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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Substrate flexibility regulates growth and apoptosis of normal but not transformed cells

One of the hallmarks of oncogenic transformation is anchorage-independent growth (27). Here we demonstrate that responses to substrate rigidity play a major role in distinguishing the growth behavior of normal cells from that of transformed cells. We cultured normal or H-ras-transformed NIH 3T3 cells on flexible collagen-coated polyacrylamide substrates with similar chemical properties but different rigidity. Compared with cells cultured on stiff substrates, nontransformed cells on flexible substrates showed a decrease in the rate of DNA synthesis and an increase in the rate of apoptosis. These responses on flexible substrates are coupled to decreases in cell spreading area and traction forces. In contrast, transformed cells maintained their growth and apoptotic characteristics regardless of substrate flexibility. The responses in cell spreading area and traction forces to substrate flexibility were similarly diminished. Our results suggest that normal cells are capable of probing substrate rigidity and that proper mechanical feedback is required for regulating cell shape, cell growth, and survival. The loss of this response can explain the unregulated growth of transformed cells.

Non-NASA Center↗
NASA NTRS2004

Volume and Mass Estimation of Three-Phase High Power Transformers for Space Applications

Spacecraft historically have had sub-1kW(sub e), electrical requirements for GN&C, science, and communications: Galileo at 600W(sub e), and Cassini at 900W(sub e), for example. Because most missions have had the same order of magnitude power requirements, the Power Distribution Systems (PDS) use existing, space-qualified technology and are DC. As science payload and mission duration requirements increase, however, the required electrical power increases. Subsequently, this requires a change from a passive energy conversion (solar arrays and batteries) to dynamic (alternator, solar dynamic, etc.), because dynamic conversion has higher thermal and conversion efficiencies, has higher power densities, and scales more readily to higher power levels. Furthermore, increased power requirements and physical distribution lengths are best served with high-voltage, multi-phase AC to maintain distribution efficiency and minimize voltage drops. The generated AC-voltage must be stepped-up (or down) to interface with various subsystems or electrical hardware. Part of the trade-space design for AC distribution systems is volume and mass estimation of high-power transformers. The volume and mass are functions of the power rating, operating frequency, the ambient and allowable temperature rise, the types and amount of heat transfer available, the core material and shape, the required flux density in a core, the maximum current density, etc. McLyman has tabulated the performance of a number of transformers cores and derived a "cookbook" methodology to determine the volume of transformers, whereas Schawrze had derived an empirical method to estimate the mass of single-phase transformers. Based on the work of McLyman and Schwarze, it is the intent herein to derive an empirical solution to the volume and mass estimation of three-phase, laminated EI-core power transformers, having radiated and conducted heat transfer mechanisms available. Estimation of the mounting hardware, connectors, etc. is not included.

Kimnach, Greg L.↗
NASA NTRS2005

On the Hilbert-Huang Transform Theoretical Developments

One of the main heritage tools used in scientific and engineering data spectrum analysis is the Fourier Integral Transform and its high performance digital equivalent - the Fast Fourier Transform (FFT). Both carry strong a-priori assumptions about the source data, such as linearity, of being stationary, and of satisfying the Dirichlet conditions. A recent development at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), known as the Hilbert-Huang Transform (HHT), proposes a novel approach to the solution for the nonlinear class of spectrum analysis problems. Using a-posteriori data processing based on the Empirical Mode Decomposition (EMD) sifting process (algorithm), followed by the normalized Hilbert Transform of the decomposition data, the HHT allows spectrum analysis of nonlinear and nonstationary data. The EMD sifting process results in a non-constrained decomposition of a source real value data vector into a finite set of Intrinsic Mode Functions (IMF). These functions form a near orthogonal adaptive basis, a basis that is derived from the data. The IMFs can be further analyzed for spectrum interpretation by the classical Hilbert Transform. A new engineering spectrum analysis tool using HHT has been developed at NASA GSFC, the HHT Data Processing System (HHT-DPS). As the HHT-DPS has been successfully used and commercialized, new applications post additional questions about the theoretical basis behind the HHT and EMD algorithms. Why is the fastest changing component of a composite signal being sifted out first in the EMD sifting process? Why does the EMD sifting process seemingly converge and why does it converge rapidly? Does an IMF have a distinctive structure? Why are the IMFs near orthogonal? We address these questions and develop the initial theoretical background for the HHT. This will contribute to the developments of new HHT processing options, such as real-time and 2-D processing using Field Programmable Array (FPGA) computational resources, enhanced HHT synthesis, and broaden the scope of HHT applications for signal processing.

