Search NASA⌕ Search

SEARCH · Search NASA

Results for “Feature engineering”

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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Hard Ferromagnetism in FePS 3 Induced by Non‐Magnetic Molecular Intercalation

Abstract Manipulating the magnetic ground states of 2D magnets is a focal point of recent research efforts. Various methods have demonstrated efficacy in modulating the magnetic properties inherent to van der Waals (vdW) magnetic systems. Herein, the emergence of robust anisotropic ferromagnetism within antiferromagnetic FePS 3 is unveiled via intercalation with non‐magnetic pyridinium ions. A one‐step ion exchange reaction facilitates the formation of energetically favorable B‐phase and metastable P‐phase. Notably, both B‐ and P‐phases manifest hard ferromagnetic behavior, featuring substantial unsaturated coercive fields (>7 T) and high Curie temperatures (72–87 K). First‐principles calculations elucidate the pivotal role of electron transfer from pyridinium ions to FePS 3 in engineering magnetic exchange interactions. Calculated effective spin Hamiltonian corroborates the observed hard ferromagnetism in intercalated FePS 3 . This study offers crucial insights into hard magnetism in intercalated vdW materials, thereby presenting promising avenues for 2D vdW magnet‐based magnetic devices.

Ou, Yunbo↗
DOE OSTI2025

From PINNs to PIKANs: recent advances in physics-informed machine learning

Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offering a promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Lastly, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.

Kolmogorov-Arnold networks↗
DOE OSTI2025

Angular-spatial hp -adaptivity for radiative transfer with discontinuous Galerkin spectral element methods

Radiative transfer is important for many science and engineering applications, and numerical simulations of radiative transfer can be challenging. For instance, the radiation field is seven-dimensional – three spatial, two angular, one wavelength, and one temporal – and often features steep gradients. Therefore, memory usage is a key issue. To reduce memory, some past work has investigated the use of adaptive mesh refinement (AMR), typically for either the spatial or angular coordinate, and typically for only h -adaptivity. Here, we propose the use of AMR for the spatial and angular coordinates together, and the use of h - and p -adaptivity together as hp -AMR for the potential for further memory savings. We implemented the proposed method for several test cases in two spatial and one angular dimension, with the discontinuous Galerkin spectral element method. These test cases featured highly anisotropic angular radiation, with or without steep spatial gradients. Our primary findings from these test cases were: (1) Angular hp -adaptivity can deliver the radiation solution with the same accuracy as, and with much less computational memory than, uniform angular h - or p -refinements, or angular h -adaptivity alone. This is most obvious when the incoming radiation is highly anisotropic, in which case the savings can be orders of magnitude. (2) Full spatial-angular hp -adaptivity is more efficient in solution representation, compared to solely spatial or solely angular -adaptivity. This is most evident when steep gradients are present in both the spatial and angular distribution. These results suggest that adaptive spatial- hp angular-refinement may perform well in large-scale seven-dimensional applications.

Adaptive refinement↗
DOE OSTI2025

GRinding Automated Classification Engine

This work is an ML-driven framework for automated surface analysis of microscopy images. We create a training dataset by imaging stainless steel samples to benchmark four developed deep neural network architectures. These models, based on a YOLOv8n-cls backend, integrate image features and process metadata using various fusion methods to distinguish between acceptable and unacceptable surface finishes. This code is associated with publication "Classifying Alloy Surface Preparation Quality with Metadata-Infused Machine Learning for Rapid Alloy Discovery" for project APEX LDRD-ER (25-ERD-039)

