Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Robustness analysis”

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.

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

At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Validation of Proposed Metrics for Two-Body Abrasion Scratch Test Analysis Standards

The objective of this work was to evaluate a set of standardized metrics proposed for characterizing a surface that has been scratched from a two-body abrasion test. This is achieved by defining a new abrasion region termed Zone of Interaction (ZOI). The ZOI describes the full surface profile of all peaks and valleys, rather than just measuring a scratch width as currently defined by the ASTM G 171 Standard. The ZOI has been found to be at least twice the size of a standard width measurement, in some cases considerably greater, indicating that at least half of the disturbed surface area would be neglected without this insight. The ZOI is used to calculate a more robust data set of volume measurements that can be used to computationally reconstruct a resultant profile for detailed analysis. Documenting additional changes to various surface roughness parameters also allows key material attributes of importance to ultimate design applications to be quantified, such as depth of penetration and final abraded surface roughness. Data are presented to show that different combinations of scratch tips and abraded materials can actually yield the same scratch width, but result in different volume displacement or removal measurements and therefore, the ZOI method is more discriminating than the ASTM method scratch width. Furthermore, by investigating the use of custom scratch tips for our specific needs, the usefulness of having an abrasion metric that can measure the displaced volume in this standardized manner, and not just by scratch width alone, is reinforced. This benefit is made apparent when a tip creates an intricate contour having multiple peaks and valleys within a single scratch. This work lays the foundation for updating scratch measurement standards to improve modeling and characterization of three-body abrasion test results.

Kobrick, Ryan L.↗
NASA NTRS2020

Robustness and Behavior of Adjoint Calculations of Observation Impacts in Numerical Weather Prediction

Adjoint models are powerful tools that can be used to estimate the impact of observations on a chosen norm for numerical weather prediction forecasts. In this study, the Global Modeling and Assimilation Office (NASA/GMAO) Observing System Simulation Experiment framework is employed to investigate the behavior of the adjoint tool in an environment where the 'true' state of the atmosphere is fully known. This allows for the calculation of adjoint estimates of observation impact for very short forecast times including the zero-hour analysis state. The adjoint calculations using self-analysis verification can also be compared to adjoint calculations using the 'truth' as verification in order to characterize the robustness of adjoint estimations in the operational setting. Results from a experiments exploring various aspects of performance of the adjoint tool will be presented.

Prive, Nikki C.↗
NASA NTRS2023

SERFE PLSS Component Lessons Learned from ISS

NASA has been developing a new spacesuit, called the Exploration Extravehicular Mobility Unit (xEMU) for over a decade. This spacesuit is under development to support missions to the International Space Station (ISS) and also to the Moon. Improvements in the life and robustness of the Portable Life Support System (PLSS) has been a major objective of these efforts. The Suit Water Membrane Evaporator (SWME) was chosen as the technology to provide cooling to the xEMU and has undergone several iterations of development over this period. An ISS flight experiment centered around the SWME and other thermal control loop (TCL) technologies was developed and tested in an ISS EXpedite PRocessing of Experiments to the Space Station (EXPRESS) rack from November of 2020 to August of 2022. In addition to the SWME, the SWME EXPRESS Rack Flight Experiment (SERFE) contains several technologies from the xEMU project and demonstrated their performance in micro-gravity and over an extended duration. These included two dissimilar water pumps, custom check valves, custom bypass relieve valves, a custom thermal control valve, development pressure and temperature sensors, and the thermal loop controller. This paper presents PLSS component lessons learned after return of the SERFE flight unit in August of 2022. The SERFE team took the flight unit apart and handed hardware components over to hardware owners to see how parts of the TCL managed after almost 2 years on the ISS and 25 simulated EVAs (Exploration Extravehicular Activity) on orbit. The team performed inspection, testing, and analysis and provided lessons learned on PLSS components for NASA’s prototype spacesuit. This analysis included how well SWME maintained its heat rejection capability, as well as looked at the robustness of the other TCL hardware.

