SEARCH · Engineering Papers

Results for “Statistical Testing Approach”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26

All sources NASA NTRS DOE OSTI

NASA NTRS1975

Dynamic Capacity and Surface Fatigue Life for Spur and Helical Gears

A mathematical model for surface fatigue life of gear, pinion, or entire meshing gear train is given. The theory is based on a previous statistical approach for rolling-element bearings. Equations are presented which give the dynamic capacity of the gear set. The dynamic capacity is the transmitted tangential load which gives a 90 percent probability of survival of the gear set for one million pinion revolutions. The analytical results are compared with test data for a set of AISI 9310 spur gears operating at a maximum Hertz stress of 1.71 billion N/sq m and 10,000 rpm. The theoretical life predictions are shown to be good when material constants obtained from rolling-element bearing tests were used in the gear life model.

Coy, J. J.↗

DOE OSTI2016

WE-DE-201-01: BEST IN PHYSICS (THERAPY): A Fast Multi-Target Inverse Treatment Planning Strategy Optimizing Dosimetric Measures for High-Dose-Rate (HDR) Brachytherapy

Purpose: Inverse treatment planning (ITP) for interstitial HDR brachytherapy of gynecologic cancers seeks to maximize coverage of the clinical target volumes (tumor and vagina) while respecting dose-volume-histogram related dosimetric measures (DMs) for organs at risk (OARs). Commercially available ITP tools do not support DM-based planning because it is computationally too expensive to solve. In this study we present a novel approach that allows fast ITP for gynecologic cancers based on DMs for the first time. Methods: This novel strategy is an optimization model based on a smooth DM-based objective function. The smooth approximation is achieved by utilizing a logistic function for the evaluation of DMs. The resulting nonconvex and constrained optimization problem is then optimized with a BFGS algorithm. The model was evaluated using the implant geometry extracted from 20 patient treatment plans under an IRB-approved retrospective study. For each plan, the final DMs were evaluated and compared to the original clinical plans. The CTVs were the contoured tumor volume and the contoured surface of the vagina. Statistical significance was evaluated with a one-sided paired Wilcoxon signed-rank test. Results: As did the clinical plans, all generated plans fulfilled the defined DMs for OARs. The proposed strategy showed a statistically significant improvement (p<0.001) in coverage of the tumor and vagina, with absolute improvements of related DMs of (6.9 +/− 7.9)% and (28.2 +/− 12.0)%, respectively. This was achieved with a statistically significant (p<0.01) decrease of the high-dose-related DM for the tumor. The runtime of the optimization was (2.3 +/− 2.0) seconds. Conclusion: We demonstrated using clinical data that our novel approach allows rapid DM-based optimization with improved coverage of CTVs with fewer hot spots. Being up to three orders of magnitude faster than the current clinical practice, the method dramatically shortens planning time.

60 APPLIED LIFE SCIENCES↗

NASA NTRSDate not supplied

Increasing Cognitive Ability/Reserve Using Software – Pilot (ICARUS-Pilot)

