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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cloud Droplet Measurement System for the ARM Tethered Balloon System (TBS) Field Campaign Report

The Mesa Photonics' cloud droplet measurement system (CDMS) performs in situ measurement of droplet size distribution and droplet number density in clouds. These characteristics are important cloud microphysical properties that are critical input parameters for atmospheric models and are also useful for proper calibration and validation of performance of other atmospheric measurement instrumentation. This small campaign was the first project (out of two) that involved integration of the CDMS into the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s tethered balloon system (TBS) and its initial testing at the ARM Southern Great Plains (SGP) atmospheric observatory. The follow-up campaign (AFC07016) involved testing of the CDMS under relevant conditions at the third ARM Mobile Facility (AMF3) at Oliktok Point, Alaska and making in situ measurements in clouds. The goal of this small field campaign was to deploy the CDMS on the ARM TBS, demonstrate its integrity and compatibility with the TBS, and test the wireless telecommunication system in preparation for subsequent in-cloud measurements at ARM’s North Slope of Alaska (NSA) observatory at Utqiagvik (formerly Barrow). The specific technical objectives included: (1) Integration of the Mesa Photonics' CDMS into the ARM TBS, (2) testing of the wireless telecommunication system of the CDMS, (3) evaluating the data acquisition software and image processing algorithms especially under high-background-illumination conditions, and (4) testing the ruggedness of the instrument's optical alignment and other performance characteristics. All objectives have been successfully met. The mounting hardware was designed and built mostly at Sandia National Laboratories (SNL), which allowed reliable mounting of the CDMS on the TBS and co-locating the CDMS with other instruments. The field tests were performed by the principal investigator (PI) in collaboration with the TBS operational crew led by D. Dexheimer. The field testing demonstrated good compatibility of the CDMS with the TBS. During the TBS flights, the crew did not experience any operational issues with the CDMS. The instrument demonstrated reliable operational characteristics, including sufficient battery life and good thermal management. The wireless telecommunication system has been successfully tested at TBS flight altitudes of up to 1000 m. During all flights, the CDMS raw data were wirelessly transmitted to the ground station and processed in real time. The ruggedness of the instrument's optical alignment was evaluated by performing calibration of the CDMS (using Mesa Photonics' fixed-size monodisperse droplet generator) before and after the campaign. The calibration was stable within the instrument's measurement precision. In summary, the campaign demonstrated good compatibility of the Mesa Photonics' CDMS with the ARM TBS. The flight tests confirmed the mechanical integrity of the CDMS and stability of its optical layout, as well as reliability of the wireless telecommunication system. Successful completion of this campaign provided a solid basis for the next campaign (AFC07016, November 2020), that involved testing of the CDMS during in-cloud TBS flights at AMF3 at Oliktok Point.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Development of performance specifications for hybrid modeling of floating wind turbines in wave basin tests

Hybrid modeling—combining physical testing and numerical simulation in real time$-$opens new opportunities in floating wind turbine research. Wave basin testing is an important validation step for floating support structure design, but the conventional approaches that use physical wind above the basin are limited by scaling problems in the aerodynamics. Applying wind turbine loads with an actuation system that is controlled by a simulation responding to the basin test in real time offers a way to avoid scaling problems and reduce cost barriers for floating wind turbine design validation in realistic coupled wind and wave conditions. This paper demonstrates the development of performance specifications for a system that couples a wave basin experiment with a wind turbine simulation. Two different points for the hybrid coupling are considered: the tower-base interface and the aero-rotor interface (the boundary between aerodynamics and the rotor structure). Analyzing simulations of three floating wind turbine designs across seven load cases reveals the motion and force requirements of the coupling system. By simulating errors in the hybrid coupling system, the sensitivity of the floating wind turbine response to coupling quality can be quantified. The sensitivity results can then be used to determine tolerances for motion tracking errors, force actuation errors, bandwidth limitations, and latency in the hybrid coupling system. These tolerances can guide the design of hybrid coupling systems to achieve desired levels of accuracy. An example demonstrates how the developed methods can be used to generate performance specifications for a system at 1:50 scale. Results show that sensitivities vary significantly between support structure designs and that coupling at the aero-rotor interface has less stringent requirements than those for coupling at the tower base. As a result, the methods and results presented here can inform design of future hybrid coupling systems and enhance understanding of how test results are affected by hybrid coupling quality.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

Server-side Log Data Analytics for I/O Workload Characterization and Coordination on Large Shared Storage Systems

