Search NASA⌕ Search

SEARCH · Search NASA

Results for “robust design”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Toward digital design at the exascale: An overview of project ICECap

High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Invertible Neural Networks for Aerodynamic Design of Wind Turbine Blades

The state-of-the-practice methods for aerodynamic design of wind turbine blads use linearized blade element momentum theory (BEM) to optimize the twist and chord profiles from a pre-selected set of 2D airfoil shapes. In this work, we apply invertible neural network (INN) tools to enable the rapid inverse aerodynamic design of wind turbine blades including component airfoils. The INN is trained on data obtained through the use of robust automated mesh generation and the HAMSTR computational fluid dynamics solver with advanced turbulence and transition models validated for turbine applications. Our design technique is a significant improvement over the state-of-the-practice linearized blade element momentum (BEM) techniques in capturing 3D nonlinear aerodynamic effects that are critical for optimal design of the rotors. This is made possible by developing sparse, invertible neural networks (INNs) for inverse design and optimization that realize a 100x cost reduction compared to adjoint-based computational fluid dynamics (CFD) approaches, while enabling increased robustness of the final design. We demonstrate the INN tool for design of a section of the NREL 5-MW blade. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of wind turbine blades.

aerodynamic design↗
DOE OSTI2025

Accelerating computational fluid dynamics simulation of post-combustion carbon capture modeling with MeshGraphNets

Packed columns are commonly used in post-combustion processes to capture CO 2 emissions by providing enhanced contact area between a CO 2 -laden gas and CO 2 -absorbing solvent. To study and optimize solvent-based post-combustion carbon capture systems (CCSs), computational fluid dynamics (CFD) can be used to model the liquid–gas countercurrent flow hydrodynamics in these columns and derive key determinants of CO 2 -capture efficiency. However, the large design space of these systems hinders the application of CFD for design optimization due to its high computational cost. In contrast, data-driven modeling approaches can produce fast surrogates to study large-scale physics problems. We build our surrogates using MeshGraphNets (MGN), a graph neural network framework that efficiently learns and produces mesh-based simulations. We apply MGN to a random packed column modeled with over 160K graph nodes and a design space consisting of three key input parameters: solvent surface tension, inlet velocity, and contact angle. Our models can adapt to a wide range of these parameters and accurately predict the complex interactions within the system at rates over 1700 times faster than CFD, affirming its practicality in downstream design optimization tasks. This underscores the robustness and versatility of MGN in modeling complex fluid dynamics for large-scale CCS analyses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Heterogeneous energetic material damage simulator (HEDS): A deep learning approach to simulate damage–sensitivity linkages

Damage in the microstructures of energetic materials (EMs), such as propellants and plastic bonded explosives (PBXs), can significantly alter their response to external loads. Both sensitization and desensitization can occur, causing concerns with safety and performance in the field; predictive models that connect damage and the sensitivity of EMs can enable design and provide confidence in their robustness and reliability. However, modeling of damage evolution is challenging for real microstructures of EMs; samples of damaged EMs are difficult to obtain, thereby hindering experiments and direct numerical simulations to determine the sensitivity of EMs at various stages of damage. Here, we develop an approach to generate synthetic, i.e., in silico produced, damaged microstructures for use in simulations to connect damage levels to sensitivity. The development of the present workflow to generate and impose varying levels of damage in microstructures, known as HEDS (Heterogeneous Energetic Material Damage Simulator), begins with a small set of images of damaged PBXs and combines a collection of deep neural network techniques to generate microstructures with varying levels of damage. By making the synthetic microstructures conform closely to those observed in available real, imaged microstructures, we develop an ensemble of damaged microstructures that can be used for in silico shock experiments. HEDS develops these microstructure ensembles as level set fields, which are directly employed in a sharp interface Eulerian hydrocode where shock simulations are performed to quantify the energy release rate from hotspot fields generated in the microstructure. These capabilities can be useful for the analysis and assessment of changes in the sensitivity of EMs and to design formulations that are less susceptible to damage-induced changes in sensitivity and performance.

