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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 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

PanDA: Production and Distributed Analysis System

The Production and Distributed Analysis (PanDA) system is a data-driven workload management system engineered to operate at the LHC data processing scale. The PanDA system provides a solution for scientific experiments to fully leverage their distributed heterogeneous resources, showcasing scalability, usability, flexibility, and robustness. The system has successfully proven itself through nearly two decades of steady operation in the ATLAS experiment, addressing the intricate requirements such as diverse resources distributed worldwide at about 200 sites, thousands of scientists analyzing the data remotely, the volume of processed data beyond the exabyte scale, dozens of scientific applications to support, and data processing over several billion hours of computing usage per year. PanDA’s flexibility and scalability make it suitable for the High Energy Physics community and wider science domains at the Exascale. Beyond High Energy Physics, PanDA’s relevance extends to other big data sciences, as evidenced by its adoption in the Vera C. Rubin Observatory and the sPHENIX experiment. As the significance of advanced workflows continues to grow, PanDA has transformed into a comprehensive ecosystem, effectively tackling challenges associated with emerging workflows and evolving computing technologies. The paper discusses PanDA’s prominent role in the scientific landscape, detailing its architecture, functionality, deployment strategies, project management approaches, results, and evolution into an ecosystem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Physics guided machine learning using simplified theories

Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines. The chief idea in our approach is to augment the knowledge of the simplified theories with the underlying learning process. To emphasize their physical importance, our architecture consists of adding certain features at intermediate layers rather than in the input layer. To demonstrate our approach, we select a canonical airfoil aerodynamic problem with the enhancement of the potential flow theory. We include the features obtained by a panel method that can be computed efficiently for an unseen configuration in our training procedure. By addressing the generalizability concerns, our results suggest that the proposed feature enhancement approach can be effectively used in many scientific machine learning applications, especially for the systems where we can use a theoretical, empirical, or simplified model to guide the learning module.

42 ENGINEERING↗
DOE OSTI2018

Gasoline Engine and Fuels Offering Reduced fuel Consumption and Emissions (GEFORCE)

The GEFORCE project resulted from a collaborative proposal submitted to a U.S. DOE funding opportunity announcement. The CRADA reported herein was put in place to support the GEFORCE project. The project objective was to establish key engine technologies and fuel characteristics that enable very high fuel efficiency with very low emissions in future vehicles. A matrix of study fuels was designed and produced to support engine experiments in an advanced engine. The advanced engine was constructed and prepared for operation in an engine test cell at a 3rd party laboratory. ORNL conducted engine experiments using a 2.0 LTG engine and used the resulting data to estimatefuel economy values for a mid-size sedan using a vehicle model established in Autonomie. The projected fuel economy trends versus driving schedule were consistent with those identified in a previous projectconducted in partnership with the Coordinating Research Council (CRC), AVFL-20. Additional vehicle modeling is anticipated when data are available from the advanced engine.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

NuScale Systems Engineering Program Overview and Status

NuScale systems engineering program overview, including technical data management and model-based engineering, product structures, architecture views, and model-based systems engineering. Includes a discussion on technical risk and opportunity management and technical readiness levels, as well as risk-informed performance-based principles and methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Vehicle Level Control Analysis for Voltec Powertrain

The next generation of the Volt vehicle with the new “Voltec” extended-range propulsion system was introduced into the market in 2016. The second-generation Volt’s powertrain architecture provides five modes of operation, including two electric vehicle operations and three extended-range operations. Vehicle testing was performed on a chassis dynamometer set within a thermal chamber at the Advanced Powertrain Research Facility at Argonne National Laboratory. The study first focused on assessing the improvement of the new Voltec system by comparing the system efficiency with the previous system. Second, control behavior and performance were analyzed under normal ambient temperature to understand the supervisory control strategy on the Voltec system based on the test data. The analysis focused on the engine on/off strategy, powertrain operation mode, energy management, and engine operating conditions. Third, test data from the control analysis were used to summarize the vehicle control logic.

