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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Avoidable errors in deposited macromolecular structures: an impediment to efficient data mining

Whereas the vast majority of the more than 85 000 crystal structures of macromolecules currently deposited in the Protein Data Bank are of high quality, some suffer from a variety of imperfections. Although this fact has been pointed out in the past, it is still worth periodic updates so that the metadata obtained by global analysis of the available crystal structures, as well as the utilization of the individual structures for tasks such as drug design, should be based on only the most reliable data. Here, selected abnormal deposited structures have been analysed based on the Bayesian reasoning that the correctness of a model must be judged against both the primary evidence as well as prior knowledge. These structures, as well as information gained from the corresponding publications (if available), have emphasized some of the most prevalent types of common problems. The errors are often perfect illustrations of the nature of human cognition, which is frequently influenced by preconceptions that may lead to fanciful results in the absence of proper validation. Common errors can be traced to negligence and a lack of rigorous verification of the models against electron density, creation of non-parsimonious models, generation of improbable numbers, application of incorrect symmetry, illogical presentation of the results, or violation of the rules of chemistry and physics. Paying more attention to such problems, not only in the final validation stages but during the structure-determination process as well, is necessary not only in order to maintain the highest possible quality of the structural repositories and databases but most of all to provide a solid basis for subsequent studies, including large-scale data-mining projects. For many scientists PDB deposition is a rather infrequent event, so the need for proper training and supervision is emphasized, as well as the need for constant alertness of reason and critical judgment as absolutely necessary safeguarding measures against such problems. Ways of identifying more problematic structures are suggested so that their users may be properly alerted to their possible shortcomings.

model validation↗
DOE OSTI2014

Modeling the transmission of community-associated methicillin-resistant Staphylococcus aureus: a dynamic agent-based simulation

Background: Methicillin-resistant Staphylococcus aureus (MRSA) has been a deadly pathogen in healthcare settings since the 1960s, but MRSA epidemiology changed since 1990 with new genetically distinct strain types circulating among previously healthy people outside healthcare settings. Community-associated (CA) MRSA strains primarily cause skin and soft tissue infections, but may also cause life-threatening invasive infections. First seen in Australia and the U.S., it is a growing problem around the world. The U.S. has had the most widespread CA-MRSA epidemic, with strain type USA300 causing the great majority of infections. Individuals with either asymptomatic colonization or infection may transmit CA-MRSA to others, largely by skin-to-skin contact. Control measures have focused on hospital transmission. Limited public health education has focused on care for skin infections. Methods: We developed a fine-grained agent-based model for Chicago to identify where to target interventions to reduce CA-MRSA transmission. An agent-based model allows us to represent heterogeneity in population behavior, locations and contact patterns that are highly relevant for CA-MRSA transmission and control. Drawing on nationally representative survey data, the model represents variation in sociodemographics, locations, behaviors, and physical contact patterns. Transmission probabilities are based on a comprehensive literature review. Results: Over multiple 10-year runs with one-hour ticks, our model generates temporal and geographic trends in CA-MRSA incidence similar to Chicago from 2001 to 2010. On average, a majority of transmission events occurred in households, and colonized rather than infected agents were the source of the great majority (over 95%) of transmission events. The key findings are that infected people are not the primary source of spread. Rather, the far greater number of colonized individuals must be targeted to reduce transmission. Conclusions: Our findings suggest that current paradigms in MRSA control in the United States cannot be very effective in reducing the incidence of CA-MRSA infections. Furthermore, the control measures that have focused on hospitals are unlikely to have much population-wide impact on CA-MRSA rates. New strategies need to be developed, as the incidence of CA-MRSA is likely to continue to grow around the world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Dynamic Simulation Technoeconomic Model for Power Generation

Sandia National Laboratories has built and successfully tested a dynamic simulation technoeconomic model of the Palo Verde Generating Station that is now being updated to help other US power plants improve operations. Palo Verde, located west of Phoenix, Arizona, is the largest electricity generator in the US at 4 GW. Palo Verde uses — 60 million gallons per day of treated wastewater from Phoenix to cool reactors, and disposes of blowdown in evaporation ponds. The model built for Palo Verde numerically evaluates the economic impact of changing, for example, alternative cooling technologies, water usage and treatment, and influent water chemistry, and is based on detailed accounting of mass, energy, and cash flows.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Sources and composition of submicron organic mass in marine aerosol particles