Fang, Irene (ORCID:0009000844557122)↗
DOE OSTI2026

Benchmarking of three DWM-based wake models at below-rated wind speeds

Wind turbine wake models are essential tools for predicting power losses and structural loads in wind farms. Among these, the dynamic wake meandering (DWM) model, included as a recommended approach in the International Electrotechnical Commission design standard, is a widely used engineering-fidelity method that balances accuracy and computational cost. This study compares the performance of three DWM-based wake model implementations (from the Technical University of Denmark, the National Renewable Energy Laboratory, and the Institute for Energy Technology) under below-rated wind speed conditions. Model predictions of wake flow, power output, and structural loads for a four-turbine row are evaluated across different ambient turbulence levels and wind-direction misalignments and compared against high-fidelity large-eddy simulation results. All three models captured the overall wake evolution and mean turbine performance with reasonable accuracy; their predicted time-averaged thrust and power were typically within 5 %–10 % of the large-eddy simulation benchmark. However, notable differences emerged in wake structure and unsteady load predictions, with discrepancies increasing for turbines further downstream. These differences highlight the importance of modelling choices such as wake summation and turbulence treatment, which strongly influence power-deficit and fatigue-load predictions. Comparison with large-eddy simulations reveals each approach's strengths and weaknesses, indicating where improvements are needed. Overall, the findings point to specific refinements for DWM models to improve their fidelity, ultimately enabling more robust wake predictions for wind farm design and operation.

17 WIND ENERGY↗
DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗
DOE OSTI2023

Underlying Substrate Effect on Electrochemical Activity for Hydrogen Evolution Reaction with Low‐Platinum‐Loaded Catalysts

Platinum is known as the best catalyst for the hydrogen evolution reaction (HER) but the scarcity and high cost of Pt limit its widespread applicability. Herein, the role of the underlying substrate on the HER activity of dispersed Pt atoms is uncovered. A direct current magnetron sputtering technique is utilized to deposit transition metal (TM) thin films of W, Ti, and Ta as underlying substrates for extremely low loading of Pt (<1.5 at%). The electrocatalytic performance of as‐synthesized samples for the HER is examined in both alkali and acidic media. The results show that despite the low loading of Pt, the Pt/TM catalysts produce hydrogen at a rate comparable to that of pristine bulk Pt. Pt/TM catalysts also display good stability with less than 5% decay in performance after 10 h of continuous HER operation. Based on the computational study, the excellent performance is attributed to the modified electronic properties of the Pt atoms, offering ideal binding energy for HER due to interaction with the underlying substrates. This work provides a robust and industry‐friendly route toward designing efficient catalytic systems for important electrochemical reactions such as HER and others.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

TRANSP integrated modeling code for interpretive and predictive analysis of tokamak plasmas

This paper provides a comprehensive review of the TRANSP code, a sophisticated tool for interpretive and predictive analysis of tokamak plasmas, detailing its major capabilities and features. It describes the equations for particle, power, and momentum balance analysis, as well as the poloidal field diffusion equations. The paper outlines the spatial and time grids used in TRANSP and details the equilibrium assumptions and solvers. Various models for heating and current drive and radiation, including updates to the NUBEAM model, are discussed. The handling of large-scale events such as sawtooth crashes and pellet injections is examined, along with the predictive capabilities for advancing plasma profiles. The integration of TRANSP with the ITER Integrated Modeling and Analysis Suite (IMAS) is highlighted, demonstrating enhanced data access and analysis capabilities. Additionally, the paper discusses best practices and continuous integration techniques to enhance TRANSP's robustness. The suite of TRANSP tools, designed for efficient data analysis and simulation, further supports the optimization of tokamak operations and coupling with other tokamak codes. Continuous development and support ensure that TRANSP remains a major code for the analysis of experimental data for controlled thermonuclear fusion, maintaining its critical role in supporting the optimization of tokamak operations and advancing fusion research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Interplay between Intrinsic Thermal Stability and Expansion Properties of Functionalized UiO-67 Metal–Organic Frameworks

