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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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NASA NTRSDate not supplied

Evaluation of Response Surface Experiment Designs for Distributed Propulsion Aircraft Aero-Propulsive Modeling

Modern distributed hybrid and electric propulsion aircraft, including vertical, short, and conventional takeoff and landing configurations, exhibit significant aero-propulsive complexity and a large number of interacting test factors. This paper presents the development and evaluation of experiment designs for aero-propulsive characterization of distributed propulsion aircraft. Five different foundational response surface designs are evaluated to inform the development of two sequential design approaches tailored to complex aircraft aerodynamic characterization experiments. The first approach, which builds on sequential face-centered central composite designs, has been used previously to develop aero-propulsive models for complex aircraft using wind tunnel testing. The second approach is a new design strategy leveraging a regular I-optimal and nested I-optimal design that was developed for this study. The two sequential design strategies are compared for experiments with a large number of test factors using pre-experiment design evaluation metrics, as well as modeling results obtained from simulated wind tunnel data for the NASA LA-8 aircraft. The design evaluation metrics show that the sequential I-optimal base design has higher statistical power, lower correlation among candidate regressors, lower prediction variance, and more precise parameter estimates. The simulated wind tunnel experiments conducted using each design reveal that the sequential I-optimal base design has better predictive capability with fewer test points. The experiment design and evaluation procedures are described in detail to inform future aerodynamic characterization experiments for complex aircraft.

design of experiments↗
NASA NTRS1988

Origination, diversity, and extinction metrics essential for analysis of mass biotic crisis events: An example from cretaceous ammonoidea

Traditional mass extinction research has predominently concentrated on statistically demonstrating that mass extinction intervals are significantly above background levels of familial and generic extinction in terms of extinction percentage, extinction rate, and per-taxon extinction rate; mass extinction intervals occur on a set periodicity throughout geologic time, which is estimated to be some 30 MYR in duration. The published literature has given little emphasis to equally important considerations and metrics such as origination rate, standing diversity, and rate of generation of new taxa DURING mass extinction intervals. The extent to which a mass extinction affects the regional or global biota, must ultimately be gauged by taking into consideration both the number of taxa which become extinct at or near the event (stage) boundary, and the number of taxa which are either not affected at all by the extinction or actually evolved during or shortly before/after the extinction interval. These effects can be seen in Cretaceous Ammonoidea (at the genus level), and their combined usage allow better insight into paleobiological dynamics and responses to mass extinction and its affect on this dominant Molluscan organism.

Collom, Christopher J.↗
NASA NTRS2015

Relationships Between Excessive Heat and Daily Mortality over the Coterminous U.S

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. Using National Land Data Assimilation System (NLDAS) meteorological reanalysis data, we have developed several measures of extreme heat to enable assessments of the impacts of heat on public health over the coterminous U.S. These measures include daily maximum and minimum air temperatures, daily maximum heat indices and a new heat stress variable called Net Daily Heat Stress (NDHS) that gives an integrated measure of heat stress (and relief) over the course of a day. All output has been created on the NLDAS 1/8 degree (approximately 12 km) grid and aggregated to the county level, which is the preferred geographic scale of analysis for public health researchers. County-level statistics have been made available through the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. We have examined the relationship between excessive heat events, as defined in eight different ways from the various daily heat metrics, and heat-related and all-cause mortality defined in CDC's National Center for Health Statistics 'Multiple Causes of Death 1999-2010' dataset. To do this, we linked daily, county-level heat mortality counts with EHE occurrence based on each of the eight EHE definitions by region and nationally for the period 1999-2010. The objectives of this analysis are to determine (1) whether heat-related deaths can be clearly tied to excessive heat events, (2) what time lags are critical for predicting heat-related deaths, and (3) which of the heat metrics correlates best with mortality in each US region. Results show large regional differences in the correlations between heat and mortality. Also, the heat metric that provides the best indicator of mortality varied by region. Results from this research will potentially lead to improvements in our ability to anticipate and mitigate any significant impacts of extreme heat events on health.

heat↗
NASA NTRS1995

Towards Principled Experimental Study of Autonomous Mobile Robots

We review the current state of research in autonomous mobile robots and conclude that there is an inadequate basis for predicting the reliability and behavior of robots operating in unengineered environments. We present a new approach to the study of autonomous mobile robot performance based on formal statistical analysis of independently reproducible experiments conducted on real robots. Simulators serve as models rather than experimental surrogates. We demonstrate three new results: 1) Two commonly used performance metrics (time and distance) are not as well correlated as is often tacitly assumed. 2) The probability distributions of these performance metrics are exponential rather than normal, and 3) a modular, object-oriented simulation accurately predicts the behavior of the real robot in a statistically significant manner.

