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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Small-scale rotor test rig capabilities for testing vibration alleviation algorithms

A test was conducted to assess the capabilities of a small scale rotor test rig for implementing higher harmonic control and stability augmentation algorithms. The test rig uses three high speed actuators to excite the swashplate over a range of frequencies. The actuator position signals were monitored to measure the response amplitudes at several frequencies. The ratio of response amplitude to excitation amplitude was plotted as a function of frequency. In addition to actuator performance, acceleration from six accelerometers placed on the test rig was monitored to determine whether a linear relationship exists between the harmonics of N/Rev control input and the least square error (LSE) identification technique was used to identify local and global transfer matrices for two rotor speeds at two batch sizes each. It was determined that the multicyclic control computer system interfaced very well with the rotor system and kept track of the input accelerometer signals and their phase angles. However, the current high speed actuators were found to be incapable of providing sufficient control authority at the higher excitation frequencies.

Jacklin, Stephen A.↗
NASA NTRS1993

RACE pulls for shared control

Maintaining and supporting an aircraft fleet, in a climate of reduced manpower and financial resources, dictates effective utilization of robotics and automation technologies. To help develop a winning robotics and automation program the Air Force Logistics Command created the Robotics and Automation Center of Excellence (RACE). RACE is a command wide focal point. Race is an organic source of expertise to assist the Air Logistic Center (ALC) product directorates in improving process productivity through the judicious insertion of robotics and automation technologies. RACE is a champion for pulling emerging technologies into the aircraft logistic centers. One of those technology pulls is shared control. Small batch sizes, feature uncertainty, and varying work load conspire to make classic industrial robotic solutions impractical. One can view ALC process problems in the context of space robotics without the time delay. The ALC's will benefit greatly from the implementation of a common architecture that supports a range of control actions from fully autonomous to teleoperated. Working with national laboratories and private industry, we hope to transition shared control technology to the depot floor. This paper provides an overview of the RACE internal initiatives and customer support, with particular emphasis on production processes that will benefit from shared control technology.

Leahy, M. B., Jr.↗
NASA NTRS2005

Boron Nitride Nanotubes for Engineering Applications

Boron nitride nanotubes (BNNT) are of significant interest to the scientific and technical communities for many of the same reasons that carbon nanotubes (CNT) have attracted wide attention. Both materials have potentially unique and important properties for structural and electronic applications. However of even more consequence than their similarities may be the complementary differences between carbon and boron nitride nanotubes While BNNT possess a very high modulus similar to CNT, they also possess superior chemical and thermal stability. Additionally, BNNT have more uniform electronic properties, with a uniform band gap of 5.5 eV while CNT vary from semi-conductive to highly conductive behavior. Boron nitride nanotubes have been synthesized both in the literature and at NASA Glenn Research Center, by a variety of methods such as chemical vapor deposition, arc discharge and reactive milling. Consistent large scale production of a reliable product has proven difficult. Progress in the reproducible synthesis of 1-2 gram sized batches of boron nitride nanotubes will be discussed as well as potential uses for this unique material.

Hurst, Janet↗
NASA NTRS2005

Synthesis of Boron Nitride Nanotubes for Engineering Applications

Boron Nitride nanotubes (BNNT) are of interest to the scientific and technical communities for many of the same reasons that carbon nanotubes (CNT) have attracted large amounts of attention. Both materials have potentially unique and significant properties which may have important structural and electronic applications in the future. However of even more interest than their similarities may be the differences between carbon and boron nanotubes. Whilt boron nitride nanotubes possess a very high modulus similaar to CNT, they are also more chemically and thermally inert. Additionally BNNT possess more uniform electronic properties, having a uniform band gap of approximately 5.5 eV while CNT vary from semi-conductin to conductor behavior. Boron Nitride nanotubes have been synthesized by a variety of methods such as chemical vapor deposition, arc discharge and reactive milling. Consistently producing a reliable product has proven difficult. Progress in synthesis of 1-2 gram sized batches of Boron Nitride nanotubes will be discussed as well as potential uses for this unique material.

Hurst, Janet↗
NASA NTRS2018

Automated Pneumothorax Diagnosis using Deep Neural Networks

Thoracic ultrasound can provide information leading to rapid diagnosis of pneumothorax with improved accuracy over the standard physical examination and with higher sensitivity than anteroposterior chest radiography. However, the clinical We have Furthermore, remote environments, such as the battlefield or deep-space exploration, may lack expertise for diagnosing developed an automated image interpretation pipeline for the analysis of thoracic ultrasound data and the classification of pneumothorax events to provide decision support in such situations. Our pipeline consists of image preprocessing, data augmentation, and deep learning architectures for medical diagnosis. In this work, we demonstrate that robust, accurate interpretation of chest images and video can be achieved using deep neural networks. A number of novel image processing techniques were employed to achieve this result. Affine transformations were applied for data augmentation. Hyperparameters were optimized for learning rate, dropout regularization, batch size, and epoch iteration by a sequential model-based Bayesian approach. In addition, we utilized pretrained architecturesinterpretation of a patient medical image is highly operator dependent. certain pathologies., applying transfer learning and fine-tuning techniques to fully connected layers. Our pipeline yielded binary classification validation accuracies of 98.3% for M-mode images and 99.8% with B-mode video frames.

