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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

A knowledge brokerage approach for assessing the impacts of the setting up young farmers policy measure in Greece

This study explores Knowledge Brokerage (KB) aspects of an ex-post Impact Assessment (IA) for the Rural Development Programme (RDP) measure of setting up young farmers, under the Common Agricultural Policy (CAP), at the regional level in Northern Greece. The measure supports the entry of young farmers in agriculture by moving land from older to younger farmers. The aim of the study was to test a set of KB tools for improving the interaction between researchers and policy makers. Our analysis mainly focused on a suite of IA Support Modules to guide practitioners, and on a technical tool kit, a web-based contextualisation platform, to support the IA of the specific test case. Offering a structured approach towards IA, both the Support Modules and LIAISE-KIT allow framing the context, organisation, scheduling and method selection in the light of KB objectives. The evaluation of how IA Support Modules influence the Science Policy Interface (SPI), in the case of the ex-post assessment, demonstrated the high relevance of KB activities for facilitating the interaction between researchers and regional policy makers. The assessment bridges the gap between knowledge producers developing scientific output to be applied in a specific context, and knowledge users, who want clear messages regarding the policy challenges they face. Other conclusions include the need for specific guidelines and training for knowledge users, especially with regard to the use of tools. According to our findings, a consequent application of KB activities is a crucial pre-condition for successfully implementing IAs in future RDP measures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Decentralised demand response market model based on reinforcement learning

A new decentralised demand response (DR) model relying on bi‐directional communications is developed in this study. In this model, each user is considered as an agent that submits its bids according to the consumption urgency and a set of parameters defined by a reinforcement learning algorithm called Q‐learning. The bids are sent to a local DR market, which is responsible for communicating all bids to the wholesale market and the system operator (SO), reporting to the customers after determining the local DR market clearing price. From local markets’ viewpoint, the goal is to maximise social welfare. Four DR levels are considered to evaluate the effect of different DR portions in the cost of the electricity purchase. The outcomes are compared with the ones achieved from a centralised approach (aggregation‐based model) as well as an uncontrolled method. Numerical studies prove that the proposed decentralised model remarkably drops the electricity cost compare to the uncontrolled method, being nearly as optimal as a centralised approach.

Shafie‐Khah, Miadreza↗
DOE OSTI2018

The Michigan New Technology Training and Research (MNTR) Translational Program in Nuclear Medicine (Final Report)

The Michigan New Technology Training and Research (MNTR) Translational Program in Nuclear Medicine and Molecular Imaging This project established the Michigan New Technology Training and Research (MNTR) translational program, focused on the acceleration of new clinical molecular imaging methods. Molecular imaging approaches to imaging are a very flexible and powerful development, with many recent examples advancing the diagnosis and guiding the management of human disease. These methods depend on the design, synthesis and testing of new radiotracers (molecules designed to target specific cells or tissues in the body), labeled with short-lived radioactive atoms (positron emitters). The new radiotracers are imaged in the human body using positron emission tomography (PET) imaging. The University of Michigan PET Center has considerable experience in the discovery, development, refinement and initial human applications of new PET radiotracers. An emerging impediment to efficient availability of new PET radiotracers, however, involves the regulatory environment at multiple levels. Many potentially successful ideas are delayed or even prevented from entering human use by these barriers. In the current grant, we provided hands-on experience for the next generation of young scientists working in molecular imaging so that they are equipped to effectively move new ideas and radiotracers from the research laboratory to the nuclear medicine clinic (bench-to-bedside). For new radiotracer development, oversight by the Food and Drug Administration (FDA), the Nuclear Regulatory Commission (NRC), and local institutional committees and reviews are required. These aspects of translation from laboratory to clinic are often omitted from the formal training of new scientists, but were provided to three trainees via direct mentorship in the MNTR program. The MNTR training program built on the translational successes of the University of Michigan Division of Nuclear Medicine and capitalized on our strengths (facilities, faculty, a wide-ranging selection of projects, and the experience and commitment to bring new radiotracers into human studies) to systematically train three fellows in radiochemistry and nuclear medicine. Integrated training in development, validation and clinical translation of new PET radiotracers occurred through practical involvement in multiple research projects, each at a different stage along the pathway from bench-to-bedside. Each trainee obtained experience of the entire process over about 2 years. Projects involved development of new radiotracers for neurodegenerative disorders (e.g. Alzheimer disease), movement disorders (e.g. Parkinson disease, progressive supranuclear palsy) and neurodevelopmental syndromes (e.g. Tourette syndrome) as well as new radiochemistry methods for synthesizing radiotracers. Each project was pursued in a close collaboration between the fellow trainee and both the clinical and basic science faculty at the University of Michigan PET Center, and provided an unprecedented opportunity for the highest-level training in academic radiochemistry and nuclear medicine research. The project has resulted in 17 peer-reviewed publications to date, and at least 4 more are in preparation. Additionally, 1 book chapter and 26 conference presentations have resulted from this grant. Upon completion of the MNTR Program, the trainees have emerged as translational scientists wielding the necessary skill-set (practical research experience, multidisciplinary training, regulatory know-how) to embark on successful careers in translational molecular imaging, bringing continued advances in the field to bear on important human disorders. As a metric of the Program’s success, one fellow has already secured employment in the field, while the other two are finishing writing papers; they wish to also stay in the radiochemistry and nuclear medicine community, and are currently applying for jobs.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS2012

