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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Establishment of a Spaceport Network Architecture

Since the beginning of the space age, the main actors in space exploration have been governmental agencies, enabling a privileged access to space, but with very restricted and rare missions. The last decade has seen the rise of space tourism, and the founding of ambitious private space mining companies, showing the beginnings of a new exploration era, that is based on a more generalized and regular access to space and which is not limited to the Earth's vicinity. However, the cost of launching sufficient mass into orbit to sustain these inspiring challenges is prohibitive, and the necessary infrastructures to support these missions is still lacking. To provide easy and affordable access into orbital and deep space destinations, there is the need to create a network of spaceports via specific waypoint locations coupled with the use of natural resources, or In Situ Resource Utilization (ISRU), to provide a more economical solution. As part of the International Space University Space Studies Program 2012, the international and intercultural team of Operations and Service Infrastructure for Space (OASIS) proposes an interdisciplinary answer to the problem of economical space access and transportation. This paper presents a summary of a detailed report [1] of the different phases of a project for developing a network of spaceports throughout the Solar System in a timeframe of 50 years. The requirements, functions, critical technologies and mission architecture of this network of spaceports are outlined in a roadmap of the important steps and phases. The economic and financial aspects are emphasized in order to allow a sustainable development of the network in a public-private partnership via the formation of an International Spaceport Authority (ISPA). The approach includes engineering, scientific, financial, legal, policy, and societal aspects. Team OASIS intends to provide guidelines to make the development of space transportation via a spaceports logistics network feasible, and believes that this pioneering effort will revolutionize space exploration, science and commerce, ultimately contributing to permanently expand humanity into space.

Larson, Wiley J.↗
NASA NTRS2012

Engineering a Multimission Approach to Navigation Ground Data System Operations

The Mission Design and Navigation (MDNAV) Section at the Jet Propulsion Laboratory (JPL) supports many deep space and earth orbiting missions from formulation to end of mission operations. The requirements of these missions are met with a multimission approach to MDNAV ground data system (GDS) infrastructure capable of being shared and allocated in a seamless and consistent manner across missions. The MDNAV computing infrastructure consists of compute clusters, network attached storage, mission support area facilities, and desktop hardware. The multimission architecture allows these assets, and even personnel, to be leveraged effectively across the project lifecycle and across multiple missions simultaneously. It provides a more robust and capable infrastructure to each mission than might be possible if each constructed its own. It also enables a consistent interface and environment within which teams can conduct all mission analysis and navigation functions including: trajectory design; ephemeris generation; orbit determination; maneuver design; and entry, descent, and landing analysis. The savings of these efficiencies more than offset the costs of increased complexity and other challenges that had to be addressed: configuration management, scheduling conflicts, and competition for resources. This paper examines the benefits of the multimission MDNAV ground data system infrastructure, focusing on the hardware and software architecture. The result is an efficient, robust, scalable MDNAV ground data system capable of supporting more than a dozen active missions at once.

Mission Design and Navigation (MDNAV)↗
NASA NTRSDate not supplied

NASA's Efforts to Pursue Commercial Communications Services for Missions in Near Space

The National Aeronautics and Space Administration (NASA) Space Communications and Navigation (SCaN) Program enables high speed, robust, secure and cost-effective space communications and navigation services to current and future science and exploration missions. Consistent with National Space Policy, NASA is seeking commercial services for all its future near Earth requirements by incorporating additional direct to Earth (DTE) providers and introducing commercial satellite communications (SATCOM) vendors as NASA’s Tracking and Data Relay Satellites (TDRS) begin to decline. Through the progression of these efforts, SCaN is also integrating commercial services in the cislunar domain. The overarching objective is to satisfy the demands from the mission community – both in terms of capacity and capability – in a robust and cost-effective way, leveraging the strong and growing commercial space sector. For near earth space relay, six SATCOM vendors were awarded Funded Space Act Agreements in June of 2022 to demonstrate the ability to serve near-Earth missions, and are working through their committed milestones. The failure of TDRS Flight-9 (F9) in late 2022 prompted an increased tempo of mission engagement to identify driving user needs that commercial SATCOM can meet in the near-term. NASA has set a course to accelerate a decision to terminate commitments for TDRS services to new missions and has developed a plan to execute validation and risk reduction efforts with early adopter “pathfinder” missions to pave the way to earlier operational services. As the Artemis Program matures and builds on the successful Artemis I flight in November/December of 2022, the definition of the supporting communications and navigation architecture has further been refined. Although NASA government assets will continue to play a key role in provision of services, such as through the Deep Space Network, SCaN is pursuing commercial services offerings to expand ground network capability and implement lunar relay services. In February of 2023, SCaN released solicitation for services that include DTE services to missions near-Earth and in cislunar space, as well as lunar relay services. Further, SCaN is seeking opportunities to integrate commercial capability into lunar surface communications infrastructure. NASA is also advocating for standards that promote interoperability and strategic technology investments in the commercial sector. This paper addresses the recent progress and future plans in the near-Earth space and lunar regimes, discusses the unified approach for industry engagement, and highlights both the common and unique challenges of commercialization efforts in the two regimes.