Gongora, AldairE [Lawrence Livermore National Labo↗
DOE OSTI2025

Optical neural engine for solving scientific partial differential equations

Abstract Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate time-consuming and computation-intensive numerical simulations of PDEs. Although optical systems offer high-throughput and energy-efficient ML hardware, their demonstration for solving PDEs is limited. Here, we present an optical neural engine (ONE) architecture combining diffractive optical neural networks for Fourier space processing and optical crossbar structures for real space processing to solve time-dependent and time-independent PDEs in diverse disciplines, including Darcy flow equation, the magnetostatic Poisson’s equation in demagnetization, the Navier-Stokes equation in incompressible fluid, Maxwell’s equations in nanophotonic metasurfaces, and coupled PDEs in a multiphysics system. We numerically and experimentally demonstrate the capability of the ONE architecture, which not only leverages the advantages of high-performance dual-space processing for outperforming traditional PDE solvers and being comparable with state-of-the-art ML models but also can be implemented using optical computing hardware with unique features of low-energy and highly parallel constant-time processing irrespective of model scales and real-time reconfigurability for tackling multiple tasks with the same architecture. The demonstrated architecture offers a versatile and powerful platform for large-scale scientific and engineering computations.

Tang, Yingheng (ORCID:0009000153622546)↗
DOE OSTI2025

Final design of outer poloidal field coils for COMPASS Upgrade

The paper aims to provide an overview of the final design of the outer poloidal field coils system for COMPASS Upgrade. Due to a relatively unique combination of design features of the new device (placement in high toroidal magnetic field, copper coils sub-cooled to liquid nitrogen temperature, hot vacuum vessel operation), the poloidal field coils design faced several interesting engineering challenges. Most notably high electromagnetic stress in the coil conductor, compatibility of the insulation to thermal and mechanical stress and design of coil supports with given spatial constraints while able to accommodate coil movements and withstand considerable mechanical stress. In conclusion, the paper describes solution to these challenges together with underlying analyses and mechanical tests.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Conceptual design of a replacement 2.1 K cold box for the Spallation Neutron Source Central Helium Liquefier

Abstract After more than 20 years of operation, the cold compressor technology in use at the Spallation Neutron Source (SNS) is obsolete, and replacement parts and service are no longer available. SNS has partnered with Jefferson Lab to design and construct a replacement sub-atmospheric cold box outfitted with modern cold compressor technology. The general design follows from Jefferson Lab’s experience on other recent sub-atmospheric cold box projects. Design decisions are backed by thorough engineering analysis to ensure technical requirements are met in a cost-effective manner. Conceptual design of the replacement cold box has been completed, and will be summarized in this paper. It features five cold compressors with an operating flow range of 80-140 g/s, and includes piping and valving to support cold compressor maintenance without interrupting flow circulation to the load. The approach to thermal shielding, insulation, and integration of the upgraded cold compressor hardware into the existing SNS control system will also be addressed.

Mastracci, B [Thomas Jefferson National Accelerato↗
DOE OSTI2026

Bioengineered algal lipids enriched in structured medium- and long-chain triacylglycerols, linoleate, and sn -2 palmitate for human milk fat substitutes

Human milk fat (HMF) contains triacylglycerol (TAG) as its primary component, providing over 50% of the calories for infant nutrition, along with structural and bioactive lipids that are important for immune and nervous system development. Palmitic acid, comprising 20-25% of the fatty acid complement of HMF, is predominantly esterified to the sn -2 position on the glycerol backbone. This regiospecific positioning facilitates absorption as 2-palmitoyl-monoacylglycerol after hydrolysis of the fatty acids at sn -1 and sn -2 by gut lipases. Other features of HMF include enrichment in structured medium- and long-chain triglycerides (MLCTs), and variation in the ratio of oleic acid to linoleic acid with maternal diet and geography. We have engineered Auxenochlorella, an oleaginous green alga, for biosynthesis of an MLCT- and sn -2 palmitate-enriched HMF substitute for infant formula, matching the regioisomeric composition and proportions of the most abundant fatty acids in HMF.