Physiological Based Simulator Fidelity Design Guidance

The evolution of the role of flight simulation has reinforced assumptions in aviation that the degree of realism in a simulation system directly correlates to the training benefit, i.e., more fidelity is always better. The construct of fidelity has several dimensions, including physical fidelity, functional fidelity, and cognitive fidelity. Interaction of different fidelity dimensions has an impact on trainee immersion, presence, and transfer of training. This paper discusses research results of a recent study that investigated if physiological-based methods could be used to determine the required level of simulator fidelity. Pilots performed a relatively complex flight task consisting of mission task elements of various levels of difficulty in a fixed base flight simulator and a real fighter jet trainer aircraft. Flight runs were performed using one forward visual channel of 40 deg. field of view for the lowest level of fidelity, 120 deg. field of view for the middle level of fidelity, and unrestricted field of view and full dynamic acceleration in the real airplane. Neuro-cognitive and physiological measures were collected under these conditions using the Cognitive Avionics Tool Set (CATS) and nonlinear closed form models for workload prediction were generated based on these data for the various mission task elements. One finding of the work described herein is that simple heart rate is a relatively good predictor of cognitive workload, even for short tasks with dynamic changes in cognitive loading. Additionally, we found that models that used a wide range of physiological and neuro-cognitive measures can further boost the accuracy of the workload prediction.

Schnell, Thomas↗
NASA NTRS2021

Trend Detection of Atmospheric Time Series: Incorporating Appropriate Uncertainty Estimates and Handling Extreme Events

This paper is aimed at atmospheric scientists without formal training in statistical theory. Its goal is to, 1) provide a critical review of the rationale for trend analysis of the time series typically encountered in the field of atmospheric chemistry; 2) describe a range of trend-detection methods; and 3) demonstrate effective means of conveying the results to a general audience. Trend detections in atmospheric chemical composition data are often challenged by a variety of sources of uncertainty, which often behave differently to other environmental phenomena such as temperature, precipitation rate, or stream flow, and may require specific methods depending on the science questions to be addressed. Some sources of uncertainty can be explicitly included in the model specification, such as autocorrelation and seasonality, but some inherent uncertainties are difficult to quantify, such as data heterogeneity and measurement uncertainty due to the combined effect of short- and long-term natural variability, instrumental stability, and aggregation of data from sparse sampling frequency. Failure to account for these uncertainties might result in an inappropriate inference of the trends and their estimation errors. On the other hand, the variation in extreme events might be interesting for different scientific questions, for example, the frequency of extremely high surface ozone events and their relevance to human health. In this study we aim to, 1) review trend detection methods for addressing different levels of data complexity in different chemical species; 2) demonstrate that the incorporation of scientifically interpretable covariates can outperform pure numerical curve fitting techniques in terms of uncertainty reduction and improved predictability; 3) illustrate the study of trends based on extreme quantiles that can provide insight beyond standard mean or median based trend estimates; and 4) present an advanced method of quantifying regional trends based on the inter-site correlations of multi-site data. All demonstrations are based on time series of observed trace gases relevant to atmospheric chemistry, but the methods can be applied to other environmental data sets.

