Trajectory Simulation Input Data for the 20mm, M56A3 Projectile Fired from a Helicopter

1981 ◽  
Author(s):  
Joseph W. Kochenderfer
Keyword(s):  
Input Data ◽  
Trajectory Simulation ◽  
Simulation Input

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

2021 ◽  
Vol 246 ◽  
pp. 04001 ◽  
Author(s):  
Andrea Ferrantelli ◽  
Hans Kristjan Aljas ◽  
Vahur Maask ◽  
Martin Thalfeldt
Keyword(s):  
Input Data ◽  
Electricity Consumption ◽  
Internal Heat ◽  
Working Hours ◽  
Energy Performance ◽  
Office Buildings ◽  
Building Performance Simulation ◽  
Electricity Use ◽  
Internal Gain ◽  
Simulation Input

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.

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

2021 ◽  
Author(s):  
Benjamin Röhm ◽  
Reiner Anderl
Keyword(s):  
Product Development ◽  
Data Management ◽  
Input Data ◽  
Integrated Design ◽  
Simulation Data ◽  
Output Data ◽  
Digital Twin ◽  
Input And Output ◽  
Simulation Input ◽  
Digital Shadow

Abstract 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.

scholarly journals Applying Genetic Algorithm to Generation of High-Dimensional Item Response Data

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
ByoungWook Kim ◽  
JaMee Kim ◽  
WonGyu Lee
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Item Response ◽  
Input Data ◽  
Real Data ◽  
Initial Population ◽  
Response Data ◽  
Item Parameters ◽  
Leibler Divergence ◽  
Simulation Input

The item response data is thenm-dimensional data based on the responses made bymexaminees to the questionnaire consisting ofnitems. It is used to estimate the ability of examinees and item parameters in educational evaluation. For estimates to be valid, the simulation input data must reflect reality. This paper presents the effective combination of the genetic algorithm (GA) and Monte Carlo methods for the generation of item response data as simulation input data similar to real data. To this end, we generated four types of item response data using Monte Carlo and the GA and evaluated how similarly the generated item response data represents the real item response data with the item parameters (item difficulty and discrimination). We adopt two types of measurement, which are root mean square error and Kullback-Leibler divergence, for comparison of item parameters between real data and four types of generated data. The results show that applying the GA to initial population generated by Monte Carlo is the most effective in generating item response data that is most similar to real item response data. This study is meaningful in that we found that the GA contributes to the generation of more realistic simulation input data.

Simulation input data analysis

1972 ◽  
Vol 3 (3) ◽  
pp. 24-31
Author(s):  
Donald V. Mathusz
Keyword(s):  
Data Analysis ◽  
Input Data ◽  
Simulation Input

scholarly journals Optimization-Based Calibration of Simulation Input Models

2019 ◽  
Vol 67 (5) ◽  
pp. 1362-1382 ◽  
Author(s):  
Aleksandrina Goeva ◽  
Henry Lam ◽  
Huajie Qian ◽  
Bo Zhang
Keyword(s):  
Performance Measures ◽  
Iterative Procedure ◽  
Numerical Experiments ◽  
Penalty Method ◽  
Input Data ◽  
Statistical Information ◽  
Data Driven ◽  
Output Data ◽  
Simulation Input ◽  
Distributionally Robust

Studies on simulation input uncertainty are often built on the availability of input data. In this paper, we investigate an inverse problem where, given only the availability of output data, we nonparametrically calibrate the input models and other related performance measures of interest. We propose an optimization-based framework to compute statistically valid bounds on input quantities. The framework utilizes constraints that connect the statistical information of the real-world outputs with the input–output relation via a simulable map. We analyze the statistical guarantees of this approach from the view of data-driven distributionally robust optimization, and show how they relate to the function complexity of the constraints arising in our framework. We investigate an iterative procedure based on a stochastic quadratic penalty method to approximately solve the resulting optimization. We conduct numerical experiments to demonstrate our performances in bounding the input models and related quantities.

Simulation input data modeling

1994 ◽  
Vol 53 (1) ◽  
pp. 47-75 ◽  
Author(s):  
Mark E. Johnson ◽  
Mansooreh Mollaghasemi
Keyword(s):  
Input Data ◽  
Data Modeling ◽  
Simulation Input

Alternatives for Railroad Traffic Simulation Analysis

2005 ◽  
Vol 1916 (1) ◽  
pp. 34-41 ◽  
Author(s):  
Thomas White
Keyword(s):  
Input Data ◽  
Simulation Analysis ◽  
Simulation Software ◽  
Accurate Assessment ◽  
Output Data ◽  
Output Analysis ◽  
Traffic Conditions ◽  
Analysis Methodology ◽  
Appropriate Care ◽  
Simulation Input

In North America, the process for determining appropriate railroad infrastructure for new service or an increased volume of existing service usually includes the use of simulation software. Decisions are generally based on statistical analysis of the simulation output. The simulation and analysis that are commonly conducted, however, may not provide an accurate assessment of the adequacy of the infrastructure. Furthermore, the output data comparisons commonly used to describe the effect of infrastructure on traffic may not be easily associated with traffic conditions. These shortcomings can be mitigated with appropriate care in developing the simulation input data and changing the output analysis methodology.

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