Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “solution 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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Constrained Bayesian Optimization of Criticality Experiments at LLNL [Slides]

The design of criticality experiments is typically an iterative process that employs a Monte Carlo transport code. The goal is to find a design that optimizes some variable, like the sensitivity of a response to a cross section, while simultaneously ensuring criticality. The high fidelity of the Monte Carlo code is a great asset, but it makes exploring the design space computationally expensive. Herein, we present how a constrained Bayesian optimization algorithm can be used to efficiently design a criticality experiment. It uses Gaussian processes as a surrogate model to probe the design space and to reduce the number of code executions that are needed to find the optimum. We demonstrate constrained Bayesian optimization with a Pu-239/polyethylene solution system and a TEX experiment that is designed for critical ity safety validation of a nuclear waste model at the Hanford Site. For both systems, a global optimum was found within 75 Monte Carlo simulations.

42 ENGINEERING↗
DOE OSTI2023

Partitioning of Radionuclides in Various Streams of the Mo-99 Purification Process

The Universal Neutron Irradiator (UNI) was designed to irradiate a small volume of uranium solution and to achieve high fission power without creating a large radionuclide (RN) inventory. The UNI system is equipped with a fully enclosed off-gas system with online hydrogen gas monitoring to prevent the production of an explosive atmosphere, as well as storage cylinders for capturing fission gases. It also includes a catalytic recombiner in the target capsule (to immediately recombine radiolytic hydrogen and oxygen gas in the system) and access ports for sampling the target solution and capsule headspace. In UNI Phase I experiments, 50 mL of uranyl sulfate solution was irradiated at 0.3–0.5 W/mL fission power using a tantalum target assembly to produce photoneutrons via irradiation with an electron beam. The purposes of the Phase I experiments were to test the efficacy of adding 200 ppm Fe 2+ to the target solution to prevent uranyl peroxide precipitation under representative irradiation conditions, and to monitor iodine speciation in the irradiated uranyl sulfate solution. The focus of the UNI Phase II experiments was to use an irradiated uranyl sulfate solution containing a mixture of fission products and monitor their distribution in various streams during the primary recovery column (titania-based column) that separates Mo-99 from uranium. Irradiations were designed to create sufficient activity for tracking their distribution in various processing streams using a combination of gamma counting and inductively coupled plasma mass spectrometry (ICP-MS) analyses. Obtained data will be used to better understand the composition of waste streams, accumulation of RN on the titania column, and identify what RNs accumulate in the uranyl sulfate solution. Furthermore, this knowledge will allow us to determine waste classifications for various streams produced during the Mo-99 purification process and develop an appropriate waste clean-up strategy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Machine Learning Tools Set for Natural Gas Fuel Cell System Design

This study is focusing on leveraging the system design tools set for the next-generation solid oxide fuel cell (SOFC) based natural gas fuel cell (NGFC) system. Conventionally, system design and optimization of NGFC systems rely heavily on traditional reduced order model (ROM) techniques and designers’ experience level. For overcoming the technical barriers of system design, multiple multi-physics models and machine learning (ML) tools have been utilized to automate the conceptual design process and enhance the reliability of solutions for the NGFC system. The proposed tools set includes a physics-informed ML tool for automated ROM construction that leverages advances in deep neural networks to significantly reduce ROM prediction error for the NGFC power island compared to traditional approaches. The constructed physics-informed ML ROM can be used in system design, and optimization tools set Institute for the Design of Advanced Energy Systems (IDAES) Process Systems Engineering (PSE) framework. The tools set also provides a user-friendly graphic user interface built within Jupyter Notebooks, and the complete tools set is open-source public available.

