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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 NTRS1983

Experimental creep data for a built-up aluminum/titanium structure subjected to heating and loading

Experimental creep, temperature, and strain data resulting from a laboratory experiment on a built-up aluminum/titanium structure are presented. The structure and the experiment are described in detail. A heating and loading experiment lasting approximately six hours is conducted on a test structure. Considerable creep strain resulted from compressive stresses in the heated skin. Large residual stresses were found after the experiment was completed. The residual stresses in the substructure frames were large enough to preclude further cycles of creep experiments with this built-up structure because of concern that the frame webs would buckle.

Jenkins, J. M.↗
NASA NTRS1978

Automated time-resolved data acquisition during static and dynamic loading of composite materials

This paper reports the development of two new nondestructive techniques for precise determination of the nature and extent of complex damage and the consequent redistribution of stress in composite materials during quasi-static and cyclic or dynamic loading. The two methods incorporate video-thermography and four-component tensor elastic modulus determination. The experimental and analytical aspects of these two methods are discussed and data is presented.

Reifsnieder, K. L.↗
NASA NTRS2016

Biomechanical Modeling of the Deadlift Exercise to Improve the Efficacy of Resistive Exercise Microgravity Countermeasures

During long-duration spaceflight missions, astronauts exposure to microgravity without adequate countermeasures can result in losses of muscular strength and endurance, as well as loss of bone mass. As a countermeasure to this challenge, astronauts engage in resistive exercise during spaceflight to maintain their musculoskeletal function. The Hybrid Ultimate Lifting Kit (HULK) has been designed as a prototype exercise device for an exploration-class vehicle; the HULK features a much smaller footprint than previous devices such as the Advanced Resistive Exercise Device (ARED) on the International Space Station (ISS), which makes the HULK suitable for extended spaceflight missions in vehicles with limited volume. As current ISS exercise countermeasure equipment represents an improvement over previous generations of such devices, the ARED is being employed as a benchmark of functional performance. This project involves the development of a biomechanical model of the deadlift exercise, and is novel in that it is the first exercise analyzed in this context to include the upper limbs in the loading path, in contrast to the squat, single-leg squat, and heel raise exercises also being modeled by our team. OpenSim software is employed to develop these biomechanical models of humans performing resistive exercises to assess and improve the new exercise device designs. Analyses include determining differences in joint and muscle forces when using different loading strategies with the device, comparing and contrasting with the ARED benchmark, and determining whether the loading is sufficient to maintain musculoskeletal health. During data collection, the number of repetitions, load, cadence, stance, and grip width are controlled in order to facilitate comparisons between loading configurations. To date, data have been collected for two human subjects performing the deadlift exercise on the HULK device using two different loading conditions. Recorded data include motion capture, electromyography (EMG), ground reaction forces, device load cell data, photos and videos, and anthropometric data. Work is ongoing to perform biomechanical analyses including inverse kinematics and inverse dynamics to compare different versions of the deadlift model in order to determine which provides an appropriate level of detail to study this exercise. This work is supported by the National Space Biomedical Research Institute through NCC 9-58.

physical exercise↗
DOE OSTI2024

An iterative bidirectional gradient boosting approach for CVR baseline estimation

Here this paper presents a novel Iterative Bidirectional Gradient Boosting Model (IBi-GBM) for estimating the baseline of Conservation Voltage Reduction (CVR) programs. In contrast to many existing methods, we treat CVR baseline estimation as a missing data retrieval problem. The approach involves dividing the load and its corresponding temperature profiles into three periods: pre-CVR, CVR, and post-CVR. To restore the missing load profile during the CVR period, the method employs a three-step process. First, a forward-pass GBM is executed using data from the pre-CVR period as inputs. Subsequently, a backward-pass GBM is applied using data from the post-CVR period. The two restored load profiles are reconciled, considering pre-calculated weights derived from forecasting accuracy, and only the leftmost and rightmost points are retained. The newly restored points are then included as inputs for the subsequent iteration. This iterative procedure continues until the original load data in the CVR period is fully restored. We develop IBi-GBM using actual smart meter and Supervisory Control and Data Acquisition (SCADA) data. Our results demonstrate that IBi-GBM exhibits robust performance across various data resolutions and in different seasons and outperforms existing methods by achieving a 1-2% reduction in normalized Root Mean Square Error (nRMSE).