Kizhner, Semion↗
NASA NTRS2006

On Certain Theoretical Developments Underlying the Hilbert-Huang Transform

One of the main traditional tools used in scientific and engineering data spectral analysis is the Fourier Integral Transform and its high performance digital equivalent - the Fast Fourier Transform (FFT). Both carry strong a-priori assumptions about the source data, such as being linear and stationary, and of satisfying the Dirichlet conditions. A recent development at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), known as the Hilbert-Huang Transform (HHT), proposes a novel approach to the solution for the nonlinear class of spectral analysis problems. Using a-posteriori data processing based on the Empirical Mode Decomposition (EMD) sifting process (algorithm), followed by the normalized Hilbert Transform of the decomposed data, the HHT allows spectral analysis of nonlinear and nonstationary data. The EMD sifting process results in a non-constrained decomposition of a source real-value data vector into a finite set of Intrinsic Mode Functions (IMF). These functions form a nearly orthogonal derived from the data (adaptive) basis. The IMFs can be further analyzed for spectrum content by using the classical Hilbert Transform. A new engineering spectral analysis tool using HHT has been developed at NASA GSFC, the HHT Data Processing System (HHT-DPS). As the HHT-DPS has been successfully used and commercialized, new applications pose additional questions about the theoretical basis behind the HHT and EMD algorithms. Why is the fastest changing component of a composite signal being sifted out first in the EMD sifting process? Why does the EMD sifting process seemingly converge and why does it converge rapidly? Does an IMF have a distinctive structure? Why are the IMFs nearly orthogonal? We address these questions and develop the initial theoretical background for the HHT. This will contribute to the development of new HHT processing options, such as real-time and 2-D processing using Field Programmable Gate Array (FPGA) computational resources,

Kizhner, Semion↗
NASA NTRS2005

Computing Instantaneous Frequency by normalizing Hilbert Transform

This invention presents Normalized Amplitude Hilbert Transform (NAHT) and Normalized Hilbert Transform(NHT), both of which are new methods for computing Instantaneous Frequency. This method is designed specifically to circumvent the limitation set by the Bedorsian and Nuttal Theorems, and to provide a sharp local measure of error when the quadrature and the Hilbert Transform do not agree. Motivation for this method is that straightforward application of the Hilbert Transform followed by taking the derivative of the phase-angle as the Instantaneous Frequency (IF) leads to a common mistake made up to this date. In order to make the Hilbert Transform method work, the data has to obey certain restrictions.

Huang, Norden E.↗
NASA NTRS1989

Architecture for time or transform domain decoding of reed-solomon codes

Two pipeline (255,233) RS decoders, one a time domain decoder and the other a transform domain decoder, use the same first part to develop an errata locator polynomial .tau.(x), and an errata evaluator polynominal A(x). Both the time domain decoder and transform domain decoder have a modified GCD that uses an input multiplexer and an output demultiplexer to reduce the number of GCD cells required. The time domain decoder uses a Chien search and polynomial evaluator on the GCD outputs .tau.(x) and A(x), for the final decoding steps, while the transform domain decoder uses a transform error pattern algorithm operating on .tau.(x) and the initial syndrome computation S(x), followed by an inverse transform algorithm in sequence for the final decoding steps prior to adding the received RS coded message to produce a decoded output message.

Shao, Howard M.↗
NASA NTRS2010

When Deriving the Spatial QRS-T Angle from the 12-lead ECG, which Transform is More Frank: Regression or Inverse Dower?

