SEARCH · Search NASA

Results for “inverse 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.

SourceResearch connectionFrom yearThrough year

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

At least 235 records · Page 13

All sources NASA NTRS DOE OSTI

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↗

DOE OSTI2021

A laser inverse Compton scattering diagnostic to study runaway electron dynamics during tokamak disruptions

The goal is to design a new diagnostic based on laser inverse Compton scattering (LICS) to study the dynamics of runaway electron formation and their subsequent loss during triggered disruptions in DIII-D. It is known that better measurements of the runaway electron population, throughout the evolution of a disruption, is critical for mitigating the impact of energetic electron beams especially in high plasma current discharges such as those in reactor relevant burning plasmas. The goal of the LICS approach being designed for DIII-D is to measure the timeresolved spatial profile and energy distribution of the runaway electrons while they are in the core of the tokamak plasma. During the last year, a code was developed to simulate the signals from a new laser scattering diagnostic for DIII-D, which will measure the relativistic electron population expected in DIII-D after disruptions triggered by argon pellet injection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Tuning magnetic antiskyrmion stability in tetragonal inverse Heusler alloys

The identification of materials supporting complex, tunable magnetic order at ambient temperatures is foundational to the development of new magnetic device architectures. In this work, we report the design of Mn 2 XY tetragonal inverse Heusler alloys that are capable of hosting magnetic antiskyrmions whose stability is sensitive to elastic strain. Here We first construct a universal magnetic Hamiltonian capturing the short- and long-range magnetic order which can be expected in these materials. This model reveals critical combinations of magnetic interactions that are necessary to approach a magnetic phase boundary, where the magnetic structure is highly susceptible to small perturbations such as elastic strain. We then computationally search for quaternary Mn 2 (X 1 , X 2 ) Y alloys where these critical interactions may be realized and which are likely to be synthesizable in the inverse Heusler structure. We identify the Mn 2 Pt 1–z X z Ga family of materials with X = Au, Ir, Ni as an ideal system for accessing all possible magnetic phases, with several critical compositions where magnetic phase transitions may be actuated mechanically.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Development of 17 N as a time-tagged neutron source for calibration of large antineutrino detectors

Large antineutrino detectors hold the potential for remote nuclear reactor monitoring and discovery, which is of great interest for nonproliferation and treaty verification applications. Correlated-particle calibration sources are especially useful for large-volume designs based on detection of inverse beta-decay events, as the timing information of the coincident particles improves background rejection and event reconstruction. 17 N is a promising calibration source since it emits beta-correlated delayed neutrons and can be produced relatively easily using high-energy neutrons. In this work, we examine the feasibility of 17 N as a time-tagged calibration source for large antineutrino detectors. 17 N was produced using a DT neutron generator from a water sample enriched in 17 O, and the observed production rates were found to be consistent with previous measurements of the 17 O(n,p) 17 N reaction cross-section. As a result, time-tagging of 17 N delayed neutrons is demonstrated via beta-neutron coincidence measurements conducted using a specialized beta detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Control Design for a Marine Hydrokinetic Cycloturbine Vehicle

Abstract Marine Hydrokinetic (MHK) cycloturbines generate sustainable power by exploiting tidal currents. By powering the turbines and using pitching foils for control, a vehicle comprised of MHK cycloturbines also has the ability to station keep and maneuver. The vehicle consists of four counter-rotating cycloturbines, with hydrofoils oriented perpendicular to the flow in a paddlewheel configuration. Lift and drag generated from these foils sum together to produce thrust. An experimentally tuned simulation model that solves the six-degrees-of-freedom rigid body equations of motion for the MHK vehicle subject to hydrodynamic, hydrostatic, and propulsive forces is used to aid the design of vehicle controllers. Global feedback controllers are initially designed by applying classical control methods to an approximate linear model of the system dynamics. A higher performing nonlinear controller is designed using the nonlinear dynamic inversion (NDI) method. NDI accounts for the nonlinearities of the MHK system and therefore is suitable for a wide range of operating conditions. The response of the classical and NDI controllers to speed, depth, roll, pitch, and yaw commands are evaluated and compared in simulation. The classical controller outperforms the NDI controller for small amplitude maneuvers, although the degradation with NDI is minor. However, in the nonlinear operating regime the NDI controller outperforms the classical controller and the classical controller exhibits instability.

