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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Getting a Cohesive Answer from a Common Start: Scalable Multidisciplinary Analysis through Transformation of a Systems Model

One of the challenges of systems engineering is in working multidisciplinary problems in a cohesive manner. When planning analysis of these problems, system engineers must trade between time and cost for analysis quality and quantity. The quality often correlates with greater run time in multidisciplinary models and the quantity is associated with the number of alternatives that can be analyzed. The trade-off is due to the resource intensive process of creating a cohesive multidisciplinary systems model and analysis. Furthermore, reuse or extension of the models used in one stage of a product life cycle for another is a major challenge. Recent developments have enabled a much less resource-intensive and more rigorous approach than hand-written translation scripts between multi-disciplinary models and their analyses. The key is to work from a core systems model defined in a MOF-based language such as SysML and in leveraging the emerging tool ecosystem, such as Query/View/Transformation (QVT), from the OMG community. SysML was designed to model multidisciplinary systems. The QVT standard was designed to transform SysML models into other models, including those leveraged by engineering analyses. The Europa Habitability Mission (EHM) team has begun to exploit these capabilities. In one case, a Matlab/Simulink model is generated on the fly from a system description for power analysis written in SysML. In a more general case, symbolic analysis (supported by Wolfram Mathematica) is coordinated by data objects transformed from the systems model, enabling extremely flexible and powerful design exploration and analytical investigations of expected system performance.

Operational QVT↗
NASA NTRS2013

A DC Transformer

A component level dc transformer is described in which no alternating currents or voltages are present. It operates by combining features of a homopolar motor and a homopolar generator, both de devices, such that the output voltage of a de power supply can be stepped up (or down) with a corresponding step down (or up) in current. The basic theory for this device is developed, performance predictions are made, and the results from a small prototype are presented. Based on demonstrated technology in the literature, this de transformer should be scalable to low megawatt levels, but it is more suited to high current than high voltage applications. Significant development would be required before it could achieve the kilovolt levels needed for de power transmission.

Youngquist, Robert C.↗
NASA NTRS2013

Environmental Monitoring of Microbe Metabolic Transformation

Mobile system and method for monitoring environmental parameters involved in growth or metabolic transformation of algae in a liquid. Each of one or more mobile apparati, suspended or partly or wholly submerged in the liquid, includes at least first and second environmental sensors that sense and transmit distinct first and second environmental, growth or transformation parameter values, such as liquid temperature, temperature of gas adjacent to and above the exposed surface, liquid pH, liquid salinity, liquid turbidity, O.sub.2 dissolved in the liquid, CO.sub.2 contained in the liquid, oxidization and reduction potential of the liquid, nutrient concentrations in the liquid, nitrate concentration in the liquid, ammonium concentration in the liquid, bicarbonate concentration in the liquid, phosphate concentration in the liquid, light intensity at the liquid surface, electrical conductivity of the liquid, and a parameter.alpha.(alga) associated with growth stage of the alga, using PAM fluorometry or other suitable parameter measurements.

Bebout, Brad↗
NASA NTRS2015

Computational Modeling and Experimental Characterization of Martensitic Transformations in Nicoal for Self-Sensing Materials

Fundamental changes to aero-vehicle management require the utilization of automated health monitoring of vehicle structural components. A novel method is the use of self-sensing materials, which contain embedded sensory particles (SP). SPs are micron-sized pieces of shape-memory alloy that undergo transformation when the local strain reaches a prescribed threshold. The transformation is a result of a spontaneous rearrangement of the atoms in the crystal lattice under intensified stress near damaged locations, generating acoustic waves of a specific spectrum that can be detected by a suitably placed sensor. The sensitivity of the method depends on the strength of the emitted signal and its propagation through the material. To study the transition behavior of the sensory particle inside a metal matrix under load, a simulation approach based on a coupled atomistic-continuum model is used. The simulation results indicate a strong dependence of the particle's pseudoelastic response on its crystallographic orientation with respect to the loading direction and suggest possible ways of optimizing particle sensitivity. The technology of embedded sensory particles will serve as the key element in an autonomous structural health monitoring system that will constantly monitor for damage initiation in service, which will enable quick detection of unforeseen damage initiation in real-time and during onground inspections.

