The thesis starts by giving a motivation to materials modeling as a way to increase profitability but also a possibility decrease the environmental impact. Fundamental concepts of relevance for this work are introduced, this include the materials genome, ICME and of course the CALPHAD method. As a demonstration promising results obtained by an ICME approach using genetic algorithms and CALPHAD on the vacuum degassing process are presented. In order to make good predictive calculations and process models it is important to have good thermodynamic descriptions. Thus most part of the work has concerned the thermodynamic assessments of systems of importance for steelmaking, corrosion and similar processes. The main focus has been the assessment of sulfur-containing systems and thermodynamic descriptions of the Fe-Mn-Ca-Mg-S, Fe-Ca-O-S, Fe-Mg-O and Mg-Mn-O systems are presented. In addition, heat capacity measurements of relevance for the Mg-Mn-O system have been performed. To summarize the efforts some application examples concerning thermodynamic calculations related to steelmaking and inclusion formation are shown. / <p>QC 20160829</p> / COMPASS

Accurately simulating material systems in a virtual environment requires the synthesis and utilization of all relevant information regarding performance mechanisms for the material occurring over a range of length and time scales. Multiscale modeling is the basis for the Integrated Computational Materials Engineering (ICME) Paradigm and is a powerful tool for accurate material simulations. However, while ICME has experienced adoption among those in the metals community, it has not gained traction in polymer research. This thesis seeks establish a hierarchical multiscale modeling methodology for simulating polymers containing secondary phases. The investigation laid out in the chapters below uses mesoscopic Finite Element Analysis (FEA) as a foundation to build a multiscale modeling methodology for polymer material systems. At the mesoscale a Design of Experiments (DOE) parametric study utilizing FEA of polymers containing defects compared the relative impacts of a selection of parameters on damage growth and coalescence in polymers. Of the parameters considered, the applied stress state proved to be the most crucial parameter affecting damage growth and coalescence. At the macroscale, the significant influence of the applied stress state on damage growth and coalescence in polymers (upscaled from the mesoscale) motivated an expansion of the Bouvard Internal State Variable (ISV) (Bouvard et al. 2013) polymer model stress state sensitivity. Deviatoric stress invariants were utilized to modify the Bouvard ISV model to account for asymmetry in polymer mechanical performance across different stress states (tension, compression, torsion). Lastly, this work implements a hierarchical multiscale modeling methodology to examine parametric effects of heterogeneities on Polymer/Clay Nanocomposite’s (PCNs) mechanical performance under uncertainty. A Virtual Composite Structure Generator (VCSG) built three-dimensional periodic Representative Volume Elements (RVEs) coupled to the Bouvard ISV model and a Cohesive Zone Model (CZM) which featured a Traction-Separation (T-S) rule calibrated to results upscaled from Molecular Dynamics (MD) simulations. A DOE parametric examination utilized the RVEs to determine the relative effects of a selection of parameters on the mechanical performance of PCNs. DOE results determined that nanoclay particle orientation was the most influential parameter affecting PCN elastic modulus while intercalated interlamellar gallery strength had the greatest influence on PCN yield stress

The usage of cobalt (Co) as binder phase material in cemented carbides has been questioned becauseof the potential health hazards associated with cobalt particle inhalation. Cobalt is used because ofits excellent adhesive and wetting properties, combined with adequate mechanical properties. Thepurpose of this work is to investigate the mechanical properties of Fe-Ni bulk alloys and WC-Cocemented carbides using Integrated Computational Materials Engineering (ICME) methods com-bined with FEM data. The report investigates the mechanical properties of several bulk alloys inthe Fe-Ni system as a function of void size and fraction. FEM indentation and FEM fracture datais interpolated and used to model the hardnessHand fracture toughnessKIc. A precipitationhardening model based on the Ashby-Orowan’s equation is implemented to predict the e↵ect on theyield strength from precipitated particles. A model for solid solution hardening is also implemented.Existing models are used to simulate the properties of WC-Co cemented carbides together with thesolid solution hardening model. Results show that the simulated properties of the Fe-Ni bulk alloysare comparable to those of cobalt. However, the results could not be confirmed due to a lack ofexperimental data. The properties of WC-Co cemented carbides are in reasonable agreement withexisting experimental data, with an average deviation of the hardness by 11.5% and of the fracturetoughness by 24.8%. The conclusions are that experimental data for di↵erent Fe-Ni bulk alloys isneeded to verify the presented models and that it is possible to accurately model the properties ofcemented carbides. / Anv¨andandet av kobolt (Co) som bindefas-material i h°ardmetall har blivit ifr°agasatt som en f¨oljdav av de potentiella h¨alsoriskerna associerade med inhalering av koboltpartiklar. Kobolt anv¨ands p°agrund av dess utm¨arkta vidh¨aftande och v¨atande egenskaper, kombinerat med tillr¨ackliga mekaniskaegenskaper. Syftet med detta arbete ¨ar att unders¨oka de mekaniska egenskaperna hos Fe-Ni bulklegeringarochWC-Co h°ardmetall genom att anv¨anda Integrated Computational Materials Engineering(ICME) metoder kombinerat med FEM-data. Rapporten unders¨oker de mekaniska egenskapernahos flera bulklegeringar i Fe-Ni systemet. FEM-indentering och FEM-fraktur data interpoleras ochanv¨ands f¨or att modellera h°ardheten H och brottsegheten KIc. En modell f¨or utskiljningsh¨ardningbaserad p°a Ashby-Orowans ekvation implementeras f¨or att f¨oruts¨aga e↵ekten p°a brottgr¨ansen av utskiljdapartiklar. ¨Aven en modell f¨or l¨osningsh¨ardning implementeras. Existerande modeller anv¨andsf¨or att simulera egenskaperna hos WC-Co h°ardmetall tillsammans med modellen f¨or l¨osningsh¨ardning.Resultaten visar att de simulerade egenskaperna hos Fe-Ni bulklegeringar ¨ar j¨amf¨orbara medde f¨or kobolt. Dock kan de inte bekr¨aftas p°a grund av avsaknad av experimentell data. Egenskapernahos WC-Co h°ardmetall st¨ammer rimligt ¨overens med existerande experimentell data, meden genomsnittlig avvikelse av h°ardheten med 11.5% och av brottsegheten med 24.8%. Slutsatserna¨ar att det beh¨ovs experimentell data f¨or Fe-Ni bulklegeringar f¨or att kunna verifiera modellernasnoggrannhet och att det ¨ar m¨ojligt att f¨oruts¨aga egenskaperna hos h°ardmetall.

