UQ and AI: data fusion, inverse identification, and multiscale uncertainty propagation in aerospace components

A key requirement for engineering designs is that they offer good performance across a range of uncertain conditions while exhibiting an admissibly low probability of failure. In order to design components that offer good performance across a range of uncertain conditions, it is necessary to take account of the effect of the uncertainties associated with a candidate design. Uncertainty Quantification (UQ) methods are statistical methods that may be used to quantify the effect of the uncertainties inherent in a system on its performance. This thesis expands the envelope of UQ methods for the design of aerospace components, supporting the integration of UQ methods in product development by addressing four industrial challenges. Firstly, a method for propagating uncertainty through computational models in a hierachy of scales is described that is based on probabilistic equivalence and Non-Intrusive Polynomial Chaos (NIPC). This problem is relevant to the design of aerospace components as the computational models used to evaluate candidate designs are typically multiscale. This method was then extended to develop a formulation for inverse identification, where the probability distributions for the material properties of a coupon are deduced from measurements of its response. We demonstrate how probabilistic equivalence and the Maximum Entropy Principle (MEP) may be used to leverage data from simulations with scarce experimental data- with the intention of making this stage of product design less expensive and time consuming. The third contribution of this thesis is to develop two novel meta-modelling strategies to promote the wider exploration of the design space during the conceptual design phase. Design Space Exploration (DSE) in this phase is crucial as decisions made at the early, conceptual stages of an aircraft design can restrict the range of alternative designs available at later stages in the design process, despite limited quantitative knowledge of the interaction between requirements being available at this stage. A histogram interpolation algorithm is presented that allows the designer to interactively explore the design space with a model-free formulation, while a meta-model based on Knowledge Based Neural Networks (KBaNNs) is proposed in which the outputs of a high-level, inexpensive computer code are informed by the outputs of a neural network, in this way addressing the criticism of neural networks that they are purely data-driven and operate as black boxes. The final challenge addressed by this thesis is how to iteratively improve a meta-model by expanding the dataset used to train it. Given the reliance of UQ methods on meta-models this is an important challenge. This thesis proposes an adaptive learning algorithm for Support Vector Machine (SVM) metamodels, which are used to approximate an unknown function. In particular, we apply the adaptive learning algorithm to test cases in reliability analysis.Open Acces

How to integrate geochemistry at affordable costs into reactive transport for large-scale systems: Abstract Book

This international workshop entitled “How to integrate geochemistry at affordable costs into reac-tive transport for large-scale systems” was organized by the Institute of Resource Ecology of the Helmholtz-Zentrum Dresden Rossendorf in Feb-ruary 2020. A mechanistic understanding and building on that an appropriate modelling of geochemical processes is essential for reliably predicting contaminant transport in groundwater systems, but also in many other cases where migration of hazardous substances is expected and consequently has to be assessed and limited. In case of already present contaminations, such modelling may help to quantify the threads and to support the development and application of suitable remediation measures. Typical application areas are nuclear waste disposal, environmental remediation, mining and milling, carbon capture & storage, or geothermal energy production. Experts from these fields were brought together to discuss large-scale reactive transport modelling (RTM) because the scales covered by such pre-dictions may reach up to one million year and dozens of kilometers. Full-fledged incorporation of geochemical processes, e.g. sorption, precipitation, or redox reactions (to name just a few important basic processes) will thus create inacceptable long computing times. As an effective way to integrate geochemistry at affordable costs into RTM different geochemical concepts (e.g. multidimensional look-up tables, surrogate functions, machine learning, utilization of uncertainty and sensitivity analysis etc.) exist and were extensively discussed throughout the workshop. During the 3-day program of the workshop keynote and regular lectures from experts in the field, a poster session, and a radio lab tour had been offered. In total, 40 scientists from 28 re-search institutes and 8 countries participated

Uncertainties and robustness with regard to the safety of a repository for high-level radioactive waste: introduction of a research initiative