SERFE↗
NASA NTRS2023

SERFE PLSS Component Lessons Learned from ISS

NASA has been developing a new spacesuit, called the Exploration Extravehicular Mobility Unit (xEMU) for over a decade. This spacesuit is under development to support missions to the International Space Station (ISS) and also to the Moon. Improvements in the life and robustness of the Portable Life Support System (PLSS) has been a major objective of these efforts. The Suit Water Membrane Evaporator (SWME) was chosen as the technology to provide cooling to the xEMU and has undergone several iterations of development over this period. An ISS flight experiment centered around the SWME and other thermal control loop (TCL) technologies was developed and tested in an ISS EXpedite PRocessing of Experiments to the Space Station (EXPRESS) rack from November of 2020 to August of 2022. In addition to the SWME, the SWME EXPRESS Rack Flight Experiment (SERFE) contains several technologies from the xEMU project and demonstrated their performance in micro-gravity and over an extended duration. These included two dissimilar water pumps, custom check valves, custom bypass relieve valves, a custom thermal control valve, development pressure and temperature sensors, and the thermal loop controller. This paper presents PLSS component lessons learned after return of the SERFE flight unit in August of 2022. The SERFE team took the flight unit apart and handed hardware components over to hardware owners to see how parts of the TCL managed after almost 2 years on the ISS and 25 simulated EVAs (Exploration Extravehicular Activity) on orbit. The team performed inspection, testing, and analysis and provided lessons learned on PLSS components for NASA’s prototype spacesuit. This analysis included how well SWME maintained its heat rejection capability, as well as looked at the robustness of the other TCL hardware.

SERFE↗
DOE OSTI2016

X-RAY SOURCES IN THE DWARF SPHEROIDAL GALAXY DRACO

We present the spectral analysis of an 87 ks XMM-Newton observation of Draco, a nearby dwarf spheroidal galaxy. Of the approximately 35 robust X-ray source detections, we focus our attention on the brightest of these sources, for which we report X-ray and multiwavelength parameters. While most of the sources exhibit properties consistent with active galactic nuclei, few of them possess the characteristics of low-mass X-ray binaries (LMXBs) and cataclysmic variable (CVs). Our analysis places constraints on the population of X-ray sources with L{sub X} > 3 × 10{sup 33} erg s{sup −1} in Draco, suggesting that there are no actively accreting black hole and neutron star binaries. However, we find four sources that could be quiescent state LMXBs/CVs associated with Draco. We also place constraints on the central black hole luminosity and on a dark matter decay signal around 3.5 keV.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Molecular ring rotation in solid ferrocene revisited

We report on quasielastic neutron spectroscopy experiments on ferrocene (bis(η{sup 5}-cyclopentadienyl)iron) in its three different crystalline phases: the disordered monoclinic crystalline phase (T > 164 K), the metastable triclinic phase (T < 164 K), and the stable orthorhombic phase (T < 250 K). The cyclopentadienyl rings in ferrocene are known to undergo rotational reorientations for which the analysis of our large data set suggests partially a revision of the known picture of the dynamics and allows for an extension and completion of previous studies. In the monoclinic phase, guided by structural information, we propose a model for rotational jumps among non-equivalent sites in contrast to the established 5-fold jump rotation model. The new model takes the dynamical disorder into account and allows the cyclopentadienyl rings to reside in two different configurations which are found to be twisted by an angle of approximately 30°. In the triclinic phase, our analysis demands the use of a 2-ring model accounting for crystallographically independent sites with different barriers to rotation. For the orthorhombic phase of ferrocene, we confirm a significantly increased barrier of rotation using neutron backscattering spectroscopy. Our data analysis includes multiple scattering corrections and presents a novel approach of simultaneous analysis of different neutron scattering data by combining elastic and inelastic fixed window temperature scans with energy spectra, providing a very robust and reliable mean of extracting the individual activation energies of overlapping processes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Integrated Cyber/Physical Grid Resiliency Modeling

This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Performance Analysis of HF Band FB-MC-SS

Abstract—In a recent paper [1] the filter bank multicarrier spread spectrum (FB-MC-SS) waveform was proposed for wideband spread spectrum HF communications. A significant benefit of this waveform is robustness against narrow and partial band interference. Simulation results in [1] demonstrated good performance in a wideband HF channel over a wide range of conditions. In this paper we present a theoretical analysis of the bit error probably for this system. Our analysis tailors the results from [2] where BER performance was analyzed for maximum ration combining systems that accounted for correlation between subcarriers and channel estimation error. Equations are give for BER that closely match the simulated performance in most situations.

