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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Circuit and Method for Communication Over DC Power Line

A circuit and method for transmitting and receiving on-off-keyed (OOK) signals with fractional signal-to-noise ratios uses available high-temperature silicon- on-insulator (SOI) components to move computational, sensing, and actuation abilities closer to high-temperature or high-ionizing radiation environments such as vehicle engine compartments, deep-hole drilling environments, industrial control and monitoring of processes like smelting, and operations near nuclear reactors and in space. This device allows for the networking of multiple, like nodes to each other and to a central processor. It can do this with nothing more than the already in-situ power wiring of the system. The device s microprocessor allows it to make intelligent decisions within the vehicle operational loop and to effect control outputs to its associated actuators. The figure illustrates how each node converts digital serial data to OOK 18-kHz in transmit mode and vice-versa in receive mode; though operations at lower frequencies or up to a megahertz are within reason using this method and these parts. This innovation s technique modulates a DC power bus with millivolt-level signals through a MOSFET (metal oxide semiconductor field effect transistor) and resistor by OOK. It receives and demodulates this signal from the DC power bus through capacitive coupling at high temperature and in high ionizing radiation environments. The demodulation of the OOK signal is accomplished by using an asynchronous quadrature detection technique realized by a quasi-discrete Fourier transform through use of the quadrature components (0 and 90 phases) of the carrier frequency as generated by the microcontroller and as a function of the selected crystal frequency driving its oscillator. The detected signal is rectified using an absolute-value circuit containing no diodes (diodes being non-operational at high temperatures), and only operational amplifiers. The absolute values of the two phases of the received signal are then summed and hard limited (digitized) by comparing them to a reference level and are then input into a microprocessor as a serial bit stream. The quasi-discrete Fourier transform is performed in high-temperature components (operational amplifiers, analog switches, resistors, and capacitors). The demodulated signal is a serial data stream that is input to the UART (universal asynchronous receiver transmitter) receiver pin of the microprocessor. The OOK of the carrier frequency uses the output of the UART pin as an enabling signal that drives the gate of the MOSFET. Logic low bits enable the carrier frequency (realized by using the 0 phase signal from the microcontroller, though either phase may be used) to be DC-coupled to the power supply bus through a current-limiting resistor mounted between the MOSFET drain and the supply rail. The presence of logic lows on the power supply rail is realized by carrier bursts while logic highs are realized by the absence of bursts.

Krasowski, Michael J.↗
DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗
DOE OSTI2021

Hybrid Imitation Learning for Real-Time Service Restoration in Resilient Distribution Systems

Self-healing capability is a critical factor for a resilient distribution system, which requires intelligent agents to automatically perform service restoration online, including network reconfiguration and reactive power dispatch. Here, the article proposes the imitation learning framework for training such an agent, where the agent will interact with an expert built based on the mixed-integer program to learn its optimal policy, and therefore significantly improve the training efficiency compared with exploration-dominant reinforcement learning (RL) methods. This significantly improved training efficiency makes the training problem under N-k scenarios tractable. A hybrid policy network is proposed to handle tie-line operations and reactive power dispatch simultaneously to further improve the restoration performance. The 33-bus and 119-bus systems with N-k disturbances are employed to conduct the training. The results indicate that the proposed method outperforms traditional RL algorithms such as the deep-Q network.

42 ENGINEERING↗
DOE OSTI2020

Approximating Trajectory Constraints With Machine Learning – Microgrid Islanding With Frequency Constraints

Here, we introduce deep earning aided constraint encoding to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Numerical modeling based machine learning approach for the optimization of falling - film evaporator in thermal desalination application

Scale formation that drastically increases thermal resistance and reduces freshwater production remains a critical challenge in thermal desalination. Novel designs of falling film evaporator and optimal operating condition hold great promise to mitigate scale formation, and increase heat transfer performance and fresh water production. In this work, CFD simulation based machine learning and multi-objective optimization are performed to identify optimal conditions and tube arrangement for evaporator. Non-dominated sorting genetic algorithm is adopted to determine and analyze the optimal pareto front for multiple objectives in desalination criteria. The errors of training, validation, and testing set are computed to identify an optimal hyperparameter set. For performance ratio, fouling resistance, and water production rate, the average relative error is 2.26%, 3.67%, and 3.24%. At pareto front, both performance ratio and water production rate increase at high temperature with fouling resistance (thermal resistance of the fouling layer) increasing as well. Tradeoffs between mitigating scale formation and enhancing desalination performance are evaluated in optimizations for different objectives. Finally, potential optima are identified and can be applied as guidelines to determine evaporator design and system operating conditions.

42 ENGINEERING↗
DOE OSTI2025

Continuous operation of a coherent 3,000-qubit system

Neutral atoms are a promising platform for quantum science, enabling advances in areas ranging from quantum simulations and computation to metrology, atomic clocks and quantum networking. Although atom losses typically limit these systems to a pulsed mode, continuous operation could substantially enhance cycle rates, remove bottlenecks in metrology and enable deep-circuit quantum evolution through quantum error correction. Here we demonstrate an experimental architecture for high-rate reloading and continuous operation of a large-scale atom-array system while realizing coherent storage and manipulation of quantum information. Our approach utilizes a series of two optical lattice conveyor belts to transport atom reservoirs into the science region, where atoms are repeatedly extracted into optical tweezers without affecting the coherence of qubits stored nearby. Using a reloading rate of 300,000 atoms in tweezers per second, we create over 30,000 initialized qubits per second, which we leverage to assemble and maintain an array of over 3,000 atoms for more than 2 hours. Furthermore, we demonstrate persistent refilling of the array with atomic qubits in either a spin-polarized or a coherent superposition state while preserving the quantum state of stored qubits. Our results pave the way for the realization of large-scale continuously operated atomic clocks, sensors and fault-tolerant quantum computers.

