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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Reducing Our Ignorance: Finding Answers to Certain Epistemic Questions for Software Systems

In previous papers, we asserted that software system safety is primarily concerned with epistemic questions, that is, questions concerning knowledge and the degree of confidence that can be placed in that knowledge. We also enumerated a set of 21 foundational epistemic questions, discussed some of the difficulties that exist in answering these questions adequately today, and speculated briefly on possible research that may provide improved confidence in the sufficiency of answers in the future. This paper focuses on three of the foundational questions. For each of these questions, current answers are discussed and potential research is proposed to help increase the justifiable level of confidence.

Holloway, C. Michael↗
NASA NTRS2012

An Initial Examination for Verifying Separation Algorithms by Simulation

An open question in algorithms for aircraft is what can be validated by simulation where the simulation shows that the probability of undesirable events is below some given level at some confidence level. The problem is including enough realism to be convincing while retaining enough efficiency to run the large number of trials needed for high confidence. The paper first proposes a goal based on the number of flights per year in several regions. The paper examines the probabilistic interpretation of this goal and computes the number of trials needed to establish it at an equivalent confidence level. Since any simulation is likely to consider the algorithms for only one type of event and there are several types of events, the paper examines under what conditions this separate consideration is valid. This paper is an initial effort, and as such, it considers separation maneuvers, which are elementary but include numerous aspects of aircraft behavior. The scenario includes decisions under uncertainty since the position of each aircraft is only known to the other by broadcasting where GPS believes each aircraft to be (ADS-B). Each aircraft operates under feedback control with perturbations. It is shown that a scenario three or four orders of magnitude more complex is feasible. The question of what can be validated by simulation remains open, but there is reason to be optimistic.

White, Allan L.↗
NASA NTRS2014

Data Mining of Historical Human Data to Assess the Risk of Injury due to Dynamic Loads

The NASA Occupant Protection Group is charged with ensuring crewmembers are protected during all dynamic phases of spaceflight. Previous work with outside experts has led to the development of a definition of acceptable risk (DAR) for space capsule vehicles. The DAR defines allowable probability rates for various categories of injuries. An important question is how to validate these probabilities for a given vehicle. One approach is to impact test human volunteers under projected nominal landing loads. The main drawback is the large number of subject tests required to attain a reasonable level of confidence that the injury probability rates would meet those outlined in the DAR. An alternative is to mine existing databases containing human responses to impact. Testing an anthropomorphic test device (ATD) at the same human‐exposure levels could yield a range of ATD responses that would meet DAR. As one aspect of future vehicle validation, the ATD could be tested in the vehicle's seat and suit configuration at nominal landing loads and compared with the ATD responses supported by the human data set. This approach could reduce the number of human‐volunteer tests NASA would need to conduct to validate that a vehicle meets occupant protection standards. METHODS: The U.S. Air Force has recorded hundreds of human responses to frontal, lateral, and spinal impacts at many acceleration levels and pulse durations. All of this data are stored on the Collaborative Biomechanics Data Network (CBDN), which is maintained by the Wright Patterson Air Force Base (WPAFB). The test device for human occupant restraint (THOR) ATD was impact tested on WPAFB's horizontal impulse accelerator (HIA) matching human‐volunteer exposures on the HIA to 5 frontal and 3 spinal loading conditions. No human injuries occurred as a result of these impact conditions. Peak THOR response variables for neck axial tension and compression, and thoracic‐spine axial compression were collected. Maximal chest deflection was determined from motion capture video of the impact test. HIC‐ 15 and BRIC were calculated from head acceleration responses. Given the number of human subjects for each test condition a confidence interval of injury probability will be obtained. RESULTS: Results will be discussed in terms of injury‐risk probability estimates based on the human data set evaluated. Also, gaps in the data set will be identified. These gaps could be one of two types. One is areas where additional THOR testing would increase the comparable human data set, thereby improving confidence in the injury probability rate. The other is where additional human testing would assist in obtaining information on other acceleration levels or directions. DISCUSSION: The historical human data showed validity of the THOR ATD for supplemental testing. The historical human data are limited in scope, however. Further data are needed to characterize the effects of sex, age, anthropometry, and deconditioning due to spaceflight on risk of injury

Wells, Jesica↗
NASA NTRS2013

Mars Biosignature - Detection Capabilities: A Method for Objective Comparison of In Situ Measurements and Sample Return