Trace gas↗
DOE OSTI2015

BCL::SAXS: GPU accelerated Debye method for computation of small angle X-ray scattering profiles

Small angle X-ray scattering (SAXS) is an experimental technique used for structural characterization of macromolecules in solution. In this work, we present BCL::SAXS--an algorithm designed to replicate SAXS profiles from rigid protein models at different levels of detail. We first show our derivation of BCL::SAXS and compare our results with the experimental scattering profile of hen egg white lysozyme. Using this protein we show how to generate SAXS profiles representing: (1) complete models, (2) models with approximated side chain coordinates, and (3) models with approximated side chain and loop region coordinates. We evaluated the ability of SAXS profiles to identify a correct protein topology from a non-redundant benchmark set of proteins. We find that complete SAXS profiles can be used to identify the correct protein by receiver operating characteristic (ROC) analysis with an area under the curve (AUC)>99%. We demonstrate how our approximation of loop coordinates between secondary structure elements improves protein recognition by SAχS for protein models without loop regions and side chains. Agreement with SAXS data is a necessary but not sufficient condition for structure determination. We conclude that experimental SAXS data can be used as a filter to exclude protein models with large structural differences from the native.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

In modern engineering, physical processes are modelled and analysed using advanced computer simulations, such as finite element models. Furthermore, concepts of reliability analysis and robust design are becoming popular, hence, making efficient quantification and propagation of uncertainties an important aspect. In this context, a typical workflow includes the characterization of the uncertainty in the input variables. In this paper, input variables are modelled by probability-boxes (p-boxes), accounting for both aleatory and epistemic uncertainty. The propagation of p-boxes leads to p-boxes of the output of the computational model. A two-level meta-modelling approach is proposed using non-intrusive sparse polynomial chaos expansions to surrogate the exact computational model and, hence, to facilitate the uncertainty quantification analysis. The capabilities of the proposed approach are illustrated through applications using a benchmark analytical function and two realistic engineering problem settings. They show that the proposed two-level approach allows for an accurate estimation of the statistics of the response quantity of interest using a small number of evaluations of the exact computational model. This is crucial in cases where the computational costs are dominated by the runs of high-fidelity computational models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

The MFiX Particle-in-Cell Method (MFiX-PIC) Theory Guide

MFiX (Multiphase Flow with Interphase eXchanges) is an open-source multiphase flow solver developed at the National Energy Technology Laboratory. Within the code, users have access to a single phase or interpenetrating continua-based multiphase two-fluid model (TFM), a discrete element model (DEM), and a particle-in-cell model (PIC). TFM, DEM, and PIC can all be used to create multiphase simulations that include hydrodynamics, chemical reactions, and heat transfer.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS1984

Measurements of mantle wave velocities and inversion for lateral heterogeneity and anisotropy. II - Analysis by the single-station method

In the present investigation, the single-station method reported by Brune et al. (1960) is utilized for an analysis of long-period Love(G) and Rayleigh(R) waves recorded on digital seismic networks. The analysis was conducted to study the lateral heterogeneity of surface wave velocities. The data set is examined, and a description is presented of the single-station method. Attention is given to an error analysis for velocity measurements, the estimation of geographical distribution of surface wave velocities, the global distribution of surface wave velocities, and the correlation between the surface wave velocities and the heat flow on the geoid. The conducted measurements and inversions of surface wave velocities are used as a basis to derive certain conclusions. It is found that the application of the single-station method to long-period surface waves recorded on digital networks makes it possible to reach an accuracy level comparable to great circle velocity measurements.

Nakanishi, I.↗
NASA NTRS1985

Polynomic nonlinear dynamical systems - A residual sensitivity method for model reduction

The motivation for using polynomic combinations of system states and inputs to model nonlinear dynamics systems is founded upon the classical theories of analysis and function representation. A feature of such representations is the need to make available all possible monomials in these variables, up to the degree specified, so as to provide for the description of widely varying functions within a broad class. For a particular application, however, certain monomials may be quite superfluous. This paper examines the possibility of removing monomials from the model in accordance with the level of sensitivity displayed by the residuals to their absence. Critical in these studies is the effect of system input excitation, and the effect of discarding monomial terms, upon the model parameter set. Therefore, model reduction is approached iteratively, with inputs redesigned at each iteration to ensure sufficient excitation of remaining monomials for parameter approximation. Examples are reported to illustrate the performance of such model reduction approaches.

Yurkovich, S.↗
NASA NTRS1990

Mapping sea ice leads with a coupled numeric/symbolic system

A method is presented which facilitates the detection and delineation of leads with single-channel Landsat data by coupling numeric and symbolic procedures. The procedure consists of three steps: (1) using the dynamic threshold method, an image is mapped to a lead/no lead binary image; (2) the likelihood of fragments to be real leads is examined with a set of numeric rules; and (3) pairs of objects are examined geometrically and merged where possible. The processing ends when all fragments are merged and statistical characteristics are determined, and a map of valid lead objects are left which summarizes useful physical in the lead complexes. Direct implementation of domain knowledge and rapid prototyping are two benefits of the rule-based system. The approach is found to be more successfully applied to mid- and high-level processing, and the system can retrieve statistics about sea-ice leads as well as detect the leads.