commercialization↗
DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗
DOE OSTI2025

Casing Annulus Monitoring of CO 2 Injection Using Wireless Autonomous Distributed Sensor Networks

Effective and secure carbon subsurface storage, involving the deep underground injection of CO 2 into geological formations where it is permanently trapped, is paramount to mitigating CO 2 emissions (Figure I). Ensuring the integrity of these storage sites and detecting potential leakage through the casing annulus necessitates robust monitoring. This work provides the first integrated demonstration of a wireless casing-annulus monitoring architecture that can operate in highly attenuating cement-brine environments relevant to CO 2 storage. This project focused on developing and validating a novel sensor system for integration with autonomous monitoring near the cement reservoir interface. The goal was a fully integrated Technology Readiness Level (TRL) 4/5 field validation of a distributed wireless intelligent sensor system providing real-time, direct subsurface formation measurements to enhance fluid movement monitoring in the cemented casing annulus. Achieving this objective required the development and integration of 1) wireless autonomous microsensor technology by California Institute of Technology (Caltech); 2) sensor packaging and emplacement technology by Research Triangle Institute (RTI); and 3) smart well completions using wireless active casing collars and NOV pipe by the Sandia National Lab (SNL). The collaboration with the Caltech team in this project aimed to develop millimeter-scale radio frequency identification (RFID) sensors capable of detecting CO 2 , pH, and/or methane levels. These sensors are engineered to be impervious to fluids, allowing them to be mixed with cement and installed within the casing annulus. They operate using RFID protocols at frequencies of 902–928 MHz for both power and communication. A Sandia National Laboratories’ team engaged their expertise in the development of a Smart Collar system designed for the wireless data collection from these RFID sensors embedded in the cement annulus and transmission of this information to the ground surface via IntelliPipe/IntelliServ NOV drill pipe. This is accomplished through inductive coupling at the collar, which facilitates data transfer through each segment of the pipe. Because the system cannot transmit a direct current signal to power the Smart Collar, both power and communication were implemented using alternating current and electromagnetic signals at varying frequencies. Furthermore, the developed microsensor technology had to be demonstrated and validated in comparison with reference transducer measurements in a field test site at The University of Texas at Austin (UT-Austin). Although the full sensor suite did not reach field-deployment readiness, the system-level integration achieved in this project establishes a validated pathway for future incorporation of advanced microsensors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Enhance Low Level Temperature and Moisture Profiles Through Combining NUCAPS, ABI Observations, and RTMA Analysis

Thermodynamic information from low levels in the atmosphere is crucial for operational weather forecasts and meteorological researchers. The NOAA Unique Combined Atmospheric Processing System (NUCAPS) sounding products have been proven beneficial to fill the data gap between synoptic radiosonde observations (RAOBs). However, compared with the upper troposphere, the accuracy of NUCAPS soundings in the low levels still needs improvement. In this study, a deep neural network (DNN) is applied to fuse multiple data sources to enhance the NUCAPS temperature and moisture profiles in the lower atmosphere. The network is developed by combining satellite observations, including NUCAPS sounding retrievals and high resolution geostationary satellite observations from the Advanced Baseline Imager, and surface analysis from the Real-Time Mesoscale Analysis (RTMA) as inputs, while collocated soundings from ECMWF re-analysis version 5 are used as the benchmark for the training. The performance of the model is evaluated by using the independent testing data set, data from a different year, as well as collocated RAOBs, showing improvement to the temperature and moisture profiles by reducing the root-mean-squared-error (RMSE) by more than 30% in the lower atmosphere (from 700 hPa to surface) in both clear sky and partially cloudy conditions. A convective event from June 18, 2017 is presented to illustrate the application of the enhanced low level soundings on high impact weather events. The enhanced soundings from fused data capture the large surface-based convective available potential energy structures in the preconvection environment, which is very useful for severe storm nowcasting and forecasting applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Scalable Risk Assessment of Rare Events in Power Systems With Uncertain Wind Generation and Loads