US Army collaboration↗
NASA NTRSDate not supplied

Designed Experiment Results for Constituent Effects on NBR Based Solid Rocket Motor Insulation Performance

Solid rocket motor internal insulations are facing serious issues with obsolescence of constituents and the need for improved performance. The design community is at a unique point with a large number of rocket motors requiring reformulation of insulation or design upgrades at the same time. Unfortunately, performance data related to component choice is not generally available to aid development. A study of insulation component materials, conducted at Marshall Space Flight Center over the last four years, used designed experiments to understand the contribution of each material to the performance of a formulation. This report covers work with Nitrile Butadiene Rubber (NBR) based formulations. In-house compounding provided modified formulations allowing for isolation of both individual and synergistic effects. All formulas were tested for mechanical, thermal and producibility properties to evaluate performance. This presentation will describe the methodology for, and results from, identifying the individual and combined effects of components used in our formulations. Specific properties investigated include, rheology, cure characteristics, density, tensile strength, along with erosion/ablation under various heat loads and flame chemistries. Scale up of several formulas from lab to production size batches was accomplished. We will discuss troubleshooting problems of scale and the new set of designed experiments with Ethylene Propylene Diene Monomer (EPDM) based formulations.

Insulation↗
DOE OSTI2023

Adaptive, Active Learning, and Multifidelity Monte Carlo Methods in the MOOSE Stochastic Tools Module

MOOSE is an open-source computational platform for constructing multi-physics models and executing them in a massively parallel fashion. It has a stochastic tools module (STM) for forward/inverse uncertainty quantification (UQ) and surrogate modeling. This presentation details some recent developments to the STM with respect to the implementation of adaptive, active learning, and multifidelity Monte Carlo methods for forward UQ of computational models. Specifically, the adaptive Monte Carlo methods include Markov Chain Monte Carlo (MCMC)-driven algorithms like adaptive importance sampling and parallelized subset simulation for statistical QoI estimation, rare events analysis, and stochastic gradient-free optimization. The active learning methods include Gaussian Process (GP) surrogates and their training via Adam optimization, design of acquisition functions, and integration with samplers like Monte Carlo, adaptive importance, and parallelized subset simulation. These active learning methods are also designed to work in a batch mode, wherein, the required calls to the full computational model are executed in parallel whenever a user-specified batch size is met. The multifidelity methods in STM are broadly divided into two categories: hierarchical, where a defined hierarchy exists among the low-fidelity models, and peer, where all the low-fidelity models are treated equally. A GP surrogate is used to learn the differences between the low- and high-fidelity models in both multifidelity categories, and acquisition functions from the active learning classes are used to decide whether to rely on a low-fidelity model or call the expensive high-fidelity model. Alongside the software description and usage, applications are also presented to nuclear engineering computational models including a TRISO nuclear fuel particle, a reactor pressure vessel, and a heat-pipe microreactor.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models

Mixture of Experts (MoE) models have enabled the scaling of Large Language Models (LLMs) and Vision Language Models (VLMs) by achieving massive parameter counts while maintaining computational efficiency. However, MoEs introduce several inference-time challenges, including load imbalance across experts and the additional routing computational overhead. To address these challenges and fully harness the benefits of MoE, a systematic evaluation of hardware acceleration techniques is essential. We present MoE-Inference-Bench, a comprehensive study to evaluate MoE performance across diverse scenarios. We analyze the impact of batch size, sequence length, and critical MoE hyperparameters such as FFN dimensions and number of experts on throughput. We evaluate several optimization techniques on Nvidia H100 GPUs, including pruning, Fused MoE operations, speculative decoding, quantization, and various parallelization strategies. Our evaluation includes MoEs from the Mixtral, DeepSeek, OLMoE and Qwen families. The results reveal performance differences across configurations and provide insights for the efficient deployment of MoEs.

