Search NASA⌕ Search

SEARCH · Search NASA

Results for “adaptive materials”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A New Method for Creating Structured High-Performance Current Collectors for Electrochemical Applications

A significant challenge in many electrochemical systems is finding a stable, high-performing current collector material that is mechanically robust, adaptable in form factor, and free of precious metals. Titanium electrodes are robust in many of these regards but exhibit poor charge transfer performance due to self-passivation. Herein, a new materials processing paradigm based on the titanium/titanium nitride (Ti/TiN) system which allows for robust, stable, and low-resistance current collectors of arbitrary form factor is presented. Specifically, a gas-nitriding process for 3D-printed titanium electrodes that results in a 20-fold improvement of charge transfer characteristics relative to the untreated material is outlined. The ability to utilize 3D-structured current collectors with a net 40-fold improvement in performance over nonstructured electrodes is further demonstrated. This novel approach to creating electrochemical current collectors requires minimal laboratory resources and can be widely adapted for a variety of applications, including desalination, electrolysis, energy storage, and basic research. In conclusion, the work described herein provides both a means for accelerating research and opens the door to hierarchical tuneability for enhanced performance.

36 MATERIALS SCIENCE↗
NASA NTRS2019

AladynPi – Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application

This report provides an overview and description of commands used in the Computational Materials mini-application, AladynPi. AladynPi is an extension of a previously released mini-application, Aladyn (https://github.com/nasa/aladyn; Yamakov, V.I., and Glaessgen, E.H., NASA/TM-2018-220104). Aladyn and AladynPi are basic molecular dynamics codes written in FORTRAN 2003, which are designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory method. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. In Aladyn, the ANN gives directly the energy of interatomic interactions. In AladynPi, the ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). The parameterized BOP function is then used to calculate the energy. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing manycore architectures based on graphic processing units (GPUs). The effort is supported by the High Performance Computing incubator (HPCi) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗
NASA NTRS1990

Graphite/epoxy composite adapters for the Space Shuttle/Centaur vehicle

The decision to launch various NASA satellite and Air Force spacecraft from the Space Shuttle created the need for a high-energy upper stage capable of being deployed from the cargo bay. Two redesigned versions of the Centaur vehicle which employed a graphite/epoxy composite material for the forward and aft adapters were selected. Since this was the first time a graphite/epoxy material was used for Centaur major structural components, the development of the adapters was a major effort. An overview of the composite adapter designs, subcomponent design evaluation test results, and composite adapter test results from a full-scale vehicle structural test is presented.

Kasper, Harold J.↗
DOE OSTI2025

Characterizing Disorders Within Cathode Materials of Lithium‐Ion Batteries

The demand for developing high-energy density cathode materials has been increasing. The energy densities of cathode materials have been improved by adapting structural deviation from the ideal fully ordered α-NaFeO2 type, but that led to limitations in terms of structural stability and safety. Although disorders in cathode materials are closely related to their electrochemical properties, unfortunately, characterizing the disorder itself in cathode materials has been challenging due to its complex parasitic reaction and strong correlation with other disorders occurring during charge/discharge. In this review, we categorize various disorders by their scales of ordering from short-range to long-range. We addressed the principles of various characterization tools to figure out how they can help to identify the structural disorder in cathode materials. Specifically, we focused on the underlying principles of each characterization technique to correlate different disorder-driven phenomena through several case studies. It underscores the substantial importance of disorder-property relationships and the corresponding characterization methods, which can provide novel research strategies for developing high-energy density cathode materials with decent structural stability.

Lee, Hakwoo↗
NASA NTRS2009

Perspectives on Highly Adaptive or Morphing Aircraft

The ability to adapt to different flight conditions has been fundamental to aircraft design since the Wright Brothers first flight. Over a hundred years later, unconventional aircraft adaptability, often called aircraft morphing has become a topic of considerable renewed interest. In the past two decades, this interest has been largely fuelled by advancements in multi-functional or smart materials and structures. However, highly adaptive or morphing aircraft is certainly a cross-discipline challenge that stimulates a wide range of design possibilities. This paper will review some of the history of morphing aircraft including recent research programs and discuss some perspectives on this work.

McGowan, Anna-Maria R.↗
NASA NTRS1973

Analysis of temperature and pressure distribution of containers for nuclear waste material disposal in space

A computer program was adapted from a previous generation program to analyze the temperature and internal pressure response of a radioactive nuclear waste material disposal container following impact on the earth. This program considers component melting, LiH dissociation, temperature dependent properties and pressure and container stress response. Analyses were performed for 21 cases with variations in radioactive power level, container geometry, degree of deformation of the container, degree of burial and soil properties. Results indicated that the integrity of SS-316 containers could be maintained with partial burials of either underformed or deformed containers. Results indicated that completely buried waste containers, with power levels above 5 kW, experienced creep stress rupture failures in 4 to 12 days.

