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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 343 records · Page 19

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NASA NTRS1992

A microwave holography methodology for diagnostics and performance improvement for large reflector antennas

This microwave holography technique utilizes the Fourier transform relation between the complex far field radiation pattern of an antenna and the complex aperture field distribution. Resulting aperture phase and amplitude distribution data can be used to precisely characterize various crucial performance parameters, including panel alignment, panel shaping, subreflector position, antenna aperture illumination, directivity at various frequencies, and gravity deformation effects. The methodology of data processing presented here was successfully applied to the Deep Space Network (DSN) 34-m beam waveguide antennas. The antenna performance was improved at all operating frequencies by reducing the main reflector mechanical surface rms error to 0.43 mm. At Ka-band (32 GHz), the estimated improvement is 4.1 dB, resulting in an aperture efficiency of 52 percent. The performance improvement was verified by efficiency measurements and additional holographic measurements.

Rochblatt, D. J.↗

NASA NTRS1992

Microwave antenna holography

Rochblatt, David J.↗

NASA NTRS2014

Investigating the Present Day Cosmic Dust Flux at the Earth's Surface: Initial Results from the Kwajalein Micrometeorite Collection

Examination of impact craters on the Long Duration Exposure Facility satellite indicate a present day micrometeoroid flux of approx. 30,000 tonnes [1 after 2]. But what portion of this material arrives at the Earth's surface as micrometeorites? Studies of available micrometeorite collections from deep sea sediments [e.g. 3], Greenland blue ice [e.g. 4] and the South Pole water well [e.g. 1] may be complicated by terrestrial weathering and, in some cases, collection bias (magnetic separation for deep sea sediments) and poorly constrained ages. We have recently set up a micrometeorite collection station on Kwajalein Island in the Republic of the Marshall Islands in the Pacific Ocean, using high volume air samplers to collect particles directly from the atmosphere. By collecting in this way, the terrestrial age of the particles is known, the weathering they experience is minimal, and we are able to constrain particle arrival times. Collecting at this location also exploits the considerably reduced anthropogenic background [5]. Method: High volume air samplers were installed on top of the two-story airport building on Kwajalein. These were fitted with polycarbonate membrane filters with 5μm diameter perforations. The flow rates were set to 0.5m3/min, and filters were changed once a week. After collection, filters were washed to remove salt and concentrate particles [see 5] in preparation for analysis by SEM. Results and Discussion: A selection of filters have been prepared and surveyed. Due to their ease of identification our initial investigations have focused on particles resembling cosmic spherules. The spheres can be divided into three main groups: 1. Silicate spherules rich in Al, Ca, K and Na (to varying degrees), 2. Silicate spherules rich in Mg and Fe and 3. Fe-rich spherules. Group 1 spherules are often vesiculated and can occur as aggregates. They are similar in appearance and composition to volcanic microspheres [e.g. 6] and are thus likely terrestrial in origin (volcanic). Those of groups 2 and 3, however, typically exhibit quenched surface textures consistent with cosmic spherules. Initial results suggest there is significant variation in the abundance of these groups from filter to filter. Work is ongoing to fully characterize these spherules and to constrain their flux with time.

Wozniakiewicz, P. J.↗

DOE OSTI2020

DeepClone: Lightweight State Replication of Deep Learning Models for Data Parallel Training

Training modern deep neural network (DNN) models involves complex workflows triggered by model exploration, sensitivity analysis, explainability, etc. A key primitive in this context is the ability to clone a model training instance, i.e. “fork” the training process in a potentially different direction, which enables comparisons of different evolution paths using variations of training data and model parameters. However, in a quest improve the training throughput, a mix of data parallel, model parallel, pipeline parallel and layer-wise parallel approaches are making the problem of cloning highly complex. In this paper, we explore the problem of efficient cloning under such circumstances. To this end, we leverage several properties of data-parallel training and layer-wise parallelism to design DeepClone, a cloning approach based on augmenting the execution graph to gain direct access to tensors, which are then sharded and reconstructed asynchronously in order to minimize runtime overhead, standby duration, readiness duration. Compared with state-of-art approaches, DeepClone shows orders of magnitude improvement for several classes of DNN models.

