Concept of a System Architecture for a Simulation Data Management In the Digital Twin

The Department of Computer Integrated Design (DiK) at the TU Darmstadt deals with the Digital Twin topic from the perspective of virtual product development. A concept for the architecture of a Digital Twin was developed, which allows the administration of simulation input and output data. The concept was built under consideration of classical CAE process chains in product development. The central part of the concept is the management of simulation input and output data in a simulation data management system in the Digital Twin (SDM-DT). The SDM-DT takes over the connection between Digital Shadow and Digital Master for simulation data and simulation models. The concept is prototypically implemented. For this purpose, real product condition data were collected via a sensor network and transmitted to the Digital Shadow. The condition data were prepared and sent as a simulation input deck to the SDM-DT in the Digital Twin based on the product development results. Before the simulation data and models are simulated, there is a comparison between simulation input data with historical input data from product development. The developed and implemented concept goes beyond existing approaches and deals with a central simulation data management in Digital Twins.

Selection and analysis of input parameters influencing pedestrian micro-simulated crossing time

Pedestrian unrestrained behaviour, sudden movements and vulnerability are elements, which can highly affect road safety, especially when interacting with motorized vehicles. Therefore, it is important to have a deep insight in pedestrian behaviour. A way to tackle this issue is micro-simulation. Modern micro-simulation tools, indeed, allow, thanks to the implemented mathematical formulation of the problem, to model and repeat a real situation in a virtual environment. Nevertheless, they need to well-fit the real observed behaviour: the calibration step allows to make the model reliable, by adapting selected, influential model input parameters. By dealing with pedestrian issues, software Vissim/Viswalk has been selected for micro-simulation, which implements Helbing's Social Force model. This model is based on several parameters, like relaxation time, side preference, strength and range of pedestrian interactions, amount of anisotropy, parameters governing the forces among pedestrians, noise, number of reacting pedestrians, queue order and straightness, which need to be set by the user when creating the model, but they can be hardly measured. This paper presents a selection of the recalled input parameters, on which statistical tests are carried out to understand their influence on the behavioural output – crossing time - that is supposed to describe pedestrian crossing behaviour. This is the first step towards the development of a new calibration methodology, which will keep advantage of artificial intelligence tools to fine-tune micro-simulation input parameters.

Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging

This work proposes a stochastic process-based inversion to estimate hydrocarbon resistivity based on multifrequency electromagnetic (EM) data. Currently, mesh-based algorithms are used for processing the EM responses which cause high time-consuming and unable to quantify uncertainty. Gaussian process (GP) is utilized as the alternative forward modeling approach to evaluate the EM profiles with uncertainty quantification. For the optimization, gradient descent is used to find the optimum by minimizing its loss function. The prior EM profiles are evaluated using finite element (FE) through computer simulation technology (CST) software. For validation purposes, mean squared deviation and its root between EM profiles evaluated by the GP and FE at the unobserved resistivities are computed. Time taken for the GP and CST to evaluate the EM profiles is compared, and absolute error between the estimate and its simulation input is also computed. All the resulting deviations were significantly small, and the GP took lesser time to evaluate the EM profiles compared to the software. The observational datasets also lied within the 95% confidence interval (CI) where the resistivity inputs were estimated by the proposed inversion. This indicates the stochastic process-based inversion can effectively estimate the hydrocarbon resistivity in the seabed logging.

Nanopore-Level Wood-Water Interactions—A Molecular Simulation Study

The nanoscale wood-water interaction strength, accessible sorption sites, and cell wall pore sizes are important factors that drive water sorption and the hysteresis phenomenon in wood. In this work, these factors were quantitatively studied using molecular simulations based on a cell wall pore model, previously developed by the authors. Specifically, the wall-water interaction strength, the sorption sites network including their number, interaction range, strength, and spatial distributions were set at a series of theoretical values as simulation input parameters. The results revealed that most of the investigated parameters significantly affected both sorption isotherms and hysteresis. Water monolayers and clusters were observed on the simulated pore surface when the wood-water interaction and sorption site strength were set at unrealistically high values. Furthermore, multiple linear regression models suggested that wood-water interaction and sorption site parameters were coupled in determining sorption isotherms, but not in determining hysteresis.

Tenant-based measured electricity use in 4 large office buildings in Tallinn, Estonia

The energy performance assessment of buildings during design is usually based on energy simulations with pre-defined input data from standards and legislations. Typically, the internal gain values and profiles are based on EN 16798–1. However, studies have shown that the real electricity use of plug load and lighting varies more smoothly than in the profiles of EN 16798–1 where zero occupancy outside working hours is assumed. This might result in sub-optimal building solutions due to inadequate building performance simulation input data. The aim of this work is to structure and analyse data from a total of 196 electricity meters in 4 large office buildings in Tallinn, Estonia. Typically, 3 to 8 electricity meters were installed per floor with the consumption coming mainly from plug loads and electric lighting. The data had been gathered between the years 2016–2020 with either 1 or 24 hour time steps, depending on the building and the electricity meter. 3 out of the 4 buildings had an average normalized energy usage slightly below the modelling value calculated according to EN16798–1. Some office spaces stood out with an abnormally high electricity consumption, however, the 24-hour distributions were fairly compact, meaning quite steady consumption patterns. When looking at the dispersion of energy consumption per 24h, averaged over all given offices in a building, no outliers stood out, either. This means that there are not many days when the average consumption and internal heat gains of all offices were simultaneously well below the mean. Additionally, major events like holidays and the COVID19-induced lockdown show up well on the graphs, but also planned changes in occupancy can be seen.

Reducing Simulation Input-Model Risk via Input Model Averaging

Input uncertainty is an aspect of simulation model risk that arises when the driving input distributions are derived or “fit” to real-world, historical data. Although there has been significant progress on quantifying and hedging against input uncertainty, there has been no direct attempt to reduce it via better input modeling. The meaning of “better” depends on the context and the objective: Our context is when (a) there are one or more families of parametric distributions that are plausible choices; (b) the real-world historical data are not expected to perfectly conform to any of them; and (c) our primary goal is to obtain higher-fidelity simulation output rather than to discover the “true” distribution. In this paper, we show that frequentist model averaging can be an effective way to create input models that better represent the true, unknown input distribution, thereby reducing model risk. Input model averaging builds from standard input modeling practice, is not computationally burdensome, requires no change in how the simulation is executed nor any follow-up experiments, and is available on the Comprehensive R Archive Network (CRAN). We provide theoretical and empirical support for our approach.