Recent studies have proposed a variety of interpretations of the sources and composition of atmospheric marine aerosol particles (aMA) based on a range of physical and chemical measurements collected during open-ocean research cruises. To investigate the processes that affect marine organic particles, this study uses the characteristic functional group composition (from Fourier transform infrared (FTIR) spectroscopy) of aMAP from five ocean regions to show that: (i) The organic functional group composition of aMAP that can be identified as atmospheric primary marine (ocean-derived) aerosol (aPMA) is 65±12% hydroxyl, 21±9% alkane, 6±6% amine, and 7±8% carboxylic acid functional groups. Contributions from photochemical reactions add carboxylic acid groups (15%-25%), shipping effluent in seawater and ship emissions add additional alkane groups (up to 70%), and coastal emissions mix in alkane and carboxylic acid groups from coastal pollution sources. (ii) The organic composition of aPMA is nearly identical to model generated primary marine aerosol particles (gPMA) from bubbled seawater (55% hydroxyl, 32% alkane, and 13% amine functional groups), indicating that its overall functional group composition is the direct consequence of the organic constituents of the seawater source. (iii) While the seawater organic functional group composition was nearly invariant across all three ocean regions studied, the gPMA alkane group fraction increased with chlorophyll-a concentrations (r = 0.79). gPMA from productive seawater had a larger fraction of alkane functional groups (35%) compared to gPMA from non-productive seawater (16%), likely due to the presence of surfactants in productive seawater that stabilize the bubble film and lead to preferential drainage of the more soluble (lower alkane group fraction) organic components. gPMA has a hydroxyl group absorption peak location characteristic of monosaccharides and disaccharides, where the seawater OM hydroxyl group peak location is closer to that of polysaccharides. This may result from the larger saccharides preferentially remaining in the seawater during gPMA and aPMA production

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Analysis of albedo versus cloud fraction relationships in liquid water clouds using heuristic models and large eddy simulation: ALBEDO VERSUS CLOUD FRACTION RELATIONSHIPS

The relationship between the albedo of a cloudy scene $A$ and cloud fraction f c is studied with the aid of heuristic models of stratocumulus and cumulus clouds. Existing work has shown that scene albedo increases monotonically with increasing cloud fraction but that the relationship varies from linear to superlinear. The reasons for these differences in functional dependence are traced to the relationship between cloud deepening and cloud widening. When clouds deepen with no significant increase in f c (e.g., in solid stratocumulus), the relationship between$A$ and f c is linear.When clouds widen as they deepen, as in cumulus cloud fields, the relationship is superlinear. A simple heuristic model of a cumulus cloud field with a power law size distribution shows that the superlinear $A$-f c behavior is traced out either through random variation in cloud size distribution parameters or as the cloud field oscillates between a relative abundance of small clouds (steep slopes on a log-log plot) and a relative abundance of large clouds (flat slopes). Oscillations of this kind manifest in large eddy simulation of trade wind cumulus where the slope and intercept of the power law fit to the cloud size distribution are highly correlated. Further analysis of the large eddy model-generated cloud fields suggests that cumulus clouds grow larger and deeper as their underlying plumes aggregate; this is followed by breakup of large plumes and a tendency to smaller clouds. The cloud and thermal size distributions oscillate back and forth approximately in unison.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Phenome‐to‐genome insights for evaluating root system architecture in field studies of maize

Abstract Understanding the genetic basis of root system architecture (RSA) in crops requires innovative approaches that enable both high‐throughput and precise phenotyping in field conditions. In this study, we evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field‐grown maize in three field experiments. We used forward and reverse genetic approaches to evaluate >1700 maize root crowns, including a diversity panel, a biparental mapping population, and maize mutant and wild‐type alleles at two known RSA genes,DEEPER ROOTING 1(DRO1) andRootless1(Rt1). We show the utility of increasing the dimensionality of traditional two‐dimensional (2D) techniques, referred to as the “2D multi‐view” method, to improve the capture of whole root system information for mapping genetic variation influencing RSA. Comparison of univariate and multivariate genome‐wide association study (GWAS) approaches revealed that multivariate traits were effective at dissecting complex RSA phenotypes and identifying pleiotropic quantitative trait loci (QTLs). Overall, three‐dimensional (3D) root models generated from X‐ray computed tomography and digital phenotyping captured a larger proportion of RSA trait variations compared to other methods of root phenotyping, as evidenced by both genome‐wide and single‐gene analyses. Among the individual root traits, root pulling force emerged as a highly heritable estimate of RSA that identified the largest number of shared QTLs with 3D phenotypes. Our study shows that integrating complementary phenotyping technologies helps to provide a more comprehensive understanding of the genetic architecture of RSA in field‐grown maize.