A Mars sample-return mission has been proposed within NASA's Mars Exploration Program. Studying Martian samples in laboratories on Earth could address many important issues in planetary science, but arguably none is as scientifically compelling as the question of whether biosignatures indicative of past or present life exist on that planet. It is reasonable to ask before embarking on a sample-return mission whether equivalent investigation of Martian biosignatures could be conducted in situ. This study presents an approach to (1)identifying an optimal instrument suite for in situ detection of biosignatures on Mars,and (2)comparing the projected confidence level of in situ detection in a 2026 timeframe to that of Earth-based analysis. We identify a set of candidate instruments, the development of which is projected to be achievable by 2026 well within a $200 million cost cap. Assuming that any biosignatures near the surface of Mars are similar to those of terrestrial life, we find that this instrument suite, if successfully developed and deployed, would enable in situ biosignature detection at essentially the same level of confidence as that of Earth-based analysis of the same samples. At a cost cap of half that amount,the confidence level of in situbiosignature detection analysis could reach about 90% that of Earth-based investigations.

National Environmental Policy Act (NEPA)↗
NASA NTRS2015

NASA DOEPOD NDE Capabilities Data Book

This data book contains the Directed Design of Experiments for Validating Probability of Detection (POD) Capability of NDE Systems (DOEPOD) analyses of the nondestructive inspection data presented in the NTIAC, Nondestructive Evaluation (NDE) Capabilities Data Book. DOEPOD is designed as a decision support system to validate inspection system, personnel, and protocol demonstrating 0.90 POD with 95% confidence at critical flaw sizes, a90/95. Although 0.90 POD with 95% confidence at critical flaw sizes is often stated as an inspection requirement in inspection documents, including NASA Standards, NASA critical aerospace applications have historically only accepted 0.978 POD or better with a 95% one-sided lower confidence bound exceeding 0.90 at critical flaw sizes, a90/95.

Generazio, Edward R.↗
NASA NTRS2014

Goal Based Testing: A Risk Informed Process

A process for life demonstration testing is developed, which can reduce the number of resources required by conventional sampling theory while still maintaining the same degree of rigor and confidence level. This process incorporates state-of-the-art probabilistic thinking and is consistent with existing NASA guidance documentation. This view of life testing changes the paradigm of testing a system for many hours to show confidence that a system will last for the required number of years to one that focuses efforts and resources on exploring how the system can fail at end-of-life and building confidence that the failure mechanisms are understood and well mitigated.

Everline, Chester↗
NASA NTRS2018

Decadal Seasonal Shifts of Precipitation and Temperature in TRMM and AIRS Data

We present results from an analysis of seasonal phase shifts in the global precipitation and surface temperatures. We use data from the TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Algorithm (TMPA), and the Atmospheric Infrared Sounder (AIRS) on Aqua satellite, all hosted at NASA Goddard Earth Science Data and Information Services Center (GES DISC). We explore the information content and data usability by first aggregating daily grids from the entire records of both missions to pentad (5-day) series which are then processed using Singular Value Decomposition approach. A strength of this approach is the normalized principal components that can then be easily converted from real to complex time series. Thus, we can separate the most informative, the seasonal, components and analyze unambiguously for potential seasonal phase drifts. TMPA and AIRS records represent correspondingly 20 and 15 years of data, which allows us to run simple “phase learning†from the first 5 years of records and use it as reference. The most recent 5 years are then phase-compared with the reference. We demonstrate that the seasonal phase of global precipitation and surface temperatures has been stable in the past two decades. However, a small global trend of delayed precipitation, and earlier arrival of surface temperatures seasons, are detectable at 95% confidence level. Larger phase shifts are detectable at regional level, in regions recognizable from the Eigen vectors to having strong seasonal patterns. For instance, in Central North America, including the North American Monsoon region, confident phase shifts of 1-2 days per decade are detected at 95% confidence level. While seemingly symbolic, these shifts are indicative of larger changes in the Earth Climate System. We thus also demonstrate a potential usability scenario of Earth Science Data Records curated at the NASA GES DISC in partnership with Earth Science Missions.

surface temperatures↗
NASA NTRS2020

A NICER View of Spectral and Profile Evolution for Three X-ray-Emitting Millisecond Pulsars