Wang, Dewei↗
DOE OSTI2025

An alternative electrode design for accurate spectral induced polarization measurements in variably saturated porous media

SUMMARY Applications of spectral induced polarization (SIP) require electrodes that maintain hydrologic contact with surrounding soils to capture small electrical responses, often observed as phase shifts in milliradians. For unsaturated soils, electrodes must overcome increased electrical contact impedance due to reduced pore fluid. Traditional designs use a ceramic membrane electrode (CME) with a water reservoir and metal conductor, requiring periodic maintenance to retain electrolytic solution. For field applications where maintenance is impractical, alternative designs are needed. This study evaluated a new electrode design (silica flour electrode, SFE) alongside a CME. SFEs use packed silica flour to store water via capillary forces against a metal conductor. This study examined both designs in three variably saturated soils at soil suctions up to 700 mbar and soil water contents below 1 percent, with SIP measurements across 0.01 to 10 000 Hz frequencies. SFEs match CMEs at high frequencies and perform better at lower frequencies, without requiring ongoing maintenance, making them ideal for field use. In water-only experiments, CMEs produced errors and high noise below 1.5 Hz, whereas SFEs were more accurate. However, CMEs performed better above 300 Hz. In fine sand, SFEs performed better due to the relatively lower contact impedance as compared to CMEs. Both electrode types performed comparably in silty sand and silt loam soils, although CMEs required ongoing maintenance, suggesting potential for long-term reliability issues.

58 GEOSCIENCES↗
DOE OSTI2022

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale (V.2.0)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Therefore, the resilience challenge for extreme-scale HPC systems requires coordination between various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. Also, due to practical limits on power consumption in future HPC systems, they are likely to embrace innovative architectures, increasing the levels of hardware and software complexities. Therefore, the techniques that seek to improve resilience must navigate the complex trade-off space between resilience and the overheads to power consumption and performance. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance & power efficiency characteristics. Additionally, few implementations of current resilience solutions are portable to newer architectures and software environments that will be deployed on future systems. We developed a new structured approach to the management of HPC resilience using the concept of resilience-based design patterns. In general, a design pattern is a repeatable solution to a commonly occurring problem. We identified the well-known solutions that are commonly used to deal with faults, errors and failures in HPC systems. In the initial design patterns specification (version 1.0), we described the various solutions, which address specific problems in the design of resilient HPC environments, in the form of patterns. Each pattern describes a problem caused by a fault, error or failure event in an HPC environment, and then describes the core of the solution of the problem in such a way that this solution may be adapted to different systems and implemented at different layers of the system stack. The catalog of these resilience design patterns provides designers with a collection of design elements. To construct complete resilience solutions using combinations of various patterns, we defined a framework that enhances HPC designers' understanding of the important constraints and the opportunities for the design patterns to be implemented and deployed at various layers of the system stack. The design framework is also useful for establishing interfaces and mechanisms to coordinate flexible fault management across hardware and software components, as well as to consider the trade-off between performance, resilience, and power consumption when constructing a solution. The resilience design patterns specification version 1.1 included more detailed explanations of the pattern solutions, the context in which the patterns are applicable, and the implications for hardware or software design. It also provided several additional examples and detailed case studies to demonstrate the use of patterns to build realistic solutions. In version 1.2 of the specification document, we have improved the pattern descriptions, including graphical representations of the pattern components. These improvements are largely based on critical comments, feedback and suggestions received from pattern experts and readers of the previous versions of the specification. The pattern classification has been modified to further clarify the relationships between pattern categories. This version of the specification also introduces a pattern language for resilience design patterns. The pattern language presents the patterns in the catalog as a network, revealing the relations among the resilience patterns. The language provides designers with the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language also enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack. The overall goal of this work is to provide hardware and software designers, as well as the users and operators of HPC systems, a systematic methodology for the design and evaluation of resilience technologies in HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types. Version 2.0 expands the resilience design pattern classification and catalog to include self-stabilization patterns and reliability, availability and performance models for each structural pattern.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Grain boundary segregation beyond the dilute limit: Separating the two contributions of site spectrality and solute interactions