The Federal Company for Radioactive Waste Disposal (BGE mbH) is tasked with the selection of a site for a high-level radioactive waste repository in Germany in accordance with the Repository Site Selection Act. In September 2020, 90 areas with favorable geological conditions were identified as part of step 1 in phase 1 of the Site Selection Act. Representative preliminary safety analyses are to be carried out next to support decisions on the question, which siting regions should undergo surface-based exploration. These safety analyses are supported by numerical simulations building on geoscientific and technical data. The models that are taken into account are associated with various sources of uncertainties. Addressing these uncertainties and the robustness of the decisions pertaining to sites and design choices is a central component of the site selection process. In that context, important research objectives are associated with the question of how uncertainty should be treated through the various data collection, modeling and decision-making processes of the site selection procedure, and how the robustness of the repository system should be improved. BGE, therefore, established an interdisciplinary research cluster to identify open questions and to address the gaps in knowledge in six complementary research projects. In this paper, we introduce the overall purpose and the five thematic groups that constitute this research cluster. We discuss the specific questions addressed as well as the proposed methodologies in the context of the challenges of the site selection process in Germany. Finally, some conclusions are drawn on the potential benefits of a large method-centered research cluster in terms of simulation data management

Reliability-constrained design optimisation of extra-large offshore wind turbine support structures

The offshore wind industry has evolved significantly over the last decade, contributing considerably to Europe’s energy mix. For further penetration of this technology, it is essential to reduce its costs to make it competitive with conventional power generation technologies. To this end, optimising the design of components while simultaneously fulfilling design criteria is a crucial requirement for producing more cost-effective strategies. Traditional design optimisation techniques rely on the optimisation of design variables against constraints such as stresses or deformation in the form of limit states and to minimise an objective function such as the total mass of a component. Although this approach leads to more optimal designs, the presence of uncertainties, for instance, in material properties, manufacturing tolerances and environmental loads, requires more systematic consideration of these uncertainties. A combination of optimisation methods with concepts of structural reliability can be a suitable approach if challenges such as the approximation of the load effect concerning global input loads and computational requirements are addressed accordingly. In this study, a reliability-constrained optimisation framework for offshore wind turbine (OWT) support structures is developed, applied, and documented for the first time. First, a parametric finite element analysis (FEA) model of OWT support structures is developed, considering stochastic material properties and environmental loads. The parametric FEA model is then combined with response surface and Monte Carlo (MC) to create an assessment model in the Six Sigma module in ANSYS, which is then further integrated with an optimisation algorithm to develop a fully coupled reliability-constrained optimisation framework. The framework is applied to the NREL 5MW OWT and OC3 sub-structure. Results indicate that the proposed optimisation framework can effectively reduce the mass of OWT support structures meeting target reliability levels focusing on realistic limit states. At the end of the optimisation loop, an LCOE comparison is done to see the effect of mass reduction on the wind turbine cost. The study expanded with a scaling-up approach and investigated the technical feasibility of increasing the system’s power and size in deeper water depth for bottom-fixed support structures. Additionally, parametric equations have been developed to estimate the wind turbine rating and weight considering water depth in the conceptual design stage. Furthermore, the sensitivity analysis was performed on the latest reference support structure of the IEA 15MW turbine to see the effect of water depth between 30m to 60m. The results showed the influences of water depth on the current structural response of the monopile. It revealed that utilising the proposed support structure is not feasible for water-depth above 50m as the analysis did not fulfil design criteria.The offshore wind industry has evolved significantly over the last decade, contributing considerably to Europe’s energy mix. For further penetration of this technology, it is essential to reduce its costs to make it competitive with conventional power generation technologies. To this end, optimising the design of components while simultaneously fulfilling design criteria is a crucial requirement for producing more cost-effective strategies. Traditional design optimisation techniques rely on the optimisation of design variables against constraints such as stresses or deformation in the form of limit states and to minimise an objective function such as the total mass of a component. Although this approach leads to more optimal designs, the presence of uncertainties, for instance, in material properties, manufacturing tolerances and environmental loads, requires more systematic consideration of these uncertainties. A combination of optimisation methods with concepts of structural reliability can be a suitable approach if challenges such as the approximation of the load effect concerning global input loads and computational requirements are addressed accordingly. In this study, a reliability-constrained optimisation framework for offshore wind turbine (OWT) support structures is developed, applied, and documented for the first time. First, a parametric finite element analysis (FEA) model of OWT support structures is developed, considering stochastic material properties and environmental loads. The parametric FEA model is then combined with response surface and Monte Carlo (MC) to create an assessment model in the Six Sigma module in ANSYS, which is then further integrated with an optimisation algorithm to develop a fully coupled reliability-constrained optimisation framework. The framework is applied to the NREL 5MW OWT and OC3 sub-structure. Results indicate that the proposed optimisation framework can effectively reduce the mass of OWT support structures meeting target reliability levels focusing on realistic limit states. At the end of the optimisation loop, an LCOE comparison is done to see the effect of mass reduction on the wind turbine cost. The study expanded with a scaling-up approach and investigated the technical feasibility of increasing the system’s power and size in deeper water depth for bottom-fixed support structures. Additionally, parametric equations have been developed to estimate the wind turbine rating and weight considering water depth in the conceptual design stage. Furthermore, the sensitivity analysis was performed on the latest reference support structure of the IEA 15MW turbine to see the effect of water depth between 30m to 60m. The results showed the influences of water depth on the current structural response of the monopile. It revealed that utilising the proposed support structure is not feasible for water-depth above 50m as the analysis did not fulfil design criteria