42 ENGINEERING↗
NASA NTRS1988

Controlled-strain rate tests at very low strain rates of 2618 aluminum at 200 C

Constant strain rate tests and constant load creep tests were performed on 2618 aluminum at 200 C. The strain rates used in the constant strain rate tests were 10 to the minus 6, 10 to the minus 7, 10 to the minum 8, and 10 to the minus 9/sec. Due to the fact that the strain rates in both tests were comparable to each other, the similarities between them can therefore be studied. It was concluded that metals are essentially rate sensitive at elevated temperatures. The traditional definition of creep and plasticity used in the classical creep analysis is actually a reflection of the material behavior under different loading conditions. A constitutive equation based on the test data under one loading condition should work well for other loading conditions as long as the strain rates are in the same range as those under which the material constants are determined.

Ding, J. L.↗
NASA NTRS1996

Thread Migration in the Presence of Pointers

Dynamic migration of lightweight threads supports both data locality and load balancing. However, migrating threads that contain pointers referencing data in both the stack and heap remains an open problem. In this paper we describe a technique by which threads with pointers referencing both stack and non-shared heap data can be migrated such that the pointers remain valid after migration. As a result, threads containing pointers can now be migrated between processors in a homogeneous distributed memory environment.

Cronk, David↗
DOE OSTI2020

Smart-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios

The explosion of new ideas for distributed energy resources and advanced distribution grid algorithms faces a critical challenge: how can one effectively develop and evaluate these breakthrough technologies without an adequate set of power systems network, load, and resource data to test against? Today's open test systems, particularly for distribution systems, are tiny and incomplete; and though with enough effort, obtaining utility data under NDA can overcome scale challenges, the results can't be openly shared, preventing fair comparison. The Smart-DS project-led by NREL in partnership with MIT, Universidad Pontificia Comillas, CYME, and EDD-is working to overcome this gap by generating multiple large-scale, open, synthetic, distribution systems that are realistic but not real. These datasets go beyond a single medium voltage feeder (~1000 customers) to cover entire metropolitan areas and their surrounds with up to hundreds of feeders and millions of customers, complete with low voltage customer connections and high voltage sub-transmission. This scale allows adequate testing and analysis of not just local controls and advanced DERs, but also multiple substation interactions, optimized switching, full-scale distribution-OPF, and more. To accompany these datasets, the project is also building a rich set of scenario generation tools that can be used for distribution and transmission systems alike. This includes automated, spatially aware access to world-class, high-resolution solar, wind, and weather data: forward looking generation mixes: and highly configurable DER, load, climate, outage, control-scheme, and other scenarios. We will also highlight DiTTo, an exciting open-source, multi-way distribution dataset transformation tool for many-to-many format translation with scenario, merge/split, and many other manipulation capabilities.

DERs↗
NASA NTRS1961

Investigation of Three Analytical Hypothesis for Determining Material Creep Behavior under Varied Loads, with an Application to 2024-T3 Aluminum-Alloy Sheet in Tension at 400 F

Three existing hypotheses are formulated mathematically to estimate tensile creep strain under varied loads and constant temperature from creep data obtained under constant load and constant temperature. hypotheses investigated include the time-hardening, strain-hardening, and life-fraction rules. Predicted creep behavior is compared with data obtained from tensile creep tests of 2024-T3 aluminum-alloy sheet at 400 F under cyclic-load conditions. creep strain under varied loads is presented on the basis of an equivalent stress, derived from the life-fraction rule, which reduces the varied-load case to a constant-load problem. Creep strain in the region of interest for structural design and rupture times, determined from the hypotheses investigated, are in fair agreement with data in most cases, although calculated values of creep strain are generally greater than the experimental values because creep recovery is neglected in the calculations.