BACKGROUND This research study was competitively awarded under the 2022 JSC Innovation Charge Account (ICA) program administered by NASA Johnson Space Center’s Joint Technology Working Group. Study period of performance was May through September 2022, with a maximum allowed procurement budget of $10K. The study sought to quantify and assess the potential benefit of using commercial-off-the-shelf (COTS) cognitive training software to improve cognitive performance in an astronaut-like terrestrial population. METHODS Five volunteer research participants were recruited from the JSC employee population to mimic certain demographic characteristics of the NASA astronaut population (age, education/discipline). Participant cognitive performance was assessed before and after executing eighteen sessions of remote cognitive training executed nominally three times per week using six exercises within an adaptive app-based COTS software package (BrainHQ, Posit Science) on study-provided tablets. Pre- and post-training cognitive performance was measured using internal assessments in BrainHQ as well as Cognition Test Battery (CTB) version ISS B01 v3 (3.0.9-201710021500), an independent software test developed specifically for NASA and used currently in research studies on astronauts. BrainHQ exercises were posited to map well or partially to several CTB sub-tests. Participants provided feedback on their study experience formally via semi-structured interview at the conclusion of testing and informally throughout the study if they encountered issues. RESULTS The enrolled ICARUS-Pilot study participants generally matched Artemis crew demographic characteristics. Four of five participants have completed study training and assessment activities as of the writing of this abstract. These test participants complied well with desired training session frequency and duration yielding an average cumulative active training duration of 15 hours over an average of 45 days; participants showed 78% average improvement in metric performance for the six trained exercises, with an associated overall 33%ile ranking increase against performance of the entire BrainHQ subscribing population for internal pre/post assessment, agreeing with post-study survey self-reported performance increases. CTB overall feedback scoring, not corrected for learning effects, showed an average of 19% performance improvement across its 10 performance measures over the training period for the completed participants. Detailed analyses will be conducted once participant data collection for the study is complete and the resulting dataset is fully populated. DISCUSSION These preliminary results provide a positive trend for the effectiveness of the training approach, but further analysis will be needed to establish significance, investigate far transfer, and suggest the needed participant pool size for subsequent efforts to achieve statistically significant outcomes given similar results. The pilot study has already been helpful by allowing the study team to learn a great deal about the capabilities and limitations of the COTS software package that will be reflected in future proposals along with revised timelines for study execution and test participant management. From participant feedback, one common thread regarding the COTS training was that it felt overly repetitive – future proposals should reassess overall training duration, available levels for each trained exercise, and the behavior of the BrainHQ internal scheduler in determining which exercises should be trained and for how long. If the final analysis of this feasibility study ultimately supports it, the study team will recommend further investigation to fully evaluate this potential countermeasure and optimize its implementation. Future proposals would cite this feasibility study’s outcome and would seek to refine the training protocol and obtain statistically significant results for cognitive performance increases as well as retention data.

cognitive training↗

NASA NTRSDate not supplied

Increasing Cognitive Ability/Reserve Using Software – Pilot (ICARUS-Pilot)

Background: This research study was competitively awarded under the 2022 JSC Innovation Charge Account (ICA) program administered by NASA Johnson Space Center’s Joint Technology Working Group. Study period of performance was May through September 2022, with a maximum allowed procurement budget of $10K. The study sought to quantify and assess the potential benefit of using commercial-off-the-shelf (COTS) cognitive training software to improve cognitive performance in an astronaut-like terrestrial population. Methods: Five volunteer research participants were recruited from the JSC employee population to mimic certain demographic characteristics of the NASA astronaut population (age, education/discipline). Participant cognitive performance was assessed before and after executing eighteen sessions of remote cognitive training executed nominally three times per week using six exercises within an adaptive app-based COTS software package (BrainHQ, Posit Science) on study-provided tablets. Pre- and post-training cognitive performance was measured using internal assessments in BrainHQ as well as Cognition Test Battery (CTB) version ISS B01 v3 (3.0.9-201710021500), an independent software test developed specifically for NASA and used currently in research studies on astronauts. BrainHQ exercises were posited to map well or partially to several CTB sub-tests. Participants provided feedback on their study experience formally via semi-structured interview at the conclusion of testing and informally throughout the study if they encountered issues. Results: The enrolled ICARUS-Pilot study participants generally matched Artemis crew demographic characteristics. Four of five participants have completed study training and assessment activities as of the writing of this abstract. These test participants complied well with desired training session frequency and duration yielding an average cumulative active training duration of 15 hours over an average of 45 days; participants showed 78% average improvement in metric performance for the six trained exercises, with an associated overall 33%ile ranking increase against performance of the entire BrainHQ subscribing population for internal pre/post assessment, agreeing with post-study survey self-reported performance increases. CTB overall feedback scoring, not corrected for learning effects, showed an average of 19% performance improvement across its 10 performance measures over the training period for the completed participants. Detailed analyses will be conducted once participant data collection for the study is complete and the resulting dataset is fully populated. Discussion: These preliminary results provide a positive trend for the effectiveness of the training approach, but further analysis will be needed to establish significance, investigate far transfer, and suggest the needed participant pool size for subsequent efforts to achieve statistically significant outcomes given similar results. The pilot study has already been helpful by allowing the study team to learn a great deal about the capabilities and limitations of the COTS software package that will be reflected in future proposals along with revised timelines for study execution and test participant management. From participant feedback, one common thread regarding the COTS training was that it felt overly repetitive – future proposals should reassess overall training duration, available levels for each trained exercise, and the behavior of the BrainHQ internal scheduler in determining which exercises should be trained and for how long. If the final analysis of this feasibility study ultimately supports it, the study team will recommend further investigation to fully evaluate this potential countermeasure and optimize its implementation. Future proposals would cite this feasibility study’s outcome and would seek to refine the training protocol and obtain statistically significant results for cognitive performance increases as well as retention data.