Risk assessment of rare events has become increasingly important in power system planning and operation with the increasing integration of renewable energy and the presence of system uncertainties. However, quantifying the risk posed by rare events via the traditional method, i.e., Monte Carlo sampling (MCS), incurs substantial computational expense stemming from the vast ensemble of power flow simulations. To accelerate the assessment, this paper proposes a Deep Neural Network (DNN)-kernelized vector-valued Gaussian Process (VVGP) approach with excellent computational efficiency while maintaining high accuracy. Consequently, serving as a surrogate model for the power flow solver, the DNN-kernelized VVGP enables significantly faster but accurate risk assessment compared to the power flow solver. The developed surrogate model evaluates low-order N - k events that contain more than 90% instances by adeptly capturing the topological features while the high-order N - k events are assessed via a power flow solver, thereby striking a balance between computational efficiency and uncertainty quantification accuracy. Moreover, the model incorporates a Support Vector Machine (SVM) classifier to resample concerning low-probability tail events to counteract the biases potentially introduced during the DNN-kernelized VVGP evaluations. Simulations conducted on the modified IEEE 24-bus, 118-bus, and European 1354-bus systems demonstrate that the proposed method maintains the accuracy benchmark set by MCS while significantly reducing computational demands in large-scale power systems as compared to other state-of-the-art methods.

17 WIND ENERGY↗
DOE OSTI2023

Trustworthy Physics-Informed Deep Learning for Predictive Scientific Computing

This project has developed powerful trustworthy physics-informed deep learning (TPiDL) models and methods to fundamentally enhance the scale and power of computational modeling in the scientific and engineering domains. Deep learning (DL) has radically advanced the state-of-the-art in machine learning, computer vision, natural language processing, and also scientific computing. Nevertheless, progress has been driven almost entirely by empirical observations, hacks, and tricks. Under the support of this project, the graph operator learning tools and advanced trustworthy physical informed neural networks have been developed. In addition, stochastic gradient replica-exchange Markov Chain Monte Carlo (MCMC) sampling algorithms have been designed to quantify the uncertainties and speed up the training of large-scale neural networks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

Mu-2 ranging

The Mu-II Dual-Channel Sequential Ranging System designed as a model for future Deep Space Network ranging equipment is described. A list of design objectives is followed by a theoretical explanation of the digital demodulation techniques first employed in this machine. Hardware and software implementation are discussed, together with the details relating to the construction of the device. Two appendixes are included relating to the programming and operation of this equipment to yield the maximum scientific data.

Martin, W. L.↗
NASA NTRS2022

A Deep Learning Approach to Fast Radiative Transfer

Due to the sheer volume of data, leveraging satellite instrument observations effectively in a data assimilation context for numerical weather prediction or for remote sensing requires a radiative transfer model as an observation operator that is both fast and accurate at the same time. Physics-based line-by-line radiative transfer (RT) models fulfil the requirement for accuracy, but are too slow and too costly in computational terms for operational applications. Therefore, fast methods were developed to be able to perform fast RT calculations using techniques such as spectral sampling or pre-computed look-up tables. The operational fast models currently calculate the absorption and scattering coefficients from the pre-computed regression coefficients and atmospheric state and cloud profiles. As a novel solution to this problem, this work investigates a deep learning approach to replace the regression coefficients in the fast RT models. A selection of hidden-layer neural network configurations is trained against atmospheric transmittance profile data computed by an accurate line-by-line model and their performance is evaluated and their advantages and disadvantages are discussed.

Machine learning↗
NASA NTRSDate not supplied

Dense Feature Tracking of Atmospheric Winds with Deep Optical Flow

Atmospheric winds are a key physical phenomenon impacting natural hazards, energy transport, ocean currents, large-scale circulation, and ecosystem fluxes. Observing winds is a complex process and presents a large gap in NASA’s Earth Observation System. Atmospheric motion vectors (AMVs) aim to fill this gap by making numerical estimates of cloud movement between sequences of multi-spectral satellite images, tracking clouds and water vapor. Recent imaging hardware and software advancements have enabled the use of numerical optical flow techniques to produce accurate and dense vector fields outperforming traditional methods. This work presents WindFlow as the first machine learning based system for feature tracking atmospheric motion using optical flow. Due to the lack of large-scale satellite-based observations, we leverage high-resolution numerical simulations from NASA's GEOS-5 Nature Run to perform supervised learning and transfer to satellite images. We demonstrate that our approach using deep learning based optical flow scales to ultra-high-resolution images of size 2881x5760 with less than 1 m/s bias and 2.5 m/s average error. Four network and learning architectures are compared and it is found that recurrent all-pairs field transforms (RAFT) produces the lowest errors on all metrics for wind speed and direction. Results on held out numerical outputs shows RAFT's good performance in each of the spatial, temporal, and physical dimensions. A comparison between WindFlow and an operational AMV product against rawinsonde observations show that RAFT transfers across simulations and thermal infrared satellite observations. This work shows that machine learning based optical flow is an efficient approach to generating robust feature tracking for AMVs consistently over large regions.

