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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Superfluid Helium On-Orbit Transfer (SHOOT) flight demonstration

The Superfluid Helium On-Orbit Transfer (SHOOT) Flight Demonstration was an attached Shuttle payload mounted on a Hitchhiker cross-bay carrier which flew on STS-57 in June of 1993. SHOOT successfully demonstrated the handling and transfer of superfluid helium between two containers, called dewars, in low gravity. SHOOT was a class C payload and for the STS-57 mission was termed a complex secondary payload. The primaries were the retrieval of the EURECA carrier and a collection of modular experiments contained in SPACEHAB. Because the liquid helium was continuously boiling off, SHOOT's activities were scheduled for the first three days of the mission, concurrent with some SPACEHAB experiments, but well before the EURECA retrieval. Control of the SHOOT experiment was highly interactive and originated primarily from the Goddard Payload Operations and Control Center (POCC). Transfer and calibration activities required continuous command windows of up to 50 minutes duration and up to 80 minutes out of each orbit. Occasionally the crew controlled the experiment using the Payload General Support Computer (PGSC) when near-real time control and monitoring was required. SHOOT also placed considerable demands on the orbiter, including a pitch rotation of 3 deg./sec for 15 minutes, and translational burns using both the aft and forward RCS jets to generate accelerations up to 7 milli-g. The basis for these and other requirements are discussed. Interacion with the crew and timing of crew activity during the mission will be detailed. The processing flow of SHOOT at KSC is described with emphasis on the tradeoffs for vertical, as opposed to horizontal, installation in the orbiter. Finally, some lessons learned are presented that are relevant to future cryogenic and Hitchhiker payloads.

Shirron, Peter↗
DOE OSTI2024

Generic and ML Workloads in an HPC Datacenter: Node Energy, Job Failures, and Node-Job Analysis

HPC datacenters offer a backbone to the modern digital society. Increasingly, they run Machine Learning (ML) jobs next to generic, compute-intensive workloads, supporting science, business, and other decision-making processes. However, understanding how ML jobs impact the operation of HPC datacenters, relative to generic jobs, remains desirable but understudied. In this work, we leverage long-term operational data, collected from a national-scale production HPC datacenter, and statistically compare how ML and generic jobs can impact the performance, failures, resource utilization, and energy consumption of HPC datacenters. Our study provides key insights, e.g., ML-related power usage causes GPU nodes to run into temperature limitations, median/mean runtime and failure rates are higher for ML jobs than for generic jobs, both ML and generic jobs exhibit highly variable arrival processes and resource demands, significant amounts of energy are spent on unsuccessfully terminating jobs, and concurrent jobs tend to terminate in the same state. We open-source our cleaned-up data traces on Zenodo (https://doi. org/10.5281/zenodo.13685426), and provide our analysis toolkit as software hosted on GitHub (https://github.com/atlarge-research/2024-icpads-hpc-workload-characterization). This study offers multiple benefits for data center administrators, who can improve operational efficiency, and for researchers, who can further improve system designs, scheduling techniques, etc.

crossanalysis↗
DOE OSTI2025

SpecSims: A Scalable Speculative Tree-based Simulation Cloning Framework for Finite Memory Machines

Simulation cloning is a technique in which cloned simulations whose state spaces differ partially from their parent simulation due to intervening events are spawned at runtime and concurrently advanced. It is a powerful method to carry out what-if analysis by speculatively exploring and evaluating the impact of various permutations of intervening cascade of events. Due to the exponential growth in the number of possible clones even for a small number of distinct intervening events, the practical efficacy of the approach is often severely limited by the maximum available memory of the computing host. In this paper, we introduce a novel speculative simulation cloning framework that executes a simulation cloning campaign capable of efficiently exploring an exponentially large space of clone simulations created by permutation of intervening events under a finite memory constraint. We provide a theoretical analysis of the runtime characteristics of our proposed approach and highlight its novel advantages such as memory-aware and as-long-as-needed execution. Furthermore, in support of our analytical findings and to demonstrate its practical feasibility, we implement a prototype of the cloning framework on a shared memory system and report its performance characteristics in the context of a heat diffusion simulation, and a power grid simulation subject to cascading disruptions from geomagnetic disturbances.

Simulation framework↗
NASA NTRS2000

Global Design Optimization for Aerodynamics and Rocket Propulsion Components

Modern computational and experimental tools for aerodynamics and propulsion applications have matured to a stage where they can provide substantial insight into engineering processes involving fluid flows, and can be fruitfully utilized to help improve the design of practical devices. In particular, rapid and continuous development in aerospace engineering demands that new design concepts be regularly proposed to meet goals for increased performance, robustness and safety while concurrently decreasing cost. To date, the majority of the effort in design optimization of fluid dynamics has relied on gradient-based search algorithms. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space, can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables, and methods for predicting the model performance. In this article, we review recent progress made in establishing suitable global optimization techniques employing neural network and polynomial-based response surface methodologies. Issues addressed include techniques for construction of the response surface, design of experiment techniques for supplying information in an economical manner, optimization procedures and multi-level techniques, and assessment of relative performance between polynomials and neural networks. Examples drawn from wing aerodynamics, turbulent diffuser flows, gas-gas injectors, and supersonic turbines are employed to help demonstrate the issues involved in an engineering design context. Both the usefulness of the existing knowledge to aid current design practices and the need for future research are identified.