Berkovits, Avraham↗
NASA NTRS1974

Statistical analysis of general aviation VG-VGH data

To represent the loads spectra of general aviation aircraft operating in the Continental United States, VG and VGH data collected since 1963 in eight operational categories were processed and analyzed. Adequacy of data sample and current operational categories, and parameter distributions required for valid data extrapolation were studied along with envelopes of equal probability of exceeding the normal load factor (n sub z) versus airspeed for gust and maneuver loads and the probability of exceeding current design maneuver, gust, and landing impact n sub z limits. The significant findings are included.

Clay, L. E.↗
NASA NTRS1956

A reevaluaion of data on atmospheric turbulence and airplane gust loads for application in spectral calculations

The available information on the spectrum of atmospheric turbulence is first briefly reviewed. On the basis of these results, methods are developed for the conversion of available gust statistics normally given in terms of counts of gusts or acceleration peaks into a form appropriate for use in spectral calculations. The fundamental quantity for this purpose appears to be the probability distribution of the root-mean-square gust velocity. Estimates of this distribution are derived from data for a number of load histories of transport operations; also, estimates of the variation of this distribution with altitude and weather condition are derived from available data and the method of applying these results to the calculation of airplane gust-response histories in operations is also outlined. (author)

Press, Harry↗
DOE OSTI2020

Modeling and Detection of Future Cyber-Enabled DSM Data Attacks

Demand-Side Management (DSM) is an essential tool to ensure power system reliability and stability. In future smart grids, certain portions of a customer’s load usage could be under the automatic control of a cyber-enabled DSM program, which selectively schedules loads as a function of electricity prices to improve power balance and grid stability. In this scenario, the security of DSM cyberinfrastructure will be critical as advanced metering infrastructure and communication systems are susceptible to cyber-attacks. Such attacks, in the form of false data injections, can manipulate customer load profiles and cause metering chaos and energy losses in the grid. The feedback mechanism between load management on the consumer side and dynamic price schemes employed by independent system operators can further exacerbate attacks. To study how this feedback mechanism may worsen attacks in future cyber-enabled DSM programs, we propose a novel mathematical framework for (i) modeling the nonlinear relationship between load management and real-time pricing, (ii) simulating residential load data and prices, (iii) creating cyber-attacks, and (iv) detecting said attacks. In this framework, we first develop time-series forecasts to model load demand and use them as inputs to an elasticity model for the price-demand relationship in the DSM loop. This work then investigates the behavior of such a feedback loop under intentional cyber-attacks. We simulate and examine load-price data under different DSM-participation levels with three types of random additive attacks: ramp, sudden, and point attacks. We conduct two investigations for the detection of DSM attacks. The first studies a supervised learning approach, with various classification models, and the second studies the performance of parametric and nonparametric change point detectors. Results conclude that higher amounts of DSM participation can exacerbate ramp and sudden attacks leading to better detection of such attacks, especially with supervised learning classifiers. We also find that nonparametric detection outperforms parametric for smaller user pools, and random point attacks are the hardest to detect with any method.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Expedited Model Building Method for Rocket Nozzle Side Loading