Analysis↗
DOE OSTI2023

Hyper Suprime-Cam Year 3 results: Cosmology from cosmic shear power spectra

Here, we measure weak lensing cosmic shear power spectra from the 3-year galaxy shear catalog of the Hyper Suprime-Cam (HSC) Subaru Strategic Program imaging survey. The shear catalog covers 416 deg 2 of the northern sky, with a mean i-band seeing of 0.59 arcsec and an effective galaxy number density of 15 arcmin —2 within our adopted redshift range. With an i-band magnitude limit of 24.5 mag, and four tomographic redshift bins spanning 0.3 ≤ z ph ≤ 1.5 based on photometric redshifts, we obtain a high-significance measurement of the cosmic shear power spectra, with a signal-to-noise ratio of approximately 26.4 in the multipole range 300 < ℓ < 1800. The accuracy of our power spectrum measurement is tested against realistic mock shear catalogs, and we use these catalogs to get a reliable measurement of the covariance of the power spectrum measurements. We use a robust blinding procedure to avoid confirmation bias, and model various uncertainties and sources of bias in our analysis, including point spread function systematics, redshift distribution uncertainties, the intrinsic alignment of galaxies and the modeling of the matter power spectrum. For a flat Λ CDM model, we find S 8 ≡ σ 8 (Ω m /0.3) 0.5 = ${0.776}_{—0.033}^{+0.032}$, which is in excellent agreement with the constraints from the other HSC Year 3 cosmology analyses, as well as those from a number of other cosmic shear experiments. This result implies a ~2σ-level tension with the Planck 2018 cosmology. We study the effect that various systematic errors and modeling choices could have on this value, and find that they can shift the best-fit value of S 8 by no more than ~ 0.5σ, indicating that our result is robust to such systematics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Development for Integrated System-Level Analysis Capabilities in SAM for Molten Salt Reactors

In recent years, there has been renewed interest in Molten Salt Reactors (MSRs) for their potential advantages compared to reactors that rely on solid fuel. In response to such interest, many methods and codes have been developed to capture the unique features of MSRs. Among them, the System Analysis Module (SAM) is a modern system analysis tool that provides fast-running, modest-fidelity, whole-plant transient analysis capabilities, essential for fast-turnaround design scoping and engineering analyses of advanced reactor concepts. For liquid-fuel MSRs, the complex physics and chemistry involved in MSR operation—such as reactor kinetics, fluid flow, heat transfer, and salt composition dynamics—pose significant challenges for system-level modeling. Specific modeling capabilities are needed for system-level transient simulation. This paper presents recent advancements in SAM capability enhancements for system-level modeling of MSRs, focusing on improved simulation fidelity, computational efficiency, and multi-physics integration. Key enhancements include the development of species transport, Delayed Neutron Precursor (DNP) drift, modified Point Kinetics Equations (PKE), decay heat modeling, key fission product behavior, salt corrosion, and thermal-hydraulic coupling, as well as code robustness and performance enhancements for MSR applications. The code enhancement allows for better predictive accuracy in safety analysis, transient behavior, and operational optimization, thus supporting the design and licensing of next-generation MSRs. Results from case studies are presented to demonstrate the benefits of these enhancements in accurately capturing key reactor transient behaviors.

Hu, Rui (ORCID:0000000237712920)↗
DOE OSTI2018

Aerosol Vacuum-Assisted Plasma Ionization (Aero-VaPI) Coupled to Ion Mobility-Mass Spectrometry

In this communication, we report on the real-time analysis of organic aerosol particles by Vacuum-assisted Plasma Ionization-Mass Spectrometry (Aero-VaPI-MS) using a home-built VaPI ion source coupled to a Synapt G2-S HDMS ion mobility-mass spectrometry (IM-MS) system. Standards of organic molecules of interest in prebiotic chemistry were used to generate aerosols. Monocaprin and decanoic acid aerosol particles were successfully detected in both the positive and negative ion modes, respectively. A complex aerosol mixture of different sizes of polymers of L-malic acid was also examined through ion mobility (IM) separations, resulting in the detection of polymers of up to eight monomeric units. This noncommercial plasma ion source is proposed as a low cost alternative to other plasma ionization platforms used for aerosol analysis, and a higher-performance alternative to more traditional aerosol mass spectrometers. VaPI provides robust online ionization of organics in aerosols without extensive ion activation, with the coupling to IM-MS providing higher peak capacity and excellent mass accuracy. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM 2.5 concentrations at 1 km resolution in the Eastern United States

To link short-term exposures of air pollutants to health outcomes, scientists must use high temporal and spatial resolution estimates of PM 2.5 concentrations. We develop a daily PM 2.5 product at 1 × 1 km 2 spatial resolution across the eastern United States (east of 90° W) with the aid of 1 × 1 km 2 MAIAC aerosol optical depth (AOD) data, 36 × 36 km 2 WRF-Chem output, 1 × 1 km 2 land-use type from the National Land Cover Database, and 0.125° × 0.125° ERA-Interim re-analysis meteorology. A gap-filling technique is applied to MAIAC AOD data to construct robust daily estimates of AOD when the satellite data are missing (e.g., areas obstructed by clouds or snow cover). The input data are incorporated into a multiple-linear regression model trained to surface observations of PM 2.5 from the EPA Air Quality System (AQS) monitoring network. The model generates a high-fidelity estimate (r 2 = 0.75 using a 10-fold random cross-validation) of daily PM 2.5 throughout the eastern United States. Of the inputs to the statistical model, WRF-Chem output (r 2 = 0.66) is the most important contributor to the skill of the model. MAIAC AOD is also a strong contributor (r 2 = 0.52). Daily PM 2.5 output from our statistical model can be easily integrated into county-level epidemiological studies. The novelty of this project is that we are able to simulate PM 2.5 in a computationally efficient manner that is constrained to ground monitors, satellite data, and chemical transport model output at high spatial resolution (1 × 1 km 2 ) without sacrificing the temporal resolution (daily) or spatial coverage (>2,000,000 km 2 ).