atomic and molecular physics↗
DOE OSTI2023

Solving multiphysics-based inverse problems with learned surrogates and constraints

Abstract Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved inversions for the important fluid-flow property, permeability, it also provides a natural platform for inverting multimodal data including well measurements and active-source time-lapse seismic data. By adding a learned constraint, we arrive at a computationally feasible inversion approach that remains accurate. This is accomplished by including a trained deep neural network, known as a normalizing flow, which forces the model iterates to remain in-distribution, thereby safeguarding the accuracy of trained Fourier neural operators that act as surrogates for the computationally expensive multiphase flow simulations involving partial differential equation solves. By means of carefully selected experiments, centered around the problem of geological carbon storage, we demonstrate the efficacy of the proposed constrained optimization method on two different data modalities, namely time-lapse well and time-lapse seismic data. While permeability inversions from both these two modalities have their pluses and minuses, their joint inversion benefits from either, yielding valuable superior permeability inversions and CO 2 plume predictions near, and far away, from the monitoring wells.

Yin, Ziyi (ORCID:0000000250248771)↗
NASA NTRS1976

Viking X-band telemetry experiment

In order to uncover operational and design problems in the use of X-band by the 1977 Mariner Jupiter-Saturn mission and future spacecraft using the Deep Space Network, the Viking X-band telemetry experiment was conducted. The experiment was conducted during the months of December 1975 and January 1976. During each of the five successful passes, a periodic sequence (in lieu of ranging) was transmitted to the spacecraft and returned by the spacecraft transponder on both S- and X-bands. These telemetry-like signals were received, demodulated, and detected. From a variety of measurements at the station, four independent measurements were made of the received signal-to-noise ratio (SNR). These four SNRs were later compared with each other and the predicted SNR. The principal result of the experiment is that X-band telemetry works as expected. That is, the measured SNRs were consistent relative to each other and to the predicted values within the accuracy of the experiment.

Weber, W. J., III↗
NASA NTRS1977

The deep space network

A Deep Space Network progress report is presented dealing with in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations.

Source record↗
NASA NTRS1979

The deep space network

A report is given of the Deep Space Networks progress in (1) flight project support, (2) tracking and data acquisition research and technology, (3) network engineering, (4) hardware and software implementation, and (5) operations.

Source record↗
NASA NTRS1986

The Telecommunications and Data Acquisition Report

This publication, one of a series formerly titled The Deep Space Network (DSN) Progress Report, documents DSN progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations. In addition, developments in Earth-based radio technology as applied to geodynamics, astrophysics, and the radio search for extraterrestrial intelligence are reported.

Posner, E. C.↗
NASA NTRS1987

A growth path for deep space communications

Increased Deep Space Network (DPN) receiving capability far beyond that now available for Voyager is achievable through a mix of increased antenna aperture and increased frequency of operation. In this note a sequence of options are considered: adding midsized antennas for arraying with the existing network at X-band; converting to Ka-band and adding array elements; augmenting the DSN with an orbiting Ka-band station; and augmenting the DSN with an optical receiving capability, either on the ground or in space. Costs of these options are compared as means of achieving significantly increased receiving capability. The envelope of lowest costs projects a possible path for moving from X-band to Ka-band and thence to optical frequencies, and potentially for moving from ground-based to space-based apertures. The move to Ka-band is clearly of value now, with development of optical communications technology a good investment for the future.

Layland, J. W.↗
NASA NTRS1988

Radar and the DSN

This paper describes how a station, designed, built, and operated for spacecraft communications has been used for scientific planetary radar studies. The thrust of the paper is the mutual advantage that the NASA/JPL Deep Space Network (DSN) and the Goldstone Solar System Radar have derived from sharing some equipment. It is concluded that, by allocating a small fraction of a DSN station's tracking time to planetary radar studies, a superb scientific instrument has been developed.

Reid, M. S.↗
NASA NTRS1992

Beam-waveguide antenna performance predictions with comparisons to experimental results

An overview of a NASA/JPL antenna project is presented, with specific focus on the methodology used to predict the microwave performance of a 34-m-diameter beam-waveguide (BWG) reflector antenna, designated DSS 13 (Deep Space Station 13). DSS 13 is the R&D facility serving the NASA/JPL Deep Space Network. Microwave performance predictions as well as a summary of test results for the antenna are given. The antenna has Cassegrain and centerline BWG operating modes at X-band (8.450-GHz) and Ka-band (32-GHz) frequencies. The performance predictions regarding antenna area efficiencies, corresponding beampeak gains, and for several (but not all) operating noise temperatures are found to agree reasonably well with the corresponding experimental results.

Bathker, Dan A.↗
DOE OSTI2023

Scalable Graph Analytics and HPC Operational Enhancement: Parallel Computing and ML/DL Innovations

Parallel computing plays a pivotal role in the efficient processing of large-scale graphs. Complex network analysis stands as a capti- vating research frontier, holding promise across diverse scientific domains such as sociology, biology, online media, and recommenda- tion systems. In this era, Machine Learning (ML) and Deep Learning (DL) have emerged as indispensable tools, underpinning remarkable technological achievements. Within this dynamic landscape, my research revolves around advancing parallel algorithms tailored for large-scale graph operations. To achieve this, I harness the power of cutting-edge technologies including OpenMP, MPI, HIP, and CUDA, on the High-Performance Computing (HPC) platforms to unlock optimal performance. I also apply ML/DL techniques to HPC operational data, to streamline the monitoring and maintenance of supercomputers, alleviating the complexities associated with their upkeep and enhancing user support. My research echoes the syn- ergy between parallel computing, large-scale graph analysis, and ML/DL, improving computational efficiency and user experience.

Sattar, Naw Safrin↗