We present two years of Neutron star Interior Composition Explorer (NICER) X-ray observations of three energetic rotation-powered millisecond pulsars (MSPs): PSRs B1937+21, B1821−24, and J0218+4232. We fit Gaussians and Lorentzians to the pulse profiles for different energy sub-bands of the soft X-ray regime to measure the energy dependence of pulse separation and width. We find that the separation between pulse components of PSR J0218+4232 decreases with increasing energy at >3σ confidence. The 95% upper limit on pulse separation evolution for PSRs B1937+21 and B1821−24 is less than 2 milliperiods per keV. Our phase-resolved spectral results provide updated constraints on the non-thermal X-ray emission of these three pulsars. The photon indices of the modeled X-ray emission spectra for each pulse component of PSR B1937+21 are inconsistent with each other at the 90% confidence level, suggesting different emission origins for each pulse. We find that the PSR B1821−24 and PSR J0218+4232 emission spectra are invariant with phase at the 90% confidence level. We describe the implications of our profile and spectral results in the context of equatorial current sheet emission models for these three MSPs with non-thermal, magnetospheric X-ray emission.

Dominick M Rowan↗
NASA NTRS2021

Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana

Low-cost sensors (LCSs) for air quality monitoring have enormous potential to improve air quality data coverage in resource-limited parts of the world such as sub-Saharan Africa. LCSs, however, are affected by environment and source conditions. To establish high-quality data, LCSs must be collocated and calibrated with reference grade PM2.5 monitors. From March 2020, a low-cost PurpleAir PM2.5 monitor was collocated with a Met One Beta Attenuation Monitor 1020 in Accra, Ghana. While previous studies have shown that multiple linear regression (MLR) and random forest regression (RF) can improve accuracy and correlation between PurpleAir and reference data, MLR and RF yielded suboptimal improvement in the Accra collocation (R2 = 0.81 and R2 = 0.81, respectively). We present the first application of Gaussian mixture regression (GMR) to air quality data calibration and demonstrate improvement over traditional methods by increasing the collocated PM2.5 correlation and accuracy to R2 = 0.88 and MAE = 2.2 μg/cu. m. Gaussian mixture models (GMMs) are a probability density estimator and clustering method from which nonlinear regressions that tolerate missing inputs can be derived. We find that even when given missing inputs, GMR provides better correlation than MLR and RF performed with complete data. GMR also allows us to estimate calibration certainty. When evaluated, 95% confidence intervals agreed with reference PM2.5 data 96% of the time, suggesting that the model accurately assesses its own confidence. Additionally, clustering within the GMM is consistent with climate characteristics, providing confidence that the calibration approach can learn underlying relationships in data.

Sensors↗
NASA NTRS2020

Demonstrating Assurance of Model-Based Fault Diagnosis Systems on an Operational Mission

Developers of robotic scientific and commercial spacecraft are trending towards use of onboard autonomous capabilities for responding quickly to dynamic environments and rapidly changing situations. These capabilities need to know the state of the spacecraft’s health. Model-based fault diagnosis (MBFD) is an approach to estimating health by continuously verifying accurate behavior and diagnosing off-nominal behavior. Proper functioning of MBFD depends on 1) the quality of the diagnostic system model that is analyzed and compared to commands and onboard measurements to estimate a system’s health state, and 2) the correct functionality of the diagnosis engine interrogating the model and comparing its analyses to observed system behavior. Our goal is to develop Verification and Validation (V&V) techniques for MBFD to provide future missions sufficient confidence in its functionality and performance to deploy it on the systems they develop. Our work has been focused on infusing the techniques we developed earlier to an operational mission. First, we are constructing diagnostic models of a spacecraft attitude control system and updating our diagnostic engine so they can be demonstrated aboard the Arcsecond Space Telescope Enabling Research in Astrophysics (ASTERIA) mission, an operational spacecraft for which experiments in autonomy are being planned and executed, using the V&V techniques we have previously developed to assure they are both correct and complete. Since it is nearing the end of its life, ASTERIA provides a unique opportunity to demonstrate MBFD since the monitored components are expected to fail. Our demonstration will give system developers additional confidence to make timely, informed MBFD deployment decisions. Second, we will be completing performance assessments of the diagnostic engine/diagnostic model ensemble both on the flight system and ground-based testbeds to gain confidence in MBFD’s ability to run successfully in a spacecraft’s resource-constrained environment without adversely affecting other on-board activities. Finally, we are capturing our experience in preparing this demonstration in a set of checklists and guidance documents. Current practice includes high-level institutional guidance documents and standards, but at a high level of abstraction that does not necessarily address specific MBFD concerns. The purpose of the new checklists is to provide future mission developers clear, unambiguous, procedure-oriented guidance on assuring MBFD. This paper describes our work in these areas. For the first area, we describe the diagnostic models and updated diagnostic engine that will be used for the on-board demonstration. We describe how the V&V techniques we developed earlier are used to assure model and engine correctness and completeness. For the second area, we identify the performance measurement and assessment techniques used to characterize the diagnostic engine and diagnostic models, and discuss the effect of measured performance on overall mission operation. Finally, we present the checklist and guidance documents and describe how they meet the goals of providing system developers with clear, unambiguous, procedure-oriented guidance on MBFD assurance. We show how the techniques we have developed map into those artifacts.

