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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Smallsat 2024 - Starling Cubesat Swarm Technology Demonstration Flight Results

The Starling swarm of four 6U CubeSats launched in July 2023 to test four key technologies to enable future swarm missions: 1) Mobile Ad-Hoc Networking (MANET) over a crosslink radio network 2) Autonomous onboard decision-making for operations 3) Optical-based absolute and relative navigation 4) Autonomous maneuver planning and execution The Starling team implemented the Better Approach to Mobile Ad-hoc Networking (B.A.T.M.A.N.) protocol to automatically manage the crosslink network of four satellites. The B.A.T.M.A.N. protocol uses a decentralized approach to managing a multi-hop mesh network of devices, in this case, a satellite swarm. The four satellites were able to successfully establish a network at multiple data rates and demonstrate file transfer and command issuance between spacecraft over the network. Starling incorporated Distributed Spacecraft Autonomy's (DSA) software to demonstrate onboard decision-making. The DSA software takes L1/L2 band GPS measurements and uses them to estimate the relative Total Electron Count (TEC) in the ionosphere. The onboard software then determines if there are any features of interest and provides that information to the other satellites over the crosslink network. The swarm of satellites then reaches a consensus on the optimal TEC observation strategy and adjusts its measurement collection tactics autonomously. The Starling Formation-Flying Optical Experiment (StarFOX), produced by Stanford's Space Rendezvous Laboratory, uses the onboard star trackers to collect images of the other swarm spacecraft and produce angles-only navigation estimates. This system is envisioned to be valuable in applications in which Global Navigation Satellite Systems (GNSS) are not available, such as in cis-lunar or deep space. StarFOX successfully applied its algorithms to multiple simultaneous spacecraft targets using the star tracker imagery. Finally, Starling used Emergent Space's Cluster Flight Application (CFA) software suite for the Reconfiguration and Orbit Maintenance Experiments Onboard (ROMEO) demonstration of autonomously planning and executing propulsive maneuvers. Large swarms will need to be able to maintain formation requirements with minimal operator involvement, especially as the size of the swarm scales up. Results from the ROMEO experiment are presented. Starling is funded by the Small Spacecraft Technology (SST) program out of NASA's Space Technology Mission Directorate (STMD).

distributed systems↗
DOE OSTI2026

A non-intrusive framework using acoustic signals and deep learning for boiling diagnostics in visual-limited environments

Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.

42 ENGINEERING↗
NASA NTRS1984

Effects of CW interference on the carrier tracking loop of the deep space network

A radio-frequency interference (RFI) environment can cause serious problems for communications systems, such as the Deep Space Network (DSN). For this reason, it is necessary to determine first the RFI susceptibility characteristics for DSN receiving systgms. The present investigation is concerned with CW RFI's which are close in frequency to the carrier frequency. The carrier tracking loop is highly sensitive, and this type of RFI will degrade the carrier loop performance before saturation effects become noticeable. The investigation has the objective to develop an analytical model which will make it possible to predict the tracking performance of the Block IV receiver when operated in the presence of a CW RFI in the receiver passband. The Block IV receiver represents one of the DSN receivers. Attention is given to the effects of a CW RFI on a phase-locked loop, the effects of a bandpass limiter, numerical results, experimental verification, and aspects of computer simulation.

Sue, M. K.↗
NASA NTRS1984

Design optimization of the 34-meter DSN-NCP antennas

The new NASA Deep Space Network (DSN) 34-m-diameter azimuth-elevation (Az-El) antenna structure is an example of an essentially computer-automated design. In addition to pivotal comptuer Lagrange multiplier design optimization software, much of the associated pre- and post-processing was also performed by computer. The construction of one of these antennas at Goldstone, California, is well advanced and will be completed this summer. A second installation is in progress in Australia. Both atennas will be used primarily for spacecraft tracking and will operate in the 8.5-GHz, 3.5-cm (1.4-in.) wavelength microwave frequency.