Inter-application I/O contention and performance interference have been recognized as severe problems. In this work, we demonstrate, through measurement from Titan (world s No. 3 supercomputer), that high I/O variance co-exists with the fact that individual storage units remain under-utilized for the majority of the time. This motivates us to propose AID, a system that performs automatic application I/O characterization and I/O-aware job scheduling. AID analyzes existing I/O traffic and batch job history logs, without any prior knowledge on applications or user/developer involvement. It identifies the small set of I/O-intensive candidates among all applications running on a supercomputer and subsequently mines their I/O patterns, using more detailed per-I/O-node traffic logs. Based on such auto- extracted information, AID provides online I/O-aware scheduling recommendations to steer I/O-intensive applications away from heavy ongoing I/O activities. We evaluate AID on Titan, using both real applications (with extracted I/O patterns validated by contacting users) and our own pseudo-applications. Our results confirm that AID is able to (1) identify I/O-intensive applications and their detailed I/O characteristics, and (2) significantly reduce these applications I/O performance degradation/variance by jointly evaluating out- standing applications I/O pattern and real-time system l/O load.

Liu, Y.↗
NASA NTRS2011

Maturation of Structural Health Management Systems for Solid Rocket Motors

Concepts of an autonomous and automated space-compliant diagnostic system were developed for conditioned-based maintenance (CBM) of rocket motors for space exploration vehicles. The diagnostic system will provide real-time information on the integrity of critical structures on launch vehicles, improve their performance, and greatly increase crew safety while decreasing inspection costs. Using the SMART Layer technology as a basis, detailed procedures and calibration techniques for implementation of the diagnostic system were developed. The diagnostic system is a distributed system, which consists of a sensor network, local data loggers, and a host central processor. The system detects external impact to the structure. The major functions of the system include an estimate of impact location, estimate of impact force at impacted location, and estimate of the structure damage at impacted location. This system consists of a large-area sensor network, dedicated multiple local data loggers with signal processing and data analysis software to allow for real-time, in situ monitoring, and longterm tracking of structural integrity of solid rocket motors. Specifically, the system could provide easy installation of large sensor networks, onboard operation under harsh environments and loading, inspection of inaccessible areas without disassembly, detection of impact events and impact damage in real-time, and monitoring of a large area with local data processing to reduce wiring.

Quing, Xinlin↗
NASA NTRS2017

Performance Optimizing Adaptive Control with Time-Varying Reference Model Modification

This paper presents a new adaptive control approach that involves a performance optimization objective. The control synthesis involves the design of a performance optimizing adaptive controller from a subset of control inputs. The resulting effect of the performance optimizing adaptive controller is to modify the initial reference model into a time-varying reference model which satisfies the performance optimization requirement obtained from an optimal control problem. The time-varying reference model modification is accomplished by the real-time solutions of the time-varying Riccati and Sylvester equations coupled with the least-squares parameter estimation of the sensitivities of the performance metric. The effectiveness of the proposed method is demonstrated by an application of maneuver load alleviation control for a flexible aircraft.

Adaptive Control↗
DOE OSTI2023

Physics constrained unsupervised deep learning for rapid, high resolution scanning coherent diffraction reconstruction

By circumventing the resolution limitations of optics, coherent diffractive imaging (CDI) and ptychography are making their way into scientific fields ranging from X-ray imaging to astronomy. Yet, the need for time consuming iterative phase recovery hampers real-time imaging. While supervised deep learning strategies have increased reconstruction speed, they sacrifice image quality. Furthermore, these methods’ demand for extensive labeled training data is experimentally burdensome. Here, we propose an unsupervised physics-informed neural network reconstruction method, PtychoPINN, that retains the factor of 100-to-1000 speedup of deep learning-based reconstruction while improving reconstruction quality by combining the diffraction forward map with real-space constraints from overlapping measurements. In particular, PtychoPINN gains a factor of 4 in linear resolution and an 8 dB improvement in PSNR while also accruing improvements in generalizability and robustness. This blend of performance and computational efficiency offers exciting prospects for high-resolution real-time imaging in high-throughput environments such as X-ray free electron lasers (XFELs) and diffraction-limited light sources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Severino, Joseph↗
DOE OSTI2021

Performing In Situ Closed-Cell Gas Reactions in the Transmission Electron Microscope

Gas reactions studied by in situ electron microscopy can be used to capture the real-time morphological and microchemical transformations of materials at length scales down to the atomic level. In situ closed-cell gas reaction (CCGR) studies performed using (scanning) transmission electron microscopy (STEM) can separate and identify localized dynamic reactions, which are extremely challenging to capture using other characterization techniques. For these experiments, we used a CCGR holder that utilizes microelectromechanical systems (MEMS)-based heating microchips (hereafter referred to as "E-chips"). The experimental protocol described here details the method for performing in situ gas reactions in dry and wet gases in an aberration-corrected STEM. This method finds relevance in many different materials systems, such as catalysis and high-temperature oxidation of structural materials at atmospheric pressure and in the presence of various gases with or without water vapor. Here, several sample preparation methods are described for various material form factors. During the reaction, mass spectra obtained with a residual gas analyzer (RGA) system with and without water vapor further validates gas exposure conditions during reactions. Integrating an RGA with an in situ CCGR-STEM system can, therefore, provide critical insight to correlate gas composition with the dynamic surface evolution of materials during reactions. In situ/operando studies using this approach allow for detailed investigation of the fundamental reaction mechanisms and kinetics that occur at specific environmental conditions (time, temperature, gas, pressure), in real-time, and at high spatial resolution.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1991

