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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
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Combined Effects of Spaceflight and Age in Astronauts as Assessed by Areal Bone Mineral Density [BMD] and Trabecular Bone Score

Spaceflight is a potential risk factor for secondary osteoporosis in astronauts. Although lumbar spine (LS) BMD declines rapidly, more than expected for age, there have been no fragility fractures in astronauts that can clearly be attributed to spaceflight. Recently, astronauts have been returning from 6‐month spaceflights with absolute BMD still above young adult mean BMD. In spite of these BMD measurements, we project that the rapid loss in bone mass over long‐duration spaceflight affects the bone microarchitecture of the LS which might predispose astronauts to premature vertebral fractures. Thus, we evaluated TBS, a novel texture index correlated with vertebral bone microarchitecture, as a means of monitoring changes to bone microarchitecture in astronauts as they age. We previously reported that TBS detects an effect of spaceflight (~6‐month duration), independent of BMD, in 51 astronauts (47+/‐4 y) (Smith et al, J Clin Densitometry 2014). Hence, TBS was evaluated in serial DXA scans (Hologic Discovery W) conducted triennially in all active and retired astronauts and more frequently (before spaceflight, after spaceflight and until recovery) in the subset of astronauts flying 4‐6‐ month missions. We used non‐linear models to describe trends in observations (BMD or TBS) plotted as a function of astronaut age. We fitted 1175 observations of 311 astronauts, pre‐flight and then postflight starting 3 years after landing or after astronaut's BMD for LS was restored to within 2% of preflight BMD. Observations were then grouped and defined as follows: 1) LD: after exposure to at least one long‐duration spaceflight > 100 days and 2) SD: before LD and after exposure to at least one short-duration spaceflight < 30 days. Data from males and females were analyzed separately. Models of SD observations revealed that TBS and BMD had similar curvilinear declines with age for both male and female astronauts. However, models of LD observations showed TBS declining with age while BMD appeared stable or trending upward. For females (n=8) LD observations were too few to discern a trend. Notably, models describing trends in TBS appeared to be more sensitive to the effects of age than the models for BMD. We conclude that TBS may provide an additional index for the lumbar spine to monitor the combined changes due to spaceflight and due to aging. This increased knowledge may enhance the ability to identify an intervention trigger for premature vertebral fractures in astronauts.

Sibonga, Jean D.↗
NASA NTRS2009

An Anomaly Correlation Skill Score for the Evaluation of the Performance of Hyperspectral Infrared Sounders

With the availability of very accurate forecasts, the metric of accuracy alone for the evaluation of the performance of a retrieval system can produce misleading results. A useful characterization of the quality of a retrieval system and its potential to contribute to an improved weather forecast is its skill, which we define as the ability to make retrievals of geophysical parameters which are closer to the truth than the six hour forecast, when the truth differs significantly from the forecast. We illustrate retrieval skill using one day of AMSU and AIRS data with three different retrieval algorithms, which result in retrievals for more than 90% of the potential retrievals under clear and cloudy conditions. Two of the three algorithms have better than 1 K rms "RAOB quality" accuracy on the troposphere, but only one has skill between 900 and 100 mb. AIRS was launched on the EOS Aqua spacecraft in May 2002 into a 705 km polar sun-synchronous orbit with accurately maintained 1:30 PM ascending node. Essentially uninterrupted data are freely available since September 2002.

retrieval↗
NASA NTRS2020

Tempe Urban Development II: Establishing an Urban Heat Exposure Severity Score for Infrastructure Prioritization in Tempe, Arizona, Using NASA Earth Observations and LiDAR

Located in the northern Sonoran Desert, Tempe, Arizona, features a semi-arid climate with summer daily maximum temperatures regularly exceeding 37.8° C (100.0° F). The area has experienced an increase in surface and air temperatures due to a steep expansion of impervious surfaces and rapid urban development. Urban heat is an increasingly pressing concern for Tempe with hundreds of heat-related deaths and thousands of heat-related hospitalizations over the past 15 years in Maricopa County. Furthermore, urban heat impacts residents’ quality of life and the economic vitality of the city. Recognizing the impacts of extreme urban heat, the City of Tempe collaborated with the Healthy Urban Environments initiative and the Fall 2020 Tempe Urban Development II NASA DEVELOP team to utilize NASA Earth observations to investigate the drivers of highest heat throughout the city. Data from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 8 Operational Land Imager (OLI), and Landsat 8 Thermal Infrared Sensor (TIRS), American Community Survey, and LiDAR data were used to identify areas experiencing the worst heat effects that could be targeted for heat mitigation and adaptation. These areas included the Escalante neighborhood, Alegre Community, University Heights, and around Dwight Park. Additionally, according to our shading analysis, the Gilliland and Escalante neighborhood walksheds do not meet the "good" shading threshold set at 30% by the Maricopa Association of Government.