Our primary objective was to ascertain which commonly used 12-to-Frank-lead transformation yields spatial QRS-T angle values closest to those obtained from simultaneously collected true Frank-lead recordings. Simultaneous 12-lead and Frank XYZ-lead recordings were analyzed for 100 post-myocardial infarction patients and 50 controls. Relative agreement, with true Frank-lead results, of 12-to-Frank-lead transformed results for the spatial QRS-T angle using Kors regression versus inverse Dower was assessed via ANOVA, Lin s concordance and Bland-Altman plots. Spatial QRS-T angles from the true Frank leads were not significantly different than those derived from the Kors regression-related transformation but were significantly smaller than those derived from the inverse Dower-related transformation (P less than 0.001). Independent of method, spatial mean QRS-T angles were also always significantly larger than spatial maximum (peaks) QRS-T angles. Spatial QRS-T angles are best approximated by regression-related transforms. Spatial mean and spatial peaks QRS-T angles should also not be used interchangeably.

Schlegel, Todd T.↗
NASA NTRS2006

Electro-Optical Imaging Fourier-Transform Spectrometer

An electro-optical (E-O) imaging Fourier-transform spectrometer (IFTS), now under development, is a prototype of improved imaging spectrometers to be used for hyperspectral imaging, especially in the infrared spectral region. Unlike both imaging and non-imaging traditional Fourier-transform spectrometers, the E-O IFTS does not contain any moving parts. Elimination of the moving parts and the associated actuator mechanisms and supporting structures would increase reliability while enabling reductions in size and mass, relative to traditional Fourier-transform spectrometers that offer equivalent capabilities. Elimination of moving parts would also eliminate the vibrations caused by the motions of those parts. Figure 1 schematically depicts a traditional Fourier-transform spectrometer, wherein a critical time delay is varied by translating one the mirrors of a Michelson interferometer. The time-dependent optical output is a periodic representation of the input spectrum. Data characterizing the input spectrum are generated through fast-Fourier-transform (FFT) post-processing of the output in conjunction with the varying time delay.

Chao, Tien-Hsin↗
NASA NTRS2011

High-Tc Superconducting Bolometer Noise Measurement Using Low Noise Transformers - Theory and Optimization

Care must always be taken when performing noise measurements on high-Tc superconducting materials to ensure that the results are not from the measurement system itself. One situation likely to occur is with low noise transformers. One of the least understood devices, it provides voltage gain for low impedance inputs (< 100 ), e.g., YBaCuO and GdBaCuO thin films, with comparatively lower noise levels than other devices for instance field effect and bipolar junction transistors. An essential point made in this paper is that because of the complex relationships between the transformer ports, input impedance variance alters the transformer s transfer function in particular, the low frequency cutoff shift. The transfer of external and intrinsic transformer noise to the output along with optimization and precautions are treated; all the while, we will cohesively connect the transfer function shift, the load impedance, and the actual noise at the transformer output.

Aslam, Shahid↗
NASA NTRS2012

Rainbow Fourier Transform

We present a novel technique for remote sensing of cloud droplet size distributions. Polarized reflectances in the scattering angle range between 135deg and 165deg exhibit a sharply defined rainbow structure, the shape of which is determined mostly by single scattering properties of cloud particles, and therefore, can be modeled using the Mie theory. Fitting the observed rainbow with such a model (computed for a parameterized family of particle size distributions) has been used for cloud droplet size retrievals. We discovered that the relationship between the rainbow structures and the corresponding particle size distributions is deeper than it had been commonly understood. In fact, the Mie theory-derived polarized reflectance as a function of reduced scattering angle (in the rainbow angular range) and the (monodisperse) particle radius appears to be a proxy to a kernel of an integral transform (similar to the sine Fourier transform on the positive semi-axis). This approach, called the rainbow Fourier transform (RFT), allows us to accurately retrieve the shape of the droplet size distribution by the application of the corresponding inverse transform to the observed polarized rainbow. While the basis functions of the proxy-transform are not exactly orthogonal in the finite angular range, this procedure needs to be complemented by a simple regression technique, which removes the retrieval artifacts. This non-parametric approach does not require any a priori knowledge of the droplet size distribution functional shape and is computationally fast (no look-up tables, no fitting, computations are the same as for the forward modeling).