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Advancing Li-plating detection: Motivating a multi-signal correlation approach

Facilitating fast charging in lithium-ion batteries (LiBs) is often linked to Li-plating, which harms performance, longevity, and safety. Early detection of Li-plating is essential for rapid technological development and for preventing performance deterioration and ensuring safety during operation. Fast and real-time detection, using commonly collected measurements like voltage (V), current (I), temperature (T), and pressure (P), is highly desirable. Existing standalone methods relying on electrochemical and mechanical signatures, using half, smaller, or specially designed cells often operated at lower temperatures, fail to account for real-world fast-charging conditions. These signatures may also have inherent unreliability in aged LiBs, a phenomenon currently not-well understood. All these uncertainties have complicated practical implementation of a robust Li-plating detection technique. This study, through multiple case studies involving real-world fast-charging conditions using automotive-grade 11.6 Ah LiBs, shows that many single signal-based diagnostic techniques may be inadequate to detect Li-plating. Among various signatures, end-of-charge rest pressure, differential pressure-sensing, and end-of-charge rest voltage were identified as particularly useful in detecting Li-plating. Furthermore, A multi-signal-based detection technique is shown to be more robust in detecting Li-plating. Using both fresh and aged cells, the results and analysis highlight how detection capabilities are influenced by various factors, including battery design, size, charging speed, operating temperature, and degradation level. Adopting such a multi-signal Li-plating detection approach may be instrumental in the rapid development of battery technology in laboratories, as well as ensuring the enhanced safety of next-generation LiBs in real-world fast-charging applications.

25 ENERGY STORAGE↗
DOE OSTI2018

A Critical Review of Existing Criteria for the Prediction of Pyrochlore Formation and Stability

Depending on intrinsic (e.g., radius ratio rule r Ln / r Zr ) and extrinsic factors (e.g., processing conditions), pyrochlore-type Ln 2 Zr 2 O 7 oxides achieve variable degrees of structural disorder. In this paper we report on a systematic study of the structural and microstructural characteristics of the Gd 2– x Ln x Zr 2 O 7 system, exploring the effect of replacing Gd with a wide range of homovalent lanthanide ions (Ln = Nd, Sm, Dy, Ho, Y, and Er; x = 0.20 and 0.80). All compositions were prepared via a mechanochemical reaction between the corresponding oxides and characterized by X-ray diffraction (standard and synchrotron sources) using the Rietveld method, as well as by Raman spectroscopy. Irrespective of chemical composition, this study reveals that all compositions exhibit a fluorite-like structure. Furthermore, by firing each sample at 800 and 1400 °C, we are able to analyze the transition to pyrochlore-like structures, featuring different degrees of disorder, in all but Gd 1.20 Y 0.80 Zr 2 O 7 , which retains the fluorite structure even after heating. The structural data are used to assess the existing criteria for predicting the formation and stability of the pyrochlore structure; according to this analysis, the simple radius ratio rule ( r Ln / r Zr ), provides a useful and sufficiently robust criterion. Because the pyrochlore structure has a strong tendency to disorder, it is not possible to define an empirical index similar to the Goldschmidt tolerance factor for perovskite.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Understanding Solvent-Induced Glass Transition in Polymer Thin Films Using Absorption–Desorption Isotherms

The fundamental thermodynamic and mechanical underpinnings of polymer thin films exposed to solvent vapor are critical for the development of advanced nanolithography and high-performance coatings. This work investigates the solvent− polymer interactions of glassy thin films by using the solvent absorption−desorption isotherms. An analogous relationship to the Flory−Fox equation was observed between solvent−induced glass transition, swelling, Flory−Huggins interaction parameter, and molecular weight. Isothermal swelling measurements revealed that the glass transition trends are more robust in the absorption curve compared to desorption, contrary to previous reports. Excess osmotic pressure analysis of the isotherm provides a measure of the degree of physical aging in thin films annealed below the glass transition. This is further validated in the ordering of block copolymer (BCP) films annealed at low solvent activity. In agreement with the thermal analysis, free-surface plasticization effects become the most prominent below 100 nm. However, solvent annealing is largely dependent on solvent mass transport, as made evident by the strong dependence on solvent viscosity. From these observations, four general types of isotherms are identified that graphically capture distinct solvent−polymer interaction regimes. More broadly, these results inform solvent vapor annealing-induced self-assembly, sequential infiltration synthesis, membrane-based separations, adsorptive processes, and swelling-based responsive materials design.

