Automatic simulation-based testing of autonomous ships using Gaussian processes and temporal logic

A methodology for automatic simulation-based testing of control systems for autonomous vessels is proposed. The work is motivated by the need for increased test coverage and formalism in the verification efforts. It aims to achieve this by formulating requirements in the formal logic Signal Temporal Logic (STL). This enables automatic evaluation of simulations against requirements using the STL robustness metric, resulting in a robustness score for requirements satisfaction. Furthermore, the proposed method uses a Gaussian Process (GP) model for estimating robustness scores including levels of uncertainty for untested cases. The GP model is updated by running simulations and observing the resulting robustness, and its estimates are used to automatically guide the test case selection toward cases with low robustness or high uncertainty. The main scientific contribution is the development of an automatic testing method which incrementally runs new simulations until the entire parameter space of the case is covered to the desired confidence level, or until a case which falsifies the requirement is identified. The methodology is demonstrated through a case study, where the test object is a Collision Avoidance (CA) system for a small high-speed vessel. STL requirements for safety distance, mission compliance, and COLREG compliance are developed. The proposed method shows promise, by both achieving verification in feasible time and identifying falsifying behaviors which would be difficult to detect manually or using brute-force methods. An additional contribution of this work is a formalization of COLREG using temporal logic, which appears to be an interesting direction for future work.

Automatic energy demand and system simulation at district level

The importance of climate protection and sustainability is steadily increasing all over the world. However, there is a large potential for reducing emissions in the heating demand reduction and renewable heat supply of buildings that needs to be addressed. Therefore, a method was developed within the scope of this work that allows local decision-makers such as energy supply companies, project developers and the public sector to calculate, evaluate and compare different scenarios to make buildings and city districts more sustainable based on few and widely available input data. It includes both the determination of the heat demand and measures for its reduction as well as the selection and simulation of centralised and decentralised supply systems. A combination of different methods from the fields of geoinformatics, heuristic decision-making and object-oriented modelling is used. The latter forms a focal point in the work with the development of a data model for energy system components to enable automatic simulation. The applicability as well as the transferability of the method is shown in several case studies. Based on the simulations results, which can be related to CO2 emissions as well as costs, recommendations for the implementation of measures can be given and implemented.The paper is a summary of the dissertation with the title “Automatische Simulation von Wärmebedarf und -versorgung auf Quartiersebene” by the first author at Karlsruhe Institute for Technology.

Simulation of Energy and Media Demand of Beverage Bottling Plants by Automatic Model Generation

Facing environmental challenges, high energy costs and a growing public awareness, the global brewing industry is increasingly publishing ambitious targets toward a more sustainable production. Small and medium-sized enterprises of the brewing and beverage industry cannot ensure energy and media efficiency mainly due to capital and knowledge inadequacy. This article addresses this problem and presents a pragmatic method to determine the energy and media demand. Accordingly, a modeling editor as well as a standardized data structure and automatic simulation parameter determination tools were developed to implement the method. A given production plant can be modeled with adequate details using the presented editor. Based on a configuration file, a holistic simulation model can be generated automatically in a simulation environment. A beverage bottling plant was studied, and the necessary datasets were obtained for implementing the proposed editor and, thereby, the method. It was confirmed that the simulated values of electrical energy and compressed air consumption match the measured empirical data. The measures to increase energy and media efficiency were also found effective. Using the presented method, enterprises of the brewing and beverage industry can easily uncover avenues for potential savings, test the effectiveness of optimization strategies, and substantiate possible investment decisions.

Development and application of artificial intelligence for military training modeling and simulation in Republic of Korea

In the era of the Fourth Industrial Revolution, efforts are being made throughout society to achieve innovation by utilizing artificial intelligence (AI). Developed countries are trying to maximize their combat capabilities using AI in defense areas. AI research on the defense of the Republic of Korea is focused on field-related applications, such as drones and robots; however, the research on the application of AI to training fields is relatively insufficient. In this paper, this study briefly summarizes the concept of AI and examines the use of AI in modeling and simulation (M&S) training, one of the defense M&S fields. Furthermore, the present study evaluates the areas where development is possible using current technology: decision support systems, intelligent tutoring systems, and automatic simulation of virtual entities.

A Geometry-Based Welding Distortion Prediction Tool

The prediction of welding distortion requires expertise in computer simulation programs, a clear definition of the nonlinear material properties, and mesh settings together with the nonlinear solution settings of a coupled thermal–structural analysis. The purpose of this paper is to present the validation of an automatic simulation tool implemented in Ansys using Python scripting. This tool allows users to automate the preparation of the simulation model with a reduced number of inputs. The goal was, based on some assumptions, to provide an automated simulation setup that enables users to predict accurate distortion during the welding manufacturing process. Any geometry prepared in a CAD software can be used as the input, which gave us much geometrical flexibility in the shapes and sizes to be modeled. A thermomechanical loosely coupled analysis approach together with element birth and death technology was used to predict the distortions. The automation of the setup enables both simulation and manufacturing engineers to perform welding-induced distortion prediction. The results showed that the method proposed predicts distortion with 80–98% accuracy.