Alexandrov, Mikhail D.↗
NASA NTRS2015

Karyotyping of Chromosomes in Human Bronchial Epithelial Cells Transformed by High Energy Fe Ions

Lung cancer induced from exposures to space radiation is one of the most significant health risks for long-term space travels. Evidences show that low- and high- Linear energy transfer (LET)-induced transformation of normal human bronchial epithelial cells (HBEC) that are immortalized through the expression of Cdk4 and hTERT. The cells were exposed to gamma rays and high-energy Fe ions for the selection of transformed clones. Transformed HBEC are identified and analyzed chromosome aberrations (i.e. genomic instability) using the multi-color fluorescent in situ hybridization (mFISH), as well as the multi-banding in situ hybridization (mBAND) techniques. Our results show chromosomal translocations between different chromosomes and several of the breaks occurred in the q-arm of chromosome 3. We also identified copy number variations between the transformed and the parental HBEC regardless of the exposure conditions. We observed chromosomal aberrations in the lowand high-LET radiation-induced transformed clones and they are imperfectly different from clones obtain in spontaneous soft agar growth.

Yeshitla, Samrawit↗
NASA NTRS2009

Sequential Principal Component Analysis -An Optimal and Hardware-Implementable Transform for Image Compression

This paper presents the JPL-developed Sequential Principal Component Analysis (SPCA) algorithm for feature extraction / image compression, based on "dominant-term selection" unsupervised learning technique that requires an order-of-magnitude lesser computation and has simpler architecture compared to the state of the art gradient-descent techniques. This algorithm is inherently amenable to a compact, low power and high speed VLSI hardware embodiment. The paper compares the lossless image compression performance of the JPL's SPCA algorithm with the state of the art JPEG2000, widely used due to its simplified hardware implementability. JPEG2000 is not an optimal data compression technique because of its fixed transform characteristics, regardless of its data structure. On the other hand, conventional Principal Component Analysis based transform (PCA-transform) is a data-dependent-structure transform. However, it is not easy to implement the PCA in compact VLSI hardware, due to its highly computational and architectural complexity. In contrast, the JPL's "dominant-term selection" SPCA algorithm allows, for the first time, a compact, low-power hardware implementation of the powerful PCA algorithm. This paper presents a direct comparison of the JPL's SPCA versus JPEG2000, incorporating the Huffman and arithmetic coding for completeness of the data compression operation. The simulation results show that JPL's SPCA algorithm is superior as an optimal data-dependent-transform over the state of the art JPEG2000. When implemented in hardware, this technique is projected to be ideally suited to future NASA missions for autonomous on-board image data processing to improve the bandwidth of communication.

Duong, Tuan A.↗
NASA NTRS2022

Effect of Ni2+, Zn2+, and Co2+ on green rust transformation to magnetite

In this study, we investigated Ni2+, Zn2+, and Co2+ mineralogical incorporation and its effect on green rust transformation to magnetite. Mineral transformation experiments were conducted by heating green rust suspensions at 85 °C in the presence of Ni2+, Zn2+, or Co2+ under strict anoxic conditions. Transmission electron microscopy and powder X-ray diffraction showed the conversion of hexagonal green rust platelets to fine grained cubic magnetite crystals. The addition of Ni2+, Zn2+, and Co2+ resulted in faster rates of mineral transformation. The conversion of green rust to magnetite was concurrent to significant increases in metal uptake, demonstrating a strong affinity for metal sorption/ co-precipitation by magnetite. Dissolution ratio curves showed that Ni2+, Zn2+, and Co2+ cations were incorporated into the mineral structure during magnetite crystal growth. The results indicate that the transformation of green rust to magnetite is accelerated by metal impurities, and that magnetite is a highly effective scavenger of trace metals during mineral transformation. The implications for using diagenetic magnetite from green rust precursors as paleoproxies of Precambrian ocean chemistry are discussed.