Automation & Control Systems↗

DOE OSTI2020

Physics-informed neural networks for inverse problems in nano-optics and metamaterials

In this paper, we employ the emerging paradigm of physics-informed neural networks (PINNs) for the solution of representative inverse scattering problems in photonic metamaterials and nano-optics technologies. In particular, we successfully apply mesh-free PINNs to the difficult task of retrieving the effective permittivity parameters of a number of finite-size scattering systems that involve many interacting nanostructures as well as multi-component nanoparticles. Our methodology is fully validated by numerical simulations based on the finite element method (FEM). The development of physics-informed deep learning techniques for inverse scattering can enable the design of novel functional nanostructures and significantly broaden the design space of metamaterials by naturally accounting for radiation and finite-size effects beyond the limitations of traditional effective medium theories.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Evolution of artificial intelligence for application in contemporary materials science

Abstract Contemporary materials science has seen an increasing application of various artificial intelligence techniques in an attempt to accelerate the materials discovery process using forward modeling for predictive analysis and inverse modeling for optimization and design. Over the last decade or so, the increasing availability of computational power and large materials datasets has led to a continuous evolution in the complexity of the techniques used to advance the frontier. In this Review, we provide a high-level overview of the evolution of artificial intelligence in contemporary materials science for the task of materials property prediction in forward modeling. Each stage of evolution is accompanied by an outline of some of the commonly used methodologies and applications. We conclude the work by providing potential future ideas for further development of artificial intelligence in materials science to facilitate the discovery, design, and deployment workflow. Graphical abstract

Materials Science↗

DOE OSTI2021

Simulation of the PBX 9501 Self-Ignited Heavily Confined Cookoff Test

The PBX 9501 self-ignited thermally damaged explosive (SITX) test was carried out by Holmes et al. A 6-inch diameter sphere made of the PBX 9501 was heated inside a steel confinement vessel until it underwent a thermal explosion. This test provided measured temperature data that was used to define a boundary condition to carry out post-experiment finite element (FE) thermal simulations and comparisons of the FE thermal response with the experimental time to thermal ignition and internal temperature measurements at the center of the PBX 9501 sphere. Finite element models of this experiment were constructed using the heat transfer code Aria. A cookoff model for PBX 9501 was used to simulate the thermal decomposition of PBX 9501 and to predict the time to thermal ignition. An inverse heat conduction scheme was designed and applied to estimate a 1D temperature boundary condition.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2021

Inverse Biot–Savart Optimization for Superconducting Accelerator Magnets

Superconducting (SC) magnets for accelerator concepts are often synthesized by numerically optimizing magnetic field waveforms, a process that requires a subsequent solution of a constrained inverse problem to identify suitable SC magnet windings. When the desired field distribution is intuitive, the inverse process is facilitated by seeding preconceived coil distributions into design optimization methods for refinement. With more complex magnetic field distributions, an initial design may be unknown, and topology optimization tools are required to synthesize current distributions without a priori guidance from a subject matter expert. In this work, we develop a constrained inverse Biot-Savart topology optimization methodology that synthesizes optimal distributions of current density in racetrack-like SC coils. The problem structure is exploited through a computationally efficient quadratic programming formulation, and the method is applied to recently published magnetic field waveforms for a recirculating proton phase shifter, a proton therapy gantry, and dipole magnets with sharp field transitions. The method and results herein identify novel winding configurations that can help magnet designers bring accelerator concepts to fruition.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Design of High-Performance Lead-Free Quaternary Antiperovskites for Photovoltaics via Ion Type Inversion and Anion Ordering

The emergence of halide double perovskites significantly increases the compositional space for lead-free and air-stable photovoltaic absorbers compared to halide perovskites. Nevertheless, most halide double perovskites exhibit oversized band gaps (>1.9 eV) or dipole-forbidden optical transition, which are unfavorable for efficient single-junction solar cell applications. The current device performance of halide double perovskite is still inferior to that of lead-based halide perovskites, such as CH 3 NH 3 PbI 3 (MAPbI 3 ). Here, by ion type inversion and anion ordering on perovskite lattice sites, two new classes of pnictogen-based quaternary antiperovskites with the formula of X 6 B 2 AA' and X 6 BB'A 2 are designed. Phase stability and tunable band gaps in these quaternary antiperovskites are demonstrated based on first-principles calculations. Further photovoltaic-functionality-directed screening of these materials leads to the discovery of 5 stable compounds (Ca 6 N 2 AsSb, Ca 6 N 2 PSb, Sr 6 N 2 AsSb, Sr 6 N 2 PSb, and Ca 6 NPSb 2 ) with suitable direct band gaps, small carrier effective masses and low exciton binding energies, and dipole-allowed strong optical absorption, which are favorable properties for a photovoltaic absorber material. The calculated theoretical maximum solar cell efficiencies based on these five compounds are all larger than 29%, comparable to or even higher than that of the MAPbI 3 based solar cell. Finally, our work reveals the huge potential of quaternary antiperovskites in the optoelectronic field and provides a new strategy to design lead-free and air-stable perovskite-based photovoltaic absorber materials.