Lin, Jon Y-T [University of California, Berkeley;]↗
DOE OSTI2024

TRUST Sensors in Environments: Fiber Optic Displacement (SE-FOD) Report, Release FY24

The objective of the Delivery Environments (DE) Testbeds to Reduce Uncertainty in Simulations and Tests (TRUST) project is to quantify and help increase confidence in specific areas of computational and experimental capabilities that are applicable to the development, on-target assessment, and qualification of current and future delivery environments. More complete quantification of confidence in experimental and computational capabilities and the sufficient increase of confidence in those capabilities is critical to improving weapons engineering design, qualification, and assessment efforts that are critical to the current and future stockpile. This work uses and provides feedback on analysis tools and experimental results databases for efficient and responsive engineering which are currently under development. Under the TRUST work package, there are five testbeds and their associated engineering analysis baseline models (EABMs): 1. Contact Thermal Conductivity (CTC), 2. Nonlinear Dynamics (ND), 3. Sensors in Environments: Accelerometers (SE-A), 4. Sensors in Environments: Fiber Optic Displacement Gauges (SE-FOD), and 5. Sensors in Environments: Thermocouples (SE-TC). The TRUST project uses single-feature testbeds to quantify uncertainties in specific models and experiments and to identify capability development needs that can help to reduce these uncertainties. Each testbed is designed, configured, and tested in collaboration with groups with design and experimental capability: E-14 and MPA-CINT. The complementary simulations are conducted using W-13 analysis tools and stored in model repositories with plans for incremental progress toward EABM requirements. W-13 extends and exercises the testbed simulations in collaboration with experimentalists for uncertainty quantification of current and future materials, geometries, and environments. Additionally, TRUST is intended to provide engineers in W-13 and E-14 with experience in both numerical simulations and experimental methods through cross-discipline collaborations. Following the introduction to the TRUST project, the remainder of this report focuses on experimental, modeling and simulation, and analysis efforts conducted in FY24 relevant to the TRUST Sensors in Environments: Fiber Optic Displacement (SE-FOD) testbed.

42 ENGINEERING↗
DOE OSTI2025

Nuclear Materials Packaging, Transportation, and Systems Analysis Group Software Quality Assurance Plan: ANSYS Mechanical Finite Element Analysis Software Version 2023R1

ANSYS Inc. develops and markets engineering simulation software and services used in the aerospace, automotive, manufacturing, electronics, biomedical, energy, defense, and many other industries. ANSYS is dedicated to engineering simulation and is the world’s leading software provider. ANSYS was founded in 1970 and is headquartered in Canonsburg, Pennsylvania. ANSYS provides an engineering analysis tool combining structural, thermal, computational fluid dynamics, acoustic, and electromagnetic simulation capabilities. ANSYS has two main programs, which use the same solvers: (1) Mechanical APDL (ANSYS Design Parametric Language), a Fortran-based coding platform, and (2) ANSYS Workbench, which uses a graphical user interface to aid in finite element analysis implementation. This plan covers both APDL and Workbench. The ANSYS computer program is a large-scale, multipurpose finite element program that can be used to solve several classes of engineering analyses. The analysis capabilities of ANSYS include the ability to solve static and dynamic structural analyses, steady-state and transient heat transfer problems, mode-frequency and buckling eigenvalue problems, static or time-varying magnetic analyses, and various types of field and coupled-field applications. The program contains many special features that allow nonlinearities or secondary effects such as plasticity, large strain, hyperelasticity, creep, swelling, large deflections, contact, stress stiffening, temperature dependency, material anisotropy, and radiation to be included in the solution. As ANSYS has been developed, other special capabilities such as substructuring, submodeling, random vibration, kinetostatics, kinetodynamics, free convection fluid analysis, acoustics, magnetics, piezoelectrics, coupled-field analysis, and design optimization have been added to the program. These capabilities contribute further to making ANSYS a multipurpose analysis tool for varied engineering disciplines. The ANSYS program has been in commercial use for over 50 years and has been used extensively in the aerospace, automotive, construction, electronic, energy services, manufacturing, nuclear, plastics, oil, and steel industries. Additionally, many consulting firms and hundreds of universities have used ANSYS for analysis, research, and educational purposes. ANSYS is recognized worldwide as one of the most widely used and capable programs of its type. Ansys design analysis software is the first created within a quality system with ISO 9001 certification, the internationally accepted quality standard. Product development, testing, maintenance and support processes also meet the United States Nuclear Regulatory Commission's quality requirements, as they have for nearly four decades. The Quality Assurance Service Agreement is suitable for the customers working in the nuclear industry who need to meet specific federal regulations including 10CRF50 Appendix B and provisions of 10CFR21. ANSYS has retained its original International Organization for Standardization (ISO) 9001 accreditation certificate since1995-05-04, It’s current certificate is valid until 2027-05-29.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Feature Based Qualification of 17-4PH Stainless Steel to Evaluate Location-Specific Variability in Wire Arc Additive Manufacturing