Shyy, Wei↗
NASA NTRS1990

Strategies for concurrent processing of complex algorithms in data driven architectures

The performance modeling and enhancement for periodic execution of large-grain, decision-free algorithms in data flow architectures is examined. Applications include real-time implementation of control and signal processing algorithms where performance is required to be highly predictable. The mapping of algorithms onto the specified class of data flow architectures is realized by a marked graph model called ATAMM (Algorithm To Architecture Mapping Model). Performance measures and bounds are established. Algorithm transformation techniques are identified for performance enhancement and reduction of resource (computing element) requirements. A systematic design procedure is described for generating operating conditions for predictable performance both with and without resource constraints. An ATAMM simulator is used to test and validate the performance prediction by the design procedure. Experiments on a three resource testbed provide verification of the ATAMM model and the design procedure.

Stoughton, John W.↗
NASA NTRS1990

Strategies for concurrent processing of complex algorithms in data driven architectures

Performance modeling and performance enhancement for periodic execution of large-grain, decision-free algorithms in data flow architectures are discussed. Applications include real-time implementation of control and signal processing algorithms where performance is required to be highly predictable. The mapping of algorithms onto the specified class of data flow architectures is realized by a marked graph model called algorithm to architecture mapping model (ATAMM). Performance measures and bounds are established. Algorithm transformation techniques are identified for performance enhancement and reduction of resource (computing element) requirements. A systematic design procedure is described for generating operating conditions for predictable performance both with and without resource constraints. An ATAMM simulator is used to test and validate the performance prediction by the design procedure. Experiments on a three resource testbed provide verification of the ATAMM model and the design procedure.

Som, Sukhamoy↗
DOE OSTI2026

Existence of a robust optimal control process for efficient measurements in a two-qubit system

The verification of quantum entanglement is essential for quality control in quantum communication. In this work we propose an efficient protocol to directly verify the two-qubit entanglement of a known target state through a single-expectation-value measurement. Our method provides exact entanglement quantification using the concurrence measure without performing quantum state tomography. We prove the existence of a unitary transformation that drives a known initial state of a two-qubit system to a designated final state, where the trace over a chosen observable directly yields the concurrence of the initial state. Furthermore, we implement an optimal control process of that transformation and demonstrate its effectiveness through numerical simulations. We also show that this process is robust to environmental noise. Our approach offers advantages in directly verifying entanglement with low circuit depth, making it suitable for industrial-scale quality control of entanglement generation. Our results presented here provide mathematical justification for our earlier computational experiments.

Rodriguez, Ricardo [McPherson Coll.] (ORCID:000000↗
DOE OSTI2020

Online Optimization for Networked Distributed Energy Resources With Time-Coupling Constraints

This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks (ADNs) with numerous photovoltaic inverters and inverter air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the social utility loss and ensuring the security of voltages. Conventional distributed optimization methods are rarely feasible to track the optimal solutions in fast variable environments using a fine-grained sampling interval where the underlying optimization problem evolves with the iterations of the algorithms. In contrast, based on the framework of online convex optimization (OCO), the developed approach uses a distributed algebraic update to compute the next round decisions relying on the current feedback of measurements. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder. Benchmark tests show that the proposed method is computationally and economically efficient, and outperforming existing algorithms.

active distribution networks↗
DOE OSTI2020

5G Enabled Energy Innovation: Advanced Wireless Networks for Science (Workshop Report)

