As Vehicle-to-Everything (V2X) communication technologies gain prominence, ensuring human safety from radiofrequency (RF) electromagnetic fields (EMF) becomes paramount. This study critically examines human RF exposure in the context of ITS-5.9 GHz V2X connectivity, employing a combination of numerical dosimetry simulations and targeted experimental measurements. The focus extends across Road-Side Units (RSUs), On-Board Units (OBUs), and, notably, the advanced vehicular technologies within a Tesla Model S, which includes Bluetooth, Long Term Evolution (LTE) modules, and millimeter-wave (mmWave) radar systems. Key findings indicate that RF exposure levels for RSUs and OBUs, as well as from Tesla’s integrated technologies, consistently remain below the International Commission on Non-Ionizing Radiation Protection (ICNIRP) exposure guidelines by a significant margin. Specifically, the maximum exposure level around RSUs was observed to be 10 times lower than ICNIRP reference level, and Tesla’s mmWave radar exposure did not exceed 0.29 W/m2, well below the threshold of 10 W/m2 set for the general public. This comprehensive analysis not only corroborates the effectiveness of numerical dosimetry in accurately predicting RF exposure but also underscores the compliance of current V2X communication technologies with exposure guidelines, thereby facilitating the protective advancement of intelligent transportation systems against potential health risks.
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Energy dissipative steel cushions (EDSCs) are simple units that can be used to join structural members. They can absorb a substantial amount of seismic energy due to their geometric shapes and the ductile behavior of mild steel. Large deformation capability and stable hysteretic behavior were obtained in monotonic and cyclic tests of EDSCs in the framework of the SAFECLADDING project. Discrete numerical modeling strategies were applied to reproduce the experimental results. The first and second models comprise two-dimensional shell elements and one-dimensional flexural frame elements, respectively. The uncertain points in the preparation of the models included the mesh density, representation of the material properties, and interaction between contacting surfaces. A zero-length nonlinear link element was used in the third attempt in the numerical modeling. Parameters are recommended for the Ramberg–Osgood and bilinear models. The obtained results indicate that all of the numerical models can reproduce the response, and the stiffness, strength, and unloading and reloading curves were fitted accurately.
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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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CC-BYNatural ventilation has been used widely in buildings to deliver a healthy and comfortable indoor environment for occupants. It also reduces the consumption of energy in the built environment and dilutes the concentration of carbon dioxide. Various methods and techniques have been used to evaluate and predict indoor airspeed and patterns in buildings. However, few studies have been implemented to investigate the relevant methods and tools for the evaluation of ventilation performance in indoor and outdoor spaces. The current study aims to review available methods, identifying reliable ones to apply in future research. This study investigates scientific databases and compares the advantages and drawbacks of methods including analytical models, empirical models, zonal models, and CFD models. wind-driven ventilation; analytical models; experimental models; zonal models; computational fluid dynamics (CFD) models; numerical discretization methods https://www.mdpi.com/2071-1050/13/22/12721Sustainability 2021, 13(22), 12721
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The assessment of the out-of-plane response of unreinforced masonry (URM) buildings with cavity walls has been a popular topic in regions such as Central and Northern Europe, Australia, New Zealand, China and several other countries.Cavity walls are particularly vulnerable as the out-of-plane capacity of each individual leaf is significantly smaller than the one of a solid wall. In the Netherlands, cavity walls are characterized by an inner load-bearing leaf of calcium silicate bricks, and by an outer veneer of clay bricks that has only aesthetic and insulation functions. The two leaves are typically connected by means of metallic ties. This paper utilizes the results of an experimental campaign conducted by the authors to calibrate a hysteretic model that represents the axial cyclic response of cavity wall tie connections. The proposednumerical model uses zero-length elements implemented in OpenSees with the Pinching4 constitutive model to account for the compression-tension cyclic behaviour of the ties. The numerical model is able to capture important aspects of the tie response such as the strength degradation, the unloading stiffness degradation and the pinching behaviour. The numerical modelling approach in this paper can be easily adopted by practitioner engineers who aim to model the wall ties more accurately when assessing the structures against earthquakes.
