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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Comparison of Sample and Detection Quantification Methods for Salmonella Enterica from Produce

The purpose of this study was to identify and optimize fast and reliable sampling and detection methods for the identification of pathogens that may be present on produce grown in small vegetable production units on the International Space Station (ISS), thus a field setting. Microbiological testing is necessary before astronauts are allowed to consume produce grown on ISS where currently there are two vegetable production units deployed, Lada and Veggie.

methods of salmonella from produce↗
NASA NTRS2016

Performance Metrics, Error Modeling, and Uncertainty Quantification

A common set of statistical metrics has been used to summarize the performance of models or measurements-­ the most widely used ones being bias, mean square error, and linear correlation coefficient. They assume linear, additive, Gaussian errors, and they are interdependent, incomplete, and incapable of directly quantifying un­certainty. The authors demonstrate that these metrics can be directly derived from the parameters of the simple linear error model. Since a correct error model captures the full error information, it is argued that the specification of a parametric error model should be an alternative to the metrics-based approach. The error-modeling meth­odology is applicable to both linear and nonlinear errors, while the metrics are only meaningful for linear errors. In addition, the error model expresses the error structure more naturally, and directly quantifies uncertainty. This argument is further explained by highlighting the intrinsic connections between the performance metrics, the error model, and the joint distribution between the data and the reference.

Quantification↗
NASA NTRS2015

Visualization and Quantification of Rotor Tip Vortices in Helicopter Flows

Helicopter aeromechanics encompasses a highly vortical flow field. The vortices generated at each blade tip contain unsteady, complex, three-dimensional structures, which interact with each other, other blades, the fuselage and various components of the helicopter. It is crucial to understand vortex kinematics and their subsequent dynamic evolution. Much research has been devoted to the understanding of helicopter vortex dynamics, including a number of experimental studies.1-6 In May 2010 Particle Image Velocimetry (PIV) measurements of a full-scale UH-60A rotor were acquired in the National Full-Scale Aerodynamics Complex (NFAC) 40- by 80-Foot Wind Tunnel.1 These measurements were taken at a plane just downstream of the advancing blade in the vicinity of the blade tip—the so-called PIV plane. The resulting PIV data were then processed using an ensemble-average approach to create graphical representations of the vortical wake velocity and vorticity fields, which, in turn, have enhanced the understanding of rotorcraft vortical wake flow field physics and have provided a more detailed validation of vortical wake computer simulations.7 A common approach used to analyze flow field features is to compute and plot color contour maps of various scalar quantities such as pressure, velocity magnitude and vorticity magnitude. For example, the color map of the vorticity magnitude is typically used to determine vortical flow structure. With this approach the vortex core may appear larger or smaller, depending on the contour levels that are selected. Thus, the resulting visualization is sensitive to user-specified contour levels. For vortex core radius measurements, it is more accurate to calculate the vortex core radius using the cross-flow velocity profile across the vortex core. The task of extracting the cross-flow velocity profile can be time consuming with existing tools since the user needs to manually select the core center then specify sampling points along the profile axis. The task becomes even more challenging when the associated grid system uses AMR (Adaptive Mesh Refinement) where the profile axis could span multiple grid blocks. There are a number of existing techniques for profiling of vortex core attributes;8-9 however, these techniques are not fully automatic in that the user still needs to select the vortex core center to compute the cross-flow velocity profile. The present study introduces a new color map scheme that is based on the vortex core radius, which is fully automatic and does not require user intervention. Analysis and visualization of blade tip vortices on the PIV plane using the proposed new color map scheme are described in Section II. The new approach is evaluated using two case studies, which are described in Section III. The paper ends with a summary in Section IV.