Genetics & Heredity↗
DOE OSTI2017

Measurement of charged-particle distributions sensitive to the underlying event in $ \sqrt{s}=13 $ TeV proton-proton collisions with the ATLAS detector at the LHC

We present charged-particle distributions sensitive to the underlying event, measured by the ATLAS detector in proton-proton collisions at a centre-of-mass energy of 13 TeV, in low-luminosity Large Hadron Collider fills corresponding to an integrated luminosity of 1.6 nb –1 . The distributions were constructed using charged particles with absolute pseudorapidity less than 2.5 and with transverse momentum greater than 500 MeV, in events with at least one such charged particle with transverse momentum above 1 GeV. These distributions characterise the angular distribution of energy and particle flows with respect to the charged particle with highest transverse momentum, as a function of both that momentum and of charged-particle multiplicity. The results have been corrected for detector effects and are compared to the predictions of various Monte Carlo event generators, experimentally establishing the level of underlying-event activity at LHC Run 2 energies and providing inputs for the development of event generator modelling. The current models in use for UE modelling typically describe this data to 5% accuracy, compared with data uncertainties of less than 1%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Deformable Registration for Longitudinal Breast MRI Screening

MRI screening of high-risk patients for breast cancer provides very high sensitivity, but with a high recall rate and negative biopsies. Comparing the current exam to prior exams reduces the number of follow-up procedures requested by radiologists. Such comparison, however, can be challenging due to the highly deformable nature of breast tissues. Automated co-registration of multiple scans has the potential to aid diagnosis by providing 3D images for side-by-side comparison and also for use in CAD systems. Although many deformable registration techniques exist, they generally have a large number of parameters that need to be optimized and validated for each new application. Here, we propose a framework for such optimization and also identify the optimal input parameter set for registration of 3D T{sub 1}-weighted MRI of breast using Elastix, a widely used and freely available registration tool. A numerical simulation study was first conducted to model the breast tissue and its deformation through finite element (FE) modeling. This model generated the ground truth for evaluating the registration accuracy by providing the deformation of each voxel in the breast volume. An exhaustive search was performed over various values of 7 registration parameters (4050 different combinations of parameters were assessed) and the optimum parameter set was determined. This study showed that there was a large variation in the registration accuracy of different parameter sets ranging from 0.29 mm to 2.50 mm in median registration error and 3.71 mm to 8.90 mm in 95 percentile of the registration error. Mean registration errors of 0.32 mm, 0.29 mm, and 0.30 mm and 95 percentile errors of 3.71 mm, 5.02 mm, and 4.70 mm were obtained by the three best parameter sets. The optimal parameter set was applied to consecutive breast MRI scans of 13 patients. A radiologist identified 113 landmark pairs (~ 11 per patient) which were used to assess registration accuracy. The results demonstrated that using the optimal registration parameter set, a registration accuracy (in mm) of 3.4 [1.8 6.8] was achieved.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Thermodynamics of Restricted Boltzmann Machines and Related Learning Dynamics

We investigate the thermodynamic properties of a restricted Boltzmann machine (RBM), a simple energy-based generative model used in the context of unsupervised learning. Assuming the information content of this model to be mainly reflected by the spectral properties of its weight matrix W, we try to make a realistic analysis by averaging over an appropriate statistical ensemble of RBMs. First, a phase diagram is derived. Otherwise similar to that of the Sherrington–Kirkpatrick (SK) model with ferromagnetic couplings, the RBM’s phase diagram presents a ferromagnetic phase which may or may not be of compositional type depending on the kurtosis of the distribution of the components of the singular vectors of W. Subsequently, the learning dynamics of the RBM is studied in the thermodynamic limit. A “typical” learning trajectory is shown to solve an effective dynamical equation, based on the aforementioned ensemble average and explicitly involving order parameters obtained from the thermodynamic analysis. In particular, this let us show how the evolution of the dominant singular values of W, and thus of the unstable modes, is driven by the input data. At the beginning of the training, in which the RBM is found to operate in the linear regime, the unstable modes reflect the dominant covariance modes of the data. In the non-linear regime, instead, the selected modes interact and eventually impose a matching of the order parameters to their empirical counterparts estimated from the data. Finally, we illustrate our considerations by performing experiments on both artificial and real data, showing in particular how the RBM operates in the ferromagnetic compositional phase.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Error-mitigated data-driven circuit learning on noisy quantum hardware

