Dinamikus folyamatrendszerek modellezése és irányítása = Modelling and control of dynamic process systems

A dinamikus folyamatrendszerek modellezése és irányítása területén folytattunk kutatást a folyamatmérnöki, valamint a rendszer- és irányításelméleti módszereket integrálva. 1. Számítógéppel segített folyamatmodellezés A folyamatmodellek osztályán bevezettük a minimális modell fogalmát, és eljárásokat javasoltunk minimális modellek felállítására modellredukcióval, illetve inkrementális modellépítéssel. A komplex folyamatrendszerek dinamikus modellezésére használatos többléptékű (multi-scale) modellekre kidolgoztunk egy diagnosztikai cél-vezérelt modellezési módszert és egy ezen alapuló intelligens diagnosztikai rendszert. Kidolgoztuk a többléptékű modellek skálatérképen alapuló egyszerűsítésének módszerét. 2. Nemlineáris folyamatrendszerek analízise és irányítása A kvázipolinom modellekkel leírható folyamatrendszerek esetén lineáris mátrix egyenlőtlenségekkel leírható feltételeket adtunk a stabilitásra, és kvadratikus hamiltoni struktúrák létezésére. Módszert adtunk statikus kvázipolinom alakú visszacsatolás tervezésére bilineáris mátrix egyenlőtlenségek megoldásával. 3. Diszkrét folyamatrendszer modellek és alkalmazásaik A folyamatrendszer modellek egyenlet-változó struktúra gráfjának segítségével gráfelméleti módszert adtunk a modell-egyszerűsítő lépések hatásának vizsgálatára modell differenciális indexére. A folyamatrendszer modellek optimális dekompozíciója érdekében új módszereket adtunk fokszám-feltételeknek eleget tevő gráfpartíciók előállítására. | An interdisciplinary research integrating process systems engineering and systems and control theory has been conducted. 1. Computer-aided process modelling The notion of minimal model has been defined on the class of lumped process models. Methods for constructing minimal models have been proposed uising model reduction and incremental model building. Based on the multi-scale modeling paradigm for complex process systems, a goal-directed multi-scale modeling method has been proposed and an intelligent diagnostic system based thereon has been developed. A scale-map based model reduction method has been developed for multi-scale process models. 2. Analysis and control of nonlinear process systems Stability conditions in the form of linear matrix inequalities have been proposed for process system models in quasi-polynomial form that guarantee the existence of local quadratic Hamiltonian structures for them. A method for designing the structure and parameters of a globally stabilizing static quasi-polynomial feedback by using bilinear matrix inequalities is also developed. 3. Discrete process models Based on the equation-variable graph of process models, a method has been developed for investigating the effect of model simplification on the differential index. In order to optimally decompose process models, efficient methods of degree-constrained decomposition of graphs have been proposed

Dealing with diversity in computational cancer modeling.

This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology

Multiscale Methods for Random Composite Materials

Simulation of material behaviour is not only a vital tool in accelerating product development and increasing design efficiency but also in advancing our fundamental understanding of materials. While homogeneous, isotropic materials are often simple to simulate, advanced, anisotropic materials pose a more sizeable challenge. In simulating entire composite components such as a 25m aircraft wing made by stacking several 0.25mm thick plies, finite element models typically exceed millions or even a billion unknowns. This problem is exacerbated by the inclusion of sub-millimeter manufacturing defects for two reasons. Firstly, a finer resolution is required which makes the problem larger. Secondly, defects introduce randomness. Traditionally, this randomness or uncertainty has been quantified heuristically since commercial codes are largely unsuccessful in solving problems of this size. This thesis develops a rigorous uncertainty quantification (UQ) framework permitted by a state of the art finite element package \texttt{dune-composites}, also developed here, designed for but not limited to composite applications. A key feature of this open-source package is a robust, parallel and scalable preconditioner \texttt{GenEO}, that guarantees constant iteration counts independent of problem size. It boasts near perfect scaling properties in both, a strong and a weak sense on over $15,000$ cores. It is numerically verified by solving industrially motivated problems containing upwards of 200 million unknowns. Equipped with the capability of solving expensive models, a novel stochastic framework is developed to quantify variability in part performance arising from localized out-of-plane defects. Theoretical part strength is determined for independent samples drawn from a distribution inferred from B-scans of wrinkles. Supported by literature, the results indicate a strong dependence between maximum misalignment angle and strength knockdown based on which an engineering model is presented to allow rapid estimation of residual strength bypassing expensive simulations. The engineering model itself is built from a large set of simulations of residual strength, each of which is computed using the following two step approach. First, a novel parametric representation of wrinkles is developed where the spread of parameters defines the wrinkle distribution. Second, expensive forward models are only solved for independent wrinkles using \texttt{dune-composites}. Besides scalability the other key feature of \texttt{dune-composites}, the \texttt{GenEO} coarse space, doubles as an excellent multiscale basis which is exploited to build high quality reduced order models that are orders of magnitude smaller. This is important because it enables multiple coarse solves for the cost of one fine solve. In an MCMC framework, where many solves are wasted in arriving at the next independent sample, this is a sought after quality because it greatly increases effective sample size for a fixed computational budget thus providing a route to high-fidelity UQ. This thesis exploits both, new solvers and multiscale methods developed here to design an efficient Bayesian framework to carry out previously intractable (large scale) simulations calibrated by experimental data. These new capabilities provide the basis for future work on modelling random heterogeneous materials while also offering the scope for building virtual test programs including nonlinear analyses, all of which can be implemented within a probabilistic setting

Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and near-term developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future.publishedVersio

Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and near-term developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future