Qualifying large-scale metal additive manufacturing (M-AM) technologies such as wire arc additive manufacturing (WAAM) can be challenging. This is especially significant in precipitation hardened martensitic stainless steels like SS 17-4PH, where thermal histories induce location-specific microstructural variability and property anisotropy. The Department of Defense (DOD) and the United States Army Combat Capabilities Development Command Ground Vehicle Systems Center (GVSC) Ground Vehicle Materials Engineering (GVME) aim to build robust and qualified large-scale M-AM workflows that could reduce the time and cost through quick and informed evaluation, testing, and development of feedstock, processes, and parts. The report presents the findings from the collaborative efforts between Oak Ridge National Laboratory (ORNL) and the U.S. Army GVSC GVME. The aim of this project was to develop a geometric feature-based qualification framework for WAAM of SS 17-4PH components. This report outlines selection methodology of representative build geometries, optimization of WAAM process parameters, in-situ monitoring, microstructure-property evaluation, thermal simulations, as well as data visualization techniques incorporated in this project. The results from this project demonstrate a clear understanding of thermal history dependent phase evolution and consequent location-specific property variations in WAAM of SS 17-4PH. These results in conjunction with the data-driven methodologies used in this project are expected to reduce qualification timelines, improve predictability, and accelerate the development of reliable feature-based qualification strategies for part production via large-scale M-AM technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Anomalous superconductivity in twisted MoTe 2 nanojunctions

Introducing superconductivity in topological materials can lead to innovative electronic phases and device functionalities. Here, we present a unique strategy for quantum engineering of superconducting junctions in moiré materials through direct, on-chip, and fully encapsulated 2D crystal growth. We achieve robust and designable superconductivity in Pd-metalized twisted bilayer molybdenum ditelluride (MoTe 2 ) and observe anomalous superconducting effects in high-quality junctions across ~20 moiré cells. Unexpectedly, the junction develops enhanced, instead of weakened, superconducting behaviors, exhibiting fluctuations to a higher critical magnetic field compared to its adjacent Pd 7 MoTe 2 superconductor. In addition, the critical current further exhibits a notable V-shaped minimum at zero magnetic field. These features are unexpected in conventional Josephson junctions and absent in junctions of natural bilayer MoTe 2 created using the same approach. We discuss implications of these observations, including the possible formation of mixed even- and odd-parity superconductivity at the moiré junctions. Our results also demonstrate a pathway to engineer and investigate superconductivity in fractional Chern insulators.