Statistical analysis of test data is currently the preferred method of creating models for uncharacterized side loading in rocket nozzles. However, statistical analysis requires multiple data sets to be reliable; getting these sets requires building and testing multiple rocket motors, which is not feasible given the incredible time and cost required to do so. By using a combination of geometry, simplified finite element analysis (FEA) analogs, and algebra we can create a limited predictive model from only one test data set. Once created, the model requires only the input from one strain gauge located at each yaw and pitch attachment bracket for the actuation system. This process is not computationally intensive, therefore saving an abundance of time and budget over using full 3D FEA models to develop a similar predictive model. To develop the model, conversion factors between the strain at the gauge locations and corresponding actuator load is derived from the data. These conversion factors are used to create a “conversion ellipse” that bounds the constitutive properties of the nozzle as one lumped factor. Using a 2D cross-sectional analog of the nozzle exit plane, we create a function that allows us to determine the azimuthal location of the unexpected load based on the ratio between yaw and pitch strain readings. Combining this with the “conversion ellipse” yields a function that given the strain reading outputs an induced actuator load, regardless of the azimuthal location of the unexpected load. As previously mentioned, this predictive model is limited in its application as there is inaccuracy introduced by the use of data by only one test as well as simplifying assumptions. This approach makes a useful tool to quickly determine whether uncharacterized actuator loading events can damage actuation systems or induce loading that exceeds design limits.

Seetha A Kolli↗
NASA NTRS1997

Application-Controlled Demand Paging for Out-of-Core Visualization

In the area of scientific visualization, input data sets are often very large. In visualization of Computational Fluid Dynamics (CFD) in particular, input data sets today can surpass 100 Gbytes, and are expected to scale with the ability of supercomputers to generate them. Some visualization tools already partition large data sets into segments, and load appropriate segments as they are needed. However, this does not remove the problem for two reasons: 1) there are data sets for which even the individual segments are too large for the largest graphics workstations, 2) many practitioners do not have access to workstations with the memory capacity required to load even a segment, especially since the state-of-the-art visualization tools tend to be developed by researchers with much more powerful machines. When the size of the data that must be accessed is larger than the size of memory, some form of virtual memory is simply required. This may be by segmentation, paging, or by paged segments. In this paper we demonstrate that complete reliance on operating system virtual memory for out-of-core visualization leads to poor performance. We then describe a paged segment system that we have implemented, and explore the principles of memory management that can be employed by the application for out-of-core visualization. We show that application control over some of these can significantly improve performance. We show that sparse traversal can be exploited by loading only those data actually required. We show also that application control over data loading can be exploited by 1) loading data from alternative storage format (in particular 3-dimensional data stored in sub-cubes), 2) controlling the page size. Both of these techniques effectively reduce the total memory required by visualization at run-time. We also describe experiments we have done on remote out-of-core visualization (when pages are read by demand from remote disk) whose results are promising.

Cox, Michael↗
NASA NTRS2019

TPSAS-NF1676L-32493-DND

The Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has supported the Open Data Cube (ODC) initiative to provide a data architecture solution that has value to its global users and increases the impact of EO satellite data. ODC is an open-source platform for processing satellite data. We have developed software products and tools around the core ODC that would help users perform machine learning on EO satellite data. The recent United Nations (UN) Sustainable Development Agenda provides a shared blueprint for peace and prosperity for people and for the planet, considering our current situation and helping to create a plan. The core of this agenda is a set of seventeen Sustainable Development Goals (SDGs), which represent an urgent call for action by all countries - both developed and developing - in a global partnership. The CEOS SEO team has recently developed and released a set of innovative Jupyter notebooks addressing UN SDGs 6.6.1 (spatial extents of water-related ecosystems), 11.3.1 (ratio of land consumption rate to population growth rate), and 15.3.1 (proportion of land that is degraded over total land area). These notebooks empower users by providing features that will assist with streamlining analysis ready data retrieval, processing, and visualization. We have recently incorporated several machine learning techniques in these notebooks. In this paper, we present the lessons learned from our experience on classifying land using supervised and unsupervised machine learning techniques using ODC framework for UN SDGs. We identify the current limitations of ODC to seamlessly support machine learning techniques. We propose features that would help machine learning, specifically within the ODC framework. We propose a thematic indexing/loading of data for both unsupervised learning as well as data annotation/labeling pipeline. Currently, ODC supports machine learning by separating data-management from the analysis process. It works as a mechanism to load cubes of data. ODC does not natively support features that are vital in machine learning such as validation splits, fair/balanced sampling, establishing load size constraints, etc. We believe that our proposed features will empower users by providing features that bring machine learning techniques closed to ODC. Enhancements to ODC to better accommodate machine learning techniques can assist in fulfilling UN SDGs such as 6.3.2, 6.4.2, 6.6.1, 11.3.1, 14.1.1, 15.1.1, 15.3.1, and 15.4.2.