The UiO family of metal-organic frameworks (MOFs) has been extensively studied for several applications owing to their high stability rendered by their robust secondary building units. The efficient design and use of these materials require a fundamental understanding of their thermal stability and its impact on chemical and structural functionality. In this work, we provide a detailed characterization of the intrinsic thermal behavior of the UiO-67 and functional analogues, UiO-67-NH 2 and UiO-67-CH 3 . Using in situ temperature-programmed X-ray diffraction, we find that distortion of the carboxylate group on the organic linker leads to negative thermal expansion (NTE) of the UiO-67 MOFs during heating. This NTE behavior is correlated with rich and reversible thermal changes observed in the MOF infrared spectral signature as samples are heated to the sample activation temperature (473 K). We find that in the absence of oxygen, activated UiO-67 samples show higher thermal stability compared to ambient or inert environments, with temperature-programmed desorption revealing an overall stability trend: UiO-67 > UiO-67- CH 3 > UiO-67-NH 2 . Two stages of change are observed during thermal treatment above 473 K, which are directly related to deformation of the inorganic node and the isotropic NTE behavior of these materials. Ultimately, these results provide a real-time interpretation of the fundamental thermoresponsive behavior of UiO-67 MOFs and offer a foundation for accurate interpretation of MOF interactions with guest molecules and their temperature dependence.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Direct Observations of Field-Intensity-Dependent Dielectric Breakdown Mechanisms in TiO 2 Single Nanocrystals

One of the main challenges for next-generation electric power systems and electronics is to avoid premature dielectric breakdown in insulators and capacitors and to ensure reliable operations at higher electric fields and higher efficiencies. However, dielectric breakdown is a complex phenomenon and often involves many different processes simultaneously. Here we show distinctly different defect-related and intrinsic breakdown processes by studying individual, single crystalline TiO 2 nanoparticles using in-situ transmission electron microscopy (TEM). As the applied electric field intensity rises, rutile-to-anatase phase transition, local amorphization/melting, or ablation are identified as the corresponding breakdown processes, the field intensity thresholds of which are found to be related to the position of the intensified field and the duration of the applied bias relative to the time of charged defects accumulation. Our observations reveal an intensity-dependent dielectric response of crystalline oxides at breakdown and suggest possible routes to suppress the iniation of premature dielectric breakdown. Hence, they will aid the design and development of next generation robust and efficient solid dielectrics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Bayesian calibration of irradiated graphite property models under high temperatures

Graphite under high temperatures and irradiation is central to advanced reactors. We develop a Bayesian calibration framework for graphite property models that explicitly represents model-data mismatch via a Gaussian-process discrepancy. The approach propagates uncertainty from parameters, experimental noise, and model form, with a hierarchical variance structure to capture group and cross-group noise. Using two predictive models across five grades (IG-110, NBG-18, PCEA, NBG-17, 2114) and four properties-irradiation-induced dimension change, creep, Young’s modulus change ratio, and coefficient of thermal expansion change ratio-we obtain average predictive-error reductions of 54%, 65%, 17%, and 17% when discrepancy is included. We illustrate engineering impact with a multiphysics model of a very-high-temperature reactor prismatic reflector brick, analyzing stresses under high fluence and temperature. Accounting for model discrepancy markedly improves predictive accuracy and provides a robust basis for reliable graphite component design in advanced reactors.

36 - MATERIALS SCIENCE↗
DOE OSTI2020

Resilience of Urban Transport Network-of-Networks under Intense Flood Hazards Exacerbated by Targeted Attacks

Natural hazards including floods can trigger catastrophic failures in interdependent urban transport network-of-networks (NoNs). Population growth has enhanced transportation demand while urbanization and climate change have intensified urban floods. However, despite the clear need to develop actionable insights for improving the resilience of critical urban lifelines, the theory and methods remain underdeveloped. Furthermore, as infrastructure systems become more intelligent, security experts point to the growing threat of targeted cyber-physical attacks during natural hazards. Here we develop a hypothesis-driven resilience framework for urban transport NoNs, which we demonstrate on the London Rail Network (LRN). We find that topological attributes designed for maximizing efficiency rather than robustness render the network more vulnerable to compound natural-targeted disruptions including cascading failures. Our results suggest that an organizing principle for post-disruption recovery may be developed with network science principles. Our findings and frameworks can generalize to urban lifelines and more generally to real-world spatial networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Development of a hardened THz energy meter for use on the kilojoule-scale, short-pulse OMEGA EP laser