Hydrosulfate green rust↗
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 aerial wildfire operations reports to better understand the risks present. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement. These applications can benefit from the use of state-of-the-art natural language processing techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of adaptation of NLP tools to the aviation domain by fine-tuning transformer based models using aviation data sets. In 2018, a novel language model based on neural units (also called transformers) was created and became known as “Bidirectional Encoder Representations from Transformers” or BERT. This architecture combined with large amounts of English training data and innovative semi-supervised training tasks set the standard for what would later emerge as Large Language Models. The performance of these models was further improved by hyperparameter tuning and refinement of the semi-supervised training task and resulted in “Robustly Optimized BERT Pre-training Approach through hyperparameter tuning” or RoBERTa models. These pre-trained Large Language Models proved to be useful for a wide variety of natural language processing tasks such as text classification and question answering through a process called fine-tuning. The transformer architecture with pre-trained weights served as the basis with the last few layers replaced with layers fine-tuned to perform a new task e.g., a layer that provides a label for the entire input text. This process of fine-tuning can also be used to adapt the models to new domains; e.g., BioBERT started with the pre-trained BERT model and was completed by additional fine-tuning and training on biomedical documents. Transformer-based architectures can also be used to create rich representations of text called embeddings which can serve as the input to other machine learning models. This allows simpler algorithms such as logistic regression to use context-rich representations of the text while still remaining quick to train and evaluate. In the world of aviation, there is a growing demand for natural language processing and understanding but the domain presents unique challenges. Due to the technical content (and specialized language) of most aviation documents, fine-tuning pre-trained Large Language Models to specific tasks has not met the benchmark on natural language processing tasks set by simpler models trained from scratch on the data. To address this deficiency, this paper evaluates the improvements from fine-tuning a Large Language Model on a large set of aviation documents using the original semi-supervised training tasks before performing specific natural language tasks. In fine-tuning, a domain-specific dataset is used on the original training task but with the pre-trained Large Language Model instead of starting from a random initialization. This approach allows the model to be adapted to the specific domain language without discarding the information gained from training on general English data. This paper utilized two major dataset types to train and assess the RoBERTa fine-tuning performance. The first are 7,057 Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the semi-supervised fine-tuning. The second type is the 494 document classification labels to be used for evaluation. This down-stream evaluation aims to show the performance of the fine-tuned model, better understand how much data is needed for an effective fine-tuning, and how fine-tuning can be adapted for different applications in-the domain. After semi-supervised training, evaluation begins by encoding the documents for classification using the fine-tuned RoBERTa model. Then a logistic regression classifier is trained to label the document type and compared against our ground truth labels. This currently leads to a 82.8% accuracy on 10-fold cross validation showing improvement over baseline RoBERTa which achieved 81.0%. We plan to measure the improvements on additional tasks and it is expected that these improvements will lead to more robust models that can tackle the natural language processing challenges present in aviation datasets.

ATM↗
DOE OSTI2025

Sub-microsecond Transformers for Jet Tagging on FPGAs

We present the first sub-microsecond transformer implementation on an FPGA achieving competitive performance for state-of-the-art high-energy physics benchmarks. Transformers have shown exceptional performance on multiple tasks in modern machine learning applications, including jet tagging at the CERN Large Hadron Collider (LHC). However, their computational complexity prohibits use in real-time applications, such as the hardware trigger system of the collider experiments up until now. In this work, we demonstrate the first application of transformers for jet tagging on FPGAs, achieving $\mathcal{O}(100)$ nanosecond latency with superior performance compared to alternative baseline models. We leverage high-granularity quantization and distributed arithmetic optimization to fit the entire transformer model on a single FPGA, achieving the required throughput and latency. Furthermore, we add multi-head attention and linear attention support to hls4ml, making our work accessible to the broader fast machine learning community. This work advances the next-generation trigger systems for the High Luminosity LHC, enabling the use of transformers for real-time applications in high-energy physics and beyond.