cognitive training↗

DOE OSTI2024

Joint inference of multiplicative and additive systematics in galaxy density fluctuations and clustering measurements

Galaxy clustering measurements are a key probe of the matter density field in the Universe. With the era of precision cosmology upon us, surveys rely on precise measurements of the clustering signal for meaningful cosmological analysis. However, the presence of systematic contaminants can bias the observed galaxy number density, and thereby bias the galaxy two-point statistics. As the statistical uncertainties get smaller, correcting for these systematic contaminants becomes increasingly important for unbiased cosmological analysis. We present and validate a new method for understanding and mitigating both additive and multiplicative systematics in galaxy clustering measurements (two-point function) by joint inference of contaminants in the galaxy overdensity field (one-point function) using a maximum-likelihood estimator (MLE). We test this methodology with Kilo-Degree Survey-like mock galaxy catalogues and synthetic systematic template maps. We estimate the cosmological impact of such mitigation by quantifying uncertainties and possible biases in the inferred relationship between the observed and the true galaxy clustering signal. Our method robustly corrects the clustering signal to the sub-percent level and reduces numerous additive and multiplicative systematics from 1.5σ to less than 0.1σ for the scenarios we tested. In addition, we provide an empirical approach to identifying the functional form (additive, multiplicative, or other) by which specific systematics contaminate the galaxy number density. Even though this approach is tested and geared towards systematics contaminating the galaxy number density, the methods can be extended to systematics mitigation for other two-point correlation measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

A multivariate approach to ensure statistical reproducibility of climate model simulations

Effective utilization of novel hybrid architectures of pre-exascale and exascale machines requires transformations to global climate modeling systems that may not reproduce the original model solution bit-for-bit. Round-off level differences grow rapidly in these non-linear and chaotic systems. This makes it difficult to isolate bugs/errors from innocuous growth expected from round-off level differences. Here, we apply two modern multivariate two sample equality of distribution tests to evaluate statistical reproducibility of global climate model simulations using standard monthly output of short (~ 1-year) simulation ensembles of a control model and a modified model of US Department of Energy's Energy Exascale Earth System Model (E3SM). Both the tests are able to identify changes induced by modifications to some model tuning parameters. We also conduct formal power analyses of the tests by applying them on designed suites of short simulation ensembles each with an increasingly different climate from the control ensemble. The results are compared against those from another such test. These power analyses provide a framework to quantify the degree of differences that can be detected confidently by the tests for a given ensemble size (sample size). This will allow model developers using the tests to make an informed decision when accepting/rejecting an unintentional non-bit-for-bit change to the model solution.

Mahajan, Salil↗

DOE OSTI2021

Lowering post‐construction yield assessment uncertainty through better wind plant power curves