A highly adaptable and robust terahertz (THz) energy meter is designed and implemented to detect energetic THz pulses from high-intensity (>10 18 W/cm 2 ) laser–plasma interactions on the OMEGA EP. THz radiation from the laser driven target is detected by a shielded pyrometer. A second identical pyrometer is used for background subtraction. The detector can be configured to detect THz pulses in the 1 mm to 30 μm (0.3- to 10-THz) range and pulse energies from joules to microjoules via changes in filtration, aperture size, and position. Additional polarization selective filtration can also be used to determine the THz pulse polarization. The design incorporates significant radiation and electromagnetic pulse shielding to survive and operate within the OMEGA EP radiation environment. We describe the design, operational principle, calibration, and testing of the THz energy meter. The pyrometers were calibrated using a benchtop laser and show linear sensitivity to up to 1000 nJ of absorbed energy. Furthermore, the initial results from four OMEGA EP THz experiments detected up to ~15 μJ at the detector, which can correspond to hundreds of mJ depending on THz emission and reflection models.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Safety Functions and Features, Events and Processes for the E-Area Performance Assessment

The DOE Technical Standard, “Disposal Authorization Statement and Tank Closure Documentation,” (DOE 2017) recommends the use of safety functions and features, events and processes (FEPs) to support development of conceptual models and identification of scenarios to be considered in a performance assessment (PA). The FEP process provides a means to describe how a PA considers and addresses the factors that could influence the performance of key barriers. Understanding the roles of barriers in terms of limiting migration helps to focus on how changes in the system could lead to a situation where those roles cannot be fulfilled and there is the potential for compromised performance. The FEPs screening and review process was used to identify FEPs that are relevant for the EArea Low-Level Waste Facility (LLWF) and specifically those FEPs that could have a detrimental impact on the effectiveness of a given safety function. For this PA, a default list of FEPs developed at the International Atomic Energy Agency (IAEA 2004) and an approach implemented for PAs at the Hanford and Idaho sites (Mehta et al. 2016, DOE-ID 2019) are used to identify processes and events that could influence the effectiveness of a given safety function for the E-Area LLWF (e.g., subsidence can impact the safety function of the cover system and lead to increased infiltration). The Hanford and Idaho PAs represent two of the most recent applications of this approach. The PA evaluates the potential impacts of changes in performance of different features of the system and demonstrates that the safety functions represent multiple and redundant barriers. Barrier analyses, assuming a safety function is not present, also test the robustness of the system in the event of the loss of one or more safety functions. Such evaluations also support a qualitative illustration of the concept of defense in depth. The safety concept for closure of the E-Area LLWF (generically referred to as “E-Area”) encompasses a variety of different features (i.e., administrative controls, natural site features, and engineered barriers) that reduce the potential impacts on human health and the environment from the residual waste that will remain after closure. These features can be represented as a collection of safety functions acting independently and as a system to provide for overall safety. In some applications, there have been attempts to assign numerical expectations to specific safety functions, but that is not the intent in this case. The concept of safety functions is used more qualitatively in two ways for this PA: 1. To illustrate the robustness of the E-Area design, operational practices and closure approach by documenting features that are and are not credited in different modeling cases. 2. To identify the roles of the different features and potential processes and events that could compromise the performance of safety features and need to be considered when developing the modeling approach. This report addresses both safety functions and FEPs for the E-Area PA.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

A combined biological and chemical flue gas utilization system towards carbon dioxide capture from coal-fired power plants (Final Report)

Photosynthetic algal cultivation has been intensively studied for CO 2 capture and utilization for several decades. The footprint for using algae to capture CO 2 emitted from carbon-intensive industrial processes (power plants, cement plants, and fermentation processes) is extremely large, which creates serious technical and economic hurdles that must be cleared if algal technologies are to be commercially implemented. Fortunately, algal biomass is rich in proteins, carbohydrates, and lipids, providing a good chemical source for organic absorbents and other value-added chemical feedstocks. In particular, amino acids from algal protein can be used to generate amino acid salt solutions that have been proven to be effective for capturing CO 2 . In order to take advantage of both algal cultivation and biomass utilization, the goal of the proposed project is to develop a combined biological and chemical system for coal-fired power plants for sequestering CO 2 in biological absorbents and generating value-added products. This approach significantly reduces the land and energy footprint of CO 2 capture, and minimizes capital and operational expenses. Three specific objectives are targeted: 1) optimizing the growth of the selected algal strain to maximize biomass accumulation from the coal-fired flue gas; 2) developing a cascade biomass utilization to produce amino acid absorbents, polyurethanes, biodiesel, and methane; and 3) conducting techno-economic analysis (TEA) and life cycle assessment (LCA) of the proposed process. Three key technical outcomes were achieved: (1) With the selected robust algal strain and unique photobioreactor design, long-term culture stability can be extended, and algal biomass productivity reached 0.5 g dry biomass/L/day year-round at a biomass concentration of 1.2 g/L in the pilot photobioreactor; (2) The biomass utilization process led to complete utilization of the algal biomass to produce amino acid salt absorbent, polyurethanes, and methane; and (3) The combined biological and chemical flue gas utilization process concluded a technically and economically feasible commercial-scale system that completely captures CO 2 in coal-fired flue gas with greatly reduced energy consumption.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Molten Salt Reactor Passive Heat Removal System Modeling