Microstructure characterization and reconstruction (MCR) is an important prerequisite for empowering and accelerating integrated computational materials engineering. Much progress has been made in MCR recently; however, in the absence of a flexible software platform it is difficult to use ideas from other researchers and to develop them further. To address this issue, this work presents MCRpy as an easy-to-use, extensible and flexible open-source MCR software platform. MCRpy can be used as a program with graphical user interface, as a command line tool and as a Python library. The central idea is that microstructure reconstruction is formulated as a modular and extensible optimization problem. In this way, arbitrary descriptors can be used for characterization and arbitrary loss functions combining arbitrary descriptors can be minimized using arbitrary optimizers for reconstructing random heterogeneous media. With stochastic optimizers, this leads to variations of the well-known Yeong–Torquato algorithm. Furthermore, MCRpy features automatic differentiation, enabling the utilization of gradient-based optimizers. In this work, after a brief introduction to the underlying concepts, the capabilities of MCRpy are demonstrated by exemplarily applying it to typical MCR tasks. Finally, it is shown how to extend MCRpy by defining a new microstructure descriptor and readily using it for reconstruction without additional implementation effort.

A case study which examines the agreement between prediction and data is performed for three, complex interplanetary shocks which were detected at the Sun-Earth Lagrange point L1 and induced moderate to intense geomagnetic storms. We use model output from previous runs of the coupled coronal-heliosphere WSA-Enlil+Cone model, available through the Community Coordinated Modeling Center (CCMC), and in-situ data from the OMNI data set. Code written in MATLAB is used to compare the model output with the in-situ measurements of the interplanetary magnetic field as well as the density, speed and temperature of the solar wind. In addition, the difference between the predicted and actual shock arrival time is computed and regions of potential temperature depression are identified. A considerable discrepancy is found between data and model for the studied events. The main reason is deemed to be an inadequate representation of the ambient solar wind as well as the complex interactions between interplanetary coronal mass ejections and corotating interaction regions. We suggest future steps to be taken for the further development of the model as well as for the general understanding of space weather and the Sun-Earth connection. / Denna fallstudie undersöker överensstämmelsen mellan modell och data för tre interplanetära chockvågor, som kunde detekteras vid jordens Lagrangepunkt 1, och som orsakade geomagnetiska stormar av måttlig till kraftig styrka. Vi använder oss av tidigare genomförda körningar av den sammansatta WSA-Enlil+Cone modellen, som avbildar fortplantningen av temporära störningar med ursprung i solens korona, såsom koronamassutkastningar, ut i heliosfären. Modellen gjordes tillgänglig av Community Coordinated Modeling Center (CCMC) och datan inhämtades från OMNI. Kod skriven i MATLAB nyttjades för att göra en jämförelse mellan modell och faktiska mätningar av det interplanetära magnetfältet samt solvindens hastighet, densitet och temperatur. Utöver detta, beräknas också skillnaden mellan förväntad och faktisk ankomsttid av respektive interplanetär chock, och tidsperioder med en temperatursänkning utöver det normala identifieras. Vi finner en omfattande avvikelse mellan modell och data, i synnerhet för de fall där på varandra följande koronamassutkastningar förväntas interagera eller rent av slås ihop samt för uppskattningen av den omgivande solvindens egenskaper och det interplanetära fältet under pågående geomagnetisk störning. Interaktionen mellan koronamassutkastningar och närliggande ko-roterande interaktionsregioner har ej heller återskapats väl av modellen ifråga. Slutligen ger vi förslag på möjliga, framtida åtgärder som kan bör tas i åtanke vid konstruerandet av framtida versioner av nämnda modell, liksom för den allmänna förståelsen för rymdvädrets inverkan på Jorden.