data-parallel training↗

NASA NTRS2010

Simulation of Earth-Moon-Mars Environments for the Assessment of Organ Doses

Space radiation environments for historically large solar particle events (SPE) and galactic cosmic rays (GCR) at solar minimum and solar maximum are simulated in order to characterize exposures to radio-sensitive organs for missions to low-Earth orbit (LEO), moon, and Mars. Primary and secondary particles for SPE and GCR are transported through the respective atmosphere of Earth or Mars, space vehicle, and astronaut s body tissues using the HZETRN/QMSFRG computer code. In LEO, exposures are reduced compared to deep space because particles are deflected by the Earth s magnetic field and absorbed by the solid body of the Earth. Geomagnetic transmission function as a function of altitude was applied for the particle flux of charged particles, and the shift or the organ exposures to higher velocity or lower stopping powers compared to those in deep space were analyzed. In the transport through Mars atmosphere, a vertical distribution of atmospheric thickness was calculated from the temperature and pressure data of Mars Global Surveyor, and the directional cosine distribution was implemented to describe the spherically distributed atmospheric distance along the slant path at each altitude. The resultant directional shielding by Mars atmosphere at solar minimum and solar maximum was used for the particle flux simulation at various altitudes on the Martian surface. Finally, atmospheric shielding was coupled with vehicle and body shielding for organ dose estimates. We made predictions of radiation dose equivalents and evaluated acute symptoms at LEO, moon, and Mars at solar minimum and solar maximum.

Kim, Myung-Hee↗

NASA NTRS2003

Low-Power SOI CMOS Transceiver

The work aims at developing a low-power Silicon on Insulator Complementary Metal Oxide Semiconductor (SOI CMOS) Transceiver for deep-space communications. RF Receiver must accomplish the following tasks: (a) Select the desired radio channel and reject other radio signals, (b) Amplify the desired radio signal and translate them back to baseband, and (c) Detect and decode the information with Low BER. In order to minimize cost and achieve high level of integration, receiver architecture should use least number of external filters and passive components. It should also consume least amount of power to minimize battery cost, size, and weight. One of the most stringent requirements for deep-space communication is the low-power operation. Our study identified that two candidate architectures listed in the following meet these requirements: (1) Low-IF receiver, (2) Sub-sampling receiver. The low-IF receiver uses minimum number of external components. Compared to Zero-IF (Direct conversion) architecture, it has less severe offset and flicker noise problems. The Sub-sampling receiver amplifies the RF signal and samples it using track-and-hold Subsampling mixer. These architectures provide low-power solution for the short- range communications missions on Mars. Accomplishments to date include: (1) System-level design and simulation of a Double-Differential PSK receiver, (2) Implementation of Honeywell SOI CMOS process design kit (PDK) in Cadence design tools, (3) Design of test circuits to investigate relationships between layout techniques, geometry, and low-frequency noise in SOI CMOS, (4) Model development and verification of on-chip spiral inductors in SOI CMOS process, (5) Design/implementation of low-power low-noise amplifier (LNA) and mixer for low-IF receiver, and (6) Design/implementation of high-gain LNA for sub-sampling receiver. Our initial results show that substantial improvement in power consumption is achieved using SOI CMOS as compared to standard CMOS process. Potential advantages of SOI CMOS for deep-space communication electronics include: (1) Radiation hardness, (2) Low-power operation, and (3) System-on-Chip (SOC) solutions.

Fujikawa, Gene↗

DOE OSTI2024

ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model

Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here, we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as the target to enable the discovery of protein materials with superior mechanical properties.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2010

The Future of NASA's Deep Space Network and Applications to Planetary Probe Missions

NASA's Deep Space Network (DSN) has been an invaluable tool in the world's exploration of space. It has served the space-faring community for more than 45 years. The DSN has provided a primary communication pathway for planetary probes, either through direct- to-Earth links or through intermediate radio relays. In addition, its radiometric systems are critical to probe navigation and delivery to target. Finally, the radio link can also be used for direct scientific measurement of the target body ('radio science'). This paper will examine the special challenges in supporting planetary probe missions, the future evolution of the DSN and related spacecraft technology, the advantages and disadvantages of radio relay spacecraft, and the use of the DSN radio links for navigation and scientific measurements.

Deep Space Network (DSN)↗

DOE OSTI2023

DeepThermo: Deep Learning Accelerated Parallel Monte Carlo Sampling for Thermodynamics Evaluation of High Entropy Alloys

Since the introduction of Metropolis Monte Carlo (MC) sampling, it and its variants have become standard tools used for thermodynamics evaluations of physical systems. However, a long-standing problem that hinders the effectiveness and efficiency of MC sampling is the lack of a generic method (a.k.a. MC proposal) to update the system configurations. Consequently, current practices are not scalable. Here we propose a parallel MC sampling framework for thermodynamics evaluation—DeepThermo. By using deep learning–based MC proposals that can globally update the system configurations, we show that DeepThermo can effectively evaluate the phase transition behaviors of high entropy alloys, which have an astronomical configuration space. For the first time, we directly evaluate a density of states expanding over a range of ~e 10,000 for a real material. We also demonstrate DeepThermo’s performance and scalability up to 3,000 GPUs on both NVIDIA V100 and AMD MI250X-based supercomputers.