The direct reactor auxiliary cooling system is a very robust, passive safety system that is designed to remove up to 2.36 MW of heat from the reactor during accident conditions. This report details a variety of DRACS degradation conditions and their effect on the safety of the reactor. This preliminary investigation shows that only two of the three DRACS loops are necessary to quickly suppress the decay heat produced by a newly shut down reactor. Even with a single DRACS loop operational, the maximum salt temperature observed was far below the safety specification of the plant (1173 K). When investigating the degraded performance of each DRACS loop, the short-term maximum salt temperature observed was strongly dependent on the DHX performance but was unaffected by the TCHX performance. However, even a heavily degraded DHX heat transfer performance was sufficient to halt the rising salt temperature due to decay heat. Further investigation should be done to characterize the effects of TCHX performance degradation at longer time scales. High levels of TCHX degradation were shown to lead to a reactor salt temperature minimum after a few hours of operation followed by a steady increase in temperature. With reduced ability to exhaust heat to the environment, it is possible the DRACS would be unable to maintain cooling during a long loss of active cooling event.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Develop a new integrated macro→micro←nano (MMN) multiscale modeling framework to optimize high strength aluminum alloys and processes for vehicle light-weighting​

Bending tests provide a means to study plane strain fracture performance of 6000 series aluminum alloys. Metrics from bending tests have been correlated with self-pierce riveting (SPR) performance of a high strength AA6111 automotive aluminum alloy in previous works. Using the ORNL HPC resources, this project developed an innovative macro→micro←nano (MMN) multiscale microstructure-based finite element (FE) code to further understanding of the relationship between microstructure and fracture properties of high-strength 6000-series alloys. This work started with microstructural characterization in both mesoscale and nanoscale and bending performance characterization of AA6111 HS2-T6 alloy at Ford, the MMN framework was applied to this alloy to simulate 3-point VDA bending. From the results of the macro-modeling of 3-point VDA bending, the critical region of fracture was identified, and the region geometry was used to construct the micro-model. The fracture criterion of micron-scale precipitates and aluminum matrix which contains submicron and nano particles (AL-SMP-NP) in the micro-model was calibrated and validated by comparing simulated and measured bending results. With the AL-SMP-NP fracture strain obtained, the fracture strain of Al-matrix containing nano particles (ALNP) will similarly be determined by a submicron scale-model using an edge-constrained FE modeling approach developed by Hu et al. With the ALNP fracture strain obtained, the fracture strain of Al-matrix containing no particles will similarly be determined by a nano scale-model using an edge-constrained FE modeling approach. After the MMN framework is built and fracture criterion calibrated, nano-model FE simulations with virtual microstructures was performed to obtain a reduced order model (ROM) of the fracture criterion of the ALNP as a function of volume fraction, size, and distribution of the nanoparticle. This nano→submicron→micro modeling part allows exploration of the influence of different material nanostructures from different process conditions on the bending properties within the multiscale bending simulation framework and the ROM of material bendability as a function of nanoparticle size and shape was established. This obtained reduced order model (ROM) could help guide the design and selection of materials to improve existing SPR process models that could replace trial-and-error rivet/die selection and help to design new rivet/die combinations capable of robustly joining new higher strength 6000 5 series alloys in automotive body structures. This would enable lightweighting of Ford vehicles leading to greater fuel efficiency and reduce manufacturing time and energy.

36 MATERIALS SCIENCE↗