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DOE OSTI2023

Promoting Fast Ion Conduction in Li-Argyrodite through Lithium Sublattice Engineering

Fundamental understanding of ionic transport plays a pivotal role in designing and optimizing fast ionic conductors. Here, through a systematic neutron scattering and theoretical investigation, we discovered new insights about how anion sublattice affects Li + distribution and transport in Li-argyrodite. We found that the promotion of Li + conductivities is strongly correlated with a previously overlooked Li + interstitial site (16e), which is critical for realizing intercage Li + migration. More isotropic Li + migration pathways with higher Li + occupancies on the interstitial 16e site are found to be the underlying reason for the much higher Li + conductivity in Li 6 PS 5 Cl relative to the Br- and I-based analogues. We further confirm that they are also the universal driving force for the ultrahigh Li + conductivities in both anion-substituted Li-poor (Li 6–a PS 5–a X a, X = Cl and Br) and cation-substituted Li-rich argyrodite (e.g., Li 6+a GeaP 1–a S 5 I and Li 6+a SiaSb 1–a S 5 I). In conclusion, it is expected this strategy can be generally adopted to improve the ionic conductivity of the broad family of Li-rich argyrodite and beyond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1977

Mass-driver reaction engine as Shuttle upper-stage

Optimization has been carried out on the design of a mass-driver intended for upgrading the Shuttle to geosynchronous and lunar-orbit capability. The machine accelerates 2,100 tons/yr of external tankage, in 14-gram segments, to exhaust velocities of 8,000-10,000 m/s. The resulting reaction force raises 1,700 tons/yr. of Shuttle payloads to high orbit, in two round trips. Exhaust velocity and thrust can be optimized for each mission segment. For a mission requiring an exhaust velocity of 8,000 m/sec, thrust is 560 newtons, power is 2.9 megawatts, and efficiency is 75%. Total electrical component mass including power supplies and waste-heat radiators is 89 tons. To insure that reaction mass does not constitute a hazard, segments may be in the form of powder, electrostatically dispersed after acceleration, and/or thrust may be vectored.

Oneill, G. K.↗

DOE OSTI2020

Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow

This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2026

Algebraic Multigrid with Filtering: An Efficient Preconditioner for Interior Point Methods in Large-Scale Contact Mechanics Optimization

Large-scale contact mechanics simulations are crucial in many engineering fields such as structural design and manufacturing. In the frictionless case, contact can be modeled by minimizing an energy functional; however, these problems are often nonlinear, nonconvex, and increasingly difficult to solve as mesh resolution increases. In this work, we employ a Newton-based interior-point (IP) filter line-search method, an effective approach for large-scale constrained optimization. While this method converges rapidly, each iteration requires solving a large saddle-point linear system that becomes ill-conditioned as the optimization process converges, largely due to IP treatment of the contact constraints. Such ill-conditioning can hinder solver scalability and increase iteration counts with mesh refinement. Here, to address this, we introduce a novel preconditioner, algebraic multigrid with filtering (AMGF), tailored to the Schur complement of the saddle-point system. Building on the classical AMG solver, commonly used for elasticity, we augment it with a specialized subspace correction that filters near null space components introduced by contact interface constraints. Through theoretical analysis and numerical experiments on a range of linear and nonlinear contact problems, we demonstrate that the proposed solver achieves mesh independent convergence and maintains robustness against the ill-conditioning that notoriously plagues IP methods. These results indicate that AMGF makes contact mechanics simulations more tractable and broadens the applicability of Newton-based IP methods in challenging engineering scenarios. More broadly, AMGF is well suited for problems, optimization or otherwise, where solver performance is limited by a low-dimensional subspace, such as those arising from localized constraints, interface conditions, or model heterogeneities. This makes the method widely applicable beyond contact mechanics and constrained optimization.

