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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 127 records · Page 7

Real-time data compressor for Eos-class missions

A conceptual design for a real-time VLSI compressor capable of processing rate up to one gigabit per second is presented. This scheme is capable of providing a three-to-one distortion-free data reduction factor to both the High Resolution Imaging Spectrometer and processed SAR imaging data. The design uses a VLSI parallel/piplined architecture capable of processing at a real time rate. The design consists of a parallel array of VLSI compressor modules. Each module is built on a single customized VLSI chip using existing state-of-the-art semiconductor technology.

Lee, Jun-Ji↗

Learning-Based Real-Time Event Identification Using Rich Real PMU Data

A large-scale deployment of phasor measurement units (PMUs) that reveal the inherent physical laws of power systems from a data perspective enables an enhanced awareness of power system operation. However, the high-granularity and non-stationary nature of PMU data and imperfect data quality could bring great technical challenges for real-time system event identification. To address these challenges, this paper proposes a two-stage learning-based framework. In the first stage, a Markov transition field (MTF) algorithm is exploited to extract the latent data features by encoding temporal dependency and transition statistics of PMU data in graphs. Then, a spatial pyramid pooling (SPP)-aided convolutional neural network (CNN) is established to efficiently and accurately identify power events. The proposed method fully builds on and is also tested on a large real-world dataset from several tens of PMU sources (and the corresponding event logs), located across the U.S., with a time span of two consecutive years. We report the numerical results validate that our method has high identification accuracy while showing good robustness against poor data quality.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A portable real-time data processing system for standard meteorological radiosondes

The UMET-1 is a microprocessor-based portable system for automatic real-time processing of flight data transmitted from the standard RAWINSONDE upper atmosphere meteorological balloonsonde. The first 'target system' is described which was designed to receive data from a mobile tracking and telemetry receiving station (TRADAT), as the balloonsonde ascends to apogee. After balloon-burst, the UMET-1 produces user-ready hardcopy.

Staffanson, F. L.↗

Interpretable Machine Learning for Characterizing Electric Vehicle Charging Behavior: Insights from Real-World Data

As electric vehicle (EV) adoption rises globally, concerns about the impact on aging electrical grids grow, particularly regarding the charging behavior of EV drivers. This study analyzes real-world driving and charging data from Ford battery electric vehicles (BEVs) collected between 2018 and 2019 to develop interpretable models that characterize charging behavior and quantify influencing factors. Prior research has relied on assumptions regarding driver behavior, often overlooking actual charging patterns. By employing generalized linear mixed models (GLMMs), this work offers insights into how various elements, such as next trip distance and state of charge (SOC), influence charging decisions. The dataset comprises over three million park-trip pairs from 1,997 vehicles, revealing that features related to driving behavior significantly dictate charging behavior, while infrastructure and regional factors have lesser impacts. The findings suggest that existing simulation models may oversimplify EV charging behavior assumptions. This work utilizes real-world EV driving and charging data to train interpretable models that describe charging behavior and quantify the factors most associated with how drivers use charging infrastructure. This research underscores the need for interpretable, data-driven methodologies to inform future EV infrastructure planning and grid management.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

PIMS Real-Time Data Reception and Environment Characterization

The methodology and system concepts developed by the PI Microgravity Services (PIMS) project to process real-time ISS microgravity acceleration data will be presented. The characterization of these data and analyses into information meaningful to microgravity scientists will also be described.

McPherson, Kevin M.↗

Real-time data acquisition system for the NASA Langley transonic dynamics tunnel

The hardware configuration of the Transonic Dynamics Wind Tunnel Data Acquisition System (DAS) which consists of an analog front end that can process up to 260 channels of data is presented. The DAS also has a multi-channel analog-to-digital subsystem that can process up to 50,000 samples of data per sec, and a digital computer with standard and nonstandard devices, with graphics capability. The software configuration of the DAS and complex hardware/software interfaces are described, which can provide automatic amplifier gain and offset adjustment for each data channel. Finally, a summary of specific DAS applications is given including the real-time processing of dynamic deflection data, unsteady pressure measurements, and flutter and buffet data.