Abstract Many operational analyses of wind power plants require a statistical relationship, which can be called the wind plant power curve, to be developed between wind plant energy production and concurrent atmospheric variables. Currently, a univariate linear regression at monthly resolution is the industry standard for post‐construction yield assessments. Here, we evaluate the benefits in augmenting this conventional approach by testing alternative regressions performed with multiple inputs, at a finer time resolution, and using nonlinear machine‐learning algorithms. We utilize the National Renewable Energy Laboratory's open‐source software package OpenOA to assess wind plant power curves for 10 wind plants. When a univariate generalized additive model at daily or hourly resolution is used, regression uncertainty is reduced, in absolute terms, by up to 1.0 % and 1.2 % (corresponding to a −59 % and −80 % relative change), respectively, compared to a univariate linear regression at monthly resolution; also, a more accurate assessment of the mean long‐term wind plant production is achieved. Additional input variables also reduce the regression uncertainty: when temperature is added as an input to the conventional monthly linear regression, the operational analysis uncertainty connected to regression is reduced, in absolute terms, by up to 0.5 % (−43 % relative change) for wind power plants with strong seasonal variability. Adding input variables to the machine‐learning model at daily resolution can further reduce regression uncertainty, with up to a −10 % relative change. Based on these results, we conclude that a multivariate nonlinear regression at daily or hourly resolution should be recommended for assessing wind plant power curves.

17 WIND ENERGY↗

DOE OSTI2023

Machine Learning technique for isotopic determination of radioisotopes using HPGe γ-ray spectra

γ-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides. Traditional methods of isotopic determination have various challenges that contribute to statistical and systematic uncertainties in the estimated isotopics. Furthermore, these methods typically require numerous pre-processing steps, and have only been rigorously tested in laboratory settings with limited shielding. Here, in this work, we examine the application of a number of machine learning based regression algorithms as alternatives to conventional approaches for analyzing γ-ray spectroscopy data in the Emergency Response arena. This approach not only eliminates many steps in the analysis procedure, and therefore offers potential to reduce this source of systematic uncertainty, but is also shown to offer comparable performance to conventional approaches in the Emergency Response Application.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Bayesian-based response expansion and uncertainty quantification using sparse measurement sets

Systems subjected to dynamic loads often require monitoring of their vibrational response, but limitations on the total number and placement of the measurement sensors can hinder the data-collection process. Here, we present an indirect approach to estimate a system’s full-field dynamic response, including all uninstrumented locations, using response measurements from sensors sparsely located on the system. This approach relies on Bayesian inference that utilizes a system model to estimate the full-field response and quantify the uncertainty in these estimates. By casting the estimation problem in the frequency domain, this approach utilizes the modal frequency response functions as a natural, frequency-dependent weighting scheme for the system mode shapes to perform the expansion. This frequency-dependent weighting scheme enables an accurate expansion, even with highly correlated mode shapes that may arise from spatial aliasing due to the limited number of sensors, provided these correlated modes do not have natural frequencies that are closely spaced. Furthermore, the inherent regularization mechanism that arises in this Bayesian-based procedure enables the utilization of the full set of system mode shapes for the expansion, rather than any reduced subset. This approach can produce estimates when considering a single realization of the measured responses, and with some modification, it can also produce estimates for power spectral density matrices measured from many realizations of the responses from statistically stationary random processes. A simply supported beam provides an initial numerical validation, and a cylindrical test article excited by acoustic loads in a reverberation chamber provides experimental validation.

42 ENGINEERING↗

DOE OSTI2020

Roundup causes embryonic development failure and alters metabolic pathways and gut microbiota functionality in non-target species

Background: Research around the weedkiller Roundup is among the most contentious of the twenty-first century. Scientists have provided inconclusive evidence that the weedkiller causes cancer and other life-threatening diseases, while industry-paid research reports that the weedkiller has no adverse effect on humans or animals. Much of the controversial evidence on Roundup is rooted in the approach used to determine safe use of chemicals, defined by outdated toxicity tests. We apply a system biology approach to the biomedical and ecological model species Daphnia to quantify the impact of glyphosate and of its commercial formula, Roundup, on fitness, genome-wide transcription and gut microbiota, taking full advantage of clonal reproduction in Daphnia. We then apply machine learning-based statistical analysis to identify and prioritize correlations between genome-wide transcriptional and microbiota changes. Results: We demonstrate that chronic exposure to ecologically relevant concentrations of glyphosate and Roundup at the approved regulatory threshold for drinking water in the US induce embryonic developmental failure, induce significant DNA damage (genotoxicity), and interfere with signaling. Furthermore, chronic exposure to the weedkiller alters the gut microbiota functionality and composition interfering with carbon and fat metabolism, as well as homeostasis. Using the “Reactome,” we identify conserved pathways across the Tree of Life, which are potential targets for Roundup in other species, including liver metabolism, inflammation pathways, and collagen degradation, responsible for the repair of wounds and tissue remodeling. Conclusions: Our results show that chronic exposure to concentrations of Roundup and glyphosate at the approved regulatory threshold for drinking water causes embryonic development failure and alteration of key metabolic functions via direct effect on the host molecular processes and indirect effect on the gut microbiota. The ecological model species Daphnia occupies a central position in the food web of aquatic ecosystems, being the preferred food of small vertebrates and invertebrates as well as a grazer of algae and bacteria. The impact of the weedkiller on this keystone species has cascading effects on aquatic food webs, affecting their ability to deliver critical ecosystem services.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1981