Application-level benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the preparation of a target quantum state via optimization over a loss landscape. This is complicated by various sources of noise, fixed hardware connectivity, and generative modeling, the choice of target distribution. Gradient-based training has become a useful benchmarking task for noisy intermediate-scale quantum computers because of the additional requirement that the optimization step uses the quantum device to estimate the loss function gradient. In this work, we use gradient-based data-driven circuit learning to qualitatively evaluate the performance of several superconducting platform devices and present results that show how error mitigation can improve the training of quantum circuit Born machines with 28 tunable parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Reproducible benchmark for the SNAP 8 experimental reactor at operating conditions

This work presents fully reproducible multiphysics benchmark models of the Systems for Nuclear Auxiliary Power (SNAP) 8 Experimental Reactor at operating conditions with coolant flow. Wet experiment (with coolant, at power) validation benchmarks are presented using both deterministic (Serpent-Griffin) and Monte-Carlo (OpenMC-Cardinal) multiphysics frameworks coupled with thermal-hydraulic solvers in MOOSE. Reactivity coefficient measurements including fuel temperature, isothermal temperature, and power coefficients show good agreement with experiments, with discrepancies within experimental uncertainty. Reactivity worth experiments for coolant, samarium, and xenon poisoning are reproduced with differences under 200 pcm. Comparison between Serpent-Griffin and OpenMC-Cardinal frameworks reveal multiphysics coupling introduces positive reactivity effects (100-200 pcm) compared to uniform temperature and density fields at nominal operating conditions. Comparison between Serpent-Griffin and reference Serpent solution shows that power distributions maintain consistent radial and axial peaking behavior. All models, assumptions, thermophysical and thermomechanical properties, and material definitions are thoroughly documented with cited references; model inputs and model generating scripts are stored in the snapReactors GitHub repository.

SNAP↗
DOE OSTI2019

Load response of a floating wind turbine to turbulent atmospheric flow

The two turbulence-generation models [Kaimal Spectrum Exponential Coherence (KSEC) and Mann] specified in the international standard for wind turbine design assume neutral atmospheric conditions and are based on statistical and spectral methods. Mainly due to the lack of physics, the flow fields simulated with these models ultimately differ in their underlying structure, especially in terms of the spatial coherence of longitudinal velocity perturbations. While this may not be critical for smaller wind turbine rotors, it becomes important when rotor sizes increase. Furthermore, it might be especially important in the context of floating technologies as they are more sensitive to large turbulent coherent structures. Previous work found that these differences between KSEC and Mann can propagate to loads predictions and thereby affect the design space of the entire wind turbine system. It is therefore crucial to determine in which ways these two models are underperforming. Up until now, validation of these models had only been done in the vertical direction because it is extremely difficult to obtain atmospheric turbulence measurements separated laterally, and sampled at heights relevant to wind energy. In this work, we address the lack of measurements by using high-fidelity, high-resolution simulation data as a reference. We perform hour-long, large-eddy simulations of turbulent velocity fields that are stability-dependent and contain three-dimensional coherent structures. These flow fields are then used to investigate which stochastic model is a better predictor of loads on a realistic spar-system floating offshore wind turbine, and to quantify how the assumption of neutral stratification propagates to short-term load estimates. Both stochastic turbulence models are found to overpredict fatigue loading in high-wind scenarios (in some cases, by more than 25%) and underpredict it when the wind speed is low (by as much as 20%). The KSEC model matches the high-fidelity flow fields more closely than Mann at high wind speeds, and the opposite is true at low wind speeds. Finally, turbine loading is found to be sensitive to atmospheric stability even when the turbulence intensity remains fairly constant. This sensitivity is most pronounced at low wind speeds, when fatigue load estimates on the spar system can differ by 40%.

17 WIND ENERGY↗
DOE OSTI2022

Analysis of controls for integrated energy storage system in energy arbitrage configuration with concrete thermal energy storage

Novel highly detailed NuScale reactor and turbo-generator models have been built in Modelica to analyze potential controls for an integrated energy system (IES) configuration combining a nuclear reactor and concrete thermal energy storage (CTES) operating in energy arbitrage. A shakedown test demonstrates the viability of the control schemes to operate across expected operating conditions while maintaining safety limits and buffers. A five-day data set from Idaho is used to show a load-following application of this system that increases the average reactor power level by 20% over the data set and matching demand at nearly all points. Here, by integrating these models, feedback mechanisms across the primary and secondary sides of the nuclear reactor are integrated, and realistic control evaluation is done. All models are consistent with the Hybrid repository, a suite of Modelica models, maintained at Idaho National Laboratory.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