Atmospheric winds↗
NASA NTRS1987

Results of using the global positioning system to maintain the time and frequency synchronization in the Deep Space Network

There are two hydrogen maser clocks located at each signal processing center (SPC) in the DSN. Close coordination of the time and frequency of the SPC clocks is needed to navigate spacecraft to the outer planets. A recent example was the Voyager spacecraft's encounter with Uranus in January 1986. The clocks were adjusted with the goal of minimizing time and frequency offsets between the SPCs at encounter. How time and frequency at each SPC is estimated using data acquired from the Global Positioning System Timing Receivers operating on the NBS-BIH (National Bureau of Standards-Bureau International de l'Heure) tracking schedule is described. These data are combined with other available timing receiver data to calculate the time offset estimates. The adjustment of the clocks is described. It was determined that long range hydrogen maser drift is quite predictable and adjustable within limits. This enables one to minimize time and frequency differences between the three SPCs for many months by matching the drift rates of the three standards. Data acquisition and processing techniques using a Kalman filter to make estimates of time and frequency offsets between the clocks at the SPCs and UTC(NBS) (Coordinated Universal Time realized at NBS) are described.

Clements, P. A.↗
NASA NTRS1996

The Mars Microprobe Mission: A Unique Solution for Network Science

The second deep-space advanced technology validation mission in NASA's New Millenium Program will demonstrate planetary microprobe technologies. Two microporobes, each consisting of a very low-mass aeroshell and penetrator system, are planned to be launched in January 1999 and arrive at at Mars in December 1999... This paper will summarize the engineering techniques that will be implemented to provide safe landing and operation of the Mars microprobe in its unique environment. This paper will also summarize some key mission and system design trades, as well as discuss some of the technologies being developed.

microprobes↗
NASA NTRS2011

Interplanetary Overlay Network Bundle Protocol Implementation

The Interplanetary Overlay Network (ION) system's BP package, an implementation of the Delay-Tolerant Networking (DTN) Bundle Protocol (BP) and supporting services, has been specifically designed to be suitable for use on deep-space robotic vehicles. Although the ION BP implementation is unique in its use of zero-copy objects for high performance, and in its use of resource-sensitive rate control, it is fully interoperable with other implementations of the BP specification (Internet RFC 5050). The ION BP implementation is built using the same software infrastructure that underlies the implementation of the CCSDS (Consultative Committee for Space Data Systems) File Delivery Protocol (CFDP) built into the flight software of Deep Impact. It is designed to minimize resource consumption, while maximizing operational robustness. For example, no dynamic allocation of system memory is required. Like all the other ION packages, ION's BP implementation is designed to port readily between Linux and Solaris (for easy development and for ground system operations) and VxWorks (for flight systems operations). The exact same source code is exercised in both environments. Initially included in the ION BP implementations are the following: libraries of functions used in constructing bundle forwarders and convergence-layer (CL) input and output adapters; a simple prototype bundle forwarder and associated CL adapters designed to run over an IPbased local area network; administrative tools for managing a simple DTN infrastructure built from these components; a background daemon process that silently destroys bundles whose time-to-live intervals have expired; a library of functions exposed to applications, enabling them to issue and receive data encapsulated in DTN bundles; and some simple applications that can be used for system checkout and benchmarking.

Burleigh, Scott C.↗
DOE OSTI2021

Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach

The dependence on advanced information and communication technology increases the vulnerability in smart grids under cyber-attacks. Recent research on unobservable false data injection attacks (FDIAs) reveals the high risk of secure system operation, since these attacks can bypass current bad data detection mechanisms. To mitigate this risk, this paper proposes a data-driven learning-based algorithm for detecting unobservable FDIAs in distribution systems. We use autoencoders for efficient dimension reduction and feature extraction of measurement datasets. Further, we integrate the autoencoders into an advanced generative adversarial network (GAN) framework, which successfully detects anomalies under FDIAs by capturing the unconformity between abnormal and secure measurements. Also, considering that the datasets collected from practical power systems are partially labeled due to expensive labeling costs and missing labels, the proposed method only requires a few labeled measurement data in addition to unlabeled data for training. Numerical simulations in three-phase unbalanced IEEE 13-bus and 123-bus distribution systems validate the detection accuracy and efficiency of this method.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Development of the Optical Communications Telescope Laboratory: A Laser Communications Relay Demonstration Ground Station