Object-Oriented Simulation↗
NASA NTRSDate not supplied

Trajectory Planning for Mission Survivability of Autonomous Vehicles in Moderately to Extremely Uncertain Environments

Trajectory planning is a particularly challenging task for autonomous vehicles when the uncertainties in their operating environment cannot be characterized statistically. In this paper, we propose a receding horizon control strategy with novel trajectory planning policies that enable dynamic updating of the planned trajectories of autonomous vehicles operating in environments where potential conflicts are, from a statistical perspective, either partially known or completely unknown. The proposed policies utilize two metrics: (1) the number of feasible trajectories; and (2) the robustness of the feasible trajectories. We measure the effectiveness of the suggested policies in terms of mission survivability, which is defined as the probability that the primary mission is accomplished or, if that is not possible, the vehicle lands safely at an alternative site. We show that a linear combination of both metrics is an effective objective function when there is a mix of partially known and unknown uncertainties. When the operating environment is dominated by unknown disturbances, maximizing the number of feasible trajectories results in the highest mission survivability. These findings have significant implications for achieving safe aviation. autonomy.

Aviation autonomy↗
NASA NTRSDate not supplied

Applying NASA-STD-7009 Standard for Models and Simulations to Surrogate and Other Statistical Models

NASA-STD-7009A, Standard for Models and Simulations, contains a worthy and insightfully-crafted credibility assessment. That set of metrics is intended to accumulate and communicate information that can be critical for users and their customers in evaluating findings due to a model or simulation. Was the model designed for the present use? Is it validated in the domain it’s being applied to? And so on. The standard was originally focused on physics-based models, but it can be applied more generally. It can – and should – be applied to high-impact statistical models, including surrogate models based on larger models and sims and regression models based directly on data. A key issue with many of such models is their relative simplicity compared to most M&S despite their use in high-risk engineering decisions. This report will make a case for using this Standard for a wider class of models, and how it might actually add value in excess of its cost for not only stakeholders, but modelers themselves. A rough example showing compliance for a statistical model is included.

K L Johnson↗
NASA NTRS2018

On-Time Performance ASPM Non-Parametric Statistical Analysis

The purpose of this analysis is to provide a data-driven examination of two selected flight metrics, i.e., (1) total taxi-out time and (2) actual off time minus scheduled off block time. More specifically, Aviation System Performance Metrics (ASPM) data were analyzed to determine any possible differences in CLT (Charlotte Douglas International Airport) departure flights on these two metrics when comparing pre-IADS (Integrated Arrival, Departure, and Surface Operations) against post-IADS metering operations. This was originally presented to the ATD-2 (Airspace Technology Demonstration-2) Analytics team in August 2018.

tech transfer↗
NASA NTRS1985

A comparison of solar helium-3-rich events with type II bursts and coronal mass ejections

The acceleration process for energetic particles in He-3-rich events and for particles in normal-abundance events are compared. A list of 66 He-3-rich events observed with the Goddard Space Flight Center particle detector on ISEE 3 is presented, and it is shown that these events are not statistically associated with either of the two common signatures of normal-abundance events, metric type II and coronal mass ejections. This result indicates that enhanced abundance events may be produced only in the impulsive phases of flares, while normal abundance events are produced in subsequent flare shock waves.

Kahler, S.↗
NASA NTRS1993

Subjective response to simulated sonic booms with ground reflections

The Sonic Boom Simulator at NASA LaRC was used for the following: (1) quantify subjective loudness of simulated composite sonic booms, each of which was comprised of a simulated direct (non-reflected) boom combined with a simulated reflection of the direct boom; and (2) evaluate several metrics as estimators of loudness for these composite booms. The direct booms consisted of selected N-wave and minimized signatures having front-shock rise times of 3, 6, and 9 milliseconds and durations of 300 milliseconds. Delay times of the reflected booms ranged from 0 to 12 milliseconds. Subjective loudness results indicated that composite booms formed using reflections with non-zero delay times were generally rated as being less loud than composite booms containing non-delayed reflections. The largest reductions in loudness occurred when delay times were equal to the front shock rise times of the direct booms and were, in some cases, equivalent to reductions in Perceived Level of 6 to 7 dB. Results also showed Perceived Level to be an effective metric for assessing subjective loudness effects for the composite signatures. This was confirmed by statistical analysis, which showed that, for equal Perceived Level, no significant differences existed between the subjective loudness responses to composite booms containing reflections with zero delay and those containing reflections with non-zero delays.

