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Cryogenic engineering, hypergolic engineering, hazardous warning, structures and mechanics, computer sciences, communications, meteorology, technology applications, safety engineering, materials analysis, biomedicine, and engineering management and training aids research are reviewed.
Research and technology projects in the following areas are described: cryogenic engineering, hypergolic engineering, hazardous warning instrumentation, structures and mechanics, sensors and controls, computer sciences, communications, material analysis, biomedicine, meteorology, engineering management, logistics, training and maintenance aids, and technology applications.
A year ago at the First Long Duration Exposure Facility (LDEF) Post-Retrieval Symposium, detailed measurements on trunnion sections, as well as results from 'intentional' samples (Co, Ni, In, Ta, and V) and spacecraft parts were reported. For this year's Symposium, some of these findings are re-evaluated in combination with more recent results, to cast a longer perspective on the LDEF experience, and to sketch some promising avenues toward more effective participation in future missions. The LDEF analysis effort has been a superb training exercise, from which lessons learned need to be applied to future missions - right back to the early phases of mission planning.
The close and productive collaborations between the NWS Warning and Forecast Office, the Short Term Prediction and Research Transition Center at NASA Marshall Space Flight Center and the University of Alabama in Huntsville have provided a unique opportunity for science sharing and technology transfer. One significant technology transfer that has provided immediate benefits to NWS forecast and warning operations is the use of data from the North Alabama Lightning Mapping Array. This network consists of ten VHF receivers deployed across northern Alabama and a base station located at the National Space Science and Technology Center. Preliminary investigations done at WFO Huntsville, along with other similar total lightning networks across the country, have shown distinct correlations between the time rate-of-change of total lightning and trends in intensity/severity of the parent convective cell. Since May 2003 when WFO HUN began receiving these data - in conjunction with other more traditional remotely sensed data (radar, satellite, and surface observations) -- have improved the situational awareness of the WFO staff. The use of total lightning information, either from current ground based systems or future space borne instrumentation, may substantially contribute to the NWS mission, by enhancing severe weather warning and decision-making processes. Operational use of the data has been maximized at WFO Huntsville through a process that includes forecaster training, product implementation, and post event analysis and assessments. Since receiving these data, over 50 surveys have been completed highlighting the use of total lightning information during significant events across the Tennessee Valley. In addition, around 150 specific cases of interest have been archived for collaborative post storm analysis. From these datasets, detailed trending information from radar and total lightning can be compared to corresponding damage reports. This presentation will emphasize the effective use of total lightning information in warning decision making along with best practices for implementation of new technologies into operations.
For 6 years prior to the retirement of the Space Shuttle Program, the shuttles carried an onboard repair kit with a tool for emergency use: two tubes of NOAX, or "good goo," as some people called it. NOAX flew on all 22 flights following the Columbia accident, and was designed to repair damage that occurred on the exterior of the shuttle. Bill McMahon, a structural materials engineer at Marshall Space Flight Center says NASA needed a solution for the widest range of possible damage to the shuttle s exterior thermal protection system. "NASA looked at several options in early 2004 and decided on a sealant. Ultimately, NOAX performed the best and was selected," he says. To prove NOAX would work effectively required hundreds of samples manufactured at Marshall and Johnson, and a concerted effort from various NASA field centers. Johnson Space Center provided programmatic leadership, testing, tools, and crew training; Glenn Research Center provided materials analysis; Langley Research Center provided test support and led an effort to perform large patch repairs; Ames Research Center provided additional testing; and Marshall provided further testing and the site of NOAX manufacturing. Although the sealant never had to be used in an emergency situation, it was tested by astronauts on samples of reinforced carbon-carbon (RCC) during two shuttle missions. (RCC is the thermal material on areas of the shuttle that experience the most heat, such as the nose cone and wing leading edges.) The material handled well on orbit, and tests showed the NOAX patch held up well on RCC.
We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.
Using NASA's High Contrast Imaging Testbed (HCIT) at the Jet Propulsion Laboratory, we have experimentally investigated the sensitivity of dark hole contrast in a Lyot coronagraph for the following factors: 1) Lateral and longitudinal translation of an occulting mask; 2) An opaque spot on the occulting mask; 3) Sizes of the controlled dark hole area. Also, we compared the measured results with simulations obtained using both MACOS (Modeling and Analysis for Controlled Optical Systems) and PROPER optical analysis programs with full three-dimensional near-field diffraction analysis to model HCIT's optical train and coronagraph.