Vanbibber, L. E.↗
NASA NTRS2004

A Robust and Engineerable Self-Assembling Protein Template for the Synthesis and Patterning of Ordered Nanoparticle Arrays

Self-assembling biomolecules that form highly ordered structures have attracted interest as potential alternatives to conventional lithographic processes for patterning materials. Here we introduce a general technique for patterning materials on the nanoscale using genetically modified protein cage structures called chaperonins that self-assemble into crystalline templates. Constrained chemical synthesis of transition metal nanoparticles is specific to templates genetically functionalized with poly-Histidine sequences. These arrays of materials are ordered by the nanoscale structure of the crystallized protein. This system may be easily adapted to pattern a variety of materials given the rapidly growing list of peptide sequences selected by screening for specificity for inorganic materials.

McMillan, R. Andrew↗
NASA NTRSDate not supplied

Digitally-Engineered Impact Resistant Aerogel Composites for MMOD Protection (DIRAC-MP)

This project implemented a digital-engineering approach to optimize the impact absorption performance of polymer aerogels and aerogel-based composites for Micrometeoroids and Orbital Debris (MMOD) containment. We developed a curated materials database and a machine-learning framework to derive composition-response relationships, enabling predictive design and targeted material selection. In support of experimental validation, a split Hopkinson pressure bar (SHPB) test rig, specifically adapted for low-density aerogel materials, was designed and built in-house. This project accelerates the development of new aerogel formulations, producing candidate materials tailored for enhanced impact-absorption behavior.

Sadeq Malakooti↗
NASA NTRS1990

Thermomechanical testing techniques for high-temparature composites: TMF behavior of SiC(SCS-6)/Ti-15-3

Thermomechanical testing techniques recently developed for monolithic structural alloys were successfully extended to continuous fiber reinforced composite materials in plate form. The success of this adaptation was verified on a model metal matrix composite (MMC) material, namely SiC(SCS-6)/Ti-15V-3Cr-3Al-3Sn. Effects of heating system type and specimen preparation are also addressed. Cyclic lives determined under full thermo-mechanical conditions were shown to be significantly reduced from those obtained under comparable isothermal and in-phase bi-thermal conditions. Fractography and metallography from specimens subjected to isothermal, out-of-phase and in-phase conditions reveal distinct differences in damage-failure modes. Isothermal metallography revealed extensive matrix cracking associated with fiber damage throughout the entire cross-section of the specimen. Out-of-phase metallography revealed extensive matrix damage associated with minimal (if any) fiber cracking. However, the damage was located exclusively at surface and near-surface locations. In-phase conditions produced extensive fiber cracking throughout the entire cross-section, associated with minimal (if any) matrix damage.

Castelli, Michael G.↗
NASA NTRS1992

Thermomechanical testing of high-temperature composites - Thermomechanical fatigue (TMF) behavior of SiC(SCS-6)/Ti-15-3

Thermomechanical testing techniques recently developed for monolithic structural alloys were successfully extended to continuous fiber reinforced composite materials in plate form. The success of this adaptation was verified on a model metal matrix composite (MMC) material, namely SiC(SCS-6)/Ti-15V-3Cr-3Al-3Sn. Effects of heating system type and specimen preparation are also addressed. Cyclic lives determined under full thermomechanical conditions were shown to be significantly reduced from those obtained under comparable isothermal and in-phase bi-thermal conditions. Fractography and metallography from specimens subjected to isothermal, out-of-phase and in-phase conditions reveal distinct differences in damage-failure modes. Isothermal metallography revealed extensive matrix cracking associated with fiber damage throughout the entire cross-section of the specimen. Out-of-phase metallography revealed extensive matrix damage associated with minimal (if any) fiber cracking. However, the damage was located exclusively at surface and near-surface locations. In-phase conditions produced extensive fiber cracking throughout the entire cross-section, associated with minimal (if any) matrix damage.