Sullivan, B. M.↗
NASA NTRS2011

Uncertainty Assessment of Hypersonic Aerothermodynamics Prediction Capability

The present paper provides the background of a focused effort to assess uncertainties in predictions of heat flux and pressure in hypersonic flight (airbreathing or atmospheric entry) using state-of-the-art aerothermodynamics codes. The assessment is performed for four mission relevant problems: (1) shock turbulent boundary layer interaction on a compression corner, (2) shock turbulent boundary layer interaction due a impinging shock, (3) high-mass Mars entry and aerocapture, and (4) high speed return to Earth. A validation based uncertainty assessment approach with reliance on subject matter expertise is used. A code verification exercise with code-to-code comparisons and comparisons against well established correlations is also included in this effort. A thorough review of the literature in search of validation experiments is performed, which identified a scarcity of ground based validation experiments at hypersonic conditions. In particular, a shortage of useable experimental data at flight like enthalpies and Reynolds numbers is found. The uncertainty was quantified using metrics that measured discrepancy between model predictions and experimental data. The discrepancy data is statistically analyzed and investigated for physics based trends in order to define a meaningful quantified uncertainty. The detailed uncertainty assessment of each mission relevant problem is found in the four companion papers.

Bose, Deepak↗
NASA NTRS2017

Network Analysis of Rodent Transcriptomes in Spaceflight

Network analysis methods leverage prior knowledge of cellular systems and the statistical and conceptual relationships between analyte measurements to determine gene connectivity. Correlation and conditional metrics are used to infer a network topology and provide a systems-level context for cellular responses. Integration across multiple experimental conditions and omics domains can reveal the regulatory mechanisms that underlie gene expression. GeneLab has assembled rich multi-omic (transcriptomics, proteomics, epigenomics, and epitranscriptomics) datasets for multiple murine tissues from the Rodent Research 1 (RR-1) experiment. RR-1 assesses the impact of 37 days of spaceflight on gene expression across a variety of tissue types, such as adrenal glands, quadriceps, gastrocnemius, tibalius anterior, extensor digitorum longus, soleus, eye, and kidney. Network analysis is particularly useful for RR-1 -omics datasets because it reinforces subtle relationships that may be overlooked in isolated analyses and subdues confounding factors. Our objective is to use network analysis to determine potential target nodes for therapeutic intervention and identify similarities with existing disease models. Multiple network algorithms are used for a higher confidence consensus.

genomics↗
NASA NTRSDate not supplied

The System Modeling and Analysis of Resiliency in STEReO (SMARt-STEReO)

Wildfire emergency response has remained rooted in relatively low-tech solutions for coordination between ground and aerial assets. These low-tech solutions are robust for the remote environments in which wildfires are usually fought, but limit strategic cross-organizational support and the ability to deploy and effectively utilize aerial assets. As aircraft become more advanced and new technology, including drones, become available to firefighters, a new, more modern method of asset coordination is needed. NASA is working on a project called ‘Scalable Traffic Management for Emergency Response Operations’ (STEReO) to integrate unmanned aerial systems (UAS)and UAS traffic management (UTM)into wildfire response. STEReO’s goals include simplifying the coordination of aerial assets, improving the existing UAS framework, and increasing the role of additional autonomous systems to reduce human risk and to increase system resilience. This paper describes the development of the ‘System Modeling and Analysis of Resiliency in STEReO’ (SMARt-STEReO) project, which aims to model wildfire response and to quantify the additional system resilience that STEReO technology provides firefighters. This paper verifies SMARt-STEReO and defines its scope; it includes experimental and statistical analysis of the impact that the addition of UAS has on both performance metrics and also on performance resiliency response to a given fault. SMARt-STEReO is a grid-based model of fire propagation that incorporates varying crew responses. Through the use of a Python package called ‘fmdtools’, the model easily allows for the addition of faults to the system. These faults allow analysts to investigate various response parameters. Factors including terrain, fuel type and wind speed can be modified to affect the fire propagation; additionally, the number of ground crews, engines, fixed wing aircraft, helicopters, and UAS can be changed to affect the crew response. The communication lines between actors mimic those used in real life situations. This paper explains the development of SMARt-STEReO including background research, verification and validation, and preliminary experimental analysis of system resilience to both a minor and major fault in systems with and without UAS.