Yin, Junqi↗

DOE OSTI2022

Non-Blind Deblurring for Fluorescence: A Deformable Latent Space Approach with Kernel Parameterization

We report N\non-blind deblurring (NBD) is a modeling method of the image deblurring problem in computer vision, where the blurring kernel is known or can be externally estimated. In this paper, we attempt to solve a parametric NBD problem, inspired by the simultaneous acquisition of ptychography and fluorescent imaging (FI). Ptychography is an imaging method that favors larger probes, i.e. convolutional kernels, while FI relies on a small probe for high resolution. Also, the kernel can be solved during ptychographic reconstruction. With Ptycho-FI using the same larger kernel, we can perform NBD on the blurred fluorescent images to achieve high-resolution FI, and thus speed up the experiments. To this end, we design a deep latent space deformation network that is directly parameterized by the kernel. The network consists of three components: encoder, deformer, and decoder, where the deformer is specifically meant to rectify the latent space representations of blurred images to a standard latent space, regardless of the kernel. The deformation network is trained with a two-stage training scheme. We conduct extensive experiments to confirm that our parametric model can adapt to drastically different blurring kernels and perform robust deblurring.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

NASA NTRS2001

Pluto/Kuiper Missions with Advanced Electric Propulsion and Power

In response to a request by NASA Code SD Deep Space Exploration Technology Program, NASA Glenn Research center performed a study to identify advanced technology options to perform a Pluto/Kuiper mission without depending on a 2004 Jupiter Gravity Assist, but still arriving before 2020. A concept using a direct trajectory with small, sub-kilowatt ion thrusters and Stirling radioisotope power system was shown to allow the same or smaller launch vehicle class (EELV) as the chemical 2004 baseline and allow launch in any year and arrival in the 2014 to 2020 timeframe. With the nearly constant power available from the radioisotope power source such small ion propelled spacecraft could explore many of the outer planetary targets. Such studies are already underway. Additional information is contained in the original extended abstract.

Oleson, S. R.↗

NASA NTRS2002

Outer Planet Exploration with Advanced Radioisotope Electric Propulsion

In response to a request by the NASA Deep Space Exploration Technology Program, NASA Glenn Research Center conducted a study to identify advanced technology options to perform a Pluto/Kuiper mission without depending on a 2004 Jupiter Gravity Assist, but still arriving before 2020. A concept using a direct trajectory with small, sub-kilowatt ion thrusters and Stirling radioisotope power systems was shown to allow the same or smaller launch vehicle class as the chemical 2004 baseline and allow a launch slip and still flyby in the 2014 to 2020 timeframe. With this promising result the study was expanded to use a radioisotope power source for small electrically propelled orbiter spacecraft for outer planet targets such as Uranus, Neptune, and Pluto.

Oleson, Steven↗

NASA NTRS2003

Reversing Flows and Heat Spike: Caused by Solar g-Modes?

The Quasi Biennial Oscillation in the Earth s upper atmosphere has an analog deep inside the Sun. As on Earth, the flow is east or west, it is at low latitude, and it reverses direction in a roughly periodic manner. The period in the solar case is 1.3 years. It was detected using solar oscillations similar to the way earthquakes are used to study the Earth's interior. But its cause was not known. We showed that global oscillations (g-modes) can supply enough angular momentum to drive zonal flows with the observed reversal period. This required a calculation of wave dissipation rates inside each flow and in the turbulent layer that separates any two flows of opposite sign. Heat that this process leaves behind causes a thermal spike inside the Sun at the same depth. This may explain an anomaly in observed sound speed that has had no sure explanation.