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The prediction of mechanical elastic response of laminated hybrid polymer composites with basic carbon nanostructure, that is carbon nanotubes and graphene, inclusions has gained importance in many advanced industries like aerospace and automotive. For this purpose, in the current work, a hierarchical, four-stage, multilevel framework is established, starting from the nanoscale, up to the laminated hybrid composites. The proposed methodology starts with the evaluation of the mechanical properties of carbon nanostructure inclusions, at the nanoscale, using advanced 3D spring-based finite element models. The nanoinclusions are considered to be embedded randomly in the matrix material, and the Halpin-Tsai model is used in order to compute the average properties of the hybrid matrix at the lamina micromechanics level. Then, the standard Halpin-Tsai equations are employed to establish the orthotropic elastic properties of the unidirectional carbon fiber composite at the lamina macromechanics level. Finally, the lamination theory is implemented in order to establish the macroscopic force-strain and moment-curvature relations at the laminate level. The elastic mechanical properties of specific composite configurations and their performance in different mechanical tests are evaluated using finite element analysis and are found to considerably increase with the nanomaterial volume fraction increase for values up to 0.5. Further, the hybrid composite structures with graphene inclusions demonstrate better mechanical performance as compared to the identical structures with CNT inclusions. Comparisons with theoretical or other numerical techniques, where it is possible, demonstrate the accuracy of the proposed technique.
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This paper seeks to make a contribution to business model experimentation for sustainability by putting forward a relatively simple tool. This tool calculates the financial and sustainability impact based on the SDG’s of a newly proposed business model (BM). BM experimentation is described by Bocken et al. (2019) as an iterative-multi-actor experimentation process. At the final experimentation phases some form of sustainability measurement will be necessary in order to validate if the new proposed business model will be achieving the aims set in the project. Despite the plethora of tools, research indicates that tools that fit needs and expectations are scarce, lack the specific focus on sustainable BM innovation, or may be too complex and demanding in terms of time commitment (Bocken, Strupeit, Whalen, & Nußholz, 2019a). In this abstract we address this gap, or current inability of calculating the financial and sustainability effect of a proposed sustainable BM in an integrated, time effective manner. By offering a practical tool that allows for this calculation, we aim to answer the research question; “How can the expected financial and sustainability impact of BMs be forecasted within the framework of BM experimentation?
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As people age, physiological changes affect their thermal perception, sensitivity and regulation. The ability to respond effectively to temperature fluctuations is compromised with physiological ageing, upsetting the homeostatic balance of health in some. As a result, older people can become vulnerable at extremes of thermal conditions in their environment. With population ageing worldwide, it is an imperative that there is a better understanding of older people’s thermal needs and preferences so that their comfort and wellbeing in their living environment can be optimised and healthy ageing achieved. However, the complex changes affecting the physiological layers of the individual during the ageing process, although largely inevitable, cannot be considered linear. They can happen in different stages, speeds and intensities throughout the ageing process, resulting in an older population with a great level of heterogeneity and risk. Therefore, predicting older people’s thermal requirements in an accurate way requires an in-depth investigation of their individual intrinsic differences. This paper discusses an exploratory study that collected data from 71 participants, aged 65 or above, from 57 households in South Australia, over a period of 9 months in 2019. The paper includes a preliminary evaluation of the effects of individual intrinsic characteristics such as sex, body composition, frailty and other factors, on thermal comfort. It is expected that understanding older people’s thermal comfort from the lens of these diversity-causing parameters could lead to the development of individualised thermal comfort models that fully capture the heterogeneity observed and respond directly to older people’s needs in an effective way. (article starts at page 13)
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Are the so-called “new” business models focused on “sharing” actually promoting new behaviour or are they simply using old behaviour of the provider/consumer in a new technological environment? Are the new tech companies in the sharing economy with their “new” business models grabbing too much power, unnoticeably?
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Organizations feel an urgency to develop and implement applications based on foundation models: AI-models that have been trained on large-scale general data and can be finetuned to domain-specific tasks. In this process organizations face many questions, regarding model training and deployment, but also concerning added business value, implementation risks and governance. They express a need for guidance to answer these questions in a suitable and responsible way. We intend to offer such guidance by the question matrix presented in this paper. The question matrix is adjusted from the model card, to match well with development of AIapplications rather than AI-models. First pilots with the question matrix revealed that it elicited discussions among developers and helped developers explicate their choices and intentions during development.
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