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Bayesian updating is used to approximate discontinuous multi-interval uncertainty representations (i.e., belief structures) of epistemic uncertainty. Several Bayesian-based approaches are examined for assessing the accuracy of approximating the mean and standard deviation of a belief structure and calculating reliability using posterior distributions. Moreover, a Bayesian-based belief structure approximation is integrated with a decomposed multilevel optimization solution strategy through analytical target cascading, where the ensuing reliability-based design optimization problem within each decomposed element is solved using a single loop single vector approach. The non-deterministic decomposed multilevel optimization approach is demonstrated through solutions to four analytical benchmark problems with mixed aleatory and epistemic uncertainties as well as a nano-enhanced composite sandwich plate problem. Consistent with the integrated computational materials engineering philosophy, the proposed solution strategy for the sandwich plate problem combines micro- and macro-level material modeling and design with structural level analysis and optimization. The orientation distribution of the carbon nanofibers in the micro-mechanical model is described through a belief structure and modeled using a Bayesian approach. Aleatory uncertainty in the ply thickness of the composite facesheets is also considered. This problem is used to demonstrate computationally efficient integration of epistemic uncertainty described through a belief structure for a complex design problem with mixed uncertainties. The results of this study show that the posterior distributions from some of the Bayesian-based approaches are suitable for direct calculation of reliability through joint probability density functions. Moreover, the Bayesian-based approach can provide a computationally efficient method for integrating epistemic and aleatory uncertainties in decomposed multilevel optimization of complex problems.

The historical success of friction stir welding (FSW) on materials such as aluminum and magnesium alloys is associated with the absence of melting and solidification during the solid-state process. However, commercial adoption of FSW on steels and other non-ferrous high-strength, high-temperature materials such as nickel-base and titanium-base alloys is limited due to the high costs associated with the process. In this dissertation, the feasibility of using an FSW approach to fabricate certain structural components made of nitrogen containing austenitic stainless steels that go into the vacuum vessel and magnetic systems of tokamak devices was demonstrated. The FSW weldments possessed superior application-specific mechanical and functional properties when compared to fusion weldments reported in the technical literature. However, as stated earlier, the industrial adoption of FSW on high temperature materials such as the ferrous alloys used in the present study is greatly limited due to the high costs associated with the process. The cost is mainly dictated by the high temperature FSW tools used to accomplish the weldments. Commercially available high temperature FSW tools are exorbitantly priced and often have short lifetimes. To overcome the high-cost barrier, we have explored the use of integrated computational materials engineering (ICME) combined with experimental prototyping validation to design next-generation tool materials with high performance and relatively low cost. Cermet compositions with either tungsten carbide or niobium carbide as the hard phase bonded by high entropy alloy binders were processed via mechanical alloying and spark plasma sintering. The feasibility and effectiveness of the newly developed cermet tool materials as potential next generation high temperature FSW tool materials was evaluated.

The material development cycle is becoming too slow if compared with other technologies sectors like IT and electronics. The materials scientists’ community needs to bring materials science back to the core of human development. ICME (Integrated Computational Materials Engineer) is a new discipline that uses advanced computational tools to simulate material microstructures, processes and their links with the final properties. There is the need for a new way to design tailor-made materials with a faster and cheaper development cycle while creating products that meet “real-world” functionalities rather than vague set of specifications. Using the ICME approach, cutting edge computational thermodynamics models were employed in order to assist the microstructure characterization and refinement during the TIG welding of a functionally graded composite material with outstanding wear and corrosion resistance. The DICTRA diffusion model accurately predicted the carbon diffusion during sintering, Thermo-Calc and TC-PRISMA models described the thermodynamic and kinetics of harmful carbide precipitation, while COMSOL Multhiphysic furnished the temperature distribution profile at every timestep during TIG welding of the material. Bainite transformation and the influence of chromium and molybdenum was studied and modelled with MAP_STEEL software. The simulations were then compared with experimental observations and a very good agreement between computational works and experiments was found for both thermodynamic and kinetics predictions. The use of this new system proved to be a robust assistance to the classic development method and the material microstructures and processes were carefully adjusted in order to increase corrosion resistance and weldability. This new approach to material development can radically change the way we think and we make materials. The results suggest that the use of computational tools is a reality that can dramatically increase the efficiency of the material development.

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