Kim, Namdoo (ORCID:000000025201539X)↗
NASA NTRS1974

Flight investigation of installation effects on a plug nozzle with a series of boattailed primary shrouds installed on an underwing nacelle

Several variations of a boattailed shroud for a 10 deg conical plug nozzle were tested using an F-106B aircraft for Mach 0.6 to 1.3. The data were obtained so that the tradeoff between boattail and plug size could be studied for an underwing nacelle location. The nozzles were tested with J85-GE-13 turbojet engine, and the data were compared to previous flight results of installed plug nozzles. Boattail area varied from 31 to 66 percent of the nacelle area. The effect of increasing projected boattail area was to increase the gross thrust coefficient in the same way as from isolated data for flight Mach numbers below 0.85. The highest gross thrust coefficient (0.958) was obtained at Mach 0.95 with a long circular arc shroud configuration with a very small amount of secondary air flow.

Head, V. L.↗
NASA NTRS1986

Application of Weibull analysis to SSME hardware

Generally, it has been documented that the wearing of engine parts forms a failure distribution which can be approximated by a function developed by Weibull. The purpose here is to examine to what extent the Weibull distribution approximates failure data for designated engine parts of the Space Shuttle Main Engine (SSME). The current testing certification requirements will be examined in order to establish confidence levels. An examination of the failure history of SSME parts/assemblies (turbine blades, main combustion chamber, or high pressure fuel pump first stage impellers) which are limited in usage by time or starts will be done by using updated Weibull techniques. Efforts will be made by the investigator to predict failure trends by using Weibull techniques for SSME parts (turbine temperature sensors, chamber pressure transducers, actuators, and controllers) which are not severely limited by time or starts.

Gray, L. A. B.↗
NASA NTRS2021

Extending the Licklider Transmission Protocol to Multi-Band Links

Most deep space missions return data to Earth using links operating at a single frequency band. Indeed, their data requirements are low enough that bandwidth regulations do not constrain the system. In contrast, spacecraft such as Kepler or Europa Clipper are transitioning to a new operational paradigm where engineering and science data are transmitted through simultaneous links operating at different frequency bands (henceforth termed multi-band links). This ensures, for instance, that critical data is correctly received using a well characterized X-band link, while science data at a much larger data rate can be returned efficiently (both in terms of bandwidth and energy) through a Ka-band link.Having a spacecraft establish two simultaneous links with a ground station opens a large span of potential improvements for space communications and mission operations. In this paper, we consider the problem of running a Licklider Transmission Protocol (LTP) session over a multi-band link. LTP is an implementation of a selective Automatic Repeat reQuest (ARQ) protocol, i.e. it ensures correct delivery of data over an error prone link with potentially long propagation delays. To maximize its efficiency in deep space environments, LTP operates in deferred-ACK mode and is typically included as one of the core protocols in the Delay Tolerant Networking (DTN) suite.The contributions of this paper are as follows: First, we propose an extension to LTP for multi-band links (denoted MBLTP) and sketch how it can be implemented without modifying the current definition of LTP data units. Next, we develop bounds on the performance of MBLTP when transmitting a single data file over a multi-band link. Three metrics are considered, file expected delivery time, total energy spent and bundle jitter. The results of the analytic model are first benchmarked against simulations to ensure validity, and then compared against the performance of both traditional LTP and Parallel LTP (PLTP).We demonstrate that MBLTP can significantly reduce the latency and jitter with which data products are delivered to destination over a deep space link compared to LTP at moderate energy cost. Similarly, we also demonstrate that MBLTP outperforms PLTP in all considered metrics.