Materials for in-vessel components

The EUROfusion materials research program for DEMO in-vessel components aligns with the European Fusion Roadmap and comprises the characterization and qualification of the in-vessel baseline materials EUROFER97, CuCrZr and tungsten, advanced structural and high heat flux materials developed for risk mitigation, as well as optical and dielectric functional materials. In support of the future engineering design activities, the focus is primarily to assemble qualified data to supply the design process and generate material property handbooks, material assessment reports, DEMO design criteria and material design limits for DEMO thermal, mechanical and environmental conditions. Highlights are provided on advanced material development including (a) steels optimized towards lower or higher operational windows, (b) heat sink materials (copper alloys or composites) and (c) tungsten based plasma facing materials. The rationale for the down-selection of material choices is also presented. The latter is strongly linked with the results of neutron irradiation campaigns for baseline material characterization (structural, high heat flux and functional materials) and screening of advanced materials. Finally, an outlook on future material development activities to be undertaken during the upcoming Concept Design Phase for DEMO will be provided, which highly depends on an effective interface between materials’ development and components’ design driven by a common technology readiness assessment of the different systems

Assessment of Concrete Structures Including Corrosion and Cracks

Reinforced concrete (RC) structures constitute a major proportion of the built environment and society relies continuously on their service. Many of these structures were built in the era following the Second World War and are thus approaching the end of their intended service life. The likelihood of deterioration increases with time and so damage caused by, say, corrosion is not uncommon. Also, increased demands are often laid on the load-carrying capacity of existing bridges, aimed at increasing utilisation of the road network by allowing heavier vehicles. Simply dismantling and re-constructing all bridges at the end of their designed service life, or taking needless strengthening measures, is unsustainable. Rather, improved methods of assessing the capacity of existing infrastructure are needed. The current work has aimed to develop improved, reliable assessment methods. Its focus areas were structures with reinforcement corrosion and structures with cracks from previous loading. Both simplified and advanced methods of evaluating anchorage capacity were developed for concrete structures with corroded reinforcement. The simplified method modifies the bond stress-slip relationship and is calibrated against a large database of bond tests, with the safety margin ensured by deriving partial safety factors. The advanced method is based on finite element (FE) analysis, with tensile material properties altered for elements positioned at the splitting cracks along the reinforcement. The latter method was also investigated for RC without corrosion damage but with cracks from previous loading. Design results from advanced nonlinear FE analyses (meaning results with a proper safety margin) are obtained by applying a “safety format”. The current work investigated whether safety formats available in fib Model Code 2010 also ensured reliable design capacities for structures with somewhat complicated load application and geometry; in this case, a concrete frame subjected to vertical and horizontal loads. The results indicate that the anchorage capacity may be reasonably well estimated by using the simplified method. The proposed partial safety factors also provided sufficient safety margin. Furthermore, in the advanced anchorage assessment, the capacity could be estimated solely from weakened tensile properties located at the position of the splitting cracks and without input concerning the corrosion level. Moreover, by including cracks from previous loading in advanced modelling, improved predictions of the failure mode, ultimate capacity and ductility were demonstrated. Lastly, in the investigation of safety formats for nonlinear FE analysis, the method of estimating a coefficient of variance of resistance (ECOV), did not reach the intended safety level. However, the global resistance factor method (GRF) and partial factor method (PSF) did. This work has the potential to improve both simplified and advanced assessment methods, providing more sustainable infrastructure management in the future