Quantification↗
NASA NTRS2019

Bayesian Inference and the Effects of Varying Uncertainty Models in Charring Ablator Calibration and Uncertainty Quantification Problems

The Mars Science Laboratory (MSL) vehicle utilized a heat shield constructed from NASA’s Phenolic-Impregnated Carbon Ablator (PICA) material to protect the main structure from the high enthalpy environment encountered during hypersonic atmospheric entry. During the vehicle’s descent through Martian atmosphere, multiple thermocouples embedded within the heat shield captured in-depth material temperature data that allow for studies to be conducted on current material response reconstruction tools. In the present work, material temperature data obtained from thermocouples within the MISP-4 plug (MEDLI (Mars Science Laboratory Entry, Descent, and Landing Instrument) Integrated Sensor Plug) are utilized in the calibration of Theoretical Ablative Composite for Open Testing (TACOT) model parameters in conjunction with NASA’s Porous material Analysis Toolbox (PATO) through Bayesian inference where uncertainty due to parametric, modeling, and experimental sources is simultaneously quantified. Prior to the study, a sensitivity analysis is performed through computation of the robust Sobol indices in an effort to study the relationship between input space and model response and to reduce the dimensionality of the statistical inverse problem. The Bayesian inference methodology necessitates an a-priori choice to be made for the uncertainty model for which numerous possibilities are available. Across most works, however, only basic additive or multiplicative models are utilized with pre-defined magnitudes of uncertainty based on a-priori knowledge or to-be-calibrated multipliers of static covariance matrix structures. The present effort explores the effects of informed uncertainty models, ones with temporal dependence that are simultaneously calibrated through Bayesian inference, on calibrated results for parameters that make up the uncertain input space.

Sensitivity Analysis↗
NASA NTRSDate not supplied

Uncertainty Quantification of Expected Time-of-Arrival in UAV Flight Trajectory

One of the foremost requirements for accurate in-flight safety monitoring of autonomous unmanned aerial vehicles (UAVs) is tracking of their flight trajectory. Existing UAVs leverage autonomous flight functionalities based on trajectory generation algorithms developed in robotic applications such as polynomial or spline curves in order to facilitate kinematic smoothness, minimum vibrations and fuel efficiency. However in practice, the actual path may be subjected to unexpected local weather conditions, unexpected obstacles along the path or abrupt traffic changes in the low-altitude airspace resulting in large errors of the predicted time-of-arrival at way-points. In this study, an approach to quantify and propagate uncertainty in 4D trajectories is proposed. The paper presents a simple error interval propagation method based on the expected cruise speed of the UAV and its associated uncertainty. The uncertainty is then propagated in time to estimate reasonable confidence intervals on the times-of-arrival of the vehicle at each way-point as well as along the entire flight-path. The uncertainty propagation is demonstrated on a state-of-the-art trajectory generation algorithm based on non-uniform rational B-spline (NURBS) curves. Further, the effect of a stationary wind field is incorporated in the uncertainty propagation approach. The proposed method is implemented on synthetic and real data obtained from flight experiments with a small UAV.

Uncertainty Quantification↗
DOE OSTI2020

Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models

We use a conditional Karhunen-Lo` eve (KL) model to quantify and reduce uncertainty in a stochastic partial differential equation (SPDE) problem with partially-known space-dependent coefficient, Y (x). We assume that a small number of Y (x) measurements are available and model Y (x) with a KL expansion. We achieve reduction in uncertainty by conditioning the KL expansion coefficients on measurements. We consider two approaches for conditioning the KL expansion: In Approach 1, we condition the KL model first and then truncate it. In Approach 2, we first truncate the KL expansion and then condition it. We employ the conditional KL expansion together with Monte Carlo and sparse grid collocation methods to compute the moments of the solution of the SPDE problem. Uncertainty of the problem is further reduced by adaptively selecting additional observation locations using two active learning methods. Method 1 minimizes the variance of the PDE coefficient, while Method 2 minimizes the variance of the solution of the PDE. We demonstrate that conditioning leads to dimension reduction of the KL representation of Y (x). For a linear diffusion SPDE with uncertain log-normal coefficient, we show that Approach 1 provides a more accurate approximation of the conditional log-normal coefficient and solution of the SPDE than Approach 2 for the same number of random dimensions in a conditional KL expansion. Furthermore, Approach 2 provides a good estimate for the number of terms of the truncated KL expansion of the conditional field of Approach 1. Finally, we demonstrate that active learning based on Method 2 is more efficient for uncertainty reduction in the SPDE’s states (i.e., it leads to a larger reduction of the variance) than active learning using Method 2.