A superstructure optimization approach for process synthesis under complex reaction networks

Here in this work, we present mixed integer linear programming methods for the synthesis of processes that involve complex reaction networks. Specifically, we consider the modeling of reactors and interconnecting streams in systems where the composition of the reactor inlet streams can vary substantially, thereby making the determination of the limiting component as well as the calculation of the stream heating/cooling and power requirements challenging. First, towards the modeling of reactors, we develop an extent-based method which detects the limiting reactant of each reaction occurring in parallel with others, based on the inlet flows of the reactants. Second, we develop a computationally tractable method for the calculation of the work and heating/cooling duty needed to condition any stream of a process based on simple calculations that can be performed offline. Finally, we present how the two aforementioned components can be integrated in an optimization model generated based on a process superstructure. We demonstrate the application of the developed methods for the synthesis of a biorefinery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Rats use memory confidence to guide decisions

Memory enables access to past experiences to guide future behavior. Humans can determine which memories to trust (high confidence) and which to doubt (low confidence). How memory retrieval, memory confidence, and memory-guided decisions are related, however, is not understood. In particular, how confidence in memories is used in decision making is unknown. We developed a spatial memory task in which rats were incentivized to gamble their time: betting more following a correct choice yielded greater reward. Rat behavior reflected memory confidence, with higher temporal bets following correct choices. We applied machine learning to identify a memory decision variable and built a generative model of memories evolving over time that accurately predicted both choices and confidence reports. Our results reveal in rats an ability thought to exist exclusively in primates and introduce a unified model of memory dynamics, retrieval, choice, and confidence.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Performance of reverse osmosis membrane with large feed pressure fluctuations from a wave-driven desalination system

Wave-driven desalination systems are proposed water treatment systems that involve reverse osmosis of seawater powered directly by wave motion. Such a configuration would result in drastic feed pressure fluctuations. For a technology conventionally operated with a constant feed condition, the effect of these variable pressures on membrane integrity and performance is unknown. Here, experiments were conducted with spiral wound membranes coupled to a system capable of producing feed pressure fluctuations of more than 400 psi. Feed composition included 5, 20, and 35 g/L NaCl, and a synthetic seawater at normal and 1.5x concentration. The variable feed conditions included sine-like pressure waves swings of 200-500 and 500-900 psi with frequencies of 1.25, 7.5, and 12 waves/min, and a model-generated random waveform. Between each wave experiment we performed membrane integrity tests at 650 psi and 25 g/L NaCl feed, which showed a 7.4% drop in the membrane's water permeability coefficient, an 18.4% flux decline, and more than 99% salt rejection over 1770 h of cumulative experimental time. Analysis of permeate samples showed high salt rejection. In general, variable feed pressure had no significant deleterious effect on membrane integrity or performance.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

How reliable is distribution of relaxation times (DRT) analysis? A dual regression-classification perspective on DRT estimation, interpretation, and accuracy

The distribution of relaxation times (DRT) has gained increasing attention and adoption in recent years as a versatile method for analyzing electrochemical impedance spectroscopy (EIS) data obtained from complex devices like fuel cells, electrolyzers, and batteries. The DRT deconvolutes the impedance without a priori specification of a generative model, which is especially useful for interpretation and model selection when the governing principles of the system under study are not fully understood. However, DRT estimation is an ill-posed inversion problem that must be addressed with a subjective choice of regularization and tuning, which leaves substantial risk of misleading interpretations of EIS data. In this work, we suggest a new classification view of the DRT inversion to clarify DRT estimation and interpretation. We introduce a dual regression-classification framework that unifies the classification and regression views of the DRT inversion with wide-reaching implications for DRT analysis. The dual framework is employed to demonstrate a new kind of DRT inversion algorithm and develop novel evaluation metrics that capture previously ignored aspects of DRT accuracy. These approaches are applied to both synthetic data and experimental spectra collected from a protonic ceramic fuel cell and a lithium-ion battery to illustrate their broad utility. The dual inversion algorithm shows promising performance for accurate DRT estimation and autonomous model identification, while the dual evaluation approach produces metrics that meaningfully assess the strengths and risks of DRT algorithms. Here this work provides valuable insight for both practical application of the DRT to experimental data and further development of EIS analysis methods.

36 MATERIALS SCIENCE↗