Nikora, Allen↗
NASA NTRSDate not supplied

Multiphase Simulations of the SLS Launch Environment

NASA’s Space Launch System (SLS), which will send astronauts back to the Moon in the next few years, is powered by four RS-25 engines and two RSRMV solid rocket boosters (SRBs). During launch the SLS propulsion system generates intense acoustics and other powerful waves, such as ignition overpressure (IOP) which, if unmitigated, have the potential to damage the vehicle and possibly cause loss of mission or crew. To protect the vehicle from these powerful waves, the SLS launch pad design includes an ignition overpressure/sound suppression (IOP/SS) system which sprays 270,000 gallons per minute of water very close to the SRB and RS-25 nozzles. The SRB and RS-25 engine plumes, and the proximity of the IOP/SS water, create a complex multiphase (gas and liquid) environment during the SLS ignition sequence. The interplay among these systems creates challenges related to water spray into/onto engine nozzles, potential debris transport, and additional transient loads due to strong plume-water interactions - all of which the SLS vehicle must be able to withstand. Prior to the Artemis I launch, the SLS multiphase liftoff environment was largely unknown due to differences from the Space Shuttle and other programs. Some data was available from tests of individual systems, but no integrated testing or analysis was available. Even post-launch analysis of Artemis I cannot provide a full understanding of the complex physics involved due to limited (or obstructed) camera views and instrumentation. Computational fluid dynamics (CFD) is being used to investigate the details of the multiphase environment which could not be measured, help comprehend the data gathered from the launch, and ultimately identify phenomena that are a concern for future flights. Project Details Engineers at NASA’s Marshall Space Flight Center (MSFC) have executed simulations using the Loci/STREAM-Volume of Fluid (VoF) multiphase CFD solver to understand this environment. Initial efforts successfully validated the CFD solver on various tests, giving confidence to simulate the SLS multiphase liftoff environment prior to the Artemis I launch. The CFD simulation of the SLS ignition sequence was conducted in three phases. First the IOP/SS water system was simulated for approximately 6 seconds to reach a quasi-steady state. Next, the RS-25 engine plumes were activated and held at full power for 1 second. Lastly, the SRB booster was activated and the simulation was carried out until just prior to vehicle motion. This simulation process mimics the conditions that exist at launch. Results and Impact The SLS ignition sequence simulation results provide deep understanding of the underlying physics occuring during launch. Observations from the simulation include reduction of water splashing into/onto the engine nozzles, change in angling of the dense water sheets, and the origin of the powerful ignition overpressure (IOP) wave. These observations directly inform the SLS program on subjects including plume-water induced side loads, debris transport, and the acoustic launch environment. Additionally, with post launch comparison of CFD observations to flight data, these tools can be applied to launch vehicles and environments other than SLS with confidence. Why HPC Matters The SLS ignition sequence CFD simulations are conducted on meshes up to hundreds of millions of cells on thousands of processors for weeks at a time. These simulations generate terabytes of data that must also be stored and archived for future use on HPC systems. Simply put, the CFD simulations would not be possible without NASA HPC resources. What’s Next Comparisons between the Artemis I flight data and the CFD simulations will be continued to both improve confidence in the CFD results and provide deeper understanding into the SLS multiphase launch environment. This will be used to provide insight for decision making for the first manned SLS flight, Artemis II. Future simulations will target new configurations of the SLS IOP/SS water required to support the more powerful variants of the SLS vehicle, such as Block 1B. Additionally, this capability provides NASA the ability to investigate launch environments for vehicles other than SLS to support other missions.

