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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Deep learning based event reconstruction for cyclotron radiation emission spectroscopy

The objective of the cyclotron radiation emission spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time–frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization—may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment—a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium β - -decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2011

Dexterous Operations on ISS and Future Applications

The Mobile Servicing System (MSS) is a complex robotics system used extensively in the assembly, inspection and maintenance of the International Space Station (ISS). Its external components are comprised of the Space Station Remote Manipulator System (SSRMS), the Mobile Base System (MBS), and the Special Purpose Dexterous Manipulator (SPDM or "Dextre"). Dexterous robotic maintenance operations on the ISS are now enabled with the launch and deployment of "Dextre" in March 2008 and the recently completed commissioning to support nominal operations. These operations include allowing for maintenance of the MSS capability to be executed uniquely via robotic means. Examples are detailed inspection and the removal and replacement of On-orbit Replaceable Units (ORUs) located outside the pressurized volume of the ISS, alleviating astronauts from performing numerous risky and time-consuming extra-vehicular activities (EVAs). In light of the proposed extension of the ISS to 2020 and beyond, "Dextre" can also be seen as a resource for the support and conduct of external ISS experiments. "Dextre" can be utilized to move experiments around ISS, as test bed for more elaborate experiments outside the original design intent, and as a unique platform for external experiments. This paper summarizes the status of "Dextre", its planned use, and future potential for dexterous operations on the ISS. Lessons learned from the planning and execution of SPDM commissioning are first introduced, and significant differences between "Dextre" and SSRMS operations are discussed. The use of ground control as the predominant method for operating "Dextre" is highlighted, along with the benefits and challenges that this poses. Finally, the latest plans for dexterous operations on ISS are summarized including visiting vehicle unloading, nominal maintenance, and operations of a more experimental flavor.

Keenan, P. Andrew↗
NASA NTRSDate not supplied

NASA GPM Status and Future Activities

The joint U.S.-Japan Global Precipitation Measurement (GPM) mission is approaching a decade of operations, and continues to pursue research, dataset production, and outreach related to precipitation. Key activities over the last year were the release of an improved “Version 07” of all GPM precipitation and latent heating products, boosting the orbit of the GPM Core Observatory (GPM CO) to 435 km, and improving quality control on precipitation retrievals from the GPM constellation of passive microwave satellites. This presentation summarizes key improvements to the GPM products and provides some examples of the changes between Versions 06 and 07 in algorithm performance. One important operational change that affected Version 07 is that the scanning strategy for the Ka-band radar channel changed in May 2018; all products that depend on Ka were revised to accommodate this change. For example, in Version 07 the Goddard Profiling (GPROF) algorithm has implemented improvements in regions where orographic enhancement and suppression take place and where the surface is snowy/icy, and again covers radiometers reaching back to 1987. The Combined Radar Radiometer Algorithm (CORRA) now incorporates modified drop-size distribution constraints that substantially reduce bias. Revisions to the Convective-Stratiform Heating (CSH) algorithm employ new radiative transfer retrievals as well as accounting for terrain in the vertical coordinates. Each algorithm was adjusted to ensure continuity for each product across the boundary in 2014 between the predecessor Tropical Rainfall Measuring Mission (TRMM) and the GPM CO. The U.S. Science Team’s Integrated Multi-satellitE Retrievals for GPM (IMERG) was upgraded to account for distortions in the probability density function of regional precipitation rates due to weighted averaging in the Kalman filter used for “morphing” the passive microwave data. Maintaining the GPM CO orbital altitude in the the current very active solar cycle has been forcing the use of more fuel than planned and consequently shortening the forecasted life of the mission from the early 2030's to the late 2020's. It was considered vital to regain some of this lifetime to ensure overlap with the upcoming Atmosphere Observing System mission to provide crosscalibration of instruments. To accomplish this, the orbital altitude was raised from 400 to 435 km on 7-8 November 2023. Thereafter, the primary GPM CO algorithms had to be revised to account for the change in observing parameters. By meeting time this action should be complete. Recently, a screening algorithm based on auto-encoding was developed that uncovered 162 orbits (out of the many thousands of orbits across all years and all satellites) of passive microwave retrievals that had highly anomalous values. Removing these defective retrievals has improved the integrity of both the GPROF and IMERG records. However, the nature of the IMERG processing interacted sufficiently badly with the now-discovered anomalous orbits that it was necessary to completely reprocess the IMERG Final Run record, now labeled Version 07B. The presentation also considers major issues that require continued attention, including the use of machine learning algorithms and the operational challenge of swarms of “small”, perhaps short-lived satellites.