Chitty-Venkata, Krishna Teja↗
DOE OSTI2026

Component-Level Inverse Design of Transmon Qubits Using Neural Networks

Designing a superconducting qubit to realize specific Hamiltonian parameters typically requires iterating through a time and compute-intensive forward loop in which the designer chooses a layout geometry, simulates it, extracts circuit parameters such as capacitances, and refines the geometry. We study the inverse version of this task using a neural-network workflow that maps target Hamiltonian parameters directly to component-level layout parameters, which we subsequently demonstrate on a planar transmon layout. During training, we pair the inverse model with a frozen forward surrogate model and evaluate the loss in Hamiltonian space rather than in layout-parameter space. In validation against a conventional EM solver, 97% of generated designs produce usable geometries, and the inverse-plus-surrogate pipeline reaches mean percent errors of 0.73% for qubit frequency and 1.58% for anharmonicity, comparable to or below the fabrication and simulation-to-measurement uncertainty expected for academic-process transmon devices of this type. A single pipeline query takes ~60 ms on CPU, versus ~2 min for a conventional EM capacitance extraction on the same hardware, a speedup of approximately 2,000x. Batching minimizes the AI model inference overhead, reducing the runtime to 3.1 microseconds per sample on CPU and 2.6 microseconds per sample on GPU at a batch size of 2048, resulting in speedups of 3.9 x 10^7 and 4.6 x 10^7, respectively, relative to a single conventional CPU EM extraction. Our results indicate that component-level inverse design usefully extends and complements conventional EM simulation, including for small datasets on the order of 1,000 samples.

Seidel, Olivia [Fermilab; Texas U., Arlington]↗
DOE OSTI2024

Batched sparse direct solver design and evaluation in SuperLU_DIST

Over the course of interactions with various application teams, the need for batched sparse linear algebra functions has emerged in order to make more efficient use of the GPUs for many small and sparse linear algebra problems. In this paper, we present our recent work on a batched sparse direct solver for GPUs. The sparse LU factorization is computed by the levels of the elimination tree, leveraging the batched dense operations at each level and a new batched Scatter GPU kernel. The sparse triangular solve is computed by the level sets of the directed acyclic graph (DAG) of the triangular matrix. Batched operations overcome the large overhead associated with launching many small kernels. For medium sized matrix batches with not-so-small bandwidth, using an NVIDIA A100 GPU, our new batched sparse direct solver is orders of magnitude faster than a batched banded solver and uses less than one-tenth of the memory.

Boukaram, Wajih↗
DOE OSTI2022

BUTTER - Empirical Deep Learning Dataset

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four learning rates, six minibatch sizes, four levels of label noise, and fourteen levels of L1 and L2 regularization each. Multiple repetitions (typically 30, sometimes 10) of each combination of hyperparameters were preformed, and statistics including training and test loss (using a 80% / 20% shuffled train-test split) are recorded at the end of each training epoch. In total, this dataset covers 178 thousand distinct hyperparameter settings ("experiments"), 3.55 million individual training runs (an average of 20 repetitions of each experiments), and a total of 13.3 billion training epochs (three thousand epochs were covered by most runs). Accumulating this dataset consumed 5,448.4 CPU core-years, 17.8 GPU-years, and 111.2 node-years.

Array↗
NASA NTRS1983

Pellet Feed for Dendritic-Web Growth

Melt replenishment system sustains continuous growth of silicon dentritic web for several days. Substantially increases size of batch, limited mainly by level of impurities and life of crucible. Silicon pellets automatically added to crucible sustain crystal growth for days.

Duncan, C. S.↗
NASA NTRS2012

Indium Tin Oxide Resistor-Based Nitric Oxide Microsensors

A sensitive resistor-based NO microsensor, with a wide detection range and a low detection limit, has been developed. Semiconductor microfabrication techniques were used to create a sensor that has a simple, robust structure with a sensing area of 1.10 ~ 0.99 mm. A Pt interdigitated structure was used for the electrodes to maximize the sensor signal output. N-type semiconductor indium tin oxide (ITO) thin film was sputter-deposited as a sensing material on the electrode surface, and between the electrode fingers. Alumina substrate (250 m in thickness) was sequentially used for sensor fabrication. The resulting sensor was tested by applying a voltage across the two electrodes and measuring the resulting current. The sensor was tested at different concentrations of NO-containing gas at a range of temperatures. Preliminary results showed that the sensor had a relatively high sensitivity to NO at 450 C and 1 V. NO concentrations from ppm to ppb ranges were detected with the low limit of near 159 ppb. Lower NO concentrations are being tested. Two sensing mechanisms were involved in the NO gas detection at ppm level: adsorption and oxidation reactions, whereas at ppb level of NO, only one sensing mechanism of adsorption was involved. The NO microsensor has the advantages of high sensitivity, small size, simple batch fabrication, high sensor yield, low cost, and low power consumption due to its microsize. The resistor-based thin-film sensor is meant for detection of low concentrations of NO gas, mainly in the ppb or lower range, and is being developed concurrently with other sensor technology for multispecies detection. This development demonstrates that ITO is a sensitive sensing material for NO detection. It also provides crucial information for future selection of nanostructured and nanosized NO sensing materials, which are expected to be more sensitive and to consume less power.