Scaling of material properties for Yucca Mountain: literature review and numerical experiments on saturated hydraulic conductivity

A review of pertinent literature reveals techniques which may be practical for upscaling saturated hydraulic conductivity at Yucca Mountain: geometric mean, spatial averaging, inverse numerical modeling, renormalization, and a perturbation technique. Isotropic realizations of log hydraulic conductivity exhibiting various spatial correlation lengths are scaled from the point values to five discrete scales through these techniques. For the variances in log{sub 10} saturated hydraulic conductivity examined here, geometric mean, numerical inverse and renormalization adequately reproduce point scale fluxes across the modeled domains. Fastest particle velocities and dispersion measured on the point scale are not reproduced by the upscaled fields. Additional numerical experiments examine the utility of power law averaging on a geostatistical realization of a cross-section similar to the cross-sections that will be used in the 1995 groundwater travel time calculations. A literature review on scaling techniques for thermal and mechanical properties is included. 153 refs., 29 figs., 6 tabs

Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids

We introduce a generalized machine learning framework to probabilistically parameterize upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based on coarse-grained atomistic simulation data of mechanical deformation and flow processes. The proposed framework utilizes a hypothesized coarse-graining methodology with manifold learning and surrogate-based optimization techniques. Coarse-grained high-dimensional data describing quantities of interest of the multiscale models are projected onto a nonlinear manifold whose geometric and topological structure is exploited for measuring behavioral discrepancies in the form of manifold distances. A surrogate model is constructed using Gaussian process regression to identify a mapping between stochastic parameters and distances. Derivative-free optimization is employed to adaptively identify a unique set of parameters of the upper-scale model capable of rapidly reproducing the system's behavior while maintaining consistency with coarse-grained atomic-level simulations. The proposed method is applied to learn the parameters of the shear transformation zone (STZ) theory of plasticity that describes plastic deformation in amorphous solids as well as coarse-graining parameters needed to translate between atomistic and continuum representations. We show that the methodology is able to successfully link coarse-grained microscale simulations to macroscale observables and achieve a high-level of parity between the models across scales.Comment: 34 pages, 12 figures, references added, Section 4 added, Section 2.1 update

A multi-level upscaling and validation framework for uncertainty quantification in additively manufactured lattice structures

Multiscale modeling techniques are playing an ever increasing role in the effective design of complex engineering systems including aircraft, automobiles, etc. Lightweight cellular lattice structures (CLSs) gained interest recently since their complex structure, composed of a network of interconnected strut members, can be fabricated by additive manufacturing (AM). However, uncertainties in the fabricated strut members of CLSs are introduced by the layer-by-layer manufacturing process. These fine scale uncertainties influence the overall product performance resulting in inaccurate predictions of reality and increased complexity in simulations. In this research, a multi-level upscaling and validation framework is established that will enable accurate estimation of the performance of AM-fabricated CLSs under uncertainties. An improved stochastic upscaling method based on Polynomial Chaos Expansion (PCE) is employed to quantify and propagate the uncertainties across multiple levels efficiently. The upscaling method is integrated with a hierarchical validation approach to ensure that accurate predictions are made with the homogenized models. The u-pooling method is incorporated with the Kolmogorov-Smirnov test as the validation metric to efficiently use the limited experimental data during validation. The framework is applied to representative examples to demonstrate its efficacy in accurately characterizing the elastic properties of CLSs under uncertainties. The framework is also used to show its applicability in designing CLSs under uncertainties without the use of expensive simulations and optimization processes. The proposed framework is generalized to apply to any complex engineering structure that incorporates computationally intensive simulations and/or expensive experiments associated with fine scale uncertainties.Ph.D