Mathematics and Computing↗

NASA NTRSDate not supplied

Generative Design and Digital Manufacturing: Using AI and Robots to Build Lightweight Instruments

Digital Engineering technologies are transforming long-stagnant development processes by applying the tremendous advancements in Information Technology (IT) to classical engineering tasks such as design, analysis, and fabrication of space-flight instrument structures. Generative Design leverages developments in Artificial Intelligence (AI) and Cloud computing to enable a paradigm shift in the design process, allowing the engineer to focus on defining the requirements and objectives of the design while AI generates optimized designs which comply with the input requirements. Digital Manufacturing allows these complex lightweight designs to be efficiently manufactured by directly fabricating from the resulting 3D models. The development of these two Digital Engineering technologies realizes significant mass savings while simultaneously reducing structure development time from months to days. This paper describes the development of the Evolved Structures process applying these technologies to spaceflight optical instrument structures including an example demonstrating greater than 10x reduction in development time/cost and greater than 3x improvement in structural performance.

Ryan McClelland↗

NASA NTRS1998

Nonlinear Control of a Reusable Rocket Engine for Life Extension

This paper presents the conceptual development of a life-extending control system where the objective is to achieve high performance and structural durability of the plant. A life-extending controller is designed for a reusable rocket engine via damage mitigation in both the fuel (H2) and oxidizer (O2) turbines while achieving high performance for transient responses of the combustion chamber pressure and the O2/H2 mixture ratio. The design procedure makes use of a combination of linear and nonlinear controller synthesis techniques and also allows adaptation of the life-extending controller module to augment a conventional performance controller of the rocket engine. The nonlinear aspect of the design is achieved using non-linear parameter optimization of a prescribed control structure. Fatigue damage in fuel and oxidizer turbine blades is primarily caused by stress cycling during start-up, shutdown, and transient operations of a rocket engine. Fatigue damage in the turbine blades is one of the most serious causes for engine failure.

Lorenzo, Carl F.↗

DOE OSTI2023

Parametric and Sensitivity Analysis of a Steam Generator Model Using Python and Machine-Learning Tools

For this study, we used Python and machine-learning tools to perform a comprehensive parametric and sensitivity analysis on a steam generator (SG) model. (The Python model was based on a previously completed MATLAB framework for the Holtec SMR-160 SG.) We investigated the influence of various input parameters (e.g., heat transfer coefficient [HTC], Nusselt number, and heat exchanger effectiveness) on the system’s output. With machine-learning tools such as the Risk Analysis Virtual Environment (RAVEN), which was developed at Idaho National Laboratory, we were then able to perform an automated analysis of the SG inputs’ effect on the HTC. The analysis results give valuable insights into the performance and optimization of SG systems. We found the inlet mass flow rate (MFR) to have the greatest impact on the HTC, followed closely by the inlet temperature, and then pressure. Shifting of the input parameters causes the location of the maximum HTC along the SG length to change incrementally. The cold leg (CL) MFR was also found to impact the HTC magnitude as well as the location of the maximum HTC. At between 0.4–0.9 of the total SG length, the input parameters experience maximum impact on the HTC, leading us to suggest that sensors be efficiently placed on the SG so as to closely and effectively monitor thermal-hydraulic properties during reactor operation. We also found that the sensitivity data calculated manually agrees with the RAVEN – based data, confirming the same range of maximum sensitivity. However, the RAVEN-based analysis showed that cold leg pressure and hot leg temperature have a greater impact on the heat transfer coefficient than the mass flow rate, implying that a manual sensitivity study taking only two samples is not accurate.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

Portable interactive visualization of large-scale simulations in geotechnical engineering using Unity3D