Key, J.↗
DOE OSTI2015

Influence of radiation dose and reconstruction algorithm in MDCT assessment of airway wall thickness: A phantom study

Purpose: Wall thickness (WT) is an airway feature of great interest for the assessment of morphological changes in the lung parenchyma. Multidetector computed tomography (MDCT) has recently been used to evaluate airway WT, but the potential risk of radiation-induced carcinogenesis—particularly in younger patients—might limit a wider use of this imaging method in clinical practice. The recent commercial implementation of the statistical model-based iterative reconstruction (MBIR) algorithm, instead of the conventional filtered back projection (FBP) algorithm, has enabled considerable radiation dose reduction in many other clinical applications of MDCT. The purpose of this work was to study the impact of radiation dose and MBIR in the MDCT assessment of airway WT. Methods: An airway phantom was scanned using a clinical MDCT system (Discovery CT750 HD, GE Healthcare) at 4 kV levels and 5 mAs levels. Both FBP and a commercial implementation of MBIR (Veo{sup TM}, GE Healthcare) were used to reconstruct CT images of the airways. For each kV–mAs combination and each reconstruction algorithm, the contrast-to-noise ratio (CNR) of the airways was measured, and the WT of each airway was measured and compared with the nominal value; the relative bias and the angular standard deviation in the measured WT were calculated. For each airway and reconstruction algorithm, the overall performance of WT quantification across all of the 20 kV–mAs combinations was quantified by the sum of squares (SSQs) of the difference between the measured and nominal WT values. Finally, the particular kV–mAs combination and reconstruction algorithm that minimized radiation dose while still achieving a reference WT quantification accuracy level was chosen as the optimal acquisition and reconstruction settings. Results: The wall thicknesses of seven airways of different sizes were analyzed in the study. Compared with FBP, MBIR improved the CNR of the airways, particularly at low radiation dose levels. For FBP, the relative bias and the angular standard deviation of the measured WT increased steeply with decreasing radiation dose. Except for the smallest airway, MBIR enabled significant reduction in both the relative bias and angular standard deviation of the WT, particularly at low radiation dose levels; the SSQ was reduced by 50%–96% by using MBIR. The optimal reconstruction algorithm was found to be MBIR for the seven airways being assessed, and the combined use of MBIR and optimal kV–mAs selection resulted in a radiation dose reduction of 37%–83% compared with a reference scan protocol with a dose level of 1 mGy. Conclusions: The quantification accuracy of airway WT is strongly influenced by radiation dose and reconstruction algorithm. The MBIR algorithm potentially allows the desired WT quantification accuracy to be achieved with reduced radiation dose, which may enable a wider clinical use of MDCT for the assessment of airway WT, particularly for younger patients who may be more sensitive to exposures with ionizing radiation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Constraining the local variance of H {sub 0} from directional analyses

We evaluate the local variance of the Hubble Constant H {sub 0} with low-z Type Ia Supernovae (SNe). Our analyses are performed using a hemispherical comparison method in order to test whether taking the bulk flow motion into account can reconcile the measurement of the Hubble Constant H {sub 0} from standard candles ( H {sub 0} = 73.8±2.4 km s{sup -1} Mpc {sup -1}) with that of the Planck's Cosmic Microwave Background data ( H {sub 0} = 67.8 ± 0.9km s{sup -1} Mpc{sup -1}). We obtain that H {sub 0} ranges from 68.9±0.5 km s{sup -1} Mpc{sup -1} to 71.2±0.7 km s{sup -1} Mpc{sup -1} through the celestial sphere (1 σ uncertainty), implying a Hubble Constant maximal variance of δ H {sub 0} = (2.30±0.86) km s{sup -1} Mpc{sup -1} towards the ( l,b ) = (315°,27°) direction. Interestingly, this result agrees with the bulk flow direction estimates found in the literature, as well as previous evaluations of the H {sub 0} variance due to the presence of nearby inhomogeneities. We assess the statistical significance of this result with different prescriptions of Monte Carlo simulations, obtaining moderate statistical significance, i.e., 68.7% confidence level (CL) for such variance. Furthermore, we test the hypothesis of a higher H {sub 0} value in the presence of a bulk flow velocity dipole, finding some evidence for this result which, however, cannot be claimed to be significant due to the current large uncertainty in the SNe distance modulus. Then, we conclude that the tension between different H {sub 0} determinations can plausibly be caused to the bulk flow motion of the local Universe, even though the current incompleteness of the SNe data set, both in terms of celestial coverage and distance uncertainties, does not allow a high statistical significance for these results or a definitive conclusion about this issue.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Flowing fluid electrical conductivity logging of a deep borehole during and following drilling: estimation of transmissivity, water salinity and hydraulic head of conductive zones