Cole, P. H.↗

Handling Flight-Research Data In Real Time

Researchers at widely separated locations able to participate in tests and analyze data immediately. Basic data-handling needs common: Communicates with vehicle, pilot, and test team; Acquires, computes, and displays data; knows exact location of research vehicle at all times. Continuing challenge for designers and operators of ground support facilities to perform tasks in real time and present integrated results to research team in real time. Paper presents several approaches to satisfaction of requirements of representative types of aircraft research programs at NASA Western Aeronautical Test Range of Ames Research Center.

Moore, Archie L.↗

Easing the Discovery of NASA and International Near-Real-Time Data Using the Global Change Master Directory

The Global Change Master Directory (GCMD) provides an extensive directory of descriptive and spatial information about data sets and data-related services, which are relevant to Earth science research. The directory's data discovery components include controlled keywords, free-text searches, and map/date searches. The GCMD portal for NASA's Land Atmosphere Near-real-time Capability for EOS (LANCE) data products leverages these discovery features by providing users a direct route to NASA's Near-Real-Time (NRT) collections. This portal offers direct access to collection entries by instrument name, informing users of the availability of data. After a relevant collection entry is found through the GCMD's search components, the "Get Data" URL within the entry directs the user to the desired data. http://gcmd.nasa.gov/r/p/gcmd_lance_nrt.

Olsen, Lola↗

CATS Near Real Time Data Products: Applications for Assimilation Into the NASA GEOS-5 AGCM

From February 2015 through October 2017, the NASA Cloud-Aerosol Transport System (CATS) backscatter lidar operated on the International Space Station (ISS) as a technology demonstration for future Earth Science Missions, providing vertical measurements of cloud and aerosols properties. Owing to its location on the ISS, a cornerstone technology demonstration of CATS was the capability to acquire, process, and disseminate near-real time (NRT) data within 6 hours of observation time. CATS NRT data has several applications, including providing notification of hazardous events for air traffic control and air quality advisories, field campaign flight planning, as well as for constraining cloud and aerosol distributions in via data assimilation in aerosol transport models. Â Recent developments in aerosol data assimilation techniques have permitted the assimilation of aerosol optical thickness (AOT), a 2-dimensional column integrated quantity that is reflective of the simulated aerosol loading in aerosol transport models. While this capability has greatly improved simulated AOT forecasts, the vertical position, a key control on aerosol transport, is often not impacted when 2-D AOT is assimilated. Here, we present preliminary efforts to assimilate CATS aerosol observations into the NASA Goddard Earth Observing System version 5 (GEOS-5) atmospheric general circulation model and assimilation system using a 1-D Variational (1-D VAR) ensemble approach, demonstrating the utility of CATS for future Earth Science Missions.

Hlavka, D. L.↗

Improving Operational Efficiency, Compliance and Community Perception with Real-time Data - 19260