Solute segregation at grain boundaries (GBs) is known to have a profound impact on material properties, and as such is becoming routinely used as an element in alloy design. Beyond the dilute limit, the extent of solute GB segregation is known to be concentration dependent. Using hybrid Monte Carlo/Molecular Statics simulations of Mg segregation in Al, in this work we decouple the two contributions to this composition dependence: (i) spectrality of atomic environments at the boundary and (ii) solute-solute interactions. Although only contribution (ii) is typically considered in the literature, we argue that both contributions are equally important to understand concentration dependence and correctly quantify GB solute segregation in a binary alloy. Finally, a thermodynamic segregation isotherm is outlined that accounts for both the spectrality of grain boundary sites and solute-solute interactions. Unlike classical isotherms like those of McLean or Fowler-Guggenheim, which can be successfully fitted to GB segregation data only over a limited range of composition and temperature, our proposed model is shown to be accurate across the composition-temperature space.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Constrained Bayesian optimization of criticality experiments

The design of criticality experiments is typically an iterative process that employs a Monte Carlo transport code. The goal is to find a design that optimizes some variable, like the sensitivity of a response to a cross section, while simultaneously ensuring criticality. The high fidelity of the Monte Carlo code is a great asset, but it makes exploring the design space computationally expensive. Herein, we present how a constrained Bayesian optimization algorithm can be used to efficiently design a criticality experiment. It uses Gaussian processes as a surrogate model to probe the design space and to reduce the number of code executions that are needed to find the optimum. Furthermore, we demonstrate constrained Bayesian optimization with a Pu-239/polyethylene solution system and a TEX experiment that is designed for criticality safety validation of a nuclear waste model at the Hanford Site. For both systems, a global optimum was found within 75 Monte Carlo simulations.

42 ENGINEERING↗
DOE OSTI2020

OPTIMUS{sup TM} Packaging for Intermediate Level Wastes and Spent Fuel - 20129

NAC's OPTIMUS{sup TM} packaging is a family of Type B(U)F transportation packages designed for maximum flexibility and cost-efficiency to support shipment of a wide range of challenging wastes and materials. They are small, modular packaging designs meeting DOT weight limits for road transport. OPTIMUS{sup TM} contents include low level waste, mixed low-level waste, irradiated fuel waste and intermediate level waste. Contents can also include aerosol cans with compressed gas and liquefied gas propellants and standard DOT 3E lecture bottles for shipment to treatment facilities. OPTIMUS{sup TM} packaging provides a cost-effective solution for challenging and unique waste contents combined with design simplicity and operational flexibility meeting the requirements of both SSR-6 and 10 CFR 71 for Type B(U)F packaging. The modular design of the packaging components combined with the flexibility to add modified internals for enhanced packaging performance makes OPTIMUS{sup TM} the preferred solution for many radioactive material transportation needs. The OPTIMUS{sup TM} product line currently includes two packaging designs; OPTIMUS{sup TM}-L and OPTIMUS{sup TM}-H. OPTIMUS{sup TM}-L is a lightweight transportation packaging intended for low-activity wastes, whereas OPTIMUS{sup TM}-H is a larger, heavier package intended for high-activities wastes. Due to its small size and weight, six (6) to ten (10) OPTIMUS{sup TM}-L packages can be accommodated on a single legal-weight truck shipment, depending on the weight of the contents. For typical payloads, two (2) OPTIMUS{sup TM}-H packages can be accommodated on a single legal-weight truck shipment. The OPTIMUS{sup TM} packaging share the same cask containment vessel (CCV) in the OPTIMUS{sup TM}-H and OPTIMUS{sup TM}-L. The large cavity size of the CCV, which can accommodate a 416-liter (110-gallon) drum, combined with the small size, low weight and modularity of the OPTIMUS{sup TM} packaging provides unmatched flexibility. In this paper, NAC provides a technical overview of the OPTIMUS{sup TM} packaging and identifies the design features and technology advancements making the OPTIMUS{sup TM} a readily adaptable and flexible solution for packaging processing facilities, reactor and decommissioning wastes. Optimized shielding performance through the addition of internal shielding components and alternative shipping configurations are discussed. In addition, an overview of the first major deployment of the OPTIMUS{sup TM} packaging for the Whiteshell Laboratories Closure Project (WLCP) is discussed. (authors)