Conditioned Karhunen-Lo` eve expanion, machine lea↗
DOE OSTI2019

Quantification of Heterogeneous Degradation in Li-Ion Batteries

The multiscale chemomechanical interplay in lithium-ion batteries builds up mechanical stress, provokes morphological breakdown, and leads to state of charge heterogeneity. Quantifying the interplay in complex composite electrodes with multiscale resolution constitutes a frontier challenge in precisely diagnosing the fading mechanism of batteries. In this work, hard X-ray phase contrast tomography, capable of nanoprobing thousands of active particles at once, enables an unprecedented statistical analysis of the chemomechanical transformation of composite electrodes under fast charging conditions. The damage heterogeneity is demonstrated to prevail at all length scales, which stems from the unbalanced electron conduction and ionic diffusion, and collectively leads to the nonuniform utilization of active particles spatially and temporally. This research highlights that the statistical mapping of the chemomechanical transformation offers a diagnostic method for the particles utilization and fading, hence could improve electrode formulation for fast-charging batteries.

25 ENERGY STORAGE↗
DOE OSTI2019

Quantification of Active Sites and Elucidation of the Reaction Mechanism of the Electrochemical Nitrogen Reduction Reaction on Vanadium Nitride

Abstract Despite recent intense interest in the development of catalysts for the electrochemical nitrogen reduction reaction (ENRR), mechanistic understanding and catalyst design principles remain lacking. In this work, we develop a strategy to determine the density of initial and steady‐state active sites on ENRR catalysts that follow the Mars–van Krevelen mechanism via quantitative isotope‐exchange experiments. This method allows the comparison of intrinsic activities of active sites and facilitates the identification and improvement of active‐site structures for ENRR. Combined with detailed density functional theory calculations, we show that the rate‐limiting step in the ENRR is likely the initial N≡N bond activation via the addition of a proton and an electron to the adsorbed N 2 on the N vacancies to form N 2 H. The methodology developed and mechanistic insights gained in this work could guide the rational catalyst design in the ENRR.

Yang, Xuan↗
DOE OSTI2020

The Influence of C(sp 3 )H–Selenium Interactions on the 77 Se NMR Quantification of the π‐Accepting Properties of Carbenes

Abstract Selenium NMR has become a standard tool for scaling the π‐accepting character of carbenes. Herein, we highlight that non‐classical hydrogen bonding (NCHB), likely resulting from hyperconjugation, can play a significant role in the carbene–selenium 77 Se NMR chemical shift, thus triggering a non‐linear behavior of the Se‐Scale.

Junor, Glen P.↗
DOE OSTI2020

Theoretical, Solid‐State, and Solution Quantification of the Hydrogen Bond‐Enhanced Halogen Bond

Abstract Proximal noncovalent forces are commonplace in natural systems and understanding the consequences of their juxtaposition is critical. This paper experimentally quantifies for the first time a Hydrogen Bond‐Enhanced Halogen Bond (HBeXB) without the complexities of protein structure or preorganization. An HBeXB is a halogen bond that has been strengthened when the halogen donor simultaneously accepts a hydrogen bond. Our theoretical studies suggest that electron‐rich halogen bond donors are strengthened most by an adjacent hydrogen bond. Furthermore, stronger hydrogen bond donors enhance the halogen bond the most. X‐ray crystal structures of halide complexes (X − =Br − , I − ) reveal that HBeXBs produce shorter halogen bonds than non‐hydrogen bond analogues. 19 F NMR titrations with chloride highlight that the HBeXB analogue exhibits stronger binding. Together, these results form the foundation for future studies concerning hydrogen bonds and halogen bonds in close proximity.

Decato, Daniel A.↗
DOE OSTI2020

The Influence of C(sp 3 )H–Selenium Interactions on the 77 Se NMR Quantification of the π-Accepting Properties of Carbenes

Selenium NMR has become a standard tool for scaling the p-accepting character of carbenes. Herein, we highlight that non-classical hydrogen bonding (NCHB), likely resulting from hyperconjugation, can play a significant role in the carbene–selenium 77 Se NMR chemical shift, thus triggering a non-linear behavior of the Se-Scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