The Laser Communications Relay Demonstration (LCRD) project will demonstrate high bandwidth space to ground bi-directional optical communications links between a geosynchronous satellite and two LCRD optical ground stations located in the southwestern United States. The project plans to operate for two years with a possible extension to five. Objectives of the demonstration include the development of operational strategies to prototype optical link and relay services for the next generation tracking and data relay satellites. Key technologies to be demonstrated include adaptive optics to correct for clear air turbulence-induced wave front aberrations on the downlink, and advanced networking concepts for assured and automated data delivery. Expanded link availability will be demonstrated by supporting operations at small sun-Earth-probe angles. Planned optical modulation formats support future concepts of near-Earth satellite user services to a maximum of 1.244 Gb/s differential phase shift keying modulation and pulse position modulations formats for deep space links at data rates up to 311 Mb/s. Atmospheric monitoring instruments that will characterize the optical channel during the link include a sun photometer to measure atmospheric transmittance, a solar scintillometer, and a cloud camera to measure the line of sight cloud cover. This paper describes the planned development of the JPL optical ground station.

optical commuication↗
NASA NTRSDate not supplied

NASA’s Efforts to Pursue Commercial Communications Services for Missions in Near Space

The National Aeronautics and Space Administration (NASA) Space Communications and Navigation (SCaN) Program enables high speed, robust, secure, and cost-effective space communications and navigation services to current and future science and exploration missions. Consistent with National Space Policy, NASA is seeking commercial services for all its future near-Earth requirements by incorporating additional direct to Earth (DTE) providers and introducing commercial satellite relay communications (SATCOM) vendors as NASA’s Tracking and Data Relay Satellites (TDRS) begin to decline. Through the progression of these efforts, SCaN is also integrating commercial services in the cislunar domain. The overarching objective is to satisfy the demands from the mission community – both in terms of capacity and capability – in a robust, reliable, and cost-effective way, leveraging the strong and growing commercial space sector. For near earth space relay, six SATCOM vendors were awarded Funded Space Act Agreements in June of 2022 to demonstrate the ability to serve near-Earth missions with space-based communication relay services. All six vendors are working through their committed milestones with end-to-end service demonstrations tacking place throughout the mid-2020’s. The failure of TDRS Flight-9 (F9) in late 2022 prompted an increased tempo of mission engagement to identify driving user needs that commercial SATCOM can meet in the near-term. NASA has set a course to accelerate a decision to terminate offering TDRS services to new missions and is developing a plan to execute validation and risk reduction efforts with early adopter “pathfinder” missions, paving the way to operational services. As the Artemis Program matures and builds on the successful Artemis I flight in November-December of 2022, the definition of the supporting communications and navigation architecture has further been refined. Although NASA government assets will continue to play a key role in provision of services, such as through the Deep Space Network, SCaN is pursuing commercial services offerings to expand ground network capability and implement lunar relay services. In February of 2023, SCaN released solicitation for services that include DTE services to missions near-Earth and in cislunar space, as well as lunar relay services. Further, SCaN is seeking opportunities to integrate commercial capability into lunar surface communications infrastructure. NASA is also advocating for standards that promote interoperability and strategic technology investments in the commercial sector. This paper addresses the recent progress and future plans in the near-Earth space and lunar regimes, discusses the unified approach for industry engagement, and highlights both the common and unique challenges of commercialization efforts in the two regimes.

commercial services↗
DOE OSTI2021

Fully Convolutional Spatio-Temporal Models for Representation Learning in Plasma Science

We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma science. The onset of major disruptions is a critically important fusion energy science issue that must be resolved for advanced tokamak plasmas such as the $25B burning plasma international thermonuclear experimental reactor (ITER) experiment. While a variety of statistical methods have been used to address the problem of tokamak disruption prediction and control, recent approaches based on deep learning have proven particularly compelling. In the present paper, we introduce further improvements to the fusion recurrent neural network (FRNN) software suite, which delivered cross-machine disruption predictions with unprecedented accuracy using a large database of experimental signals from two major tokamaks. Up to now, FRNN was based on the long short-term memory (LSTM) variant of recurrent neural networks to leverage the temporal information in the data. Here, we implement and apply the "temporal convolutional neural network (TCN)" architecture to the time-dependent input signals. Furthermore, this allows highly optimized convolution operations to carry the majority of the computational load of training, thus enabling a reduction in training time, and the effective use of high-performance computing resources for hyperparameter tuning. At the same time, the TCN-based architecture achieves better predictive performance when compared with the LSTM architecture for various tasks for a representative fusion database.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