Castelli, Michael G.↗
NASA NTRS2000

A Parallel Genetic Algorithm for Automated Electronic Circuit Design

Parallelized versions of genetic algorithms (GAs) are popular primarily for three reasons: the GA is an inherently parallel algorithm, typical GA applications are very compute intensive, and powerful computing platforms, especially Beowulf-style computing clusters, are becoming more affordable and easier to implement. In addition, the low communication bandwidth required allows the use of inexpensive networking hardware such as standard office ethernet. In this paper we describe a parallel GA and its use in automated high-level circuit design. Genetic algorithms are a type of trial-and-error search technique that are guided by principles of Darwinian evolution. Just as the genetic material of two living organisms can intermix to produce offspring that are better adapted to their environment, GAs expose genetic material, frequently strings of 1s and Os, to the forces of artificial evolution: selection, mutation, recombination, etc. GAs start with a pool of randomly-generated candidate solutions which are then tested and scored with respect to their utility. Solutions are then bred by probabilistically selecting high quality parents and recombining their genetic representations to produce offspring solutions. Offspring are typically subjected to a small amount of random mutation. After a pool of offspring is produced, this process iterates until a satisfactory solution is found or an iteration limit is reached. Genetic algorithms have been applied to a wide variety of problems in many fields, including chemistry, biology, and many engineering disciplines. There are many styles of parallelism used in implementing parallel GAs. One such method is called the master-slave or processor farm approach. In this technique, slave nodes are used solely to compute fitness evaluations (the most time consuming part). The master processor collects fitness scores from the nodes and performs the genetic operators (selection, reproduction, variation, etc.). Because of dependency issues in the GA, it is possible to have idle processors. However, as long as the load at each processing node is similar, the processors are kept busy nearly all of the time. In applying GAs to circuit design, a suitable genetic representation 'is that of a circuit-construction program. We discuss one such circuit-construction programming language and show how evolution can generate useful analog circuit designs. This language has the desirable property that virtually all sets of combinations of primitives result in valid circuit graphs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm and circuit simulation software, we present experimental results as applied to three analog filter and two amplifier design tasks. For example, a figure shows an 85 dB amplifier design evolved by our system, and another figure shows the performance of that circuit (gain and frequency response). In all tasks, our system is able to generate circuits that achieve the target specifications.

Long, Jason D.↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
DOE OSTI2024

Targeted Adaptive Design

Modern advanced manufacturing and advanced materials design often require searches of relatively high-dimensional process control parameter spaces for settings that result in optimal structure, property, and performance parameters. The mapping from the former to the latter must be determined from noisy experiments or from expensive simulations. Here, we abstract this problem to a mathematical framework in which an unknown function from a control space to a design space must be ascertained by means of expensive noisy measurements, which locate control settings generating desired design features within specified tolerances, with quantified uncertainty. We describe targeted adaptive design (TAD), a new algorithm that performs this sampling task efficiently. TAD creates a Gaussian process surrogate model of the unknown mapping at each iterative stage, proposing a new batch of control settings to sample experimentally and optimizing the updated expected log-predictive probability density of the target design. TAD either stops upon locating a solution with uncertainties that fit inside the tolerance box or uses a measure of expected future information to determine that the search space has been exhausted with no solution. TAD thus embodies the exploration-exploitation tension in a manner that recalls, but is essentially different from, Bayesian optimization and optimal experimental design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Text Mining for Process–Structure–Properties Relationships in Metals

With the advent of large language models (LLMs), the vast unstructured text within millions of academic papers is increasingly accessible for materials discovery—although significant challenges remain. While LLMs offer promising few- and zero-shot learning capabilities, particularly valuable in the materials domain where expert annotations are scarce, general-purpose LLMs often fail to address key materials-specific queries without further adaptation. To bridge this gap, fine-tuning LLMs on human-labeled data is essential for effective structured knowledge extraction (Liu in The Importance of Human-Labeled Data in the Era of LLMs, 2023). Here, in this study, we introduce a novel annotation schema designed to extract generic process–structure–properties relationships from scientific literature. We demonstrate the utility of this approach using a dataset of 128 abstracts, with annotations drawn from two distinct domains: high-temperature materials (Domain I) and uncertainty quantification in simulating materials microstructure (Domain II). Initially, we developed a conditional random field (CRF) model based on MatBERT—a domain-specific BERT variant—and evaluated its performance on Domain I. Subsequently, we compared this model with a fine-tuned LLM (GPT-4o from OpenAI) under identical conditions. Our results indicate that fine-tuning LLMs can significantly improve entity extraction performance over the BERT-CRF baseline on Domain I. However, when additional examples from Domain II were incorporated, the performance of the BERT-CRF model became comparable to that of the GPT-4o model. These findings underscore the potential of our schema for structured knowledge extraction and highlight the complementary strengths of both modeling approaches.