Science & Technology - Other Topics↗
DOE OSTI2026

Circularity in Sequence-Controlled Copolyamides Enabled by Regioselective Enzymatic Hydrolysis

Sequence-controlled polymers enable precise control over macromolecular structures and function, but both their synthesis and end-of-life management remain fundamental challenges. Achieving high sequence fidelity is synthetically demanding, and conventional depolymerization methods lack regioselectivity, leading to irreversible loss of encoded molecular information and limiting polymer circularity. Enzymatic catalysis offers a potential solution by combining substrate specificity with selective bond cleavage. Here, we report the synthesis, characterization, and regioselective enzymatic depolymerization of poly- (X,AMA), a sequence-controlled copolyamide composed of alternating hexamethylenediamine−adipic acid (MA) and pxylylenediamine− adipic acid (XA) repeat units. Poly(X,AMA) was synthesized via solid-state polycondensation (SSP) of sequence-defined oligomers, enabling precise control over repeat-unit order. Polymer microstructure and sequence fidelity were confirmed by 13 C NMR spectroscopy and MALDI−TOF mass spectrometry. Comparison with a statistical copolymer analogue and Nylon-66 demonstrated pronounced differences in crystallinity, morphology, and thermal behavior arising from sequence control. Screening of 96 Nylon hydrolase homologues against poly(X,AMA) revealed strongly enzyme-dependent depolymerization profiles. While tetrad formation was generally favored, enzymes displayed pronounced sequence selectivity, preferentially releasing distinct sequence-defined tetrads XAMA or MAXA. SSP of sequence-defined tetrad MAXA produced a copolyamide with near identical monomer ordering as poly(X,AMA). Computational modeling of enzyme−substrate complexes identified structural features consistent with the observed regioselectivity. Together, these results establish selective enzymatic depolymerization as a viable strategy for the circular recycling of sequence-controlled polymers and provide a foundation for the rational engineering of enzymes for programmable polymer deconstruction.

Amides↗
DOE OSTI2025

hashin_shtrikman_mp: a package for the optimal design and discovery of multi-phase composite materials

hashin_shtrikman_mp is a tool for composites designers who have desired composite properties in mind, but who do not yet have an underlying formulation. The library utilizes the tightest theoretical bounds on the effective properties of composite materials with unspecified microstructure – the Hashin-Shtrikman bounds – to identify candidate theoretical materials, find real materials that are close to the candidates, and determine the optimal volume fractions for each of the constituents in the resulting composite. Its features include (i) leveraging of materials in the Materials Project database, (ii) integration with the Materials Project API, (iii) use of genetic machine-learning, (iv) agnosticism to underlying microstructure, and (v) ultimate engineering application, make it a tool with much broader applications than its predecessors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Zero Power Reactor Database (ZPRD) Development Plan

Past sodium-cooled fast reactors (SFR) were built with an active experimental program in place to support the design and development work. Most of the experimental facilities in the United States that were important for SFR design were shutdown in the 1980s and 1990s. Reactor licensing and construction requires any reactor design to be verified against existing reactor facilities or experimental measurements. With the absence of those experimental facilities, modern SFR projects must rely on historical measurements to demonstrate that the engineering modeling software and data being used for the new reactor design work are reliable. There has been a considerable push in the last 6 years by both DOE and commercial companies to obtain historical experimental measurements that are relevant for SFRs, in particular those with features that are important for the new reactor designs of interest. The zero power reactor experiments carried out at Argonne National Laboratory’s critical facilities (ZPR-3, ZPR-6, ZPR-9, and ZPPR) from the 1950s to the 1980s are some of the best reactor physics experiments on SFR technology that are available today. Of particular interest today are the ZPPR-15 measurements done at the ZPPR facility for the Integral Fast Reactor project in the 1980s as they are in line with most commercial and DOE interests today. In the past 10 years, the measurements done on ZPPR-15 have been processed into both Monte Carlo (MCNP) and deterministic models (MC2-3 and DIF3D) useable for validating the engineering modeling software for key parts of the SFR design work. To achieve this, a detailed model description must be created for the experiment and the experimental measurement that the engineering modeling software is to reproduce. Then, an assessment of the uncertainty on the measured quantity which considers all of the sources of uncertainty in defining the model must be obtained and documented. The models created for ZPPR-15 provide the best validation basis available today for neutronics modeling software. Reference 2 is a good resource to understand how these models were built and how the uncertainties on the measured quantities were derived. The intention of the Zero Power Reactor Database (ZPRD), hosted at frdb.ne.anl.gov, is to make available the experimental measurements and models that have been constructed to-date. Though ZPPR-15 measurements are the primary data requested for validation needs, other measurements on ZPPR, ZPR-6, and ZPR-9 in support of the Clinch River Breeder Reactor (CRBR) and Fast Test Reactor (FFTF) should also be considered important for future software validation needs. In this manuscript, the details of available measurements on ZPR-3, ZPR-6, ZPR-9, and ZPPR facilities are summarized, and a general organization of the web interface is displayed. Many of the documents associated with the measurements are export controlled information so access to the database will also have to be controlled.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Library of Advanced Materials for Engineering (LAMÉ) 5.20

Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAMÉ advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options and flexibility, however, comes at the cost of many capabilities, features, and responses and the ensuing complexity in the resulting implementation. Therefore, to enhance confidence and enable the utilization of the LAMÉ library in application, this effort seeks to document and verify the various models in the LAMÉ library. Specifically, the broader strategy, organization, and interface of the library itself is first presented. The physical theory, numerical implementation, and user guide for a large set of models is then discussed. Importantly, a number of verification tests are performed with each model to not only have confidence in the model itself but also highlight some important response characteristics and features that may be of interest to end-users. Finally, in looking ahead to the future, approaches to add material models to this library and further expand the capabilities are presented.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Biomanufacturing from gaseous C1 feedstocks: A perspective on opportunities and challenges

Single-carbon (C1) substrates including carbon dioxide, carbon monoxide, and methane are abundantly available from natural and anthropogenic sources and present potential feedstocks for biomanufacturing. Utilizing these C1 gas feedstocks in bioprocesses for sustainable production of chemicals and fuels could prove pivotal in removing excess carbon from the atmosphere. This perspective describes the spectrum and sources of CO2, CO, and CH4 and examines emerging opportunities in microbial bioconversion and bioelectrochemical processes for these feedstocks. We discuss existing challenges in bioprocess development that currently restrict the commercialization of C1 biomanufacturing technologies. We detail different aerobic and anaerobic bioconversion approaches for C1 feedstocks employing pure and mixed cultures and examine the suitability of each scenario for producing specific molecules. Beyond strain engineering and bioprocess constraints, we address often overlooked factors that limit the development of efficient and reliable bioprocesses, including technology availability for research and safety considerations. We then discuss and recommend the necessary safety features and technological research tools for developing fast, safe, and efficient bioprocesses using gaseous feedstocks to support the scale-up and commercialization of C1 biomanufacturing technologies. This perspective provides an overview of the current scientific and industrial state of the art and offers insights into future technological needs that must be addressed to realize the potential of biomanufacturing from gaseous feedstocks. Synopsis: C1 gases offer a sustainable feedstock for biomanufacturing of fuels and chemicals. This work analyzes bioconversion methods, challenges, and safety considerations, and emphasizes the need for improved technology to enable commercialization.

Biomanufacturing↗
DOE OSTI2025

State-dependent motion of a genetically encoded fluorescent biosensor

Genetically encoded biosensors can measure biochemical properties such as small-molecule concentrations with single-cell resolution, even in vivo. Despite their utility, these sensors are “black boxes”: Very little is known about the structures of their low- and high-fluorescence states or what features are required to transition between them. We used LiLac, a lactate biosensor with a quantitative fluorescence-lifetime readout, as a model system to address these questions. X-ray crystal structures and engineered high-affinity metal bridges demonstrate that LiLac exhibits a large interdomain twist motion that pulls the fluorescent protein away from a “sealed,” high-lifetime state in the absence of lactate to a “cracked,” low-lifetime state in its presence. Understanding the structures and dynamics of LiLac will help to think about and engineer other fluorescent biosensors.

Rosen, Paul C. (ORCID:000000017414454X)↗