Flowing fluid electrical conductivity (FFEC) logging is a hydrogeologic testing method that is usually conducted in an existing borehole. However, for the 2,500-m deep COSC-1 borehole, drilled at Åre, central Sweden, it was done within the drilling period during a scheduled 1-day break, thus having a negligible impact on the drilling schedule, yet providing important information on depths of hydraulically conductive zones and their transmissivities and salinities. In this paper, we present a reanalysis of this set of data together with a new FFEC logging data set obtained soon after drilling was completed, also over a period of 1 day, but with a different pumping rate and water-level drawdown. Their joint analysis not only results in better estimates of transmissivity and salinity in the conducting fractures intercepted by the borehole, but also yields the hydraulic head values of these fractures, an important piece of information for the understanding of hydraulic structure of the subsurface. Two additional FFEC logging tests were done about 1 year later, and are used to confirm and refine this analysis. Results show that from 250 to 2,000 m depths, there are seven distinct hydraulically conductive zones with different hydraulic heads and low transmissivity values. For the final test, conducted with a much smaller water-level drawdown, inflow ceased from some of the conductive zones, confirming that their hydraulic heads are below the hydraulic head measured in the wellbore under non-pumped conditions. Lastly, the challenges accompanying 1-day FFEC logging are summarized, along with lessons learned in addressing them.

58 GEOSCIENCES↗
DOE OSTI2024

Contributions From Cloud Morphological Changes to the Interannual Shortwave Cloud Feedback Based on MODIS and ISCCP Satellite Observations

The surface temperature-mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel “double decomposition method” that employs the “cloud radiative kernel” and “cloud regime” concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low- and mid-level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm-track clouds to thinner oceanic low-level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid- and low-level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies.

58 GEOSCIENCES↗
DOE OSTI2018

Dissection of the multichannel reaction of acetylene with atomic oxygen: from the global potential energy surface to rate coefficients and branching dynamics

The O( 3 P) + C 2 H 2 reaction is the first step in acetylene oxidation. The accurate kinetic data and the understanding of the reaction dynamics is of great importance. To this end, in this study a full-dimensional global potential energy surface (PES) for the ground triplet state of the O( 3 P) + C 2 H 2 reaction is constructed based on approximately 85,000 ab initio points calculated at the level of explicitly correlated unrestricted coupled cluster single, double, and perturbative triple excitations with the explicitly correlated polarized valence triple zeta basis set (UCCSD(T)-F12b/VTZ-F12). The PES is fit using the permutation invariant polynomial-neural network (PIP-NN) approach with a total root mean square error of 0.21 kcal mol –1 . The key topographic features of the PES, including multiple potential wells and saddle points along different reaction pathways, are well represented by this fit PES. The kinetics and dynamics of the O( 3 P) + C 2 H 2 reaction are investigated using the quasi-classical trajectory (QCT) method. The calculated rate coefficients are in good agreement with experimental data over a wide temperature range, especially when the temperature is lower than 1500 K. The product branch ratio has also been determined, which indicates the H + HCCO channel as the dominant reaction pathway at 298–3000 K, accounting for 80–90% of the overall rate coefficient, in agreement with experimental observations. The dynamics of the reaction is analyzed in detail.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Results of the first implementation of RF phase signature matching at LANSCE

The LINAC at the Los Alamos Neutron Science Center (LANSCE) has been utilizing the Delta-t method to match the RF cavities to the design acceleration parameters since its commissioning in 1972. The differences in time-of-flight between two subsequent Beam Position and Phase Monitors (BPPMs) are measured with both accelerated and drifting beams, depending on the whether the module is set to on or off. The algorithm optimizes the module amplitude and phase via iterative measurements if the initial phase is in the vicinity of the design value. With an upgrade to a faster readout system, a scan over the whole RF cavity phase range requires relatively less time than the classical optimization procedure. The Phase Scan Signature Matching (PSSM) method provides a time-efficient method that ensures the phase selection lands on the bunching side and empowers future analyses to build module-specific models. The PSSM also utilizes a direct model to determine the correct amplitude to sub-percent level instead of using linearized matrices. Furthermore, lacking a reliable energy measurement method in the LINAC, we measure the beam phases at two downstream locations to increase the precision of energy measurements. In this letter, we also discuss the sensitivities of PSSM, error propagation, and the implementation results for the 2019 and 2020 beam cycles.

47 OTHER INSTRUMENTATION↗