Syed R Rizvi↗
DOE OSTI2024

Design, Detection, and Countermeasure of Frequency Spectrum Attack and Its Impact on Long Short-Term Memory Load Forecasting and Microgrid Energy Management

This paper introduces a frequency-domain false data injection attack called Frequency Spectrum Attack (FSA) and explores its effects on load forecasting and the energy management system (EMS) in a microgrid. The FSA analyzes time-series signals in the frequency domain to identify patterns in their frequency spectrum. It learns the distribution of dominant frequencies in a dataset of healthy signals. Subsequently, it manipulates the amplitudes of dominant frequencies within this healthy distribution, ensuring a stealthy attack against statistical analysis of the signal spectrum. We evaluated the performance of FSA on LSTM, a state-of-the-art network for load forecasting. The results show that FSA can triple the Mean Absolute Error (MAE) of predictions compared to the normal case and increase it by 70% compared to noise injection attacks. Furthermore, FSA indirectly enhances battery utilization in the EMS by 45%. We then proposed a detection method that combines statistical analysis and machine-learning-based classification techniques with features. The model effectively distinguishes FSA from healthy and noisy signals, achieving an accuracy of 98.7% and an F1-score of 98.1% on a load dataset, covering healthy, FSA, and noisy load data. Finally, a countermeasure was introduced based on the statistical analysis of the frequency spectrum of healthy signals to mitigate the impact of FSA. This countermeasure successfully reduces the MAE of the attacked model from 0.135 to 0.053, validating its effectiveness in mitigating FSA.

Nazeri, Amirhossein↗
DOE OSTI2021

Heuristic Dispatch Based on Price Signals for Behind-the-Meter PV-Battery Systems in the System Advisor Model

The economic potential of a behind-the-meter (BTM) PV-battery system depends greatly on how the battery is dispatched. Different utility rates, system sizes, generation and load profiles can all require different dispatch strategies. This paper presents price signals dispatch, a new algorithm for automated economic dispatch of BTM PV-battery systems, which utilizes 24-hour PV and load forecasts, degradation data, and utility rates. The algorithm is integrated with the System Advisor Model (SAM) tool and is tested with a nonlinear generic electrochemical battery model. Price signals dispatch outperforms SAM's existing algorithms in cases requiring a balance between demand charge management and energy arbitrage, and in cases where battery degradation imposes a significant cost.

batteries↗
NASA NTRS1974

Space shuttle SRM interim contract, part 1

Essential studies and analyses required to integrate the SRM into the booster and overall space shuttle system. Emphasis was placed on the case, nozzle, insulation, and propellant components with resulting performance, weight, and structural load characteristics being generated. Effort conducted during the time period of this contract included studies, analyses, planning, and preliminary design activities. Technical requirements identified in the SRM Project Request for Proposal No. 8-1-4-94-98401 and Thiokol's proposed SRM design (designated Configuration 0) established the basis for this effort. The requirements were evaluated jointly with MSFC and altered where necessary to incorporate new information that evolved after issuance of the RFP and during the course of this interim contract. Revised water impact loads and load distributions were provided based on additional model test data and analytical effort conducted by NASA subsequent to the RFP release. Launch pad peaking loads into the SRM aft skirt were provided which also represented a change from RFP requirements. A modified SRM/External Tank (ET) attachment configuration with new structural load data was supplied by NASA, and direction was received to include a 2 percent inert weight contingency.

Source record↗