GPM↗
DOE OSTI2017

Lignite-plus-Biomass to Synthetic Jet Fuel with CO2 Capture and Storage: Design, Cost, and Greenhouse Gas Emissions Analysis for a Near-Term First-of-a-Kind Demonstration Project and Prospective Future Commercial Plants

We report on a 30-month design study for a first-of-a-kind (FOAK) demonstration plant that would be built at a site near Meridian, Mississippi, to coprocess lignite coal and woody biomass into jet fuel. The design uses an oxygen-blown TRIG™ gasifier developed by KBR and Southern Company. Fischer-Tropsch conversion of the syngas produces synthetic paraffinic kerosene (SPK) as the primary product, plus naphtha. Other co-products include electricity sold to the grid and CO 2 sold for use in enhanced oil recovery (EOR). Previous studies have identified coprocessing of various coals and biomass with CO 2 capture as promising options for cost-competitive production of low net lifecycle greenhouse gas (GHG) emissions synthetic fuels. The effort reported here goes beyond earlier studies in the level of detail in process design and cost estimation with the aim of improving the understanding of the economic prospects for lignite and woody biomass coprocessing systems. Key objectives in the design of the FOAK lignite/biomass-to-jet (LBJ) plant were to achieve net lifecycle GHG emissions for the SPK that are less than for conventional petroleum-derived jet fuel and to make process design and equipment selections such that the plant could be built and operated in the near term, e.g., before 2025. The process design was developed by researchers at Princeton University and the University of Queensland and validated by engineers at the WorleyParsons Group (WP). Commercial vendors provided design and cost estimates for several major plant components. Bare-erected capital costs and operating and maintenance costs were estimated by WP. The primary objective in building and operating the FOAK plant would be to demonstrate the technical viability of the LBJ concept as an essential first step toward launching commercial-scale plants in the longer term. With this in mind, the project team developed a set of principles to help guide the process design: the total plant cost should be less than 2 billion USD (to limit investment risk); the level of input biomass should be both proportionally significant to reflect biomass/lignite co-gasification and sufficient to achieve GHG emissions goals; process design decisions and vendor/equipment selections should prioritize the likelihood of technical success over minimizing the cost of jet fuel production. The resulting FOAK plant design capacity is 1,551 metric t/d (45.5% moisture) lignite and 556 t/d (43.3% moisture) biomass, for a total input of 295 MW HHV , of which 25% is biomass. The design output is 1,252 actual barrels per day of liquids, of which 80% is SPK (62.3 MW LHV ) and 20% is naphtha (13.9 MW LHV ). It exports 15 MW e of electricity after satisfying a 38 MW e onsite auxiliary load. Additional products are 1,326 t/d of pressurized pipeline-quality CO 2 and 49 t/day of sulfuric acid (93 wt% H 2 SO 4 ). With thinnings from sustainably-managed southern pine plantations as the biomass, and with captured CO 2 stored underground via EOR, the net lifecycle emissions for the SPK product are estimated to be about one-quarter of those for petroleum-derived jet fuel. The bare-erected cost (BEC) estimated by WP for this plant is 588 million USD (2015). The authors’ best estimate of total plant cost (TPC) is 1,230 million USD, arrived at by assuming engineering, procurement and construction management services (20% of BEC), process 5 contingencies (35% of BEC), and project contingencies (35% of the sum of all other costs, i.e., 35% of 1.55xBEC). Not surprisingly, an annual discounted cash flow (DCF) analysis determined that it would be impossible to generate a positive net present value (NPV) over a wide range in key input assumptions. An SPK production cost subsidy of nearly 400/bbl USD over a 20-year plant life would be required to achieve zero NPV under a baseline set of assumptions (including 3% real weighted average cost of capital and a levelized crude oil price of 80/bbl USD). Alternatively, a capital grant in excess of the TPC value would also achieve zero NPV. The poor financial results reflect the small scale of the plant, the design principle to prioritize technical success, the levels of contingencies appropriate for the relatively early stage of project development, and the first-of-a-kind nature of the plant. Technology innovations, learning via construction and operating experience, alternative plant configurations, and larger scale should improve economics of future plants. To help understand these prospects, a preliminary analysis of N th -of-a-kind (NOAK) plants was developed, but with the limitation that plants would use only equipment components that for the most part are already commercial today: consideration of advanced, not-yet-commercial technologies and of R&D-driven improvements in existing technologies were beyond the scope of this analysis. The analysis found that a variety of NOAK plant designs that coprocess lignite and woody biomass to make jet fuel are unlikely to be economically competitive without subsidy even in the presence of a high future carbon tax or equivalent greenhouse gas mitigation policy. This conclusion applies to process configurations and input biomass/lignite ratios that result in net GHG emissions as high as those for petroleum-derived jet fuel and as low as zero. In contrast, encouraging results were found for plants processing only biomass. The economics of these “BECCS” plants (biomass energy with CCS) improve dramatically with the strength of carbon mitigation policies because of their strongly negative net GHG emissions. These findings do not imply that coal/biomass coprocessing strategies for making synfuels with CCS are not economically promising – only that, in the case of lignite, much more than a 25% biomass coprocessing rate would be needed. However, our analysis shows that all such systems are unlikely to be economic in the absence of a strong carbon mitigation policy. Future R&D driven technological innovations could modify this conclusion. Among other R&D priorities, an emphasis on better understanding and reducing plant auxiliary loads is warranted.