The nuclear and medical radiation industry has always faced a significant burden in effectively balancing exposure and productivity. Maintenance operations and development of new techniques require significant staffing resources to monitor and review personnel exposures and changes in operational radiation levels. Currently utilised monitoring technology and review techniques remain unchanged since the 1950's, relying upon mostly analogue instrumentation and paper reporting. After utilising isolated-area monitoring during a reactor decommissioning project it became apparent that current systems created false assurance as the units continually failed without warning due to 'dirty' power. These instrument failures could have easily led to an unnecessary exposure while operators continued to assume they were being safely monitored. This paper is a philosophical discussion on how current shortcomings could be addressed, security increased, and exposure risks minimised, by utilising real-time monitoring and automated reporting. The community and media expectations of reported data and nuclear monitoring systems continue to be coloured by sensational technology seen in movies and emerging technology available through smart homes and smart cities initiatives. For the general public to be more engaged with real radiation levels and effects, the nuclear industry needs to more effectively communicate safe levels. Organisations can have regain control of exposure levels discussions through the controlled release of background and expected exposure level data. An increase in real world monitoring data communicated in a controlled and more effective way will aid in the general public accepting, and being educated about, the minimal increase above natural background levels of the nuclear industry and other radiation facilities. This is particularly as Australia is in the process of implementing its Nuclear Waste Facility, with community acceptance the number one inhibitor of being able to move forward. Storage, mobile assets, and processing all present unique management, security, exposure, and telemetry challenges for a facility operations manager. Active monitoring and automated review of the monitoring data can alert operators to any unexpected events before any resultant increases in radiation levels cause an unplanned exposure to operators or the environment. Integration of radiation monitoring data with global positioning telemetry enables better organisational awareness of mobile asset location and early detection of any potential shifting and reduction in shielding integrity of sources and encapsulated waste during transport. Additionally, the real-time monitoring can be set-up to enhance an organisations' or nation's security capabilities. For instance, the real time monitor will send an automated alert in the case of an unexpected movement of a source through a monitored facility or for an unscheduled movement of a monitored vehicle. There are further benefits to be gained in control, ALARA principles and effluent management by increasing real-time monitoring capabilities through the stages of the waste cycle. Increasing operational effectiveness while delivering operational cost savings sounds like an attractive proposition. With increased access to operational area radiation data comes drastically improved real-time management of exposures for new, and experimental practices. Increased live data can be utilised to identify optimum practices for reducing operational and environmental exposures or releases. This increased operational data effectively comes with the added benefit of a reduction in the required health physics personnel resources for general operations. By reducing exposure and the detachment of required health physics resources from routine operations, there is greater availability for these resources to support maintenance operations and project-based work. The review of this increased data will create internal capability to identify low-- and high-risk activities, resulting in the ability to reduce the number of highly skilled workers in an industry where the availability of this talent is declining. During my time as the Radiation Safety and Nuclear Security Manager at a research facility, I implemented a real-time monitoring system. The system identified a secondary risk that had previously been unidentified and posed a higher exposure and security risk than the radiation practice being monitored. What stops industry from implementing technology that encourages agile work practices, while minimising security risks and personnel exposure? (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning

In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts (0.2 < z < 1.1). Our method consists of two stages: feature extraction (obtaining the SN redshift from photometry and estimating light-curve shape parameters) and machine learning classification. We study the performance of different algorithms such as Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia samples. Using the Area Under the Curve (AUC) metric, where perfect classification is given by 1, we find that our best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC of 0.98.We show that it is possible to obtain a large photometrically selected type Ia SN sample with an estimated contamination of less than 5%. When applied to data from the first three years of SNLS, we obtain 529 events. We investigate the differences between classifying simulated SNe, and real SN survey data. In particular, we find that applying a thorough set of selection cuts to the SN sample is essential for good classification. This work demonstrates for the first time the feasibility of machine learning classification in a high- z SN survey with application to real SN data.

79 ASTRONOMY AND ASTROPHYSICS↗

A real-time ISEE data system

A real time International Sun Earth Explorer (ISEE) data system directed toward the prediction of geomagnetic substorms and storms is discussed in detail. Such a system may allow up to 60 minutes or more, advance warning of magnetospheric substorms and up to 30 minute warnings of geomagnetic storms induced by high speed streams and solar flares. The proposed system utilizes existing capabilities of several agencies thereby minimizing costs.

Tsurutani, B. T.↗

Validation and Calibration of Energy Models with Real Vehicle Data from Chassis Dynamometer Experiments