This briefing is an overview of NASA's hypersonic portfolio and core capabilities. The scope of work is fundamental research spanning technology readiness and system complexity levels; critical technologies enabling re-usable hypersonic systems; system-level research, design, analysis, validation; and, engage, invigorate and train the next generation of engineers. This briefing was requested by the Aeronautics Subcommittee of the NASA Advisory Council.
In 2015, NASA’s Jet Propulsion Laboratory partnered with Ames Research Center, Langley Research Center, and AeroVironment to develop Ingenuity, a small coaxial helicopter capable of flying within Mars’ unique atmospheric conditions. Ingenuity was successfully deployed from its protective shroud on the underside of the Mars 2020 Perseverance Rover and has flown 17 flights on Mars as of December 2021. A number of rotorcraft analysis tools were utilized, and a series of experimental tests were performed to ready Ingenuity for its launch with the Perseverance Rover in July 2020. In this paper, RotCFD, a Reynolds-averaged Navier-Stokes flow solver, is used to model Ingenuity in hover and forward flight for the purposes of validating tools to aid in the development of a future generation of Mars rotorcraft. The results from the RotCFD modeling are benchmarked against results from hover performance tests of the Ingenuity prototype in the 25-Foot Space Simulator at the Jet Propulsion Laboratory and are also compared to hover and forward flight predictions made by CAMRAD II, a well-known comprehensive rotorcraft analysis code. Surrogate performance models are trained to obtain a set of trimmed rotor settings for Ingenuity at different forward flight speeds, which are then used as inputs for the RotCFD forward flight simulations. Additionally, a study of the airframe-rotor interaction and a study of the aerodynamics of the individual airframe components of Ingenuity in forward flight are performed. Finally, to better understand performance predictions by RotCFD and CAMRAD II, a study is conducted on how sectional angles of attack in each code vary with radial station and azimuth.
Prior work in FY24 developed an adversarial AI agent aid in path analysis of physical protection systems. This agent, trained using a model-based reinforcement learning algorithm, was able to successfully learn the most vulnerable path in facilities. It was able to extend the current state of practice for physical protection design by exhibiting dynamic behavior based on current environmental conditions. Whereas PathTrace largely performs a static, graph-based analysis, the AI agent was able to make decisions based on relative position in the facility, current conditions (was the adversarial agnet discovered?), and proximity to secondary targets. The agent demonstrated some novel capabilities, but had limitations that need to be resolved before it can be used for production purposes. For example, the adversarial agent generalizes poorly and takes a relatively long time to train. Nonetheless, there is still considerable promise for developing the adversarial agent further in order to explore even richer, more dynamic behaviors (e.g., adversary motivations, environmental debris, and more). This work considers a complementary idea; development of a planning agent. The planning agent is envisioned as an auto-complete-like tool that can help accelerate security system design by human experts. The agent would respect existing barriers and sensors placed by a human expert while offering cost-effective suggestions (i.e., implicitly balancing effectiveness with cost) to improve the design. The goal is for this agent to be part of an expert’s toolbox, not to totally upend the current state-of-practice, or to displace human experts. The ultimate goal would be concurrent training of both the adversarial and planning agent together, to learn entirely through self-play. This would represent an entirely new way of performing system deign. We selected a hierarchical, model-based reinforcement learning algorithm to serve as the planning agent. This is an extension of concepts used in the prior FY24 adversarial agent work. There, we had a single agent acting an environment. Here, we have two different sub-agents (policies), working together, to form a complete agent. There is a manager policy, which can select abstract goals on slower time scales, and a worker, which performs primitive actions to reach goals selected by the manager. It is worth noting that this class of algorithm is challenging to work with. From our understanding, our work is one of the first successful uses of model-based reinforcement learning (MBRL) in nuclear energy1 , and likely the first hierarchical model-based reinforcement learning application in nuclear energy. Further, this work is one of the first known attempts to apply AI to perform a design tasks in nuclear energy. Consequently, there were significant implementation challenges and the bulk of the work was focused on successful implementation and algorithm design. The results presented here are very low technology readiness level as a consequence of the lack of related literature, but still represent a significant step forward in the pursuit of applied AI for design.
Seven heterogeneous areas within the Des Moines, Iowa area test site were selected to define candidate spectral training classes using a clustering algorithm. In addition to the 91 cluster (nonsupervised) classes, three supervised training classes were defined and subsequently included in the training statistics file. The identity of all 94 candidate classes were determined using available reference data. Through analysis of the interclass separabilities, the original 94 candidate training classes were reduced to 42 spectrally separable final classes. The minimum average transformed divergence values for the 42 spectral classes and for the best subsets of TM spectral bands are shown in a table.