On the measurement of precipitation frequencies by passive microwave radiometery

Over most of the microwave spectrum, raindrops both absorb and scatter radiation producing large changes in brightness temperatures relative to clear or cloudy conditions. Since the structure of rain varies substantially for different rain rates and climatological backgrounds, the raindrop size distribution, the rain layer thickness and the ice clouds above the rain layer are all important inputs to the model computations. The subsequent modeling involves applying the Mie theory to derive the absorption and scattering effects and the radiative transfer calculation is based upon a variational iterative approach which takes account of the multiple scattering effect of the rain layer. Results over both ocean and land backgrounds are demonstrated. It is also demonstrated that by using discrimination tests of the radiometric data, the rain/no rain decision can be made and the rainfall rate can be retrieved from a statistical inversion technique.

Kidder, S. Q.↗

NASA NTRS1984

Allowable response delay for large aircraft

The degree of permissible time delay in an airliner flight control system is presently sought by means of a pilot-in-the-loop moving base flight simulator, assuming the characteristics of an advanced L-1011 aircraft variant. Test pilots and engineers from the U.S. Navy, the airliner manufacturer, and NASA Langley were used in a task which encompassed approach and landing after recovery from offsets in localizer and glide slope, during calm, turbulent, and cross-wind conditions. The data obtained in the course of 279 runs included statistics on pilot workload and performance as well as pilot opinion. Preliminary results indicate that requirements for a 0.1-sec maximum delay in aircraft response are excessively conservative for large aircraft, where an offset landing maneuver is the critical design task. Lateral axis delays appear to be more critical than longitudinal ones.

Meyer, R. T.↗

NASA NTRS2017

Statistical Characterization of 18650-Format Lithium-Ion Cell Thermal Runaway Energy Distributions

Effective thermal management systems, designed to handle the impacts of thermal runaway (TR) and to prevent cell-to-cell propagation, are key to safe operation of lithium-ion (Li-ion) battery assemblies. Critical factors for optimizing these systems include the total energy released during a single cell TR event and the fraction of the total energy that is released through the cell casing vs. through the ejecta material. A unique calorimeter was utilized to examine the TR behavior of a statistically significant number of 18650-format Li-ion cells with varying manufacturers, chemistries, and capacities. The calorimeter was designed to contain the TR energy in a format conducive to discerning the fractions of energy released through the cell casing vs. through the ejecta material. Other benefits of this calorimeter included the ability to rapidly test of large quantities of cells and the intentional minimization of secondary combustion effects. High energy (270 Wh/kg) and moderate energy (200 Wh/kg) 18650 cells were tested. Some of the cells had an imbedded short circuit (ISC) device installed to aid in the examination of TR mechanisms under more realistic conditions. Other variations included cells with bottom vent (BV) features and cells with thin casings (0.22 1/4m). After combining the data gathered with the calorimeter, a statistical approach was used to examine the probability of certain TR behavior, and the associated energy distributions, as a function of capacity, venting features, cell casing thickness and temperature.