Resiliency↗
NASA NTRS2017

The Relationships Between the Trends of Mean and Extreme Precipitation

This study provides a better understanding of the relationships between the trends of mean and extreme precipitation in two observed precipitation data sets: the Climate Prediction Center Unified daily precipitation data set and the Global Precipitation Climatology Program (GPCP) pentad data set. The study employs three kinds of definitions of extreme precipitation: (1) percentile, (2) standard deviation and (3) generalize extreme value (GEV) distribution analysis for extreme events based on local statistics. Relationship between trends in the mean and extreme precipitation is identified with a novel metric, i.e. area aggregated matching ratio (AAMR) computed on regional and global scales. Generally, more (less) extreme events are likely to occur in regions with a positive (negative) mean trend. The match between the mean and extreme trends deteriorates for increasingly heavy precipitation events. The AAMR is higher in regions with negative mean trends than in regions with positive mean trends, suggesting a higher likelihood of severe dry events, compared with heavy rain events in a warming climate. AAMR is found to be higher in tropics and oceans than in the extratropics and land regions, reflecting a higher degree of randomness and more important dynamical rather than thermodynamical contributions of extreme events in the latter regions.

global warming↗
NASA NTRS2014

Clustering Days with Similar Airport Weather Conditions

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

traffic flow management↗
NASA NTRS2014

Clustering Days with Similar Airport Weather Conditions

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

weather↗
NASA NTRSDate not supplied

An Overview of Printed Circuit Board Risks and Supplier Capability

This NESC Webinar provides the audience a snapshot of quality variations in printed circuit boards, as assessed, using experiences in processing and risk analysis of PCB structural integrity coupons. The presentation will focus on PCB quality metrics, the relative type and number of non-conformances observed and trend analysis using statistical methods. Trend analysis shows the top five non-conformances observed across suppliers, the associated root cause(s) behind these non-conformance, suggestions of mitigation plans and most importantly – the relation of these non-conformances to end item reliability. The statistical trends will then be matched with the current state of the domestic PCB supplier base, its key challenges and opportunities. The presentation further discusses the methods applied at GSFC for evaluating candidate PCB technologies that promote the adoption of higher throughput and faster processing technology for GSFC missions.

printed cricuit boards, supplier capability, risk ↗
NASA NTRSDate not supplied

An Overview of Printed Circuit Board Risks and Supplier Capability (Reuse Document ID 20205007963)

This HDPUG Webinar provides the audience a snapshot of quality variations in printed circuit boards, as assessed, using experiences in processing and risk analysis of PCB structural integrity coupons. The presentation will focus on PCB quality metrics, the relative type and number of non-conformances observed and trend analysis using statistical methods. Trend analysis shows the top five non-conformances observed across suppliers, the associated root cause(s) behind these non-conformance, suggestions of mitigation plans and most importantly – the relation of these non-conformances to end item reliability. The statistical trends will then be matched with the current state of the domestic PCB supplier base, its key challenges and opportunities. The presentation further discusses the methods applied at GSFC for evaluating candidate PCB technologies that promote the adoption of higher throughput and faster processing technology for GSFC missions.

printed cricuit boards, supplier capability, risk ↗
NASA NTRS2017

It's All Relative: A Validation of Radiation Quality Comparison Metrics

The difference between high-LET and low-LET radiation is quantified by a measure called relative biological effectiveness (RBE). RBE is defined as the ratio of the dose of a reference radiation to that of a test radiation to achieve the same effect level, and thus, is described either as an iso-effector dose-to-dose ratio. A single dose point is not sufficient to calculate an RBE value; therefore, studies with only one dose point usually calculate an effect-to-effect ratio. While not formally used in radiation protection, these iso-dose values may still be informative. Shuryak, et al 2017 investigated the use of an iso-dose metric termed "radiation effects ratio" (RER) and used both RBE and RER to estimate high-LET risks. To apply RBE or RER to risk prediction, the selected metric must be uniquely defined. That is, the calculated value must be consistent within a model given a constant set of constraints and assumptions, regardless of how effects are defined using statistical transformations from raw endpoint data. We first test the RBE and the RER to determine whether they are uniquely defined using transformations applied to raw data. Then, we test whether both metrics can predict heavy ion response data after simulated effect size scaling between human populations or when converting animal to human endpoints.

Chappell, Lori J.↗