Rapidly expanding, new telecommunications infrastructure based on 5G technologies will disrupt and transform how we design, build, operate, and optimize scientific infrastructure and the experiments and services enabled by that infrastructure, from continental-scale sensor networks to centralized scientific user facilities, from intelligent Internet of Things devices to supercomputers. Concurrently, 5G will introduce, or exacerbate, challenges related to protecting infrastructure and associated scientific data as well as to fully leveraging opportunities related to expanded infrastructure scale and complexity. The U.S. Department of Energy (DOE) Office of Science operates scientific infrastructure, supporting some of the nation’s most advanced intellectual discoveries, spanning the country and including 30 world-class user facilities from supercomputers to accelerators. Along with field experiments and remote observatories, every aspect of DOE’s scientific enterprise will be affected by 5G, which amounts to a complete renovation of the underpinnings of the nation’s information infrastructure. In this report we explore the scientific opportunities and new research challenges associated with 5G, ranging from scalability to heterogeneity to cybersecurity. The rapid commercial deployment of 5G opens the opportunity to rethink and reinvent DOE’s scientific infrastructure and experimentation, from intelligent sensor networks at unprecedented scales to a digital continuum of cyberinfrastructure spanning low-power sensors, high-performance computing embedded within and at the edge of the network, and DOE’s large-scale user instrument and computing facilities. New programming paradigms, workflow and data frameworks, and AI-based system design, operation, and autonomous adaptation and optimization will be necessary in order to exploit these new opportunities. Field deployments and centralized scientific instruments can also be revolutionized, moving (without traditional performance penalties) from wired to wireless connectivity for data and control systems, improving flexibility, and opening new sensing modalities, including the use of the 5G electromagnetic spectrum itself as an environmental probe. For DOE science, in contrast to commercial 5G applications and settings, devices will be deployed in extreme environments such as cryogenically cooled instrument control systems and in remote settings with harsh conditions, requiring the design of new materials for RF communication and edge processing to operate in these regimes. Concurrently, 5G infrastructure comprises both hardware and sophisticated software systems - currently closed and proprietary. The cybersecurity challenges to 5G-empowered reinvention mirror the complexity and variety of new 5G features, from virtualization to private network slices to ubiquitous access. Research is also needed in order to accelerate the development of secure and open 5G software infrastructure, reducing reliance on hardware and software produced outside the United States and providing the transparency and rigorous evaluation and testing afforded through open software. Twelve broad research thrusts are laid out in four chapters, with a companion fifth chapter (and three additional research thrusts) underscoring the needs and opportunities for an aggressive testbed program co-designed by networking experts and scientists involved in the 15 research thrusts. The urgency of undertaking this research is fueled by a global, accelerating deployment of new telecommunications infrastructure that is designed for entertainment and commercial applications - barely scratching the surface of what 5G can do to extend U.S. leadership in scientific discovery.

42 ENGINEERING↗
DOE OSTI2021

Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)

Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1979

Integrated testing and verification system for research flight software design document

The NASA Langley Research Center is developing the MUST (Multipurpose User-oriented Software Technology) program to cut the cost of producing research flight software through a system of software support tools. The HAL/S language is the primary subject of the design. Boeing Computer Services Company (BCS) has designed an integrated verification and testing capability as part of MUST. Documentation, verification and test options are provided with special attention on real time, multiprocessing issues. The needs of the entire software production cycle have been considered, with effective management and reduced lifecycle costs as foremost goals. Capabilities have been included in the design for static detection of data flow anomalies involving communicating concurrent processes. Some types of ill formed process synchronization and deadlock also are detected statically.

Taylor, R. N.↗
NASA NTRS1986

Software fault-tolerance by design diversity DEDIX: A tool for experiments

The use of multiple versions of a computer program, independently designed from a common specification, to reduce the effects of an error is discussed. If these versions are designed by independent programming teams, it is expected that a fault in one version will not have the same behavior as any fault in the other versions. Since the errors in the output of the versions are different and uncorrelated, it is possible to run the versions concurrently, cross-check their results at prespecified points, and mask errors. A DEsign DIversity eXperiments (DEDIX) testbed was implemented to study the influence of common mode errors which can result in a failure of the entire system. The layered design of DEDIX and its decision algorithm are described.

Avizienis, A.↗
NASA NTRS1996

Software Development Technologies for Reactive, Real-Time, and Hybrid Systems

The research is directed towards the design and implementation of a comprehensive deductive environment for the development of high-assurance systems, especially reactive (concurrent, real-time, and hybrid) systems. Reactive systems maintain an ongoing interaction with their environment, and are among the most difficult to design and verify. The project aims to provide engineers with a wide variety of tools within a single, general, formal framework in which the tools will be most effective. The entire development process is considered, including the construction, transformation, validation, verification, debugging, and maintenance of computer systems. The goal is to automate the process as much as possible and reduce the errors that pervade hardware and software development.

Manna, Zohar↗
DOE OSTI2021

Protein-Mimetic Self-Assembly with Synthetic Macromolecules

Protein-mimetic amphiphiles have significant promise as a platform to access the complex functions of natural biological materials and incorporate the tunability and environmental resilience of synthetic materials. The fields of polymer chemistry and chemical biology have concurrently approached the development of biomimetic amphiphiles with materials ranging from random amphiphilic copolymers to peptide–lipid conjugates. In this Perspective, we incorporate strategies from diverse chemical arenas for controlling assembled morphologies and dynamics of protein-mimetic synthetic macromolecules. An overview of significant advances in peptide amphiphiles and single-chain polymer nanoparticles provides the foundation for comparing recent advances in the implementation of multiple intermolecular interactions and computational strategies to fine-tune the assembled structures. We aim to bridge these fields, combining insights from multiple disciplines to inspire new approaches for the development of protein-mimetic materials, as these assemblies have far-reaching applications including in the development of new sensors, catalysts, and therapeutics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