Wallace, T. A.↗
NASA NTRS2016

Cu-Al-Ni Shape Memory Single Crystal Wires with High Transformation Temperature

CN-250X is a new material with higher performance than Nickel-Titanium Shape Memory Alloy (SMA). For space mechanisms, the main disadvantage of Nickel-Titanium Shape Memory Alloy is the limited transformation temperature. The new CN-250X Nimesis alloy is a Cu-Al-Ni single crystal wire available in large quantity because of a new industrial process. The triggering of actuators made with this Cu-Al-Ni single crystal wire can range from ambient temperature to 200 C in cycling and even to 250 C in one-shot mode. Another advantage of CN-250X is a better shape recovery (8 to 10%) than Ni-Ti (6 to 7%). Nimesis is the first company able to produce this type of material with its new special industrial process. A characterization study is presented in this work, including the two main solicitation modes for this material: tensile and torsion. Different tests measure the shape recovery of Cu-Al-Ni single crystals wires during heating from room temperature to a temperature higher than temperature of end of martensitic transformation.

Hautcoeur, Alain↗
NASA NTRS2016

Transformational Autonomy and Personal Transportation: Synergies and Differences Between Cars and Planes

Highly automated cars have undergone tremendous investment and progress over the past ten years with speculation about fully-driverless cars within the foreseeable, or even near future, becoming common. If a driverless future is realized, what might be the impact on personal aviation? Would self-piloting airplanes be a relatively simple spin-off, possibly making travel by personal aircraft also commonplace? What if the technology for completely removing human drivers turns out to be further in the future rather than sooner; would such a delay suggest that transformational personal aviation is also somewhere over the horizon or can transformation be achieved with less than full automation? This paper presents a preliminary exploration of these questions by comparing the operational, functional, and implementation requirements and constraints of cars and small aircraft for on-demand mobility. In general, we predict that the mission management and perception requirements of self-piloting aircraft differ significantly from self-driving cars and requires the development of aviation specific technologies. We also predict that the highly-reliable control and system automation technology developed for conditionally and highly automated cars can have a significant beneficial effect on personal aviation, even if full automation is not immediately feasible.

Goodrich, Kenneth H.↗
NASA NTRS2017

Transformers for Lunar Extreme Environments: Ensuring Long-Term Operations in Regions of Darkness and Low Temperatures: II - Phase

This report shows how solar power could enable robotic operations in permanently shaded regions at lunar poles, to extract water ice and further produce liquid hydrogen and oxygen (LH2/LO2) propellant. The power needs are derived from an Architecture for Human Exploration of Mars based entirely on lunar propellant. The extraction of 10 metric tons of water per day (at 10% water in regolith) requires approx. 0.6 MW thermal power. Additional approx. 2 MW electric power are required to produce 7.5 metric tons of LH2/LO2 propellant per day, as needed by the architecture. To provide power to processing equipment inside Shackleton Crater, optimal locations are determined on the crater rim, from which several reflecting TransFormers (TFs) would redirect sunlight, achieving a combined period of illumination of approx. 99% of the year. A single 40-m diameter reflector could provide up to 1 MW solar power. Inflatable rigidizable tower support structures raise reflectors above ground for better solar exposure. There are trade-offs: e.g., two reflectors at ground level would provide the same combined total illumination as a single tower approx. 100-m tall. Such a TF based on a 100-m tower made with inflatable 2-m beams and 40-m diameter reflectors would be of similar dimensions as an MSL-class rover (approx. 1000 kg, 10 m(exp 3)). A TF-prospector rover combo could be designed and deployed in a Discovery-class mission searching for water. The TransFormers would be nodes of a Lunar Utilities Infrastructure that provides solar power year-round in the proximity of the pole, as well as local data transmission andintermittent direct to earth communications. This infrastructure would be instrumental infacilitating the development of a lunar economy.

fuel↗
NASA NTRS2019

NASA's Digital Transformation (DT) Initiative

NASA's Digital Transformation (DT) Initiative will dramatically enhance NASA's mission impact by reinventing mission and mission support processes, products, and capabilities, enabled by an innovation culture, digital-savvy workforce, and advanced digital technologies, building on a foundation of modern data management and IT security. This presentation, as part of a conference panel, provides an overview of NASA's DT Initiative, and describes how the Initiative supports an "antidisciplinary" future, where the greatest mission transformation opportunities will be found at the intersections and mergers of multiple disciplines.