Thermal Runaway Energy Distribution↗

DOE OSTI2025

Dynamical dark energy in light of the DESI DR2 baryonic acoustic oscillations measurements

Understanding whether cosmic acceleration arises from a cosmological constant or a dynamical component is a central goal of cosmology, and the Dark Energy Spectroscopic Instrument (DESI) enables stringent tests with high-precision distance measurements. Here we analyse measurements of baryon acoustic oscillations in DESI Data Release 1 and Data Release 2 and consider type Ia supernovae and a distance prior for the cosmic microwave background. With the larger statistical power and wider redshift coverage of Data Release 2, the preference for dynamical dark energy does not diminish relative to Data Release 1. Using both a shape-function reconstruction and non-parametric approaches with a Horndeski-motivated correlation prior, we find that the equation of state for dark energy w(z) varies with redshift. Baryon acoustic oscillation data alone yield modest constraints, but in combination with independent supernova compilations and the prior for the cosmic microwave background, they strengthen the evidence for dynamics. A Bayesian comparison of models shows moderate support for departures from Λ cold dark matter (ΛCDM) when several degrees of freedom in w(z) are allowed, corresponding to ~3σ tension with ΛCDM (and higher for some datasets). Despite methodological differences, our results are consistent with companion DESI papers, underscoring the complementarity of the approaches. Possible systematics remain under study; forthcoming DESI, Euclid and next-generation cosmic microwave background data will provide decisive tests.

cosmology↗

DOE OSTI2025

Dynamical dark energy in light of the DESI DR2 baryonic acoustic oscillations measurements

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Monitoring Aqueous Reprocessing Systems for Detection of Facility Misuse

Traditional approaches to material control and accounting at used nuclear fuel reprocessing facilities relies in large part on laboratory-based destructive assay of periodic samples taken from key stages. These techniques are highly accurate, but limited inspection frequency may limit the size and operation of a reprocessing facility. Online monitoring techniques using inline detectors can be applied to fuel reprocessing systems to improve the situational awareness as the facility is operating to provide additional indicators of the evolving state of the facility between normal inspection periods, potentially alerting regulators to the need for additional inspection. The Multi-Isotope Process (MIP) Monitor combines medium-resolution gamma detectors with statistical data analysis techniques to monitor reprocessing systems for anomalous conditions resulting from process control issues or malfeasance. Gamma spectra collected from the H-Canyon reprocessing facility were modeled using principal component analysis to demonstrate the efficacy of the MIP Monitor for detecting notional diversion scenarios.

safeguards, nuclear fuel reprocessing, gamma spect↗

DOE OSTI2021

An Active Detection Scheme for Sensor Spoofing in Grid-tied PV Systems

In this paper an active detection scheme for sensor spoofing (manipulated externally via a cyber attack) in grid-tied PV systems is discussed. The core of the proposed active detection scheme is to introduce a private (secret) watermarking signal into the control inputs of the DC-DC converter and DC-AC inverter stages to detect any malicious spoofing (manipulation) of voltage/current sensor measurements controlling both the DC-DC converter maximum power point tracking (MPPT) stage and the DC-AC inverter of the grid-tied PV system. Several types of possible spoofing mechanisms (attack models) are discussed. The proposed sensor spoofing attack detector system consists of injecting a small magnitude of digital watermarking signal (DWS) and conduct three statistical watermark tests on the reported sensor measurements to determine if a) the proposed system is healthy and operating as expected b) if sensor signals were spoofed (manipulated) externally or c) if a particular sensor is malfunctioning due to a faulty hardware. It is shown via extensive simulations that the proposed DWS approach is robust in detecting malicious external manipulation of sensors controlling the grid tied PV system. A testing platform is currently under development and the experimental results will be discussed in the conference presentation.

Ibrahim, Hasan↗

DOE OSTI2018

Simulated execution of hybrid quantum computing systems

We use modeling and simulation to study the behavior and performance of hybrid quantum computing systems. Our approach is based on a layered design with abstract machine models, which identify the key components and interfaces for quantum processing units, quantum programming models, and hybrid execution models. We use discrete-event simulating to track the dynamical state of hierarchical abstract machine models while executing test programs, and we collect statistics on time and energy consumption to forecast the resources required by quantum processors for future scientific computation.

Humble, Travis↗