CMSSW Scaling Limits on Many-Core Machines

Today the LHC offline computing relies heavily on CPU resources, despite the interest in compute accelerators, such as GPUs, for the longer term future. The number of cores per CPU socket has continued to increase steadily, reaching the levels of 64 cores (128 threads) with recent AMD EPYC processors, and 128 cores on Ampere Altra Max ARM processors. Over the course of the past decade, the CMS data processing framework, CMSSW, has been transformed from a single-threaded framework into a highly concurrent one. The first multithreaded version was brought into production by the start of the LHC Run 2 in 2015. Since then, the framework's threading efficiency has gradually been improved by adding more levels of concurrency and reducing the amount of serial code paths. The latest addition was support for concurrent Runs. In this work we review the concurrency model of the CMSSW, and measure its scalability with real CMS applications, such as simulation and reconstruction, on mode rn many-core machines. We show metrics such as event processing throughput and application memory usage with and without the contribution of I/O, as I/O has been the major scaling limitation for the CMS applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1997

Gamma-Ray Burst Arrival Time Localizations: Simultaneous Observations by Mars Observer, Compton Gamma Ray Observatory and Ulysses

Between 1992 October 4 and 1993 August 1, concurrent coverage by the Compton Gamma Ray Observatory (CGRO), Mars Observer (MO), and Ulysses spacecraft was obtained for 78 gamma-ray bursts (GRBs). Although most of these were below the MO and Ulysses thresholds, nine were positively detected by all three spacecraft, with data quality adequate for quantitative localization analysis. All were localized independently to approximately 2 deg accuracy by the CGRO Burst and Transient Source Experiment (BATSE). We computed arrival-time error boxes with larger dimensions ranging from a few arcminutes to the diameters of the BATSE-only boxes and with smaller dimensions in the arcminute range. Three events are of particular interest: GB 930704 (BATSE 2428) has been described as a possible repeater. The arrival-time information is consistent with that hypothesis, but only just so. The GB 930706 (2431) box, at approximately 1 min x 4 min, is the only one this small obtained since Pioneer Venus Orbiter (PVO) entered the Venusian atmosphere in 1992 October. Sensitive radio and optical observations of this location were made within 8 and 9 days of the burst, but no counterpart candidates were identified. GB 930801 (2477) is the first GRB that had its localization improved by taking into account BATSE Earth occultation.

Laros, J. G.↗
NASA NTRSDate not supplied

Evaluation of Extreme Soil Moisture Patterns over the Sahel during the 2020 Growing Season

The African Sahel is an ecologically and climatically sensitive region, and thus is a valuable test case for examination of climate extremes. Above-average rainfall during the 2020 growing season (June-October) led to flooding in the West, Central and East Sahel, with implications for infrastructure, agriculture and disease outbreaks. In this study, we evaluate soil moisture patterns in the region during 2020 to assess and quantify the extremeness of the event. The primary tool is the NASA Soil Moisture Active Passive (SMAP) Level 4 surface soil moisture data. Daily, monthly, and seasonal anomalies are computed relative to SMAP’s long-term mean (2015-2021). Additional comparisons are made with longer-time-series data sets, including surface soil moisture from NASA’s Modern-Era Retrospective analysis for Research and Applications, Version 2(MERRA-2; 1981-present) and precipitation from the African Rainfall Climatology, Version 2 (ARC2; 1983-present).Possible drivers of the extreme wet event are examined, including potential links to the concurrent 2020-21 La Niña event. Finally, we explore the connections between the extreme soil moisture and vector-borne disease outbreaks in the region in2020, namely, Rift Valley Fever in Mauritania and Chikungunya in Chad.

Soil Moisture↗
NASA NTRS1993

A minimum cost tolerance allocation method for rocket engines and robust rocket engine design

Rocket engine design follows three phases: systems design, parameter design, and tolerance design. Systems design and parameter design are most effectively conducted in a concurrent engineering (CE) environment that utilize methods such as Quality Function Deployment and Taguchi methods. However, tolerance allocation remains an art driven by experience, handbooks, and rules of thumb. It was desirable to develop and optimization approach to tolerancing. The case study engine was the STME gas generator cycle. The design of the major components had been completed and the functional relationship between the component tolerances and system performance had been computed using the Generic Power Balance model. The system performance nominals (thrust, MR, and Isp) and tolerances were already specified, as were an initial set of component tolerances. However, the question was whether there existed an optimal combination of tolerances that would result in the minimum cost without any degradation in system performance.

Gerth, Richard J.↗