In-person engineering instruction relies on the availability of equipment and space. Cost, safety, and scheduling may pose barriers to conducting in-person labs. Virtual experiments may be used to enhance the student experience by, for example, incorporating pre-labs for in-person experiments and providing access to equipment that cannot be safely used in-person. Virtual learning is used in many fields, but there remain questions as to how it should be employed in engineering, an area largely reliant on in-person lab and classroom setups. Earlier studies reviewed the advantages of virtual labs such as demonstrating hard-to-observe phenomena and allowing unlimited trials. This project attempts to leverage these strengths by developing experiments on three virtual platforms: LabVIEW, MATLAB, and Unity. The first version of the Jet Engine Virtual Laboratory is developed in LabVIEW and implemented in UCF's Measurements 1 course during Summer 2020. Student feedback is sought using a survey that suggests positive reception and informs the creation of a MATLAB version of the Jet Engine Virtual Laboratory which is being implemented in Fall 2020. A Unity version of this experiment is in production. This project is expected to fuel the development of more virtual experiments that enhance engineering education at UCF and beyond.

Underground Research Laboratories (URLs) allow geoscientific in-situ experiments at large scale. At the Mont Terri URL in Switzerland, international research groups conduct numerous experiments in parallel. The measured and simulated data as well as research results obtained from them are highly relevant as they improve the general understanding of geological processes, for example in the context of radioactive waste disposal. Unfortunately, the data obtained at the test site is often only available to researchers who are directly involved in a particular experiment. Furthermore, typical visualisation techniques of such data by domain scientists often lack spatial context and accessing and exploring the data requires prior technical knowledge and a high level of effort.We created a digital replica of the Mont Terri URL and thereby implemented a prototype of a Virtual Experiment Information System that integrates highly heterogeneous data from several different sources. It allows accessing and exploring the relevant data embedded in its spatial context without much prior technical knowledge. Both, simulation results and observation data are displayed within the same system. The 4D visualisation approach focuses on three exemplary experiments conducted at Mont Terri and is easily transferable to other experiments or even other URLs. The Unity Game Engine has been used to develop the prototype. This allowed to build the application for various output devices like desktop computers or Virtual Reality hardware without much additional effort. The implemented system reduces the technical effort required to access and explore highly relevant research data and lowers the cognitive effort usually needed to gain insights from measurements, simulation models and context data. Moreover, it promotes exchange among research groups by enabling interactive visualisations embedded in the URL’s spatial context. In addition, a future use of the system for the communication of scientific methods and results to stakeholders or the general public is plausible.

In the published article, there was an error concerning the FE Experiment. Incorrect information was used regarding the heaters’ power and temperature. A correction has been made to Chapter 3: Visualisation of Selected Experiments, Sub-section 3.3 “Full- Scale Emplacement Experiment”, Paragraph 1. The sentence previously stated: “They work with up to 1,500W each and emit heat up to 195°C.” The corrected sentence now states: “They work with up to 1,350W each and emit heat up to 135°C.” The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

The continuous monitoring of the state-of-health (SOH) of electric vehicles (EV) represents a problem with great research relevance due to the time-consuming battery cycling and capacity measurements that are usually required to create a SOH estimation model. Instead of the widely used approach of modelling the battery’s degradation behaviour with as little cycling effort as possible, the applied SOH monitoring approach is the first of its kind that is solely based on commonly logged battery management system (BMS) signals and does not rely on tedious capacity measurements. These are used to train the digital battery twins, which are subsequently subjected to virtual capacity tests to estimate the SOH. In this work, transfer-learning is applied to increase the data and computational efficiency of the digital battery twins training process to facilitate a real-world application as it enables SOH estimation for unknown ageing states due to the selective parameter initialisation at less than a tenth of the common training time. However, the successful SOH estimation with a mean SOH deviation of 0.05% using transfer-learning still requires the presence of pauses in the dataset. Meta-learning extends the idea of transfer-learning as the baseline model simultaneously takes several ageing states into account. Learning the basic battery-electric behaviour it is forced to preserve a certain level of uncertainty at the same time, which seems crucial for the successful fine-tuning of the model parameters based on three pause-free load profiles resulting in a mean SOH deviation of 0.85%. This optimised virtual SOH experiment framework provides the cornerstone for a scalable and robust estimation of the remaining battery capacity on a pure data basis.