Sanchez Net, Marc↗
NASA NTRS1949

Correlation of cylinder-head temperatures and coolant heat rejections of a multicylinder, liquid-cooled engine of 1710-cubic-inch displacement

Data obtained from an extensive investigation of the cooling characteristics of four multicylinder, liquid-cooled engines have been analyzed and a correlation of both the cylinder-head temperatures and the coolant heat rejections with the primary engine and coolant variables was obtained. The method of correlation was previously developed by the NACA from an analysis of the cooling processes involved in a liquid-cooled-engine cylinder and is based on the theory of nonboiling, forced-convection heat transfer. The data correlated included engine power outputs from 275 to 1860 brake horsepower; coolant flows from 50 to 320 gallons per minute; coolants varying in composition from 100 percent water to 97 percent ethylene glycol and 3 percent water; and ranges of engine speed, manifold pressure, carburetor-air temperature, fuel-air ratio, exhaust-gas pressure, ignition timing, and coolant temperature. The effect on engine cooling of scale formation on the coolant passages of the engine and of boiling of the coolant under various operating conditions is also discussed.

Lundin, Bruce T↗
NASA NTRS2011

Pattern Recognition for a Flight Dynamics Monte Carlo Simulation

The design, analysis, and verification and validation of a spacecraft relies heavily on Monte Carlo simulations. Modern computational techniques are able to generate large amounts of Monte Carlo data but flight dynamics engineers lack the time and resources to analyze it all. The growing amounts of data combined with the diminished available time of engineers motivates the need to automate the analysis process. Pattern recognition algorithms are an innovative way of analyzing flight dynamics data efficiently. They can search large data sets for specific patterns and highlight critical variables so analysts can focus their analysis efforts. This work combines a few tractable pattern recognition algorithms with basic flight dynamics concepts to build a practical analysis tool for Monte Carlo simulations. Current results show that this tool can quickly and automatically identify individual design parameters, and most importantly, specific combinations of parameters that should be avoided in order to prevent specific system failures. The current version uses a kernel density estimation algorithm and a sequential feature selection algorithm combined with a k-nearest neighbor classifier to find and rank important design parameters. This provides an increased level of confidence in the analysis and saves a significant amount of time.

Restrepo, Carolina↗
NASA NTRS1987

Application of advanced diagnostics to airblast injector flows

This effort is concerned with the application of both conventional laser velocimetry and phase Doppler anemometry to the flow produced by an airblast nozzle. The emphasis is placed on the acquisition of data using actual engine injector/swirler components at (noncombusting) conditions simulating those encountered in the engine. The objective of the effort was to test the applicability of the instrumentation to real injector flows, to develop information on the behavior of injectors at high flow, and to provide data useful in the development of physical models of injector flows.

Mcvey, John B.↗
DOE OSTI2022

Privacy policy robustness to reverse engineering

Differential privacy policies allow one to preserve data privacy while sharing and analyzing data. However, these policies are susceptible to an array of attacks. In particular, often a portion of the data desired to be privacy protected is exposed online. Access to these pre-privacy protected data samples can then be used to reverse engineer the privacy policy. With knowledge of the generating privacy policy, an attacker can use machine learning to approximate the full set of originating data. Bayesian inference is one method for reverse engineering both model and model parameters. We present a methodology for evaluating and ranking privacy policy robustness to Bayesian inference-based reverse engineering, and demonstrated this method across data with a variety of temporal trends.

Kusne, Aaron Gilad↗
NASA NTRSDate not supplied

Managing the Digital Thread for Structural Applications With Fit for Purpose Materials

With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Consequently, at NASA Glenn Research Center a robust information management system that manages the digital thread across the full material life (i.e., capture, analysis, maintenance, and dissemination of data) cycle directed at the design of ‘fit-for-purpose materials’ is under development. To this end the Application Table has been incorporated within NASA Glenn Research Center’s ICME Information Management framework within the ANSYS Granta MI tool. The Application Table provides a place where material and structural application information/requirements can be linked to marry the “design-the-material” (structural engineering) and the “design-with-material” (material science) paradigms and thereby enable application-driven design and optimization of materials and structures. In additional several associated toolsets, specifically: AIMAOS (Automated Information Management Across Organizations and Scales), Py MILab, and JARIMIS (Just A Rather Intelligent Material Interrogation System) are also under development to assist in the judicious automation of this process. AIMOAS offers users an interactive graphical user interface for connecting material information management systems with both commercial and in-house simulation tools at various length scales to enable such automation in the handoff across scales and maintenance of material digital twins and the digital thread. Py MILab, is an automatic framework for the capture, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. Finally, JARIMIS is an expert system that integrates various materials informatics tools (e.g., MicroNet, Surrogate ML models, ANSYS Granta MI, etc.) to enable inverse design of materials and facilitate the application of machine learning (ML) and data science with human in the loop decision making to rapidly discover and optimize new materials.