09 BIOMASS FUELS↗
NASA NTRS2007

International Space Station Materials: Selected Lessons Learned

The International Space Station (ISS) program is of such complexity and scale that there have been numerous issues addressed regarding safety of materials: from design to manufacturing, test, launch, assembly on-orbit, and operations. A selection of lessons learned from the ISS materials perspective will be provided. Topics of discussion are: flammability evaluation of materials with connection to on-orbit operations; toxicity findings for foams; compatibility testing for materials in fluid systems; and contamination control in precision clean systems and critical space vehicle surfaces.

Golden, Johnny L.↗
DOE OSTI2025

Artificial Intelligence for Data Center Operations (AIOps): Cooperative Research and Development (Final Report)

High performance computing data centers will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence and machine learning approaches provide the means to improve HPC data center operational efficiency, by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. NREL has developed methods of real-time collection, aggregation and streaming of these data in the ESIF HPC Data Center and has collected a significant dataset of relevant metrics across computer systems, racks, environmental, building and utility sources for research into various predictive analytics problems. HPE's Advanced Technology Group (ATG) is doing comprehensive research into exascale monitoring and management for High Performance Computing (HPC) systems (hereinafter HPE's Data Monitoring/ Management Technology). NREL and HPE will collaborate to add Artificial Intelligence (AI) to NREL's real-time data collection/ aggregation/ streaming system and HPE's Data Monitoring/ Management System, with the goal of improving the operational efficiency of NREL's Energy Systems Integration Facility (ESIF) HPC Data Center through data analytics on both historical and real-time data from IT systems and facilities operations. This collaboration will consist of efforts in Data Management, Data Analytics, and AI/ML Optimization for both manual and autonomous intervention in data center operations. This will be a multi-year, multi-staged effort with a goal towards building capabilities for an Advanced Smart Facility, and demonstration of these techniques in the NREL ESIF HPC Data Center.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

Hierarchical representation and machine learning from faulty jet engine behavioral examples to detect real time abnormal conditions

The theoretical basis and operation of LEBEX, a machine-learning system for jet-engine performance monitoring, are described. The behavior of the engine is modeled in terms of four parameters (the rotational speeds of the high- and low-speed sections and the exhaust and combustion temperatures), and parameter variations indicating malfunction are transformed into structural representations involving instances and events. LEBEX extracts descriptors from a set of training data on normal and faulty engines, represents them hierarchically in a knowledge base, and uses them to diagnose and predict faults on a real-time basis. Diagrams of the system architecture and printouts of typical results are shown.

Gupta, U. K.↗
DOE OSTI2025

Robustness of Deep Learning Classification to Adversarial Input on GPUs: Asynchronous Parallel Accumulation Is a Source of Vulnerability

The ability of machine learning (ML) classification models to resist small, targeted input perturbations—known as adversarial attacks—is a key measure of their safety and reliability. We show that floating-point non associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any perturbation to the input. Additionally, we show that this misclassification is particularly significant for inputs close to the decision boundary and that standard adversarial robustness results may be overestimated up to 4.6 when not considering machine-level details. We first study a linear classifier, before focusing on standard Graph Neural Network (GNN) architectures and datasets used in robustness assessments. We develop a novel black-box attack using Bayesian optimization to discover external workloads that can change the instruction scheduling which bias the output of reductions on GPUs and reliably lead to misclassification. Motivated by these results, we present a new learnable permutation (LP) gradient-based approach to learning floating-point operation orderings that lead to misclassifications. The LP approach provides a worst-case estimate in a computationally efficient manner, avoiding the need to run identical experiments tens of thousands of times over a potentially large set of possible GPU states or architectures. Finally, using instrumentation-based testing, we investigate parallel reduction ordering across different GPU architectures under external background workloads, when utilizing multi-GPU virtualization, and when applying power capping. Our results demonstrate that parallel reduction ordering varies significantly across architectures under the first two conditions, substantially increasing the search space required to fully test the effects of this parallel scheduler-based vulnerability. These results and the methods developed here can help to include machine-level considerations into adversarial robustness assessments, which can make a difference in safety and mission critical applications.