Levy, R.↗
NASA NTRS1992

A demonstration of real-time connected element interferometry for spacecraft navigation

Connected element interferometry is a technique of observing a celestial radio source at two spatially separated antennas, and then interfering the received signals to extract the relative phase of the signal at the two antennas. The high precision of the resulting phase delay data type can provide an accurate determination of the angular position of the radio source relative to the baseline vector between the two stations. A connected element interferometer on a 21-km baseline between two antennas at the Deep Space Network's Goldstone, CA tracking complex is developed. Fiber optic links are used to transmit the data at 112 Mbit/sec to a common site for processing. A real-time correlator to process these data in real-time is implemented. The architecture of the system is described, and observational data is presented to characterize the potential performance of such a system. The real-time processing capability offers potential advantages in terms of increased reliability and improved delivery of navigational data for time-critical operations. Angular accuracies of 50-100 nrad are achievable on this baseline.

Edwards, C.↗
NASA NTRS2015

A Journey with MOM

In late 2013, the Indian Space Research Organization (ISRO) launched its "Mars Orbiter Mission" (MOM). ISRO engaged NASA's Jet Propulsion Laboratory (JPL) for navigation services to support ISRO's objectives of MOM achieving and maintaining Mars orbit. The navigation support included planning, documentation, testing, orbit determination, maneuver design /analysis, and tracking data analysis. Several of MOM's attributes had an impact on navigation processes, e.g., S -band telecommunications, Earth Orbit Phase maneuvers, and frequent angular momentum desaturation s (AMDs). The primary source of tracking data was NASA/ JPL's Deep Space Network (DSN); JPL also conducted a performance assessment of Indian Deep Space Network (IDSN) tracking data. Planning for the Mars Orbit Insertion (MOI) was complicated by a pressure regulator failure that created uncertainty regarding MOM's main engine and raised potential planetary protection issues. A successful main engine test late on approach resolved these issues; it was quickly followed by a successful MOI on 24-September - 2014 at 02:00 UTC. Less than a month later, Comet Siding Spring's Mars flyby necessitated plans to minimize potential spacecraft damage. At the time of this writing, MOM's orbital operations continue, and plans to extend JPL 's support are in progress. This paper covers the JPL 's support of MOM through the Comet Siding Spring event.

Mars orbiter↗
DOE OSTI2022

Device-Centric Ransomware Detection using Machine Learning-Based Memory Forensics for Smart Inverters

Ransomware attacks are the fastest-growing form of cyberattacks worldwide. Recently, ransomware attacks have targeted industrial control systems (ICSs), including power grids. Lessons learned from recent incidents in ICSs show that ransomware groups can deliver ransomware into not only the organization’s control servers, but also the operational technology (OT) devices such as smart inverters and smart grid devices. This paper proposes a machine learning (ML)- based memory forensics method enabling the detection of ransomware binaries stored in the memory of a commercial smart inverter. Device firmware binary files are extracted from a Serial Peripheral Interface (SPI) flash memory, and samples of both benign and ransomware binaries are generated by a binary manipulation method and a real-world ransomware encryption, separately. A deep transfer learning (DTL) method is used to retrain a convolutional neural network (CNN)-based ransomware detection algorithm using the generated samples. The experimental result validates that the proposed ML-based memory forensics method can accurately detect ransomware files.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation

Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R 2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m 3 /m 3 ). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Learning diffractive optical communication around arbitrary opaque occlusions

Abstract Free-space optical communication becomes challenging when an occlusion blocks the light path. Here, we demonstrate a direct communication scheme, passing optical information around a fully opaque, arbitrarily shaped occlusion that partially or entirely occludes the transmitter’s field-of-view. In this scheme, an electronic neural network encoder and a passive, all-optical diffractive network-based decoder are jointly trained using deep learning to transfer the optical information of interest around the opaque occlusion of an arbitrary shape. Following its training, the encoder-decoder pair can communicate any arbitrary optical information around opaque occlusions, where the information decoding occurs at the speed of light propagation through passive light-matter interactions, with resilience against various unknown changes in the occlusion shape and size. We also validate this framework experimentally in the terahertz spectrum using a 3D-printed diffractive decoder. Scalable for operation in any wavelength regime, this scheme could be particularly useful in emerging high data-rate free-space communication systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. Here, in this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Smart Methane Emission Detection System Development Final Report

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators.