Accurate estimation of vehicle fuel consumption typically requires detailed modeling of complex internal powertrain dynamics, often resulting in computationally intensive simulations. However, many transportation applications-such as traffic flow modeling, optimization, and control-require simplified models that are fast, interpretable, and easy to implement, while still maintaining fidelity to physical energy behavior. This work builds upon a recently developed model reduction pipeline that derives physics-like energy models from high-fidelity Autonomie vehicle simulations. These reduced models preserve essential vehicle dynamics, enabling realistic fuel consumption estimation with minimal computational overhead. While the reduced models have demonstrated strong agreement with their Autonomie counterparts, previous validation efforts have been confined to simulation environments. This study extends the validation by comparing the reduced energy model's outputs against real-world vehicle data. Focusing on the MidSUV category, we tune the baseline Autonomie model to closely replicate the characteristics of a Toyota RAV4. We then assess the accuracy of the resulting reduced model in estimating fuel consumption under actual drive conditions. Our findings suggest that, when the reference Autonomie model is properly calibrated, the simplified model produced by the reduction pipeline can provide reliable, semi-principled fuel rate estimates suitable for large-scale transportation applications.

42 ENGINEERING↗

Co-Optimization Scheme for Hybrid Electric Vehicles Powertrain and Exhaust Emission Control System Using Future Speed Prediction

Hybrid electric vehicles (HEVs) have been an effective solution for improved vehicle fuel efficiency and reduced emission pollution. In this paper, a co-optimization scheme is proposed to optimize fuel efficiency for HEVs. Here, the proposed optimization scheme uses obtainable future speed prediction as the basis to optimally tune control parameters for the existing powertrain control system. The ramp-up time of the catalyst temperature to reach its light-off level in the exhaust emission system is also considered as an additional optimization constraint to reduce emission. The Toyota Prius Hybrid Simulink model which is an integrated model for a powertrain and exhaust emission system is validated using real data from several real driving cycle scenarios. Then, to simplify the formulation of the proposed algorithm, the model for the optimization for powertrain and exhaust emission systems is represented by a set of equivalent neural network (NN) models learned using the data generated from the well-validated Toyota Prius Hybrid Simulink model. Using NN models, a co-optimization algorithm is established that provides an optimal tuning of some fuel-sensitive powertrain control parameters using future speed prediction, leading to a novel co-optimization algorithm, achieving on average a further 9.22% fuel savings for the Toyota Prius Hybrid Simulink model.

33 ADVANCED PROPULSION SYSTEMS↗

REACH: Real-Time Data Awareness in Multi-Spacecraft Missions

Missions have been proposed that will use multiple spacecraft to perform scientific or commercial tasks. Indeed, in the commercial world, some spacecraft constellations already exist. Aside from the technical challenges of constructing and flying these missions, there is also the financial challenge presented by the tradition model of the flight operations team (FOT) when it is applied to a constellation mission. Proposed constellation missions range in size from three spacecraft to more than 50. If the current ratio of three-to-five FOT personnel per spacecraft is maintained, the size of the FOT becomes cost prohibitive. The Advanced Architectures and Automation Branch at the Goddard Space Flight Center (GSFC Code 588) saw the potential to reduce the cost of these missions by creating new user interfaces to the ground system health-and-safety data. The goal is to enable a smaller FOT to remain aware and responsive to the increased amount of ground system information in a multi-spacecraft environment. Rather than abandon the tried and true, these interfaces were developed to run alongside existing ground system software to provide additional support to the FOT. These new user interfaces have been combined in a tool called REACH. REACH-the Real-time Evaluation and Analysis of Consolidated Health-is a software product that uses advanced visualization techniques to make spacecraft anomalies easy to spot, no matter how many spacecraft are in the constellation. REACH reads a real-time stream of data from the ground system and displays it to the FOT such that anomalies are easy to pick out and investigate. Data visualization has been used in ground system operations for many years. To provide a unique visualization tool, we developed a unique source of data to visualize: the REACH Health Model Engine. The Health Model Engine is rule-based software that receives real-time telemetry information and outputs "health" information related to the subsystems and spacecraft that the telemetry belong to. The Health Engine can run out-of-the-box or can be tailored with a scripting language. Out of the box, it uses limit violations to determine the health of subsystems and spacecraft; when tailored, it determines health using equations combining the values and limits of any telemetry in the spacecraft. The REACH visualizations then "roll up" the information from the Health Engine into high level, summary displays. These summary visualizations can be "zoomed" into for increasing levels of detail. Currently REACH is installed in the Small Explorer (SMEX) lab at GSFC, and is monitoring three of their five spacecraft. We are scheduled to install REACH in the Mid-sized Explorer (MIDEX) lab, which will allow us to monitor up to six more spacecraft. The process of installing and using our "research" software in an operational environment has provided many insights into which parts of REACH are a step forward and which of our ideas are missteps. Our paper explores both the new concepts in spacecraft health-and-safety visualization, the difficulties of such systems in the operational environment, and the cost and safety issues of multi-spacecraft missions.

