for a simple city community in Los Angeles, U.S. The results highlight the importance of the neighbourhood effect. For short-term modelling of sealed scenarios, the difference between the prediction of the hourly averaged external convection using the coupling method and that of the standalone conventional BES models is up to 64 %. Furthermore, for short-term modelling of ventilated scenarios, the proposed model substantially reduces the computational cost of the dynamic coupling procedure, taking almost 1/200 of time as the conventional method. Concerning the medium-to-long-term modelling using a virtual dynamic coupling, the predictions of the local CHTCs on the external surfaces are found satisfactory with an accuracy of 0.88. Moreover, ten is the effective number of days to train the neural network tools for a one-month simulation—the proposed approach saves approximately 2/3 of the required computational time using an ordinary approach

A new regression model to predict BIPV cell temperature for various climates using a high-resolution CFD microclimate model

Understanding of cell temperature of Building Integrated Photovoltaics (BIPV) is essential in the calculation of their conversion efficiency, durability and installation costs. Current PV cell temperature models mainly fail to provide accurate predictions in complex arrangement of BIPVs under various climatic conditions. To address this limitation, this paper proposes a new regression model for prediction of the BIPV cell temperature in various climates and design conditions, including the effects of relative PV position to the roof edge, solar radiation intensity, wind speed, and wind direction. To represent the large number of possible climatic and design scenarios, the advanced technique of Latin Hypercube Sampling was firstly utilized to reduce the number of investigated scenarios from 13,338 to 374. Then, a high-resolution validated full-scale 3-dimensional Computational Fluid Dynamics (CFD) microclimate model was developed for modelling of BIPV’s cell temperature, and then was applied to model all the reduced scenarios. A nonlinear multivariable regression model was afterward fit to this population of 374 sets of CFD simulations. Eventually, the developed regression model was evaluated with new sets of unused climatic and design data when a high agreement with a mean discrepancy of 3% between the predicted and simulated BIPV cell temperatures was observed

Development of a dynamic external CFD and BES coupling framework for application of urban neighbourhoods energy modelling

© 2018 Elsevier Ltd Current building energy models are weak at representing the interactions between neighbourhoods of buildings in cities. The effect of a neighbourhood on the local microclimate is complex, varying from one building to another, meaning that neighbourhood effects on the airflow around a particular building. A failure to account for this may lead to the miss-calculation of heat transfer and energy demand. Current building energy simulation (BES) tools apply convective heat transfer coefficient (CHTC) correlations, which were developed by using a simplified model of wind flow that neglects neighbourhood effects. Computational Fluid Dynamics (CFD) techniques are able to model these neighbourhood effects and can be used to improve CHTC correlations. This work aims to develop a framework that couples CFD and BES tools to enhance the modelling of outdoor convective heat transfer in different urban neighbourhoods. A dynamic external coupling method was used to combine the benefits from both domains. Firstly, a microclimate CFD model was validated before the coupling stage using wind tunnel data. Secondly, the framework was tested using a benchmark model of a building block. Fully converged values of the surface temperature and CHTC were achieved at each time-step by the BES and CFD domains. The results highlight the importance of neighbourhood effect while the prediction of the hourly averaged external convection using coupling method can amend the simulation by up to 64% comparing to the standalone conventional BES models with DOE-2 CHTC approach

Influence of Al and Al_2O_3 Nanoparticles on the Thermal Decay of 1,3,5-Trinitro-1,3,5-triazinane (RDX): Reactive Molecular Dynamics Simulations

Metallic additives, Al nanoparticles in particular, have extensively been used in energetic materials (EMs), of which thermal decomposition is one of the most basic properties. Nevertheless, the underlying mechanism for the highly active Al nanoparticles and their oxidized counterparts, the Al_2O_3 nanoparticles, influencing the thermal decay of aluminized EMs has not fully been understood. Herein, we explore the influence of Al and Al2O3 nanoparticles on the thermal decomposition of 1,3,5-trinitro-1,3,5-triazinane (RDX), one of the most common EMs, based on large-scale reactive force field molecular dynamics simulations within three heating schemes (constant-temperature, programmed, and adiabatic heating). The presence of Al nanoparticles significantly reduces the induction time and energy required to activate the RDX decay and greatly increases energy release. The fundamental reason for these results is that Al changes the primary decay pathway from the unimolecular N–NO_2 scission of RDX to bimolecular barrier-free or low-barrier Al-involved reactions and possesses a strong O-extraction capability and a moderate one to react with C/H/N. It is also responsible for the growth of the Al-containing clusters. In addition, Al_2O_3 nanoparticles can also demonstrate such catalysis capability but contribute less to the enhancement of energy release. Moreover, the detailed evolutions of key thermodynamic properties, intermediate and final gaseous products, and Al-containing products are also presented. Besides, under the programmed heating and adiabatic heating conditions, the catalysis of the Al and Al_2O_3 nanoparticles becomes more distinct. Therefore, many properties of aluminized EMs are expected to well be understood by our simulation results