03 NATURAL GAS↗
NASA NTRS2006

Lower-cost architectures for large arays of small antennas

Future plans for NASA's Deep Space Network (DSN) call for the construction of arrays of small antennas to compliment and eventually replace the existing network of large single antennas. The motivation for this transformation is the need to support much higher downlink data rates in the future, along with the realization that the most cost-effective way to do this is though a large increase in total collecting area on the ground. As currently designed, the DSN arrays will consist of approximately four hundred 12-m diameter antennas at each of three longitudes, operating at X and Ka bands (8 and 32 GHz).

small antennas↗
DOE OSTI2024

D2NO: Efficient handling of heterogeneous input function spaces with distributed deep neural operators

Neural operators have been applied in various scientific fields, such as solving parametric partial differential equations, dynamical systems with control, and inverse problems. However, challenges arise when dealing with input functions that exhibit heterogeneous properties, requiring multiple sensors to handle functions with minimal regularity. To address this issue, discretization-invariant neural operators have been used, allowing the sampling of diverse input functions with different sensor locations. However, existing frameworks still require an equal number of sensors for all functions. We propose a novel distributed approach to further relax the discretization requirements and solve the heterogeneous dataset challenges. Our method involves partitioning the input function space and processing individual input functions using independent and separate neural networks. A centralized neural network is used to handle shared information across all output functions. This distributed methodology reduces the number of gradient descent back-propagation steps, improving efficiency while maintaining accuracy. Here, we demonstrate that the corresponding neural network is a universal approximator of continuous nonlinear operators and present three numerical examples to validate its performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Wavelet and Deep-Learning-Based Approach for Generation System Problematic Parameters Identification and Calibration

Accurate models of generation systems are critical for maintaining reliable and secure grid operations. In this paper, a novel and systematic approach is proposed to identify and calibrate the generation system problematic parameters using continuous wavelet transform (CWT) and advanced deep-learning technology. The phasor measurement unit (PMU) data are used through “event playback” to check whether the parameter calibration is required, and if yes, a group of suspicious parameters will be identified as the primary problematic parameter candidates (PPCs). These primary PPCs are randomly perturbed to generate the event playback simulation data, which are used by the CWT and convolutional neural networks (CNNs) to further narrow down the primary PPCs into a smaller set of candidates. Then, the identified candidates are perturbed again to generate massive event playback simulation data for training a parameter calibration neural network. Here, we designed a multi-output neural network structure to find the mappings between the perturbed parameters and the simulation data using both CNN and long short-term memory (LSTM) models. Finally, the well-trained and tested CNN-LSTM model is used to estimate the accurate value of the suspicious parameters with actual PMU measurements. The proposed CNN-LSTM network can accurately and reliably estimate the generation-system problematic parameters, and has better performance when compared to other machine-learning methods, such as the multilayer perceptron network and the conditional variational autoencoder method. The accuracy and effectiveness of the proposed approach have been validated through simulation and real-world data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1987

Sorption compressor/mechanical expander hybrid refrigeration

Experience with Deep Space Network (DSN) ground-based cryogenic refrigerators has proved the reliability of the basic two-stage Gifford-McMahon helium refrigerator. A very long life cryogenic refrigeration system appears possible by combining this expansion system or a turbo expansion system with a hydride sorption compressor in place of the usual motor driven piston compressor. To test the feasibility of this system, a commercial Gifford-McMahon refrigerator was tested using hydrogen gas as the working fluid. Although no attempt was made to optimize the system for hydrogen operation, the refrigerator developed 1.3 W at 30 K and 6.6 W at 60 K. The results of the test and of theoretical performances of the hybrid compressor coupled to these expansion systems are presented.