Development in large-scale geotechnical engineering simulation places tremendous demand for efficient visualization of such simulation data. This study presents a lightweight software tool, i.e. Geotechnical Interactive Visualization (GIV), as a solution to this challenge, which achieves efficient interactive visualization of large-scale simulations data in geotechnical engineering. Visualization data flow and algorithms specifically optimized for common geotechnical engineering applications are implemented in GIV. GIV can visualize geotechnical structure models with time-varying attributes attached to mesh with fixed topology, and also models with time-varying mesh topologies but no attributes attached, the two most common visualization tasks in geotechnical engineering. Furthermore, challenges for large-scale simulation data visualization, including parallel simulation data redundancy, massive data size, dynamic user interaction, and portability are overcome via specifically designed algorithms for simulation data preprocessing and optimized visualization modules using the powerful 3D rendering and interactive game engine Unity3D. Comparison of GIV with several widely used visualization tools for the visualization of large-scale idealized datasets and realistic geotechnical simulations highlights the visualization efficiency, smooth interactivity, and lightweight features of GIV.

42 ENGINEERING↗

DOE OSTI2025

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration

This paper proposes novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate. Furthermore, to facilitate Bayesian inference with intractable likelihoods, we propose to utilize optimization iterates for maximum a posteriori estimation to build a Gaussian process surrogate model for the unnormalized log-posterior density. We provide bounds for the Hellinger distance between the true and the approximate posterior distributions in terms of the number of design points. We demonstrate the effectiveness of our Bayesian optimization algorithms in nonconvex benchmark objective functions, in a machine learning hyperparameter tuning problem, and in a black-box engineering design problem. The effectiveness of our posterior approximation approach is demonstrated in two Bayesian inference problems for parameters of dynamical systems.

Bayesian inference↗

NASA NTRS2023

Human Systems Integration: Managing Risk in Anesthesia

The practice of anesthesia relies on clinicians’ ability to safely manage increasingly complex equipment. Devices such as ventilators, drug infusion pumps, and physiologic monitors use sophisticated algorithms to deliver care, but most clinicians are only trained to manage automated systems during normal operation. Few if any receive training on how to manage system failures. Although manufacturers are required to consult with human factors engineers as part of the equipment design process, most pieces of equipment are ultimately brought to market without extensive input from clinicians. Systems in the operating room can be as simple as an oxygen tank, or as complex as a multi-institutional healthcare organization. Humans are also a complex system, and play a critical role in the domains of operations, design, fabrication, maintenance, repair, and ultimately, dismantling and closeout. HSI seeks to provide a means for advocating the human side of the system. Human Systems Integration (HSI) is the cross disciplinary process used as part of the Systems Engineering process to reduce risk in systems. HSI professionals consider the human, hardware, and software elements of system design to optimize system performance and improve safety. HSI professionals work in domains of study that include training, management, human factors engineering, safety, and occupational health, among others. This article discusses the role of systems in the practice of anesthesia, and how consideration of the human during all phases of the system life cycle helps manage risks and promote a better patient outcome.

Human Factors↗

NASA NTRS1998

A Framework for Preliminary Design of Aircraft Structures Based on Process Information

This report discusses the general framework and development of a computational tool for preliminary design of aircraft structures based on process information. The described methodology is suitable for multidisciplinary design optimization (MDO) activities associated with integrated product and process development (IPPD). The framework consists of three parts: (1) product and process definitions; (2) engineering synthesis, and (3) optimization. The product and process definitions are part of input information provided by the design team. The backbone of the system is its ability to analyze a given structural design for performance as well as manufacturability and cost assessment. The system uses a database on material systems and manufacturing processes. Based on the identified set of design variables and an objective function, the system is capable of performing optimization subject to manufacturability, cost, and performance constraints. The accuracy of the manufacturability measures and cost models discussed here depend largely on the available data on specific methods of manufacture and assembly and associated labor requirements. As such, our focus in this research has been on the methodology itself and not so much on its accurate implementation in an industrial setting. A three-tier approach is presented for an IPPD-MDO based design of aircraft structures. The variable-complexity cost estimation methodology and an approach for integrating manufacturing cost assessment into design process are also discussed. This report is presented in two parts. In the first part, the design methodology is presented, and the computational design tool is described. In the second part, a prototype model of the preliminary design Tool for Aircraft Structures based on Process Information (TASPI) is described. Part two also contains an example problem that applies the methodology described here for evaluation of six different design concepts for a wing spar.