Travis Rivord↗
NASA NTRSDate not supplied

Creating Gridded Fire Probability Maps using NASA Data

Fire is a nationally and globally significant process that strongly affects human–dominated and wild landscapes. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes, providing value by decreasing fuels at the Wildland Urban Interface (WUI) to promote safe communities. However, uncontained wildfires can devastate communities, threaten our health, and result in substantial economic losses. There has been greater than a $50B increase in wildfire insurance claims from 2017-2021, which has been exacerbated by climate change. Our partners at Kettle reinsurance are focused on building a smarter reinsurance model for protecting today’s globalized world from the catastrophic effects of climate change. Our objective is to develop the world's first grid-based wildfire probability product using multiple sources of satellite data to determine whether a ‘conflagration' (fire larger than 999+ acres) has ‘breached’ a grid cell. This will substantially decrease the time it takes for homeowners to receive payouts, from over a year to a couple months. Working with our partners at Kettle reinsurance, we use multiple satellites and ancillary data to weigh the likelihood of fire, based on a number of sources that verify a fire burning in a grid cell and the level of confidence in the data source. For example, Sentinel-2 vegetation-change indices have a higher level of confidence than VIIRS (Visible Infrared Imaging Radiometer Suite) active-fire detection data; and VIIRS active-fire detection data have a higher-level of confidence than MODIS (Moderate Resolution Imaging Spectroradiometer) active-fire detection data. The first iteration has been developed for responding to wildfires in California, with the possibility to expand nationwide and globally.

Emily Gargulinski↗
NASA NTRSDate not supplied

Impact Real World System Validation

Introduction NASA has developed a new evidence-based data-driven probabilistic risk assessment and tradespace analysis tool as a successor to the Integrated Medical Model. This updated decision support tool is known as IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces). IMPACT estimates the frequency and consequences of medical conditions that might arise during exploration missions. A validation analysis of IMPACT was performed with respect to a set of International Space Station (ISS) and Shuttle Transportation System (STS) real world system (RWS) referent data due to the limited referent data available from exploration missions. Methods Observed mission and crew characteristics from STS and ISS missions were used as model inputs within MEDPRAT (Medical Extensible Dynamic Probabilistic Risk Assessment Tool). For each mission, two hundred thousand simulations were generated. For each mission, model outputs included occurrence counts for each condition, total medical events (TME), and the probability of loss of crew life (LOCL). These simulated model outputs were compared to the RWS referent data. Results The predicted number of total medical events exceeded the total RWS medical events for ISS missions and combined ISS and STS missions and fell within the 90% confidence interval for STS missions. For the 32 ISS missions simulated by IMPACT, the number of total medical events was overpredicted for 19 missions and fell within the 90% confidence interval for 13 missions. For the 21 STS missions, the total number of medical events was overpredicted for 3 missions, fell within the 90% confidence interval for 16 missions, and was underpredicted for 2 missions. Combined, 29 missions were in range, 22 were overpredicted, and 2 were underpredicted. The predicted LOCL probability for the 32 ISS missions, the 21 STS missions, and the combined ISS and STS missions was consistent with the zero LOCL events observed in the RWS referent data. The validation analysis included a comparison of the number of medical events predicted by IMPACT and the number of medical events observed in the RWS data on a condition-by-condition basis. For ISS missions, 50 conditions were in range, 52 conditions were statistically underpowered (not enough observed sample to draw any conclusions on precision), 8 conditions were overpredicted, and 9 conditions were underpredicted. Overall, only 14% (17/119) of conditions were out of range for STS missions, 40 conditions were in range, 59 conditions were statistically underpowered, 10 conditions were overpredicted, and 10 conditions were underpredicted. Overall, only 17% (20/119) of conditions were out of range. For combined ISS and STS missions, 11 conditions were overpredicted, and 11 conditions were underpredicted. Overall, only 18% (22/119) of conditions were out of range. For combined ISS and STS missions, 49 conditions were in range, 46 conditions were statistically underpowered, 18 conditions were overpredicted, and 8 conditions were underpredicted. Overall, 21% (26/121) of conditions were out of range. Conclusion The results of this validation analysis should not be interpreted as a pass/fail test of the validity of IMPACT. Instead, this validation analysis should be used to assess some of the IMPACT outcomes in terms of consistencies and inconsistencies with the ISS and STS RWS referent data.