Maks, Lori↗

Data Quality Assessment Process for Real-Time Data-Driven Traffic Microsimulation of Smart Corridor

Smart corridor digital twins are often created for the development and evaluation of emerging intelligent transportation systems and Connected and Autonomous Vehicle (CAV) technologies. However, limited guidance exists for data quality assessment for digital twin development. To address this, this paper discusses the data quality assessment utilized to develop data-driven real-time microscopic simulation models, i.e., digital twins, for two separate smart corridors: the North Avenue Smart Corridor in Atlanta, GA, and the Martin Luther King Smart Corridor in Chattanooga, Tennessee. This paper provides a summary of the author’s investigations of data requirements and data characteristics for the given smart corridor digital twin development efforts. With a focus on data, this summary includes a description of the data investigation process, key data issues observed, and strategies to address observed issues. Discussion is provided to help expand the lessons from these studies to other digital twin development efforts.

Saroj, Abhilasha [ORNL] (ORCID:0000000191178063)↗

An Accurate Fast Fluid Dynamics Model for Data Center Applications

Traditional CFD is broadly useful in the design and operation of reliable and efficient data centers. It is nevertheless computationally expensive, particularly when employed for design optimization, which usually requires multiple simulations. To speed-up calculations while retaining essential airflow physics, several researchers have turned to an alternative CFD methodology, namely, fast fluid dynamics (FFD). FFD has been reported to be much faster than traditional CFD, but at an assumed (acceptable) trade-off of reduced accuracy. However, a recent comparison of FFD and traditional CFD for data center plenum applications produced nearly indistinguishable results and suggested that previously-reported FFD/traditional-CFD differences were due primarily to inconsistent: 1) advection schemes, 2) computational grids, and 3) turbulence models. The present paper extends this FFD/traditional-CFD comparison to the data center whitespace and confirms the finding that a true like-for-like comparison produces nearly identical predictions. Our FFD implementation utilizes a first-order upwind finite-volume scheme for advection like traditional CFD, so alternative advection schemes (e.g., semi-Lagrangian) are not considered further here. Likewise, the effect of grid choice on traditional-CFD predictions is well known. However, the effect of turbulence model for data center applications has not been reported extensively so we do consider this topic further here. We compare the standard k-e and a simpler algebraic model to benchmark experimental data from a real data center. We find that the algebraic turbulence model predicts rack-inlet temperatures at a similar level-of-accuracy as the k-e model for our fairly-simple-airflow reference data center.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Hypothetical Case and Scenario Description for International Transportation of Spent Nuclear Fuel.

To support more rigorous analysis on global security issues at Sandia National Laboratories (SNL), there is a need to develop realistic data sets without using "real" data or identifying "real" vulnerabilities, hazards or geopolitically embarrassing shortcomings. In response, an interdisciplinary team led by subject matter experts in SNL's Center for Global Security and Cooperation (CGSC) developed a hypothetical case description. This hypothetical case description assigns various attributes related to international SNF transportation that are representative, illustrative and indicative of "real" characteristics of "real" countries. There is no intent to identify any particular country and any similarity with specific real-world events is purely coincidental. To support the goal of this report to provide a case description (and set of scenarios of concern) for international SNF transportation inclusive of as much "real-world" complexity as possible -- without crossing over into politically sensitive or classified information -- this SAND report provides a subject matter expert-validated (and detailed) description of both technical and political influences on the international transportation of spent nuclear fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