Posterpresentatie gegeven tijdens bezoek SBE (Samenwerkende Bedrijven Eemsdelta) aan de Hanzehogeschool
This paper presents the design of the offshore energy simulation CEL as a flow network, and its integration in the MSP Challenge 2050 simulation game platform. This platform is designed to aid learning about the key characteristics and complexity of marine or maritime spatial planning (MSP). The addition of CEL to this platform greatly AIDS MSP authorities in learning about and planning for offshore energy production, a highly topical and big development in human activities at sea. Rather than a standard flow network, CEL incorporates three additions to accommodate for the specificities of energy grids: an additional node for each team's expected energy, a split of each node representing an object into input and output parts to include the node's capacity, and bidirectional edges for all cables to enable more complex energy grid designs. Implemented with Dinic's algorithm it takes less than 30ms for the simulation to run for the average amount of grids included in an MSP Challenge 2050 game session. In this manner CEL enables MSP authorities and their energy stakeholders to use MSP Challenge 2050 for designing and testing more comprehensive offshore energy grids.
This paper focuses on the use of discrete event simulation (DES) as a decision support tool for airport land use development. As a study case, Querétaro Airport (Mexico) is used, due to its rapid growth and the different services it offers. The SIMIO® software was used to carry out a macro-level simulation of the airport’s processes, considering generic process times, flight types and demand schedules. The resulting strategic simulation model can be used to diagnose the current growth situation, analyse the airport's growth potential, and evaluate different expansion scenarios using the available land, including the expansion of the terminal building, cargo operations or MRO. The arrival and departure of aircraft (commercial, cargo, maintenance, aviation school and private aviation) at the airport were simulated to detect bottlenecks for different expansion scenarios, that aim to find an optimal balance between the growth options in the different airport grounds. The objective is to compare the potential growth of different layout expansion possibilities. Preliminary results indicate that land use options have a great impact on the growth potential of the airport and some general aviation activities, such as the aviation school, are interfering with the potential growth of other activities at Querétaro Airport.
Nederland kent ongeveer 220.000 bedrijfsongevallen per jaar (met 60 mensen die overlijden). Vandaar dat elke werkgever verplicht is om bedrijfshulpverlening (BHV) te organiseren, waaronder BHV-trainingen. Desondanks brengt slechts een-derde van alle bedrijven de arbeidsrisico’s in kaart via een Risico-Inventarisatie & Evaluatie (RI&E) en blijft het aandeel werknemers met een arbeidsongeval hoog. Daarom wordt er continu geïnnoveerd om BHV-trainingen te optimaliseren, o.a. door middel van Virtual Reality (VR). VR is niet nieuw, maar is wel doorontwikkeld en betaalbaarder geworden. VR biedt de mogelijkheid om veilige realistische BHV-noodsimulaties te ontwikkelen waarbij de cursist het gevoel heeft daar echt te zijn. Ondanks de toename in VR-BHV-trainingen, is er weinig onderzoek gedaan naar het effect van VR in BHV-trainingen en zijn resultaten tegenstrijdig. Daarnaast zijn er nieuwe technologische ontwikkelingen die het mogelijk maken om kijkgedrag te meten in VR m.b.v. Eye-Tracking. Tijdens een BHV-training kan met Eye-Tracking gemeten worden hoe een instructie wordt opgevolgd, of cursisten worden afgeleid en belangrijke elementen (gevaar en oplossingen) waarnemen tijdens de simulatie. Echter, een BHV-training met VR en Eye-Tracking (interacties) bestaat niet. In dit project wordt een prototype ontwikkeld waarin Eye-Tracking wordt verwerkt in een 2021 ontwikkelde VR-BHV-training, waarin noodsituaties zoals een kantoorbrand worden gesimuleerd (de BHVR-toepassing). Door middel van een experiment zal het prototype getest worden om zo voor een deel de vraag te beantwoorden in hoeverre en op welke manier Eye-Tracking in VR een meerwaarde biedt voor (RI&E) BHV-trainingen. Dit project sluit daarmee aan op het missie-gedreven innovatiebeleid ‘De Veiligheidsprofessional’ en helpt het MKB dat vaak middelen en kennis ontbreekt voor onderzoek naar effectiviteit rondom innovatieve-technologieën in educatie/training. Het project levert onder meer een prototype op, een productie-rapport en onderzoeks-artikel, en staat open voor nieuwe deelnemers bij het schrijven van een grotere aanvraag rondom de toepassing en effect van VR en Eye-Tracking in BHV-trainingen.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
