no / The complexity of powertrain calibration has increased significantly with the development and introduction of new technologies to improve fuel economy and performance while meeting increasingly stringent emissions legislation with given time and cost constraints. This paper presents research to improve the model-based engine calibration optimization using an integrated sequential Design of Experiments (DoE) strategy for engine mapping experiments. This DoE strategy is based on a coherent framework for a model building - model validation sequence underpinned by Optimal Latin Hypercube (OLH) space filling DoEs. The paper describes the algorithm development and implementation for generating the OLH space filling DoEs based on a Permutation Genetic Algorithm (PermGA), subsequently modified to support optimal infill strategies for the model building - model validation sequence and to deal with constrained non-orthogonal variables space. The development, implementation and validation of the proposed strategy is discussed in conjunction with a case study of a GDI engine steady state mapping, focused on the development of an optimal calibration for CO₂ and particulate number (Pn) emissions. The proposed DoE framework applied to the GDI engine mapping task combines a screening space filling DoE with a flexible sequence of model building - model validation mapping DoEs, all based on optimal DoE test plan augmentation using space filling criteria. The case study results show that the sequential DoE strategy offers a flexible way of carrying out the engine mapping experiments, maximizing the information gained and ensuring that a satisfactory quality model is achieved.

<p dir="ltr">Nuclear energy, renowned for its clean, carbon-free attributes and cost-effectiveness, stands as a pivotal pillar in the global quest for sustainable energy sources. Additionally, nuclear power, being a spatially high-concentrated industry, offers an unparalleled energy density compared to other sources of energy. Despite its numerous advantages, if a nuclear power plant (NPP) is not operated safely, it can lead to long-term shutdowns, radiation exposure to workers, radiation contamination of surrounding areas, or even a national-scale disaster, as witnessed in the Chernobyl incident of 1986. Therefore, ensuring the safe operation of nuclear reactors is considered the most important factor in their operation. Recognizing the intricate tradeoff between safety and economy, economic considerations are often sacrificed in favor of safety.</p><p dir="ltr">Given this context, it becomes crucial to develop technologies that ensure NPPs’ safety while optimizing their operational efficiency, thereby minimizing the sacrifice of economic benefits. In response to this critical need, scientists introduced the term “digital twin (DT)”, derived from the concept of product lifecycle management. As the first instance of the term, the DT model comprises the physical product, its digital representation, data flowing from the physical to the DT, and information flowing from the digital to the physical twin. In this regard, various nuclear stakeholders such as reactor designers, researchers, operators, and regulators in the nuclear sector, are pursuing the DT technologies which are expected to enable NPPs to be monitored and operated/controlled in an automated and reliable manner. DT is now being actively sought given its wide potential, including increased operational effectiveness, enhanced safety and reliability, uncertainty reduction, etc.</p><p dir="ltr">While a number of technical challenges must be overcome to successfully implement DT technology, this Ph.D. work limits its focus on one of the DT’s top challenges, i.e., model validation, which ensures that model predictions can be trusted for a given application, e.g., the domain envisaged for code usage. Model validation is also a key regulatory requirement in support of the various developmental stages starting from conceptual design to deployment, licensing, operation, and safety. To ensure a given model to be validated, the regulatory process requires the consolidation of two independent sources of knowledge, one from measurements collected from experimental conditions, and the other from code predictions that model the same experimental conditions.</p><p dir="ltr">and computational domains in an optimal manner, considering the characteristics of predictor and target responses. Successful model validation necessitates a complete data analytics pipeline, generally including data preprocessing, data analysis (model training), and result interpretation. Therefore, this Ph.D. work begins by revisiting fundamental concepts such as uncertainty classification, sensitivity analysis (SA), similarity/representativity metrics, and outlier rejection techniques, which serve as robust cornerstones of validation analysis.</p><p dir="ltr">The ultimate goal of this Ph.D. work is to develop an intelligent inference framework that infers/predicts given responses, adaptively handling various levels of data complexities, i.e., residual shape, nonlinearity, heteroscedasticity, etc. These Ph.D. studies are expected to significantly advance DT technology, enabling support for various levels of operational autonomy in both existing and first-of-a-kind reactor designs. This extends to critical aspects such as nuclear criticality safety, nuclear fuel depletion dynamics, spent nuclear fuel (SNF) analysis, and the introduction of new fuel designs, such as high burnup fuel and high-assay low-enriched uranium fuel (HALEU). These advancements are crucial in scenarios where constructing new experiments is costly, time-consuming, or infeasible with new reactor systems or high-consequence events like criticality accidents.</p>

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Le monde industriel, et en particulier l’industrie automobile, cherche à représenter au mieux le réel pour concevoir des outils et produits les plus adaptés aux enjeux et marchés actuels. Dans cette optique, le groupe Volvo a développé de puissants outils pour la simulation de la dynamique des véhicules industriels. Ces outils permettent notamment l’optimisation de composants véhicules ou de stratégies de contrôle. De nombreuses activités de recherche portent sur des technologies innovantes permettant de réduire la consommation des véhicules industriels et d’accroitre la sécurité de leurs usages dans différents environnements. En particulier, le développement des systèmes d’aide à la conduite automobile ITS et ADAS. Afin de pouvoir développer ces systèmes, un environnement de simulation permettant de prendre en compte les différents facteurs pouvant influencer la conduite d’un véhicule doit être mis en place. L’étude se concentre sur la simulation de l’environnement du véhicule et des interactions entre le véhicule et son environnement direct, i.e. le véhicule qui le précède. Les interactions entre le véhicule étudié et le véhicule qui le précède sont modélisées à l’aide de modèles mathématiques, nommés lois de poursuites. De nombreux modèles existent dans la littérature mais peu concernent le comportement des véhicules industriels. Une étude détaillée de ces modèles et des méthodes de calage est réalisée. L’environnement du véhicule peut être représenté par deux catégories de paramètres : statiques (intersections, nombre de voies…) et dynamiques (état du réseau). A partir d’une base de données de trajets usuels, ces paramètres sont calculés, puis utilisés pour générer de manière automatisée des scénarios de simulation réalistes. / The industrial world, and in particular the automotive industry, is seeking to best represent the real world in order to design tools and products that are best adapted to current challenges and markets, by reducing development times and prototyping costs. With this in mind, the Volvo Group has developed powerful tools to simulate the dynamics of industrial vehicles. These tools allow the optimization of vehicle components or control strategies. Many research activities focus on innovative technologies to reduce the consumption of industrial vehicles and increase the safety of their use in different environments. Particularly, the development of ITS and ADAS is booming. In order to be able to develop these systems, a simulation environment must be set up to take into account the various factors that can influence the driving of a vehicle. The work focuses on simulating the vehicle environment and the interactions between the vehicle and its direct environment, i.e. the vehicle in front of it. The interactions between the vehicle under study and the vehicle in front of it are modelled using mathematical models, called car-following models. Many models exist in the literature, but few of them deals specifically with heavy duty vehicles. A specific focus on these models and their calibration is realized. The vehicle environment can be represented by two categories of parameters: static (intersections, number of lanes) and dynamic parameters (state of the network). From a database of usuals roads, these parameters are computed, then, they are used to automatically generate realist traffic simulation scenarios.