Jones, J. A.↗
NASA NTRS2010

Delay/Disruption Tolerance Networking (DTN) Implementation and Utilization Options on the International Space Station

The International Space Station (ISS) is in an operational configuration and nearing final assembly. With its maturity and diverse payloads onboard, the opportunity exists to extend the orbital lab into a facility to exercise and demonstrate Delay/Disruption Tolerant Networking (DTN). DTN is an end-to-end network service providing communications through environments characterized by intermittent connectivity, variable delays, high bit error rates, asymmetric links and simplex links. The DTN protocols, also known as bundle protocols, provide a store-and-forward capability to accommodate end-to-end network services. Key capabilities of the bundling protocols include: the Ability to cope with intermittent connectivity, the Ability to take advantage of scheduled and opportunistic connectivity (in addition to always up connectivity), Custody Transfer, and end-to-end security. Colorado University at Boulder and the Huntsville Operational Support Center (HOSC) have been developing a DTN capability utilizing the Commercial Generic Bioprocessing Apparatus (CGBA) payload resources onboard the ISS, at the Boulder Payload Operations Center (POC) and at the HOSC. The DTN capability is in parallel with and is designed to augment current capabilities. The architecture consists of DTN endpoint nodes on the ISS and at the Boulder POC, and a DTN node at the HOSC. The DTN network is composed of two implementations; the Interplanetary Overlay Network (ION) and the open source DTN2 implementation. This paper presents the architecture, implementation, and lessons learned. By being able to handle the types of environments described above, the DTN technology will be instrumental in extending networks into deep space to support future missions to other planets and other solar system points of interest. Thus, this paper also discusses how this technology will be applicable to these types of deep space exploration missions.

Holbrook, Mark↗
DOE OSTI2022

DNN-based policies for stochastic AC OPF

We report a prominent challenge to the safe and optimal operation of the modern power grid arises due to growing uncertainties in loads and renewables. Stochastic optimal power flow (SOPF) formulations provide a mechanism to handle these uncertainties by computing dispatch decisions and control policies that maintain feasibility under uncertainty. Most SOPF formulations consider simple control policies such as affine policies that are mathematically simple and resemble many policies used in current practice. Motivated by the efficacy of machine learning (ML) algorithms and the potential benefits of general control policies for cost and constraint enforcement, we put forth a deep neural network (DNN)-based policy that predicts the generator dispatch decisions in real time in response to uncertainty. The weights of the DNN are learnt using stochastic primal–dual updates that solve the SOPF without the need for prior generation of training labels and can explicitly account for the feasibility constraints in the SOPF. The advantages of the DNN policy over simpler policies and their efficacy in enforcing safety limits and producing near optimal solutions are demonstrated in the context of a chance constrained formulation on a number of test cases.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

TwinMe4AD: WGAN-based Digital Twins for Anomaly Detection

SAND2024-08373O TwinMe4AD is a Python-based software tool designed for anomaly detection using digital twins that closely mimic real, wearable healthcare datasets. The tool is invaluable for scenarios where collecting data is either expensive or impractical, serving as a privacy-preserving solution. Sensitive information is protected by training deep learning models on synthetic data derived from real datasets. One of TwinMe4AD's key features is its anomaly detection capability, which is based on fourth-order moments of parameters. This versatile approach can be applied across a range of datasets, from univariate to multivariate, making it compatible with various types of data. It also generates synthetic twins using Wasserstein Generative Adversarial Networks (WGANs), allowing users to create a small cohort of a population similar to that of a village population. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Poorey, Kunal↗