L. Boley↗
NASA NTRSDate not supplied

Creating IR-verified Gridded Fire Burn Maps using Public NASA and Satellite Data

Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes, providing value by decreasing fuels at the Wildland Urban Interface (WUI) to promote safe communities. However, uncontained wildfires can devastate communities, threaten our health, and result in substantial economic losses. There has been greater than a $50B increase in wildfire insurance claims from 2017-2021, which has been exacerbated by climate change. Our partners at Kettle reinsurance are focused on building a smarter reinsurance model for protecting today’s globalized world from the catastrophic effects of climate change. Our objective is to develop a high-confidence grid-based wildfire burn product using multiple sources of satellite data to determine whether a ‘conflagration' (fire larger than 999+ acres) has ‘breached’ a grid cell. This product will substantially decrease the time it takes for homeowners to receive payouts, from over a year to a couple months. Working with our partners at Kettle reinsurance, we use VIIRS (Visible Infrared Imaging Radiometer Suite) 375 m fire detections and Sentinel-2 10 m satellite imagery to create a 20-m gridded fire burn product. Our process is based on the level of confidence in the data source and takes into account vegetation change throughout the life of the fire. For example, Sentinel-2 vegetation-change indices have a higher level of confidence when congruent with VIIRS active-fire detection data, rather than VIIRS detections alone. We have also verified our fire burn product against MODIS/ASTER Airborne Simulator (MASTER) Infrared (IR) data from the Fire Influence on Regional to Global Environments Experiment - Air Quality (FIREX-AQ) 2019 campaign, with 88% overall agreement. The first iteration has been developed for responding to wildfires in California, with the possibility to expand nationwide and globally.

Emily Gargulinski↗
NASA NTRSDate not supplied

Improving Satellite-Based Hotspot Detection Through Deep Learning-Enabled Smoke Recognition

While geostationary satellites, such as the GOES-R series, provide wildland fire hotspot readings at a high temporal resolution, they are prone to false negative readings and decreased confidence. One cause of decreased hotspot confidence is cloud contamination. Smoke produced from wildfire is often misinterpreted as cloud contamination, resulting in inaccurate and unsure sensor readings. To this end, we built a deep learning image segmentation model to identify smoke and cloud in true color satellite images. The model is pre-trained using self-supervised learning on over 10,000 GOES-R images to learn the underlying structure of satellite imagery. Then, the model is fine-tuned on a set of 130 labeled documents using supervised learning. The resulting model performs multi-class image segmentation with 85% accuracy and runs in under a minute on a standard personal computer. When paired alongside hotspot data, the model’s outputs can help increase confidence in wildfire location by identifying cases of cloud contamination that are due to smoke. The resulting model can be deployed in a stand-alone application or bundled in an Open Data Integration for wildland fire management (ODIN) application.

Earth observation↗
NASA NTRS1996

An efficient approach to ARMA modeling of biological systems with multiple inputs and delays

This paper presents a new approach to AutoRegressive Moving Average (ARMA or ARX) modeling which automatically seeks the best model order to represent investigated linear, time invariant systems using their input/output data. The algorithm seeks the ARMA parameterization which accounts for variability in the output of the system due to input activity and contains the fewest number of parameters required to do so. The unique characteristics of the proposed system identification algorithm are its simplicity and efficiency in handling systems with delays and multiple inputs. We present results of applying the algorithm to simulated data and experimental biological data In addition, a technique for assessing the error associated with the impulse responses calculated from estimated ARMA parameterizations is presented. The mapping from ARMA coefficients to impulse response estimates is nonlinear, which complicates any effort to construct confidence bounds for the obtained impulse responses. Here a method for obtaining a linearization of this mapping is derived, which leads to a simple procedure to approximate the confidence bounds.

Non-NASA Center↗
NASA NTRS1966

Design reliability goal developed from small sample

Sampling distributions, constructed by Monte Carlo simulation are used in hardware development to establish a design reliability goal, to place a confidence coefficient on reliability estimates, and to determine whether sample stress/strength data demonstrate a specified reliability at a specified confidence level.

Burrows, D. L.↗
NASA NTRS1972

Aerodynamic parameters of the Navion airplane extracted from flight

An iterative method, which is characterized as a maximum-likelihood minimum-variance technique, was used to extract the aerodynamic parameters of a Navion airplane from flight data. The purposes were to compare the results with parameters obtained from wind-tunnel tests and with results obtained by analog matching the same data, and to develop techniques for application of the parameter extraction program. Results from the study showed that the parameter-extraction program can produce aerodynamic parameters which will permit close estimation of the aircraft time histories used in the extraction process. The program determined an estimate of the standard deviations of the states and parameters. These estimates were used to indicate how well the calculated states fit the flight data and the confidence in the values of the estimated parameters. The study also showed that the values of the parameters were affected by the data and mathematical model used during the extraction process. Because of the lack of confidence in the parameters extracted by use of some of the sets of data, several parameters were estimated by other methods. By using a combination of methods, a set of parameters which gave a fit to the data was obtained.

Suit, W. T.↗