Shanmugavelu, Sanjif [Maxeler Technologies, a Groq↗
DOE OSTI2022

Operation-adversarial scenario generation

This paper proposes a modified conditional generative adversarial network (cGAN) model to generate net load scenarios for power systems that are statistically credible, conditioned by given labels (e.g., seasons), and, at the same time, “stressful” to the system operations and dispatch decisions. The measure of stress used in this paper is based on the operating cost increases due to net load changes. The proposed operation-adversarial cGAN (OA-cGAN) internalizes a DC optimal power flow model and seeks to maximize the operating cost and achieve a worst-case data generation. The training and testing stages employed in the proposed OA-cGAN use historical day-ahead net load forecast errors and has been implemented for the realistic NYISO 11-zone system. In conclusion, our numerical experiments demonstrate that the generated operation-adversarial forecast errors lead to more cost-effective and reliable dispatch decisions.

42 ENGINEERING↗
DOE OSTI2024

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗
DOE OSTI2023

Differentiable physics-enabled closure modeling for Burgers’ turbulence

Abstract Data-driven turbulence modeling is experiencing a surge in interest following algorithmic and hardware developments in the data sciences. We discuss an approach using the differentiable physics paradigm that combines known physics with machine learning to develop closure models for Burgers’ turbulence. We consider the one-dimensional Burgers system as a prototypical test problem for modeling the unresolved terms in advection-dominated turbulence problems. We train a series of models that incorporate varying degrees of physical assumptions on an a posteriori loss function to test the efficacy of models across a range of system parameters, including viscosity, time, and grid resolution. We find that constraining models with inductive biases in the form of partial differential equations that contain known physics or existing closure approaches produces highly data-efficient, accurate, and generalizable models, outperforming state-of-the-art baselines. Addition of structure in the form of physics information also brings a level of interpretability to the models, potentially offering a stepping stone to the future of closure modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Machine learning of hidden variables in multiscale fluid simulation

Solving fluid dynamics equations often requires the use of closure relations that account for missing microphysics. For example, when solving equations related to fluid dynamics for systems with a large Reynolds number, sub-grid effects become important and a turbulence closure is required, and in systems with a large Knudsen number, kinetic effects become important and a kinetic closure is required. By adding an equation governing the growth and transport of the quantity requiring the closure relation, it becomes possible to capture microphysics through the introduction of ‘hidden variables’ that are non-local in space and time. The behavior of the ‘hidden variables’ in response to the fluid conditions can be learned from a higher fidelity or ab-initio model that contains all the microphysics. In our study, a partial differential equation simulator that is end-to-end differentiable is used to train judiciously placed neural networks against ground-truth simulations. We show that this method enables an Euler equation based approach to reproduce non-linear, large Knudsen number plasma physics that can otherwise only be modeled using Boltzmann-like equation simulators such as Vlasov or particle-in-cell modeling.

42 ENGINEERING↗
DOE OSTI2018

Production Facility Prototype Blower 1000 Hour Test Results III

Long duration operations tests of the Aerzen GM 12.4 roots style blower in a closed loop configuration provides valuable data and lessons learned for long-term operation at the Mo-99 production facility. The blower was operated in a closed loop configuration with the flow conditions anticipated in plant operation with a Mo-100 target inline. The additional thermal energy generated from beam heating of the Mo-100 disks was not included in these tests. Eight long duration 500 and 1000 hour tests have been completed since the first test was performed in January of 2016. All eight long duration tests have proven successful in exposing preventable issues related to oil and helium leaks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Spectral Methods in Time-dependent Data Analysis

We aim to create a new model for time-dependent data analysis, named dynamical learning, that integrates data-driven manifold learning techniques with operator-theoretic methods from dynamical systems theory. This approach has the potential to deliver more efficient methods for analyzing time-dependent data, such as video streams, by naturally separating out the temporal and spatial features of the data. We aim to apply the newly developed methods to video surveillance data related to Sandia mission applications, and particularly focus on the problems of image segmentation and object tracking. This project ended early due to the departure of the PI from Sandia about 18 months into the project. Therefore, this document reports on partial progress towards the initial goals of the project. In addition, this document reports on part of the work conducted during the project; see the Appendix for a summary of all the work conducted during the 18 months.

97 MATHEMATICS AND COMPUTING↗