Hendeniya, Nayanathara [Iowa State Univ., Ames, IA↗
DOE OSTI2025

Dehydrogenation vs Apparent Hydrogenation: Unraveling the Mechanisms of He and O 2 Plasma Etching on Colloidal Nanocrystal Films

Removing organic ligands from colloidal nanoparticles is critical for fabricating solid-state devices, yet accurately quantifying this removal remains a significant analytical challenge. Here, we establish a robust and accessible method for this quantification by calibrating Raman spectroscopy against precise ion beam analysis (IBA) for nanoparticle assemblies (CNAs) processed by helium (He) and oxygen (O 2 ) plasmas. We demonstrate that the calibration curves are remarkably independent of plasma power and pressure, depending critically only on the choice of feed gas. He plasma induces rapid dehydrogenation and cross-linking, evidenced by a much faster decrease in the C–H Raman signal relative to the actual carbon loss. Conversely, O 2 plasma leads to a surprising “apparent hydrogenation”, where the carbon backbone is removed significantly faster than the C–H signal diminishes. This counterintuitive effect is explained by a serial mechanism of oxidative fragmentation; β-scission cleaves the alkyl chains, and subsequent stabilization steps enrich the remaining film with hydrogen-rich methyl-terminated fragments, while carbon is efficiently removed as volatile CO. This work provides calibrated functions that enable the rapid determination of absolute carbon content in processed CNAs using simple Raman spectroscopy with uncertainties of ∼8% for O 2 and ∼12% for He plasma, offering a vital tool for both process diagnostics and fundamental studies of plasma–matter interactions in colloidal nanocrystal films.

Animal feed↗
DOE OSTI2022

Evaluation of CMIP6 GCMs Over the CONUS for Downscaling Studies

Despite the necessity of Global Climate Models (GCMs) sub-selection in downscaling studies, an objective approach for their selection is currently lacking. Building on the previously established concepts in GCMs evaluation frameworks, we develop a weighted averaging technique to remove the redundancy in the evaluation criteria and rank 37 GCMs from the sixth phase of the Coupled Models Intercomparison Project over the contiguous United States. GCMs are rated based on their average performance across 66 evaluation measures in the historical period (1981–2014) after each metric is weighted between zero and one, depending on its uniqueness. The robustness of the outcome is tested by repeating the process with the empirical orthogonal function analysis in which each GCM is ranked based on its sum of distances from the reference in the principal component space. The two methodologies work in contrasting ways to remove the metrics redundancy but eventually develop similar GCMs rankings. A disparity in GCMs' behavior related to their sensitivity to the size of the evaluation suite is observed, highlighting the need for comprehensive multi-variable GCMs evaluation at varying timescales for determining their skillfulness over a region. The sub-selection goal is to use a representative set of skillful models over the region of interest without substantial overlap in their future climate responses and modeling errors in representing historical climate. Additional analyses of GCMs' independence and spread in their future projections provide the necessary information to objectively select GCMs while keeping all aspects of necessity in view.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data

The detection of illicit radiological materials is critical to establishing a robust second line of defence in nuclear security. Neutron-capture prompt-gamma activation analysis (PGAA) can be used to detect multiple radioactive materials across the entire Periodic Table. However, long detection times and a high rate of false positives pose a significant hindrance in the deployment of PGAA-based systems to identify the presence of illicit substances in nuclear forensics. In the present work, six different machine-learning algorithms were developed to classify radioactive elements based on the PGAA energy spectra. The model performance was evaluated using standard classification metrics and trend curves with an emphasis on comparing the effectiveness of algorithms that are best suited for classifying imbalanced datasets. We analyse the classification performance based on Precision, Recall, F1-score, Specificity, Confusion matrix, ROC-AUC curves, and Geometric Mean Score (GMS) measures. The tree-based algorithms (Decision Trees, Random Forest and AdaBoost) have consistently outperformed Support Vector Machine and K-Nearest Neighbours. Based on the results presented, AdaBoost is the preferred classifier to analyse data containing PGAA spectral information due to the high recall and minimal false negatives reported in the minority class.

97 MATHEMATICS AND COMPUTING↗