Stardust is a comet sample return mission that successfully returned to Earth on January 15, 2006. Stardust's targeted landing area was the Utah Test and Training Range in the Northwest corner of Utah. Requirements for the risks associated with landing were levied on Stardust by the Utah Test and Training Range and NASA. This paper describes the analysis to verify that these requirements were met and and includes calculation of debris survivability, generation of landing site selection plots, and identification of keep-out zones, as well as appropriate selection of the landing site. Operationally the risk requirements were all met for both of the GOMO-GO polls, so entry was authorized.
Large language models (LLMs) have demonstrated remarkable success as foundational models, benefiting various downstream applications through fine-tuning. Loss scaling studies have demonstrated the superior performance of larger LLMs compared to their smaller counterparts. Nevertheless, training LLMs with billions of parameters poses significant challenges and requires considerable computational resources. For example, training a one trillion parameter GPT-style model on 20 trillion tokens requires a staggering 120 million exaflops. This research explores efficient distributed training strategies to extract this computation from Frontier, the world's first exascale supercomputer. We enable and investigate various model and data parallel training techniques, such as tensor parallelism, pipeline parallelism, and sharded data parallelism, to facilitate training a trillion-parameter model on Frontier. We empirically assess these techniques and their associated parameters to determine their impact on memory footprint, communication latency, and GPU's computational efficiency. We analyze the complex interplay among these techniques and find a strategy to combine them to achieve high throughput through hyperparameter tuning. We have identified efficient strategies for training large LLMs of varying sizes through empirical analysis and hyperparameter tuning. For 22 Billion, 175 Billion, and 1 Trillion parameters, we achieved GPU throughputs of 38.38%, 36.14%, and 31.96%, respectively. For the training of the 175 Billion parameter model and the 1 Trillion parameter model, we achieved 100% weak scaling efficiency on 1024 and 3072 Mi250X GPUs, respectively. We also achieved strong scaling efficiencies of 89% and 87% for these two models. We trained these models only tens of iterations instead of training till completion.
As part of NASA's Aviation Safety and Security Program, research has been in progress to develop aerodynamic modeling methods for simulations that accurately predict the flight dynamics characteristics of large transport airplanes in upset conditions. The motivation for this research stems from the recognition that simulation is a vital tool for addressing loss-of-control accidents, including applications to pilot training, accident reconstruction, and advanced control system analysis. The ultimate goal of this effort is to contribute to the reduction of the fatal accident rate due to loss-of-control. Research activities have involved accident analyses, wind tunnel testing, and piloted simulation. Results have shown that significant improvements in simulation fidelity for upset conditions, compared to current training simulations, can be achieved using state-of-the-art wind tunnel testing and aerodynamic modeling methods. This paper provides a summary of research completed to date and includes discussion on key technical results, lessons learned, and future research needs.
Some earlier work by the Search for Extraterrestrial Intelligence (SETI) Science Working Group (SWG) on the derivation of spectrum analyzer thresholds for a pulse detection algorithm based on an analysis of false alarm rates is extended. The algorithm previously analyzed was intended to detect a finite sequence of i periodically spaced pulses that did not necessarily occupy the entire observation interval. This algorithm would recognize the presence of such a signal only if all i-received pulse powers exceeded a threshold T(i): these thresholds were selected to achieve a desired false alarm rate, independent of i. To simplify the analysis, it was assumed that the pulses were synchronous with the spectrum sample times. This analysis extends the earlier effort to include infinite and/or asynchronous pulse trains. Furthermore, to decrease the possibility of missing an extraterrestrial intelligence signal, the algorithm was modified to detect a pulse train even if some of the received pulse powers fall below the threshold. The analysis employs geometrical arguments that make it conceptually easy to incorporate boundary conditions imposed on the derivation of the false alarm rates. While the exact results can be somewhat complex, simple closed form approximations are derived that produce a negligible loss of accuracy.
The artificial neural networks (ANN) methodology is an outgrowth of research in artificial intelligence. In this study, the feed-forward network model that was proposed by Rumelhart, Hinton, and Williams was applied to the mapping of functions that are encountered in structural mechanics problems. Several different network configurations were chosen to train the available data for problems in materials characterization and structural analysis of plates and shells. By using the recall process, the accuracy of these trained networks was assessed.