Real Time Data System (RTDS)

Lessons learned from operational real time expert systems are examined. The basic system architecture is discussed. An expert system is any software that performs tasks to a standard that would normally require a human expert. An expert system implies knowledge contained in data rather than code. And an expert system implies the use of heuristics as well as algorithms. The 15 top lessons learned by the operation of a real time data system are presented.

Muratore, John F.↗
NASA NTRS1989

Generation of precedence relations for mechanical assemblies

Planning of assembly sequences is essential to the manufacturing system design process. Several methodologies have been proposed to represent all the feasible assembly sequences. In this thesis, three algorithms are presented to generate three sets of precedence relations based on all the infeasible assembly tasks, all the infeasible assembly states, and all the feasible assembly sequences, respectively. The equivalence of the resulting sets of precedence relations to the AND/OR graph is established. A new property, the real time property, of a representation of assembly sequences is defined and discussed. A representation of assembly sequences is said to have the real time property, if it is possible to generate the next assembly task by testing locally in the representation, and it will guarantee that the generated assembly task will not lead the assembly sequence to a dead end situation, in which no feasible assembly task can be performed any more. It is shown that the correctness and completeness of one representation can not guarantee the real time property of the representation. It is proven that the directed graph representation and the set of precedence relations based on all the infeasible assembly states have the real time property, while the AND/OR graph representation and the set of precedence relations based on all the infeasible assembly tasks do not have the real time property. Finally in the thesis, the PLEIDEAS system, a PLanning Environment for Integrated DEsign of Assembly Systems, is described and illustrated by an example.

Zhang, Hui↗
DOE OSTI2026

Exploring AI/ML-based Real-time Anomaly Detection in DUNE for Supernova Burst Neutrinos

The Deep Underground Neutrino Experiment (DUNE) is currently under construction with far detectors consisting of 4 liquid argon time projection chamber (LArTPC) modules at SURF (South Dakota Underground Research Facility) and a near detector complex with neutrino beam production at Fermilab to unambiguously determine neutrino mass ordering, to discover and precisely measure Charge-Parity (CP) violation phase in leptonic sector, to search for Beyond Stand Model (BSM) physics, and to study solar and supernova burst neutrinos. Anomalies in this project are classified in three categories: new physics signals, supernova burst neutrinos, and detector malfunction. We report here on promising early studies toward an Artificial Intelligence/Machine Learning-based real-time anomaly detection system, using a prototype autoencoder model currently under development. Additionally, the current status of an improved model and its performance will be presented. The model will be evaluated not only for its sensitivity to supernova neutrinos, but also to BSM physics signals and detector malfunctions. We will also consider how such a real-time algorithm might be used in DUNE.

de Jonge, Anselm [Kirchhoff Inst. Phys.] (ORCID:00↗
DOE OSTI2018

Real-Time Observation of Order-Disorder Transformation of Organic Cations Induced Phase Transition and Anomalous Photoluminescence in Hybrid Perovskites

A fundamental understanding of the interplay between the microscopic structure and macroscopic optoelectronic properties of organic-inorganic hybrid perovskite materials is essential to design new materials and improve device performance. However, how exactly the organic cations affect the structural phase transition and optoelectronic properties of the materials is not well understood. Here, real-time, in situ temperature-dependent neutron/X-ray diffraction and photoluminescence (PL) measurements reveal a transformation of the organic cation CH 3 NH 3 + from order to disorder with increasing temperature in CH 3 NH 3 PbBr 3 perovskites. The molecular-level order-to-disorder transformation of CH 3 NH 3 + not only leads to an anomalous increase in PL intensity, but also results in a multidomain to single-domain structural transition. Lastly, this discovery establishes the important role that organic cation ordering has in dictating structural order and anomalous optoelectronic phenomenon in hybrid perovskites.