three-dimensional electronic transport↗

DOE OSTI2022

On the Ohmic-dominant heating mode of capacitively coupled plasma inverted by boundary electron emission

Electron emission from the boundary is ubiquitous in a capacitively coupled plasma (CCP) and precipitates nonnegligible influence on the discharge properties. Here, we present Particle-in-Cell/Monte Carlo Collision simulation of an Ohmic-dominant heating mode of the capacitively coupled plasma, where the stochastic heating vanishes and only Ohmic heating sustains the discharge due to sheath inversion by boundary electron emission. The inverted CCP features negative sheath potential without Bohm presheath, hence excluding plasma heating due to sheath edge oscillation. The particle and energy transport of the proposed heating mode is analyzed. The influence of boundary electron emission flux, source voltage, and neutral pressure on the transition between classic and Ohmic-dominant CCP heating modes is shown with designated simulation scans. Further, a modified inverse sheath–plasma coupling due to excessive ionization is discovered. In the end, key indicators of the proposed heating mode in plasma diagnostics are provided for future experimental verifications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning

Determining process–structure–property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process–structure and structure–property linkages. In this work, we seek to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through a crystal plasticity finite element model matches a target distribution on materials properties. This stochastic inversion formulation infers a distribution of acceptable/consistent microstructures, as opposed to a deterministic solution, which expands the range of feasible designs in a probabilistic manner. Furthermore, to solve this stochastic inverse problem, we employ a recently developed uncertainty quantification framework based on push-forward probability measures, which combines techniques from measure theory and Bayes’ rule to define a unique and numerically stable solution. This approach requires making an initial prediction using an initial guess for the distribution on model inputs and solving a stochastic forward problem. To reduce the computational burden in solving both stochastic forward and stochastic inverse problems, we combine this approach with a machine learning Bayesian regression model based on Gaussian processes and demonstrate the proposed methodology on two representative case studies in structure–property linkages.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments

The Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observations from a real physical/field experiment and correct for any modeling bias (second GP) when predicting under new field conditions (design inputs). There are well-established methods for placement of design inputs for data-efficient planning of a simulation campaign in isolation, that is, without field data: space-filling, or via criterion like minimum integrated mean-squared prediction error (IMSPE). Analogues within the coupled GP KOH framework are mostly absent from the literature. Here, in this study, we derive a closed form IMSPE criterion for sequentially acquiring new simulator data for KOH. We illustrate how acquisitions space-fill in design space, but concentrate in calibration space. Closed form IMSPE precipitates a closed-form gradient for efficient numerical optimization. We demonstrate that our KOH-IMSPE strategy leads to a more efficient simulation campaign on benchmark problems, and conclude with a showcase on an application to equilibrium concentrations of rare earth elements for a liquid–liquid extraction reaction.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Smart Droplets Stabilized by Designer Surfactants: From Biomimicry to Active Motion to Materials Healing

The science and technologies of emulsion droplets have been a long‐term focus of extensive research endeavors for their practical utility across a breadth of industries, including pharmaceutical products, oil recovery processes, and the food sciences. However, with advances in materials chemistry and characterization tools, new emerging areas are arising with a focus on “smart droplets”. The versatility of emulsion droplets across is based on their ability to partition and create isolated systems with properties defined by the liquid–liquid interface, while preparative routes allow manipulation of droplet size, stability, and encapsulated contents. As described in this article, significant efforts are being devoted to creating new types of droplets by “activating” this interface through the incorporation of reactive structures that trigger droplet response to applied or environmental stimuli (e.g., pH, temperature, salt, or external fields). Moreover, parallels between droplets and live cells inspire efforts to conceive systems that resemble biological motifs or that can produce cellular behaviors that imitate biology (e.g., swarming, communication, or motion). Here, the authors highlight recent advances in smart droplets, with emphasis on organic, polymer, and/or particle surfactants that give rise to inter‐droplet communication (via aggregation, fusion, division, or mass transfer), droplet vehicles for controlled delivery, autonomous droplet motion, and tunable emulsion inversion. Especially emphasized is the macromolecular design to produce reactive and functional surfactants, which are crucial to responsive droplet behavior and their underlying mechanisms. More generally, the exquisite interplay between materials science and biology inspires the review of this research area that provides unique opportunities for insight and inspiration into the capabilities of new droplet designs.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Roadmap for Optical Metasurfaces