DEVELOP Project Summary↗
NASA NTRS2020

Tempe Urban Development II: Establishing an Urban Heat Exposure Severity Score for Infrastructure Prioritization in Tempe, Arizona, Using NASA Earth Observations and LiDAR

Located in the northern Sonoran Desert, Tempe, Arizona, features a semi-arid climate with summer daily maximum temperatures regularly exceeding 37.8° C (100.0° F). The area has experienced an increase in surface and air temperatures due to a steep expansion of impervious surfaces and rapid urban development. Urban heat is an increasingly pressing concern for Tempe with hundreds of heat-related deaths and thousands of heat-related hospitalizations over the past 15 years in Maricopa County. Furthermore, urban heat impacts residents’ quality of life and the economic vitality of the city. Recognizing the impacts of extreme urban heat, the City of Tempe collaborated with the Healthy Urban Environments initiative and the Fall 2020 Tempe Urban Development II NASA DEVELOP team to utilize NASA Earth observations to investigate the drivers of heat exposure throughout the city. Data from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 8 Operational Land Imager (OLI), and Landsat 8 Thermal Infrared Sensor (TIRS), American Community Survey, and LiDAR data were used to identify areas experiencing the worst heat effects that could be targeted for heat mitigation and adaptation. These areas included the Escalante neighborhood, Alegre Community, University Heights, and around Dwight Park. Additionally, according to our shading analysis, the Gilliland and Escalante neighborhood walksheds do not meet the "good" shading threshold set at 30% by the Maricopa Association of Government.

DEVELOP Tech Paper↗
DOE OSTI2022

A Framework to Integrate Human Reliability Data Obtained from Different Sources Based on the Complexity Scores of Proceduralized Tasks

For many decades, PSA (Probabilistic Safety Assessment) or PRA (Probabilistic Risk Assessment) techniques have been used to enhance the operational safety of nuclear power plants (NPPs) based on the consideration of potential hazards that could result in an unexpected consequence. As human error is one of the potential hazards, diverse human reliability analysis (HRA) methods have been proposed to provide a systematic way to estimate the likelihood of human errors (i.e., human error probability, HEP) in specific task contexts. Accordingly, it is evident that the quality of HRA results strongly depends on the credibility of HEP estimations. This implies that, in terms of enhancing this credibility, the collection of raw information (HRA data) that is helpful for understating when and why human errors occur is a crucial issue. In order to address this issue, in this study, the feasibility of a framework to integrate HRA data obtained from different sources is investigated based on the complexity of proceduralized tasks.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Hybrid Cyber-attack Detection in Photovoltaic Farms

Here, to address the cyber-physical security in PV farms, a hybrid cyber-attack detection is proposed in this manuscript. To secure PV farms, the proposed method integrates model-based and data-driven methods by fusing the detection score at the device and system levels. First, a model-based cyber-attack detection method is developed for each PV inverter. A residual between the estimation of the Kalman filter and measurement is calculated. By leveraging the calculated residual from all inverters, a squared Mahalanobis distance is developed for device detection score generation. At the system level, a convolutional neural network (CNN) is proposed to detect cyber-attack using the waveform data at the point of common coupling (PCC) in PV farms. To improve the CNN detection accuracy, a set of well-designed features are extracted from the raw waveform data. Finally, a weighted detection score fusion method is proposed to combine device and system detection scores by using their complementary strength. The feasibility and robustness of the proposed method are validated by testing cases and a comparative experiment.

14 SOLAR ENERGY↗
DOE OSTI2024

Building ControlScore: General Service Administration Office Building Deployment

Improvements to building control systems can lead to energy savings and increased occupant comfort. In an optimized system, process variables such as air temperature will closely follow their desired setpoints and avoid excess energy use. Typically, experts must manually inspect individual control loops to identify poor performance and opportunities for improvement. However, this approach is difficult in modern buildings that have a prohibitively large number of controllers. To address this issue, Pacific Northwest National Laboratory (PNNL) created the ControlScore concept, which takes operating data from the many controllers within a building and generates standardized scores for each loop on a scale of 0 to 10 (a score of 0 indicates poor control, a score of 10 indicates good control). PNNL applied the Building ControlScore application to all available data from a General Services Administration office building within the period of January 1, 2023, to March 9, 2023. The building scored a 4.7 overall, with all 74 of the building’s loops fitting a roughly normal distribution centered around 5. These results indicate that the analyzed systems have below-average performance with room for improvement, especially in the poorly scored systems. Airflow loops tended to have much lower scores than zone temperature loops. The lowest and highest performing systems in the building section were identified, as were all loops with a score less than 1. While the ControlScore identifies loops and systems that aren’t meeting their designated setpoints, it does not indicate the cause of those issues. For example, consider a supply air terminal unit’s airflow loop that received a low score due to it delivering less air than specified by the setpoint. The lower-than-desired airflow could be due to equipment limitations (e.g., the terminal unit or duct serving is too small to accommodate that airflow), malfunctioning equipment (e.g., a stuck damper or bad sensor), or something else entirely. The ControlScore does not diagnose problems it simply identifies the symptoms that can be explored and addressed by building operators.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2002