Materials science↗
DOE OSTI2026

Innovating Distributed Embedded Energy Prize (InDEEP): A Lessons Learned Report

The U.S. Department of Energy's Water Power Technologies Office (WPTO) launched the Innovating Distributed Embedded Energy Prize (InDEEP) in March 2023 to accelerate innovation in Distributed Embedded Energy Conversion Technologies (DEEC-Tec) for ocean wave energy. Administered by the National Laboratory of the Rockies (NLR) with technical support from Sandia National Laboratories (SNL), InDEEP focused on the development of small, distributed, and embeddable energy converters (DEECs) and their integration into scalable DEEC-Tec metamaterials for marine renewable energy applications. Spanning three phases over two years, InDEEP awarded approximately $2.3 million to teams from academia, industry, and startups. Phase I emphasized conceptual design. Phase II moved into the prototyping of individual DEECs. Phase III required integration into functional DEEC-Tec metamaterial prototypes. Across 60 submissions, teams explored a wide range of energy conversion mechanisms - including piezoelectric, variable-capacitance, ionic, and inductive methods. Note, the prize did not include the design nor demonstration of ocean wave energy conversion systems. Rather, the prize only required participants to design and demonstrate individual DEECs and corresponding DEEC-Tec metamaterials. This prize utilized a mix of novel and proven techniques to attract participants from outside marine energy, including an engagement leaderboard, robust recruitment, technical expert mentorship, and a suite of technical trainings. Key insights from the competition emphasized that DEEC-Tec metamaterials must be intentionally designed to produce beneficial emergent behaviors - advantages that go beyond simply combining multiple DEEC units. Top-performing teams showed that thoughtful design of system architecture, coordinated deformation, and systems adaptabilities could unlock meaningful performance gains both at the DEEC system level and DEEC-Tec metamaterial system level. A critical realization was that many DEEC-Tec metamaterials could benefit from being designed to accept lower-frequency energy inputs and shift those into higher-frequencies per each DEEC making up the respective DEEC-Tec metamaterial. Other important takeaways included the need for rigorous and quantitative performance testing, effective integration of power conditioning electronics, and the pivotal role of material science in enabling innovative, adaptive DEEC-Tec-based energy conversion designs. InDEEP also helped establish a growing DEEC-Tec community of practitioners, attracting participants from beyond traditional marine energy sectors. Through a strong support infrastructure, InDEEP fostered early-stage innovation and laid a foundation for future DEEC-Tec-based ocean wave energy conversion solutions - positioning DEEC-Tec as a promising pathway toward scalable, resilient ocean wave energy conversion. Through focused R&D of individual DEECs and their integration into DEEC-Tec metamaterials, alongside a growing, multidisciplinary community catalyzed by InDEEP, there is a strong opportunity to drive a disruptive shift in ocean wave energy conversion design and development. This convergence of novel architectures, emergent behaviors, and collaborative innovation positions DEEC-Tec as a transformative approach, moving the field from rigid, centralized energy conversion-based designs to resilient, modular systems highly adaptable for real-world ocean wave energy conversion applications.

16 TIDAL AND WAVE POWER↗
NASA NTRS1917

Thermodynamic efficiency of present types of internal combustion engines for aircraft

Report presents requirements of internal combustion engines suitable for aircraft. Topics include: (1) service requirements for aeronautic engines - power versus weight, reliability, and adaptability factors, (2) general characteristics of present aero engines, (3) aero engine processes and functions of parts versus power-weight ratio, reliability, and adaptability factors, and (4) general arrangement, form, proportions, and materials of aero parts - power-weight ratio, reliability, and adaptability.

Lucke, Charles E↗
DOE OSTI2024

System and method for computed axial lithography (CAL) for 3D additive manufacturing

In one aspect the present disclosure relates to a system for forming a three dimensional (3D) object from a volume of photo-responsive material contained within a material container. A plurality of optical projection subsystems are adapted to be arranged at least partially circumferentially around a material container containing the volume of photo-responsive material, and are controlled by a controller. The optical projection subsystems direct optical projections at a plurality of angles θ through the volume of photo-responsive material. The optical projections are further directed about a z axis extending through the volume of photo-responsive material. The plurality of optical subsystems are further controlled by the controller to provide each of the optical projections with a calculated three-dimensional intensity distribution acting over a fixed temporal exposure period, which is sufficient to at least one of cure or remove selected portions of the volume of photo-responsive material, and leave other portions unmodified, to form a desired 3D part.

Kelly, Brett↗