Rais-Rohani, Masoud↗

NASA NTRS1976

Nozzle selection for optimized single-stage shuttles

A computerized preliminary design procedure has been used to determine rocket-engine bell-nozzle configurations which achieve either minimum gross mass or minimum dry mass on an advanced technology winged single-stage-to-orbit launch vehicle. This procedure evaluates the trade offs between the optimized ascent flight trajectory performance and the flight vehicle sizing driven by the engine mass and propellant requirements. Numerous mission, flight, and vehicle related requirements and constraints are satisfied in this process. Propulsion system configurations which incorporate dual-position nozzles are evaluated and compared to configurations using all fixed nozzles.

Eldred, C. H.↗

NASA NTRSDate not supplied

Process Development and Hot-fire Testing of Additively Manufactured NASA HR-1 for Liquid Rocket Engine Applications

Additive manufacturing (AM) has provided new design and manufacturing opportunities to reduce cost and schedules, consolidate parts, and optimize performance. One technique being evaluated is Laser Powder Directed Energy Deposition (LP-DED), which provides a significant increase in scale compared to Laser Powder Bed Fusion (L-PBF). NASA along with industry partners have been developing the LP-DED process to demonstrate internal channel geometry and development components for use in liquid rocket engine channel cooled nozzles. Optimized materials in the extreme high pressure and hydrogen environment for liquid rocket engines remains a key challenge. NASA has advanced an enabling material called NASA HR-1 (Hydrogen Resistant -1) as a solution using AM techniques. NASA HR-1 is a high-strength Fe-Ni superalloy designed to resist high pressure, hydrogen environment embrittlement, oxidation, and corrosion. NASA HR-1 meets materials requirements for liquid rocket engine components, including good hydrogen resistance, high conductivity, good low cycle fatigue performance, and high elongation and strength for components in high heat flux environments. Material properties and process characterization have been completed on the high density thin-wall material in addition to advancements of the supply chain. NASA has also completed fabrication of several subscale and full-scale channel wall nozzles in LP-DED NASA HR-1 and completed hot-fire testing. This includes refinement of the process to produce thin-walls and various channel geometries to meet the requirements for channel wall nozzle applications. This paper will provide an overview of the LP-DED process development, material characterization and properties, component manufacturing, and hot-fire testing. Hot-fire testing was completed for a lander-class 7K-lbf thrust chamber using Liquid Oxygen (LOX)/Methane. The design overview and results from hot-fire testing will be presented in addition to hardware development for future testing on 2K-lbf and 35k-lbf thrust chambers and large-scale manufacturing technology demonstrators.

DED↗

NASA NTRSDate not supplied

Process Development and Hot-fire Testing of Additively Manufactured NASA HR-1 for Liquid Rocket Engine Applications

Additive Manufacturing↗

NASA NTRS1998

HOTCFGM-1D: A Coupled Higher-Order Theory for Cylindrical Structural Components with Through-Thickness Functionally Graded Microstructures

The objective of this three-year project was to develop and deliver to NASA Lewis one-dimensional and two-dimensional higher-order theories, and related computer codes, for the analysis, optimization and design of cylindrical functionally graded materials/structural components for use in advanced aircraft engines (e.g., combustor linings, rotor disks, heat shields, blisk blades). To satisfy this objective, a quasi one-dimensional version of the higher-order theory, HOTCFGM-1D, and four computer codes based on this theory, for the analysis, design and optimization of cylindrical structural components functionally graded in the radial direction were developed. The theory is applicable to thin multi-phased composite shell/cylinders subjected to macroscopically axisymmetric thermomechanical and inertial loading applied uniformly along the axial direction such that the overall deformation is characterized by a constant average axial strain. The reinforcement phases are uniformly distributed in the axial and circumferential directions, and arbitrarily distributed in the radial direction, thereby allowing functional grading of the internal reinforcement in this direction.