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

Q-VR: System-Level Design for Future Mobile Collaborative Virtual Reality

High Quality Mobile Virtual Reality (VR) is what the incoming graphics technology era demands: users around the world, regardless of their hardware and network conditions, can all enjoy the immersive virtual experience. However, the state-of-the-art software-based mobile VR designs cannot fully satisfy the realtime performance requirements due to the highly interactive nature of user's actions and complex environmental constraints during VR execution. Inspired by the unique human visual system effects and the strong correlation between VR motion features and realtime hardware-level information, we propose Q-VR, a novel dynamic collaborative rendering solution via software-hardware co-design for enabling future low-latency high-quality mobile VR. At software-level, Q-VR provides flexible high-level tuning interface to reduce network latency while maintaining user perception. At hardware-level, Q-VR accommodates a wide spectrum of hardware and network conditions across users by effectively leveraging the computing capability of the increasingly powerful VR hardware. Extensive evaluation on real-world games demonstrates that Q-VR can achieve an average end-to-end performance speedup of 3.4x (up to 6.7x) over the traditional local rendering design in commercial VR devices, and a 4.1x frame rate improvement over the state-of-the-art static collaborative rendering.

Xie, Chenhao↗
DOE OSTI2021

Intersystem crossing and exciton–defect coupling of spin defects in hexagonal boron nitride

Despite the recognition of two-dimensional (2D) systems as emerging and scalable host materials of single-photon emitters or spin qubits, the uncontrolled, and undetermined chemical nature of these quantum defects has been a roadblock to further development. Leveraging the design of extrinsic defects can circumvent these persistent issues and provide an ultimate solution. Here, we established a complete theoretical framework to accurately and systematically design quantum defects in wide-bandgap 2D systems. With this approach, essential static and dynamical properties are equally considered for spin qubit discovery. In particular, many-body interactions such as defect–exciton couplings are vital for describing excited state properties of defects in ultrathin 2D systems. Meanwhile, nonradiative processes such as phonon-assisted decay and intersystem crossing rates require careful evaluation, which competes together with radiative processes. From a thorough screening of defects based on first-principles calculations, we identify promising single-photon emitters such as Si VV and spin qubits such as Ti VV and Mo VV in hexagonal boron nitride. This work provided a complete first-principles theoretical framework for defect design in 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Small-sized specimen design with the provision for high-frequency bending-fatigue testing

The testing and study of emerging materials—such as additively manufactured materials—demands for specimen designs that are cost effective and time saving. Here the design of a small-sized bending-fatigue test specimen for an ultrasonic fatigue testing system is reported in this paper. The design is optimized based on the finite element analysis and analytical-solution results to achieve the proper vibration shape and stress distribution. The proposed design is evaluated in the high- and very-high-cycle fatigue regimes under 20-kHz frequency. Both simulation and testing results confirm that the desirable vibration mode occurs and the specimen fails at the designated test (gauge) section, where the maximum stress exists. The stress–life (S–N) curve is obtained for Inconel alloy 718 and indicates an expected trend.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Rational designs to enable 10-min fast charging and long cycle life in lithium-ion batteries