Digital Transformation↗
NASA NTRS1987

Fiber optic data transmission

The Ohio University Avionics Engineering Center is currently developing a fiber optic data bus transmission and reception system that could eventually replace copper cable connections in airplanes. The original form of the system will transmit information from an encoder to a transponder via a fiber optic cable. An altimeter and an altitude display are connected to a fiber optic transmitter by copper cable. The transmitter converts the altimetry data from nine bit parallel to serial form and send these data through a fiber optic cable to a receiver. The receiver converts the data using a cable similar to that used between the altimeter and display. The transmitting and receiving ends also include a display readout. After completion and ground testing of the data bus, the system will be tested in an airborne environment.

Shreve, Steven T.↗
NASA NTRS2001

A summary of spacecraft engineering sensors

This paper gives a data table summary of the different types of engineering sensors that are used onboard a typical spacecraft.

vehicle health management fault protection spacecr↗
DOE OSTI2025

Engineered Accumulation of Protocatechuate in Corn Biomass to Enhance Biomanufacturing

The in-planta accumulation of coproducts in crops can enhance the value of lignocellulosic biomass and facilitate a sustainable bioeconomy. Corn stover represents a major renewable source of lignocellulose for the production of advanced biofuels and bioproducts. In this study, we engineered corn with a bacterial gene encoding a dehydroshikimate dehydratase (QsuB) to overproduce protocatechuate (DHBA). Transgenic corn lines accumulate up to 2.9% DHBA on a dry weight basis in leaf and stem biomass. DHBA occurs in the form of glucosides that are extractable from biomass using aqueous methanol as the solvent. The analysis of lignin did not show any evidence for the incorporation of DHBA; however, an increase in the lignin syringyl to guaiacyl ratio and a higher relative abundance of p-coumarate groups compared with total lignin units were observed in QsuB-modified corn. Alkaline hydrolysates prepared from QsuB corn were enriched in DHBA compared to the hydrolysates obtained from wild-type biomass, which contained mostly p-coumarate and ferulate. Using engineered Novosphingobium aromaticivorans as a production host, a 375% improvement in 2-pyrone-4,6-dicarboxylate titers was achieved through biological upgrading of alkaline hydrolysates derived from QsuB corn compared to unmodified biomass. Our data demonstrate an engineering strategy to overproduce DHBA in corn that can facilitate sustainable manufacturing of other valuable bioproducts using stover as a feedstock.

2-pyrone-4,6-dicarboxylate↗
NASA NTRS1976

Aerodynamic characteristics of a 1/6-scale model of the rotor systems research aircraft with the rotors removed

A wind-tunnel investigation was conducted to refine the aerodynamic characteristics of the rotor systems research aircraft. For the investigation, a 1/6-scale model without a main rotor or a tail rotor was used. The model provided the capability for testing different engine nacelle sizes, engine pylon fairings, and tail configurations. The engine thrust effects were modeled by small engine simulators (fans). Data were obtained primarily over an angle-of-attack range from -13 deg to 13 deg at several values of sideslip. Stability characteristics and control effectiveness were investigated. The model with the scaled engine nacelles and the combination T-tail and lower horizontal tail displayed longitudinal and lateral-directional stability. Results show that by reducing the horizontal or vertical-tail span the longitudinal stability is decreased. Reducing the engine nacelle size increases the static stability of the model. Effective dihedral is essentially zero at 0 deg angle of attack and 0 deg wing incidence.

Mineck, R. E.↗