Biegel, Bryan↗
NASA NTRS2021

Expanding the application of soil moisture monitoring systems through regression-based transformation

Relative to other geophysical variables, soil moisture (SM) estimates derived from land surface models (LSMs) and land data assimilation systems (LDAS) are difficult to transfer between platforms and applications. This difficulty stems from the highly model-dependent nature of LSM SM estimates and differences in the vertical support of discretized SM values. As a result, operational SM estimates generated by one LSM (or LDAS) cannot generally be directly applied to a hydrologic monitoring or forecast system designed around a second LSM. This lack of transferability is particularly problematic for LDAS applications, where the time, expertise, and computational resources required to generate an operational LDAS analysis cannot be practically duplicated for every LSM-specific application. Here, we develop a set of simple regression tools for translating SM estimates between LSMs and multiple LDAS analyses. Results demonstrate that simple multivariate linear regression - utilizing independent variables based on multi-layer and temporally lagged SM estimates - can significantly improve upon baseline transformation approaches using direct percentile matching. The proposed regression approaches are effective for both the LSM-to-LSM and LDAS-to-LDAS transformation of multi-layer SM percentiles. Application of this approach will expand the utility of existing, high-quality (but LSM-specific) operational sources of SM information like the NASA Soil Moisture Active Passive Level-4 Soil Moisture product.

Soil Moisture↗
NASA NTRS2021

Utilizing local phase transformation strengthening for nickel-based superalloys

Almost 75 years of research has been devoted to producing superalloys capable of higher operating temperatures in jet turbine engines, and there is an ongoing need to increase operating temperature further. Here, a new disk Nickel-base superalloy is designed to take advantage of strengthening atomic-scale dynamic complexions. This local phase transformation strengthening provides the alloy with a three times improvement in creep strength over similar disk superalloys and comparable strength to a single crystal blade alloy at 760 °C. Ultra-high-resolution chemical mapping reveals that the improvement in creep strength is a result of atomic-scale η (D024) and χ (D019) formation along superlattice stacking faults. To understand these results, the energy differences between the L12 and competing D024 and D019 stacking fault structures and their dependence on composition are computed by density functional theory. This study can help guide researchers to further optimize local phase transformation strengthening mechanisms for alloy development.

Timothy M. Smith↗
NASA NTRS2023

Microstructural Changes and Pb Mobility During the Zircon to Reidite Transformation: Implications for Planetary Impact Chronology

Impact events modify and leave behind a complex history of rock metamorphism on terrestrial planets. Evidence for an impact event may be recorded in physical changes to minerals, such as mineral deformation and formation of high P-T polymorphs, but also in the form of chemical fingerprints, such as enhanced elemental diffusion and isotopic mixing. Here we explore laboratory shock-induced physical and chemical changes to zircon and feldspar, the former of which is of interest because its trace elements abundances and isotope ratios are used extensively in geochemistry and geochronology. To this end, a granular mixture of Bishop Tuff sanidine and Kuehl Lake zircon, both with well characterized Pb isotope compositions, was prepared and then shocked via a flat plate accelerator. The peak pressure of the experiment, as calculated by the impedance matching method, was ~24 GPa although a broader range of P-T conditions is anticipated due to starting sample porosity. Unshocked and shocked materials were characterized via scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and Raman spectroscopy. These methods show that the starting zircon material had abundant metamict regions, and the conversion of the feldspar to glass in the post-shock material. Analyses of the shocked product also yielded multiple occurrences of the high-pressure ZrSiO 4 polymorph reidite, with some domains up to 300 μm across. The possibility of U-Pb system disturbance was evaluated via laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) and secondary ion mass spectrometry (SIMS). The isotopic data reveal that disturbance of the U-Pb geochronometer in the reidite was minimal (<2% for the main U-Pb geochronometers). To better constrain the P-T conditions during the shock experiment, we complement impedance matching pressure calculations with iSALE2D impact simulations. The simulated results yield a range of P-T conditions experienced during the experiment and show that much of the sample may have reached >30 GPa, which is consistent with formation of reidite. In the recovered shocked material, we identified lamellae of reidite, some of which interlock with zircon lamellae. Reidite {112} twins were identified, which we interpret to have formed to reduce stress between the crystal structure of the host zircon and reidite. These two findings support the interpretation that shear transformation enabled the transition of zircon to reidite. The size and presence of reidite found here indicate that this phase is probably common in impact-shocked crustal rocks that experienced ~25 to ~35 GPa, especially when the target material has porosity. Additionally, shock loading of the zircon and transformation to reidite at these pressures in porous materials is unlikely to significantly disturb the U-Pb system in zircon and that the reidite inherits the primary U and Pb elemental and isotopic ratios from the zircon.