25 ENERGY STORAGE↗
NASA NTRS2018

Sensor Integrated Pilot Mask for On-Board, Real-Time, Monitoring of Pilot Breathing Gas

A pilot's job is unique in the demands it places on the human body, and many conditions can seriously affect pilot performance, threatening mission completion and pilot safety. The gas in a flight mask contains key indicators of the pilot's physiological state, including oxygen inhaled and carbon dioxide exhaled, which can signal hypoxia, hyperoxia, hypocapnia, and hypercapnia. A fiber optic-based sensor system, integrated into the pilot mask, has been developed to monitor in real time, during flight, the pilot breathing gas levels. Monitoring the partial pressure of oxygen and carbon dioxide in the pilot mask supports real-time closed loop control of the on-board oxygen generation system, based on a direct reading of what the pilot is actually breathing. The pilot Mask Sensor (MASES) system incorporates luminescence sensors for pO2, pCO2, relative humidity, pressure, and temperature in a compact probe in the pilot mask; it is based on sensor technology developed for gas monitoring in space suit systems, in work supported by NASA under the Small Business Innovation Research program. Relevant requirements for the MASES system include sensor operation while wet, operation at reduced pressure, ability to withstand rapid decompression, operation in a pure oxygen atmosphere, low power consumption, a compact readout unit, and flexible miniature sensors; many of these requirements are shared with gas monitors in space suits. Data are presented from tests conducted with human subjects in an altitude chamber and in a centrifuge.

Delgado Alonso, Jesus↗
NASA NTRS1986

Microcomputer-based programmable optical signal processor

A microcomputer-based real-time programmable optical signal processing system utilizing a magneto-optic spatial light modulator (MOSLM) and a liquid crystal light valve (LCLV) is described. This system can perform a myriad of complicated optical operations, such as image correlation, image subtraction, and matrix multiplication. Its important asserts are its programmability and the capability of real-time addressing. These are important for telerobotic vision in space automation applications. Design specifications and suggestions for practical implementation of the system are discussed. Some preliminary experimental demonstrations are conducted to demonstrate applications of the proposed system to image correlation for optical pattern recognition, image subtraction for lC chip inspection, and matrix multiplication for optical computing.

Yu, F. T. S.↗
DOE OSTI2016

High cycle fatigue in the transmission electron microscope

One of the most common causes of structural failure in metals is fatigue induced by cyclic loading. Historically, microstructure-level analysis of fatigue cracks has primarily been performed post mortem. However, such investigations do not directly reveal the internal structural processes at work near micro- and nanoscale fatigue cracks and thus do not provide direct evidence of active microstructural mechanisms. In this paper, the tension–tension fatigue behavior of nanocrystalline Cu was monitored in real time at the nanoscale by utilizing a new capability for quantitative cyclic mechanical loading performed in situ in a transmission electron microscope (TEM). Controllable loads were applied at frequencies from one to several hundred hertz, enabling accumulations of 10 6 cycles within 1 h. The nanometer-scale spatial resolution of the TEM allows quantitative fatigue crack growth studies at very slow crack growth rates, measured here at ~10 –12 m·cycle –1 . This represents an incipient threshold regime that is well below the tensile yield stress and near the minimum conditions for fatigue crack growth. Evidence of localized deformation and grain growth within 150 nm of the crack tip was observed by both standard imaging and precession electron diffraction orientation mapping. Finally, these observations begin to reveal with unprecedented detail the local microstructural processes that govern damage accumulation, crack nucleation, and crack propagation during fatigue loading in nanocrystalline Cu.

36 MATERIALS SCIENCE↗
NASA NTRS1980

Near-infrared spectral imaging Michelson interferometer for astronomical applications

The design and operation of an imaging Michelson interferometer-spectrometer used for near-infrared (0.8 micron to 2.5 microns) spectral imaging are reported. The system employs a rapid scan interferometer modified for stable low resolution (250/cm) performance and a 42 element PbS linear detector array. A microcomputer system is described which provides data acquisition, coadding, and Fourier transformation for near real-time presentation of the spectra of all 42 scene elements. The electronic and mechanical designs are discussed and telescope performance data presented.

Wells, C. W.↗
DOE OSTI2026

The Value of Forecasters‐in‐the‐Loop in Real‐Time Flood Forecasting in the Age of Machine Learning

Machine learning (ML) applications in hydrological forecasting are increasingly prevalent and show great potential. However, many previous studies have only evaluated performance through reanalysis or retrospective simulations compared to simplified baselines. This study provides the first assessment of ML performance against actual operational forecasting systems operated by the California Nevada River Forecast Center (CNRFC), which combines the Community Hydrologic Prediction System (CHPS) with forecasters-in-the-loop. Results demonstrate that forecasters-in-the-loop systems consistently outperform ML models in both general forecasts and flood alerting across lead times up to 96 hr, even when ML models use observed forcings, while CNRFC operational process relies on biased weather forecasts. Our analysis reveals that forecaster expertise maintains forecast reliability despite inaccurate precipitation inputs, with human-guided systems showing superior performance degradation characteristics at extended lead times. These findings highlight the irreplaceable value of human expertise in operational forecasting and caution against overstating current ML capabilities in real-world applications.

Tran, Vinh Ngoc [Univ. of Michigan, Ann Arbor, MI ↗