Pindera, Marek-Jerzy↗

NASA NTRS2012

Small Fast Spectrum Reactor Designs Suitable for Direct Nuclear Thermal Propulsion

Advancement of U.S. scientific, security, and economic interests through a robust space exploration program requires high performance propulsion systems to support a variety of robotic and crewed missions beyond low Earth orbit. Past studies, in particular those in support of the Space Exploration Initiative (SEI), have shown nuclear thermal propulsion systems provide superior performance for high mass high propulsive delta-V missions. The recent NASA Design Reference Architecture (DRA) 5.0 Study re-examined mission, payload, and transportation system requirements for a human Mars landing mission in the post-2030 timeframe. Nuclear thermal propulsion was again identified as the preferred in-space transportation system. A common nuclear thermal propulsion stage with three 25,000-lbf thrust engines was used for all primary mission maneuvers. Moderately lower thrust engines may also have important roles. In particular, lower thrust engine designs demonstrating the critical technologies that are directly extensible to other thrust levels are attractive from a ground testing perspective. An extensive nuclear thermal rocket technology development effort was conducted from 1955-1973 under the Rover/NERVA Program. Both graphite and refractory metal alloy fuel types were pursued. Reactors and engines employing graphite based fuels were designed, built and ground tested. A number of fast spectrum reactor and engine designs employing refractory metal alloy fuel types were proposed and designed, but none were built. The Small Nuclear Rocket Engine (SNRE) was the last engine design studied by the Los Alamos National Laboratory during the program. At the time, this engine was a state-of-the-art graphite based fuel design incorporating lessons learned from the very successful technology development program. The SNRE was a nominal 16,000-lbf thrust engine originally intended for unmanned applications with relatively short engine operations and the engine and stage design were constrained to fit within the payload volume of the then planned space shuttle. The SNRE core design utilized hexagonal fuel elements and hexagonal structural support elements. The total number of elements can be varied to achieve engine designs of higher or lower thrust levels. Some variation in the ratio of fuel elements to structural elements is also possible. Options for SNRE-based engine designs in the 25,000-lbf thrust range were described in a recent (2010) Joint Propulsion Conference paper. The reported designs met or exceeded the performance characteristics baselined in the DRA 5.0 Study. Lower thrust SNRE-based designs were also described in a recent (2011) Joint Propulsion Conference paper. Recent activities have included parallel evaluation and design efforts on fast spectrum engines employing refractory metal alloy fuels. These efforts include evaluation of both heritage designs from the Argonne National Laboratory (ANL) and General Electric Company GE-710 Programs as well as more recent designs. Results are presented for a number of not-yet optimized fast spectrum engine options.

Schnitzler, Bruce G.↗

DOE OSTI2025

Advancing the STS Neutron Moderator Design with an Automated Optimization Workflow and Unstructured Mesh Modeling

With the Second Target Station approaching its final design phase, a detailed neutronics evaluation of its critical components is necessary. Optimizing the dimensions of the two cold-source moderators that are at the heart of this facility presents a multi-objective optimization problem for which an accurate geometric description is crucial. We have applied a fully automated optimization workflow in which a detailed unstructured mesh geometry is automatically generated with Attila4MC, starting from a parametrized CREO geometry followed by preprocessing with SpaceClaim. With this geometry, a MCNP run is performed to calculate the brightness metrics, which are subsequently provided to the optimization algorithm in DAKOTA that provides new parameters and drives the optimization loop until convergence. In this paper, we show the results of the analysis that are used for the final design of the cylindrical and tube moderator. The optimization simulations provide a refinement to and confirmation of the conclusions of the previous design iteration. Additional to the optimization, a sensitivity study is performed to study the effect of minor geometry changes, which is important for the final engineering design. In conclusion, with these studies, we demonstrate that the automated workflow and high-fidelity unstructured mesh modeling are efficient tools for a thorough design evaluation.

DAKOTA↗

DOE OSTI2022

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