Mayr, Hans G.↗

NASA NTRS2003

The Visible Nulling Coronagraph--Progress Toward Mission and Technology Development

This paper describes a space mission for visible direct detection and spectroscopy of Earth like extrasolar planets using a nulling coronagraph instrument behind a moderately sized (approximately 4m) telescope in space. In our design, a 4 beam nulling interferometer is synthesized from the telescope pupil, producing a deep null proportional to theta (sup 4) which is then filtered by a coherent array of single mode fibers to suppress the residual scattered light. With diffraction limited telescope optics and similar quality components in the optical train (lambda/20), suppression of the starlight to 10 (exp -10) is achievable. We describe key features of the basic analysis, show how this effects a space mission design, present latest results of laboratory measurements demonstrating achievable null depth and component development, and discuss future key technical milestones.

coronagraph↗

NASA NTRS1994

(abstract) Spacecraft Doppler Tracking with the Deep Space Network in the Search for Gravitational Waves

The Deep Space Network generates accurate radio science data observables for investigators who use radio links between spacecraft and the Earth to examine small changes in the phase and/or amplitude of the signal to study a wide variety of structures and phenomena in space. Several such studies are directed at aspects of the theory of general relativity such as gravitational redshift and gravitational waves. A gravitational wave is a propagating, polarized gravitational field, a ripple in the curvature of space-time. In Einstein's theory of general relativity, the waves are propagating solutions of the Einstein field equations. Their amplitudes are dimensionless strain amplitudes that change the fractional difference in distance between test masses and the rates at which separated clocks keep time. Predicted by all relativistic theories of gravity, they are extremely weak (the ratio of gravitational forces to electrical forces is about 10(sup -40)) and are generated at detectable levels only by astrophysical sources - very massive sources under violent dynamical conditions. The waves have never been detected but searches in the low-frequency band using Doppler tracking of many spacecraft have been conducted and others are being planned. Upper limits have been placed on the gravitational wave strength with the best sensitivities to date are for periodic waves being 7 x 10(sup -15).

gravitational waves theory of general relativity D↗

NASA NTRS2007

Collaborative Resource Allocation

Collaborative Resource Allocation Networking Environment (CRANE) Version 0.5 is a prototype created to prove the newest concept of using a distributed environment to schedule Deep Space Network (DSN) antenna times in a collaborative fashion. This program is for all space-flight and terrestrial science project users and DSN schedulers to perform scheduling activities and conflict resolution, both synchronously and asynchronously. Project schedulers can, for the first time, participate directly in scheduling their tracking times into the official DSN schedule, and negotiate directly with other projects in an integrated scheduling system. A master schedule covers long-range, mid-range, near-real-time, and real-time scheduling time frames all in one, rather than the current method of separate functions that are supported by different processes and tools. CRANE also provides private workspaces (both dynamic and static), data sharing, scenario management, user control, rapid messaging (based on Java Message Service), data/time synchronization, workflow management, notification (including emails), conflict checking, and a linkage to a schedule generation engine. The data structure with corresponding database design combines object trees with multiple associated mortal instances and relational database to provide unprecedented traceability and simplify the existing DSN XML schedule representation. These technologies are used to provide traceability, schedule negotiation, conflict resolution, and load forecasting from real-time operations to long-range loading analysis up to 20 years in the future. CRANE includes a database, a stored procedure layer, an agent-based middle tier, a Web service wrapper, a Windows Integrated Analysis Environment (IAE), a Java application, and a Web page interface.

Wang, Yeou-Fang↗

DOE OSTI2025

Tools for unbinned unfolding

Machine learning has enabled differential cross section measurements that are not discretized. Going beyond the traditional histogram-based paradigm, these unbinned unfolding methods are rapidly being integrated into experimental workflows. Here, in order to enable widespread adaptation and standardization, we develop methods, benchmarks, and software for unbinned unfolding. For methodology, we demonstrate the utility of boosted decision trees for unfolding with a relatively small number of high-level features. This complements state-of-the-art deep learning models capable of unfolding the full phase space. To benchmark unbinned unfolding methods, we develop an extension of existing dataset to include acceptance effects, a necessary challenge for real measurements. Additionally, we directly compare binned and unbinned methods using discretized inputs for the latter in order to control for the binning itself. Lastly, we have assembled two software packages for the OmniFold unbinned unfolding method that should serve as the starting point for any future analyses using this technique. One package is based on the widely-used RooUnfold framework and the other is a standalone package available through the Python Package Index (PyPI).

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening ∗

Abstract Interface structures in complex oxides remain an active area of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D STEM datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO 3 and SrTiO 3 , using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We test the DCNN on simulated data and show that it is possible (with >95% accuracy) to identify a physically rough from a chemically diffuse interface and create a DCNN regression model to predict step positions. We quantify the applicability of the model to different thicknesses and the transferability of the approach. The method shown here is general and can be applied for any inverse imaging problem where forward models are present.

42 ENGINEERING↗