A daunting challenge in the design of lithium ion batteries (LiBs) is enabling 10-min extreme fast charging (XFC) while achieving appreciable charge acceptance and cycle life. This desirable outcome requires both a comprehensive understanding of LiB operation and aging behavior at different length scales and careful optimization. Lithium plating has been a critical bottleneck because, at XFC rates, it consumes cyclable lithium causing distinct aging and safety concerns even in moderate-loading LiBs. Here, we propose combining multiple solutions, including materials-to-electrode design-to-charging protocols, that are intended to overcome limitations in lithium-ion transport in the electrolyte phase, thus enabling 10-min XFC in LiBs. Some implemented strategies include cathode chemistry, optimized carbon binder domain in the cathode, dual layer anode design, improved separator and advanced electrolyte. Further, innovative charging protocols in moderately loading (~3 mAh/cm 2 anode/2.7 mAh/cm 2 cathode) single-layer pouch cells are proposed, together with demonstrated 10-min XFC with higher charge acceptance between 87.3 and 92.1% (or 2–2.1 mAh/cm 2 ) for 600 cycles without lithium plating. This methodical study with well-defined cells shows promise in combining multiple solution strategies to enable 10-min XFC, charting a pathway to achieve XFC in higher-loading energy-optimized LiBs.

25 ENERGY STORAGE↗
DOE OSTI2024

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

42 ENGINEERING↗
DOE OSTI2022

Impact of hydrophobic pendant phenyl groups on transport and co-transport of methanol and acetate in PEGDA-SPMAK cation exchange membranes

CO 2 reduction cells are innovative devices that reduce CO 2 into valuable chemicals (i.e. methanol (MeOH) and acetate (OAc – )) on the cathode. A major challenge with such devices is to develop ion exchange membranes that allow ion-selective transport (i.e., protons for cation exchange membranes, CEM) and suppress the crossover of CO 2 reduction products. To design such membranes, it is important to understand multi-solute transport behavior of these solutes. Previously, our group reported acetate diffusivities in co-transport with MeOH increase in sulfonated CEMs, where we speculated charge screening of the electrostatic interactions by co-diffusing MeOH has an impact. Here, crosslinked membranes fabricated by photopolymerization of poly(ethylene glycol) diacrylate (PEGDA), 3-sulfopropyl methacrylate potassium (SPMAK, SO 3 – -containing ionomer), and a phenyl-containing comonomer either phenoxyethyl acrylate (PEA) or poly(ethylene glycol) phenyl ether acrylate (PEGPEA)) are investigated. We observe OAc – diffusivities to both (1) PEGDA-SPMAK and (2) PEGDA-PEGPEA increase in co-diffusion with MeOH and those to (3) PEGDA-SPMAK/PEGPEA decrease. To rationalize this emergent co-transport behavior, we speculate (1) electrostatic interactions are interfered with by co-diffusing MeOH, (2) chain mobility (segmental dynamics) increases in the presence of MeOH and (3) for films with both hydrophobic (PEGPEA) and hydrophilic (SPMAK) comonomers chain mobility (segmental dynamics) decrease due to interactions between the comonomer sidechains depressing overall solute diffusivities. Finally, while further investigations are needed, this work contributes to improving our fundamental understanding of the relationships between polymer film chemistry, solute chemistry, and emergent cotransport behavior observed and described in this work (and others) towards enabling the design of improved membrane materials.

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

A scoping study of far-SOL main-wall protection limiters for steady-state operation of compact pilot plant tokamaks

We present a novel method for handling steady-state heat fluxes incident on the main wall of pilot plant-scale magnetic fusion devices, based on the utilization of protection limiters in the far scrape-off layer (SOL). This method helps avoid large plasma-wall gaps, without excessively compromising blanket performance. We present an optimization algorithm for determining the appropriate size and scale of these protection limiters given (1) probability distributions of SOL plasma parameters and (2) assumed risk tolerance. As part of this optimization, we have developed an analytic description of parallel heat fluxes across limiter shadows, and an objective cost function (the ‘Far-SOL Marginal Cost’) to quantify the impact that different main-wall thermal management design choices have on reactor capital cost. Applying the model to a midscale fusion pilot plant concept shows that making use of far-SOL protection limiters can reduce capital costs on the order of $500 M, relative to naively increasing the plasma-wall gap. Our analysis demonstrates that the far-SOL power decay length is the highest-leverage plasma assumption for thermal loading of the first wall, and the primary cost driver for main wall thermal management. The relative cost efficiency of protection limiters increases as assumptions on the far-SOL heat flux become more pessimistic. The concepts described in this paper motivate the further development of far-SOL protection limiters as part of larger efforts to design economical core-edge-wall compatible solutions for a fusion pilot plant.