Metasurfaces have recently risen to prominence in optical research, providing unique functionalities that can be used for imaging, beam forming, holography, polarimetry, and many more, while keeping device dimensions small. Despite the fact that a vast range of basic metasurface designs has already been thoroughly studied in the literature, the number of metasurface-related papers is still growing at a rapid pace, as metasurface research is now spreading to adjacent fields, including computational imaging, augmented and virtual reality, automotive, display, biosensing, nonlinear, quantum and topological optics, optical computing, and more. At the same time, the ability of metasurfaces to perform optical functions in much more compact optical systems has triggered strong and constantly growing interest from various industries that greatly benefit from the availability of miniaturized, highly functional, and efficient optical components that can be integrated in optoelectronic systems at low cost. This creates a truly unique opportunity for the field of metasurfaces to make both a scientific and an industrial impact. Furthermore, the goal of this Roadmap is to mark this “golden age” of metasurface research and define future directions to encourage scientists and engineers to drive research and development in the field of metasurfaces toward both scientific excellence and broad industrial adoption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Can a transport model predict inverse signatures in Lithium metal batteries without modifying kinetics?

In this study, a one-dimensional transport model is developed and analyzed that predicts the inverse signatures obtained in lithium metal batteries. This simple approach’s ability to predict inverse signatures stems from the competing interplay between moving boundary rates and mass transfer limitations to yield inverse signatures without modifying the kinetics. The numerical scheme used for the present model simulations is presented in detail which has been further used to study the effect of design parameters on the prevalence and strength of inverse signatures. It was found that the proposed model and the analysis is more pertinent to thick symmetric cells, commonly used for in-depth fundamental studies.

model, transport, dendrite, lithium metal battery,↗

DOE OSTI2023

Rational Design and Reticulation of Infinite qbe Rod Secondary Building Units into Metal–Organic Frameworks through a Global Desymmetrization Approach for Inverse C 3 H 8 /C 3 H 6 Separation

Abstract The development of reticular chemistry has enabled the construction of a large array of metal–organic frameworks (MOFs) with diverse net topologies and functions. However, dominating this class of materials are those built from discrete/finite secondary building units (SBUs), yet the designed synthesis of frameworks involving infinite rod‐shaped SBUs remain underdeveloped. Here, by virtue of a global linker desymmetrization approach, we successfully targeted a novel Cu‐MOF (Cu‐ASY) incorporating infinite Cu‐carboxylate rod SBUs with its structure determined by micro electron diffraction (MicroED) crystallography. Interestingly, the rod SBU can be simplified as a unique cylindric sphere packingqbetubule made of [4 3 .6 2 ] tiles, which further connect the tritopic linkers to give a newly discovered 3,5‐connectedgfcnet. Cu‐ASY is a permanent ultramicroporous material featuring 1D channels with highly inert surfaces and shows a preferential adsorption of propane (C 3 H 8 ) over propene (C 3 H 6 ). The efficiency of C 3 H 8 selective Cu‐ASY is validated by multicycle breakthrough experiments, giving C 3 H 6 productivity of 2.2 L/kg. Density functional theory (DFT) calculations reveal that C 3 H 8 molecules form multiple C−H⋅⋅⋅π and atypical C−H⋅⋅⋅ H−C van der Waals interactions with the inner nonpolar surfaces. This work therefore highlights the linker desymmetrization as an encouraging and intriguing strategy for achieving unique MOF structures and properties.

Chemistry↗

DOE OSTI2022

A deep learning-enhanced framework for multiphysics joint inversion

Joint inversion has drawn considerable attention due to the availability of multiple geophysical data sets, ever-increasing computational resources, the development of advanced algorithms, and its ability to reduce inversion uncertainty. A key issue of joint inversion is to develop effective strategies to link different geophysical data in a unified mathematical framework, in which the information obtained from different models can complement each other. We have developed a deep learning-enhanced joint inversion framework to simultaneously reconstruct different physical models by fusing different types of geophysical data. Traditionally, structure similarity constraints are pursued by joint inversion algorithms using manually crafted formulations (e.g., cross gradient). The constraint is constructed by a deep neural network (DNN) during the learning process. The framework is designed to combine the DNN and a traditional independent inversion workflow and improve the joint inversion result iteratively. The network can be easily extended to incorporate multiphysics without structural changes. Numerical experiments on the joint inversion of 2D DC resistivity data and seismic traveltime are used to validate our method. In addition, this learning-based framework demonstrates excellent generalization abilities when tested on data sets using different geologic structures. It also can handle different sensing configurations and nonconforming discretization.

Geochemistry & Geophysics↗