Xu, Jennifer C.↗
DOE OSTI2023

Effect of particle size on the capture of uranium oxide colloidal particles from aqueous suspensions via high-gradient magnetic filtration

The effectiveness of High Gradient Magnetic Filtration (HGMF) in capturing uranium oxide particles from suspensions was investigated in this study. Two sets of experiments were performed to evaluate the importance of size on the capture of uranium oxide particles. The first considered two batches sieved into size bins of< 5, 5–10, 10–15, and 15–20 µm, while the second was performed using two suspensions with diameters smaller than 1.0 µm and between 1.0 and 1.5 µm. Iron oxide experiments, with particles between 0.3 and 0.8 µm, were performed for calibration purposes. In all experiments, a surfactant (Triton-X100 or sodium dodecyl sulfate) was used to prevent particle aggregation and limit the influence of non-magnetic capture mechanisms. A magnetic field of approximately 1.1 Tesla was generated using a water cooled electromagnet. HGMF was performed using tubular filters packed with ferromagnetic stainless-steel wool. Of the initial four uranium oxide particle sizes, magnetic capture was only observed for particles with a diameter of less than 5 µm, while larger particles experienced no magnetic and minimal total capture. For particles with diameters smaller than 1.0 µm and between 1.0 and 1.5 µm, capture efficiencies increased by 39 ± 9% and 34 ± 6% respectively, solely due to the magnetic field. Although the magnetic force is proportional to particle diameter, the capture efficiency decreased as diameter increased. So these results suggest that Brownian diffusion, which is influential for micron sized particles and increases with decreasing particle size, is acting in conjunction with the magnetic force to influence the efficacy of HGMF for uranium oxide. This important finding underscores the effectiveness of Brownian diffusion in increasing the rate of collision between particles and collector fibers. A stochastic trajectory model was developed to incorporate the influence of Brownian motion on particle behavior and filter removal efficiency. Modeling results are discussed and compared for uranium and iron oxide particles.

42 ENGINEERING↗
NASA NTRS2014

Development of a Batch Fabrication Process for Chemical Nanosensors: Recent Advancements at NASA Glenn Research Center

A major objective in aerospace sensor development is to produce sensors that are small in size, easy to batch fabricate and low in cost, and have low power consumption. Chemical sensors involving nanostructured materials can provide these characteristics as well as the potential for the development of sensor systems with unique properties and improved performance. However, the fabrication and processing of nanostructures for sensor applications currently is limited by the ability to control their location on the sensor platform, which in turn hinders the progress for batch fabrication. This presentation will discuss the following: the development of a novel room temperature methane (CH4) sensor fabricated using porous tin oxide (SnO2) nanorods as the sensing material, the advantages of using nanomaterials in sensor designs, the challenges encountered with the integration of nanostructures into microsensordevices, and the different methods that have been attempted to address these challenges. An approach for the mass production of sensors with nanostructures using a method developed by our group at the NASA Glenn Research Center to control the alignment of nanostructures onto a sensor platform will also be described.

Nanotechnology↗
DOE OSTI2020

Performance Assessment of AFQMC Implementation (ECP Milestone 5.2 Report)

The auxiliary field quantum Monte Carlo algorithm in QMCPACK was ported to run on AMD GPUs using HIP. Initial performance measurements on early access hardware indicate a 4x slowdown compared to NVIDIA V100s. The slowdown was determined to be a result of suboptimal ROCM libraries for small matrix sizes coupled with some poorly performing hipified CUDA kernels. Through a preliminary analysis of HIP kernel performance we determined a strategy for pro ling and tuning kernels. We request that ROCM libraries are optimized for smaller problem sizes particularly for batched operations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2013

Whole Wafer Design and Fabrication for the Alignment of Nanostructures for Chemical Sensor Applications

A major objective in aerospace sensor development is to produce sensors that are small in size, easy to batch fabricate and low in cost, and have low power consumption The fabrication of chemical sensors involving nanostructured materials can provide these properties as well as the potential for the development of sensor systems with unique properties and improved performance. However, the fabrication and processing of nanostructures for sensor applications currently is limited in the ability to control their location on the sensor. Currently, our group at NASA Glenn Research Center has demonstrated the controlled placement of nanostructures in sensors using a sawtooth patterned electrode design. With this design the nanostructures are aligned between opposing sawtooth electrodes by applying an alternating current.

Biaggi-Labiosa, Azlin M.↗