Design under uncertainty↗
DOE OSTI2022

Clean Energy Solutions Center Services

The Clean Energy Solutions Center is a web-based resource that helps governments design and adopt policies and programs that support the deployment of clean energy technologies. The Solutions Center's three main offerings are Ask an Expert policy assistance, training and peer-to- peer learning forums and a rich library of technical tools and publications. These services are provided at no cost to users.

ask an expert↗
DOE OSTI2025

Early Fault Detection in Nuclear Systems: A Digital Engineering Approach

Nuclear energy systems present unique challenges in terms of ensuring safety, reliability, and efficiency during their design and operation. Early fault detection is critical for mitigating risks and fostering system resilience. However, current methods often fall short at identifying faults during early stages, potentially leading to costly delays and safety risks. The present work proposes a comprehensive digital engineering approach that leverages digital twins, digital threads, model-based systems engineering, artificial intelligence, and immersive extended reality to support early fault detection in nuclear systems. Through a series of case studies, we highlight specific gaps in the fault detection mechanisms of traditional nuclear design and operation processes, then demonstrate a suite of solutions we are working to implement to address these shortcomings in similar projects. Our findings suggest that a digital engineering approach to design and operation can significantly improve fault detection, ultimately leading to reductions in risk.

42 - ENGINEERING↗
DOE OSTI2022

Preconditioned least‐squares Petrov–Galerkin reduced order models

Abstract In this article, we introduce a methodology for improving the accuracy and efficiency of reduced order models (ROMs) constructed using the least‐squares Petrov–Galerkin (LSPG) projection method through the introduction of preconditioning. Unlike prior related work, which focuses on preconditioning the linear systems arising within the ROM numerical solution procedure to improve linear solver performance, our approach leverages a preconditioning matrix directly within the minimization problem underlying the LSPG formulation. Applying preconditioning in this way has the potential to improve ROM accuracy for several reasons. First, preconditioning the LSPG formulation changes the norm defining the residual minimization, which can improve the residual‐based stability constant bounding the ROM solution's error. The incorporation of a preconditioner into the LSPG formulation can have the additional effect of scaling the components of the residual being minimized to make them roughly of the same magnitude, which can be beneficial when applying the LSPG method to problems with disparate scales (e.g., dimensional equations, multi‐physics problems). Importantly, we demonstrate that an “ideal preconditioned” LSPG ROM (a ROM in which the preconditioner is the inverse of the Jacobian of its corresponding full order model) emulates projection of the full order model solution increment onto the reduced basis. This quantity defines a lower bound on the error of a ROM solution for a given reduced basis. By designing preconditioners that approximate the Jacobian inverse—as is common in designing preconditioners for solving linear systems—it is possible to obtain a ROM whose error approaches this lower bound. The proposed approach is evaluated on several mechanical and thermo‐mechanical problems implemented within the Albany HPC code and run in the predictive regime, with prediction across material parameter space. We demonstrate numerically that the introduction of simple Jacobi, Gauss‐Seidel, and ILU preconditioners into the proper orthogonal decomposition/LSPG formulation reduces significantly the ROM solution error, the reduced Jacobian condition number, the number of nonlinear iterations required to reach convergence, and the wall time (thereby improving efficiency). Moreover, our numerical results reveal that the introduction of preconditioning can deliver a robust and accurate solution for test cases in which the unpreconditioned LSPG method fails to converge.

Lindsay, Payton↗