A simple way to measure daily lifestyle regularity

A brief diary instrument to quantify daily lifestyle regularity (SRM-5) is developed and compared with a much longer version of the instrument (SRM-17) described and used previously. Three studies are described. In Study 1, SRM-17 scores (2 weeks) were collected from a total of 293 healthy control subjects (both genders) aged between 19 and 92 years. Five items (1) Get out of bed, (2) First contact with another person, (3) Start work, housework or volunteer activities, (4) Have dinner, and (5) Go to bed were then selected from the 17 items and SRM-5 scores calculated as if these five items were the only ones collected. Comparisons were made with SRM-17 scores from the same subject-weeks, looking at correlations between the two SRM measures, and the effects of age and gender on lifestyle regularity as measured by the two instruments. In Study 2 this process was repeated in a group of 27 subjects who were in remission from unipolar depression after treatment with psychotherapy and who completed SRM-17 for at least 20 successive weeks. SRM-5 and SRM-17 scores were then correlated within an individual using time as the random variable, allowing an indication of how successful SRM-5 was in tracking changes in lifestyle regularity (within an individual) over time. In Study 3 an SRM-5 diary instrument was administered to 101 healthy control subjects (both genders, aged 20-59 years) for two successive weeks to obtain normative measures and to test for correlations with age and morningness. Measures of lifestyle regularity from SRM-5 correlated quite well (about 0.8) with those from SRM-17 both between subjects, and within-subjects over time. As a detector of irregularity as defined by SRM-17, the SRM-5 instrument showed acceptable values of kappa (0.69), sensitivity (74%) and specificity (95%). There were, however, differences in mean level, with SRM-5 scores being about 0.9 units [about one standard deviation (SD)] above SRM-17 scores from the same subject-weeks. SRM-5 scores also deviated more from a Gaussian distribution than did SRM-17 ones. In a study with a sample size of 101, the new SRM-5 instrument yielded scores with a mean of 4.11 and an SD of 1.13. Correlations between lifestyle regularity and age, and between lifestyle regularity and morningness appeared similar whether 5-item or 17-item SRM measures were used. When a gender difference in lifestyle regularity appeared, it was detected by both SRM-5 and SRM-17 measures.

Non-NASA Center↗
NASA NTRS2019

Method for Tracking and Communicating Aggregate Risk Through the Use of Model-Based Systems Engineering (MBSE) Tools

Large, complex projects can identify a significant number and variety of risks, throughout the project life cycle. These risks are analyzed, mitigated, closed or accepted as independent uncertainties. Once closed or accepted, it is easy for projects to lose awareness of their impact. In reality, each of these risks contributes some amount to the overall risk posture of the project. The ability to track and effectively communicate this aggregate risk has represented a challenge to project management. There have been previous attempts to create a schema to communicate the aggregate effect of risks, without notable success. Most of these attempts have centered on some additive metric derived from the scoring of likelihood and consequence values. This, in and of itself, is a logical approach, but all too often the scores were then aggregated to a level where all context was lost. One weakness has been a lack of attempt to create linkages or logical groups of the risks upon which useful aggregation could then occur. The overall move to model-based (systems) engineering (MBSE) has opened up a vast frontier of opportunities to better integrate all project data. MBSE provides an underlying layer that links data items to each other. Objectives link to requirements, which then link to functions, functions to physical architecture items, and so on, as far down as projects want to model. While it started with a focus on modeling requirements based on things like use cases, efforts are now underway to integrate safety and mission assurance (S&MA) information and analyses, such as risks. This effort, called Model Based Mission Assurance (MBMA), is yielding models that are more useful and are a more accurate representations of the systems. MBSE models, with this ability to link related items, provide a new means of tracking and communicating aggregate risks. In the proposed method, risks are added into the models as distinct items, having attributes that communicate a scoring derived from the likelihood and consequence values as charted on the standard NASA 5x5 risk matrix. Like earlier efforts, each box in the 5x5 has an associated scoring, which may include both a current score and potential post-mitigation/control score. The risk items are then linked to elements of the model, such as system objectives/goals, requirements, functions, or physical architecture items, with "Risk to" relationships. These risks will then be communicated by use of reports generated from the model, detailing all risks and/or hazards linked to model elements. These reports can include aggregate impacts, including a current scoring and potential future state scoring based on the planned mitigations and/or controls. These reports will show all risks, open, accepted, and closed, linked to project objectives or requirements. When run as part of an upcoming risk acceptance discussion, these reports will serve to remind the team of all previous risks that relate to the effected portion of the system. When included as part of periodic program or project reviews, risk reviews, and safety reviews, this method can improve the overall understanding of the system's true risk posture. This proposed method takes full advantage of the advances that modern modeling techniques provide, with a minimal investment of additional time. Utilizing the model environment also enables a near constant access to current state of aggregate risks.

model based mission assurance↗