Laatu, Lauri [Imperial Coll., London]↗
DOE OSTI2026

Dynamic Nanoscale Spatial Heterogeneity in a Perovskite-to-Brownmillerite Topotactic Phase Transformation

Phase transitions are omnipresent in modern condensed matter physics and its applications. In solids, first-order phase transformations typically occur by nucleation and growth under nonequilibrium conditions. Under constant external conditions, e.g., constant annealing temperature and pressure, the nucleation and growth dynamics are often thought of as spatially and temporally independent. Here, in situ Bragg X-ray photon correlation spectroscopy (XPCS) reveals nanoscale spatial and dynamical heterogeneity in the perovskite-to-brownmillerite topotactic phase transformation in La 0.7 Sr 0.3 CoO 3 thin films annealed under constant reducing conditions over a time span of multiple hours. Specifically, a time scale associated with domain growth remains stable, with a corresponding domain wall speed of v d = 6 ± 0.5 × 10 –4 nm/s (2 ± 0.2 nm/h), while a slower time scale, associated with temperature-driven depinning of domains, leads to accelerating dynamics with time scales following an aging power law with exponent −2.2 ± 0.5. This experiment demonstrates that Bragg XPCS is a powerful tool to study nanoscale dynamics in structural phase transformations, with the ability to extract quantitative average values related to nanodomain motion in situ. Furthermore, the results are relevant for phase engineering of phase-change devices, as they show that nanoscale dynamics, linked to domain and domain-wall motion, can continuously evolve and speed up with time, even hours after the initiation of the phase transformation, with potential repercussions on electrical performance.

X-ray photon correlation spectroscopy↗
DOE OSTI2023

Atomic Dynamics of Multi‐Interfacial Migration and Transformations

Redox-induced interconversions of metal oxidation states typically result in multiple phase boundaries that separate chemically and structurally distinct oxides and suboxides. Directly probing such multi-interfacial reactions is challenging because of the difficulty in simultaneously resolving the multiple reaction fronts at the atomic scale. Using the example of CuO reduction in H 2 gas, a reaction pathway of CuO → monoclinic m-Cu 4 O 3 → Cu 2 O is demonstrated and identifies interfacial reaction fronts at the atomic scale, where the Cu 2 O/m-Cu 4 O 3 interface shows a diffuse-type interfacial transformation; while the lateral flow of interfacial ledges appears to control the m-Cu 4 O 3 /CuO transformation. Together with atomistic modeling, it is shown that such a multi-interface transformation results from the surface-reaction-induced formation of oxygen vacancies that diffuse into deeper atomic layers, thereby resulting in the formation of the lower oxides of Cu 2 O and m-Cu 4 O 3 , and activate the interfacial transformations. In conclusion, these results demonstrate the lively dynamics at the reaction fronts of the multiple interfaces and have substantial implications for controlling the microstructure and interphase boundaries by coupling the interplay between the surface reaction dynamics and the resulting mass transport and phase evolution in the subsurface and bulk.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Applications of Solid-State Transformers for Electric Power Grid HEMP/GMD Resilience

A high-altitude electromagnetic pulse (HEMP) or geomagnetic disturbance (GMD) can disrupt power grids by inducing low-frequency common-mode (CM) currents. When these currents flow through grounded transformers, they can bias the magnetic core, driving it into saturation and reducing performance or damaging equipment. This work presents a solid-state transformer (SST) to replace vulnerable assets and improve grid resilience. The paper reviews half-cycle saturation in conventional transformers, then describes an SST architecture that neutralizes and redirects CM currents during HEMP/GMD events. A prototype SST is built and validated, demonstrating stable CM-disturbance operation and protection of nearby transformers. Finally, large-scale simulations show how coordinated SST deployment mitigates CM disturbances and strengthens overall grid resilience.

four-leg inverter↗