The long-term operation (LTO) of nuclear power plant (NPP) beyond their original design life of 40 years, can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Frequent NDE requirement may lead to frequent shutdown of the NPP. This in turn can lead to power outage and additional NDE-inspection-cost related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing more digital approach such as by using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. Towards this goal Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program is working on the development of a DT framework that can be used for real time environmental fatigue prediction of reactor components. The DT framework is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics such as finite element (FE) based modeling tools. Towards this overall goal, following are some of the major contributions made during the FY21: 1) Multiple 82/182 dissimilar metal weld (DMW) specimens (both solid-weld and joint-weld representing the actual reactor multi-metal nozzles) were fatigue tested. The resulting fatigue lives were compared to the NUREG-6909 based best-fit and design fatigue curves. Additionally, the results of 52/152 DMW fatigue specimens (which were recently tested at Republic of Korea under the sponsorship of International Nuclear Energy Research Initiative - INERI program) were compared to the NUREG-6909 based best-fit and design fatigue curves. From the comparison of 82/182 and 52/152 DMW test data with NUREG-6909 best-fit curve, most of the reported test data fall way away from the NUREG-6909 suggested best-fit or mean curve. The NUREG-6909 suggested best-fit curve is the best-fit curve of austenitic stainless steel and due to lack of enough data on Nickel-based welds, this is currently being used for predicting the life of Nickel-alloy-based welded components. However, the above observation may require higher scaling factor (e.g., ASME suggested factor of 20 on cycles rather than the current NUREG-6909 suggested factor of 12 on cycles) for scaling the austenitic-stainless-steel best-fit-curve for estimating the design or safe-life of a welded component. Accordingly, for example, if a DMW component experience a strain amplitude of 0.6% the PWR-water life of the component would be 52 cycles instead of 85 cycles. However, more DMW tests are required to further ascertain the above-mentioned observations. 2) A system level CAD and finite element model were developed which consists of reactor pressure vessel (RPV), part of steam generator (SG), part of pressurizer (PRZ), hot leg (HL), and surge line (SL). This is with detailed nozzle geometry and thermal-mechanical material properties of different metals to simulate realistic thermal-mechanical stress under connected system global thermal-mechanical boundary conditions. 3) Different system level heat transfer analyses were performed with estimation of relevant heat transfer coefficients. The resulting data were used in subsequent system level thermal-mechanical stress analysis and for generating spatial-temporal training and validation data for a system level digital-twin based temperature predictor. Transient heat transfer analyses were performed considering thermal boundary condition under design-basis (DB) loading and EDF (Électricité de France) data-based grid-load-following (EDF-GLF) loading cycles. 4) System level thermal-mechanical stress analysis was performed for identifying damage-prone hotspots and for future extension of the model for cyclic state prediction. From the system-level model simulation under DB loading cycle it is found that HL and the SL nozzle that connect to the HL can experience significant stress and strain and could be one of the weakest links in the overall reactor coolant system (RCS). 5) An AI/ML based DT model was developed for multi-time-series temperature prediction at any inside/outside thickness locations of PWR pressure boundary components. This is by using Recurrent-neural-network (RNN) and keras machine learning libraries. The RNN model was validated against two laboratory test-based data sets with one obtained through ANL’s in-air fatigue test system and other through PWR-water test loop. The experimentally validated DT model further validated against FE model results to predict thermal scarification related spatialtemporal temperatures at random locations of a component. The well validated DT model was then used for demonstrating spatial-temporal temperature prediction under 100+ years of reactor operation subjected to combined DB, EDF-GLF and randomized grid-load-following (RANDOMGLF) loading Cycles. The expert-elicitation DT model framework was developed assuming field/input/process measurements can be available from a few existing plant sensors and can readily be used by the NPP operators. The above temperature prediction model will feed to the next-step stress analysis model based on which the life of a component can be predicted in realtime, which is one of our future works.
Herein this paper describes a system-level view of a fully automated transit system comprising a fleet of automated vehicles (AVs) in driverless operation, each with an SAE level 4 Automated Driving System, along with its related safety infrastructure and other system equipment. This AV system-level control is compared to the automatic train control system used in automated guideway transit technology, particularly that of communications-based train control (CBTC). Drawing from the safety principles, analysis methods, and risk assessments of CBTC systems, comparable functional subsystem definitions are proposed for AV fleets in driverless operation. With the prospect of multiple AV fleets operating within a single automated mobility district, the criticality of protecting roadway junctions requires an approach like that of automated fixed-guideway transit systems, in which a guideway switch zone "interlocking" at each junction location deconflicts railway traffic, affirming safe passage. The analogous AV protection safety subsystem is defined as fail-safe equipment that monitors roadway intersections and junctions, communicates traffic signal status, perceives and communicates alerts and signals to AV connected vehicles concerning potential unsafe conditions, and performs related primary safety functions. Conclusions are drawn that the AV protection roadway intersection functions must be performed by local roadside equipment dedicated to protecting each roadway intersection and junction. Further, it is concluded that the communications technology connecting the infrastructure with the vehicle to perform this vital, fail-safe protection should meet specific functional and performance criteria.