Bishop Tuff↗
NASA NTRS2024

How the INCOSE Model-Based Capability Matrix Has Steered Model-Based Systems Engineering Transformation at NASA

The National Aeronautics and Space Administration (NASA) is embarking on new, complex, and diverse missions to accomplish its scientific and exploration objectives, and it views digital transformation as a key enabler for those missions. The NASA Model-Based Systems Engineering (MBSE) Lead-ership Team (MLT) is leading the charge in the digital transformation of the systems engineering domain at NASA, and it is using the INCOSE Model-Based Capability Matrix (MBCM) as a roadmap. This paper discusses the modifications and tailoring of the INCOSE MBCM (Hale & Hoheb, 2020) for use at NASA, the process the team has taken on multiple rounds of assessment, findings to date, and work products that have been generated as a result of the assessment. The paper will also discuss findings and potential changes that should be made to the original product.

MBSE↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗
DOE OSTI2024

Human Machine Interface and Controls Development for a Medium Voltage Solid State Transformer

The demand for a more capable electric power grid has increased due to greater energy generation and consumption. • Controlling energy flow bidirectionally with new technologies such as solid state transformers can meet this demand, improving power quality, efficiency, and grid security. • A human machine interface was created to control a medium voltage solid state transformer.

Rafferty, Ezekiel↗
DOE OSTI2024

Transformer with integral inductor

In accordance with one embodiment is a transformer with a core comprising a perimeter portion and central intervening portion. The central intervening portion is separated from the perimeter portion by air gaps, creating an opening on either side of the intervening portion. A primary winding and secondary winding are wound around the central intervening portion of the core. The primary winding is capable of electromagnetic interaction with the secondary winding. A pair of ferrite members arranged outward from a central axis of the central intervening portion of the core and increases a series inductance with the primary winding. In accordance with another aspect of the disclosure, each ferrite member may have an air gap associated with the core to facilitate heat dissipation from the transformer.

Rastogi, Sagar K.↗
DOE OSTI2025

SHF: Symmetrical Hierarchical Forest with Pretrained Vision Transformer Encoder for High-Resolution Medical Segmentation

This paper presents a novel approach to addressing the long-sequence problem in high-resolution medical images for Vision Transformers (ViTs). Using smaller patches as tokens can enhance ViT performance, but quadratically increases computation and memory requirements. Therefore, the common practice for applying ViTs to high-resolution images is either to: (a) employ complex sub-quadratic attention schemes or (b) use large to medium-sized patches and rely on additional mechanisms within the model to capture the spatial hierarchy of details. We propose Symmetrical Hierarchical Forest (SHF), a lightweight approach that adaptively patches the input image to increase token information density and encode hierarchical spatial structures into the input embedding. We then apply a reverse depatching scheme to the output embeddings of the transformer encoder, eliminating the need for convolution-based decoders. Unlike previous methods that modify attention mechanisms or use a complex hierarchy of interacting models, SHF can be retrofitted to any ViT model to allow it to learn the hierarchical structure of details in high-resolution images without requiring architectural changes. Experimental results demonstrate significant gains in computational efficiency and performance: on the PAIP WSI dataset, we achieved a 3∼32×speedup or a 2.95%∼7.03% increase in accuracy (measured by Dice score) at a 64K2 resolution with the same computational budget, compared to state-of-the-art production models. On the 3D medical datasets BTCV and KiTS, training was 6×faster, with accuracy gains of 6.93% and 5.9%, respectively, compared to models without SHF.

Zhang, Enzhi [Hokkaido University, Japan]↗
DOE OSTI2026

Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging

Real-time jet tagging is critical for identifying short-lived particle decays in the high-throughput detectors of the Large Hadron Collider, where real-time trigger systems responsible for deciding which collision events to store impose strict latency and accuracy constraints. While transformer architectures achieve the highest jet tagging accuracy when compute is unconstrained, their quadratic self-attention cost makes inference restrictive on trigger budget. Existing efficient variants reduce the computational cost, but hinder the classification performance. To address this limitation, we introduce the Patch Hierarchical Attention Transformer (PHAT-JeT), which combines two mechanisms: a physics-inspired geometric message-passing module that encodes local detector-plane structure, and a hierarchical patch-based attention scheme that computes exact attention within small particle groups while preserving global context through lightweight patch-token communication. Within a restricted budget, PHAT-JeT achieves state-of-the-art accuracy and background rejection among all resource-constrained jet tagging models on four benchmarks (\textsc{hls4ml}, JetClass, Top Tagging, and Quark--Gluon). Our code is available at https://github.com/aaronw5/PHAT-JeT.

Wang, Aaron [Illinois U., Chicago] (ORCID:00000003↗