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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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NASA NTRS2000

The Turbulent/Non-Turbulent Interface Bounding a Far-Wake

The velocity fields of a turbulent wake behind a flat plate obtained from the direct numerical simulations of Moser et al. are used to study the structure of the flow in the intermittent zone where there are, alternately, regions of fully turbulent flow and non-turbulent velocity fluctuations either side of a thin randomly moving interface. Comparisons are made with a wake that is 'forced' by amplifying initial velocity fluctuations. There is also a random temperature field T in the flow; T varies between constant values of 0.0 and 1.0 on the sides of the wake. The value of the Reynolds number based on the centreplane mean velocity defect and halfwidth b of the wake is Re approx. = 2000. It is found that the thickness of the continuous interface is about equal to 0.07b, whereas the amplitude of fluctuations of the instantaneous interface displacement y(sub I)(t) is an order of magnitude larger, being about 0.5b. This explains why the mean statistics of vorticity in the intermittent zone can be calculated in terms of the probability distribution of y(sub I) and the instantaneous discontinuity in vorticity across the interface. When plotted as functions of y - y(sub I), the conditional mean velocity (U) and temperature (T) profiles show sharp jumps Delta(U) and Delta(T) at the interface adjacent to a thick zone where (U) and (T) vary much more slowly. Statistics for the vorticity and velocity variances, available in such detail only from DNS data, show how streamwise and spanwise components of vorticity are generated by vortex stretching in the bulges of the interface. Flow fields around the interface, analyzed in terms of the local streamline pattern, confirm previous results that the advancement of the vortical interface into the irrotational flow is driven by large-scale eddy motion. It is argued that because this is an inviscid mechanism the entrainment process is not sensitive to the value of Re, and that small-scale nibbling only plays a subsidiary role. While mean Reynolds stresses decrease gradually in the intermittent zone, conditional stresses are found to decrease sharply towards zero at the interface. Using one-point turbulence models applied to either unconditional or conditional statistics for the turbulent region and then averaged, the entrainment rate E(sub b) would, if calculated exactly, be zero. But if computed with standard computational methods, E(sub b) would be non-zero because of numerical diffusion. It is concluded that the current practice in statistical models of approximating entrainment by a diffusion process is computationally arbitrary and physically incorrect. An analysis shows how E(sub b) is related to Delta(U) and the jump in shear stress at the interface, and correspondingly to Delta(T) and the heat flux.

Bisset, David K.↗
DOE OSTI2023

Evaluation of Uncertainties in the Anthropogenic SO 2 Emissions in the USA from the OMI Point Source Catalog

Satellite remote sensing is a promising method of monitoring emissions that may be missing in inventories, but the accuracy of these estimates is often not clear. We demonstrate here a comprehensive evaluation of errors in anthropogenic sulfur dioxide (SO 2 ) emission estimates from NASA’s OMI point source catalog for the contiguous US by comparing emissions from the catalog with high-quality emission inventory data over different dimensions including size of individual sources, aggregate vs individual source errors, and potential bias in individual source estimates over time. For sources that are included in the catalog, we find that errors in aggregate (sum of error for all included sources) are relatively low. Errors for individual sources in any given year can be substantial, however, with over- or underestimates in terms of total error ranging from –80 to 110 kt (roughly 10–90th percentile). In this study, we find that these errors are not necessarily random over time and that there can be consistently positive or negative biases for individual sources. We did not find any overall statistical relationship between the degree of isolation of a source and bias, either at a 40 or 70 km scales. For a sub-set of sources where inventory emissions over a radius of 70 km around an OMI detection are larger than twice the emissions within 40 km, the OMI value is consistently overestimated. We find, as expected, that emission sources not included in the catalog are the largest aggregate source of difference between the satellite estimates and inventories, especially in more recent years where source emission magnitudes have been decreasing and note that trends in satellite detections do not necessarily track trends in total emissions. We find that the OMI-based SO 2 emissions are accurate in aggregate, when summed over a number of sources, but must be interpreted more cautiously at the individual source level. Similar analyses would be valuable for other satellite emission estimates; however, in many cases, the appropriate high-quality reference data may need to be generated.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Origins of Protein Functions in Cells

In modern organisms proteins perform a majority of cellular functions, such as chemical catalysis, energy transduction and transport of material across cell walls. Although great strides have been made towards understanding protein evolution, a meaningful extrapolation from contemporary proteins to their earliest ancestors is virtually impossible. In an alternative approach, the origin of water-soluble proteins was probed through the synthesis and in vitro evolution of very large libraries of random amino acid sequences. In combination with computer modeling and simulations, these experiments allow us to address a number of fundamental questions about the origins of proteins. Can functionality emerge from random sequences of proteins? How did the initial repertoire of functional proteins diversify to facilitate new functions? Did this diversification proceed primarily through drawing novel functionalities from random sequences or through evolution of already existing proto-enzymes? Did protein evolution start from a pool of proteins defined by a frozen accident and other collections of proteins could start a different evolutionary pathway? Although we do not have definitive answers to these questions yet, important clues have been uncovered. In one example (Keefe and Szostak, 2001), novel ATP binding proteins were identified that appear to be unrelated in both sequence and structure to any known ATP binding proteins. One of these proteins was subsequently redesigned computationally to bind GTP through introducing several mutations that introduce targeted structural changes to the protein, improve its binding to guanine and prevent water from accessing the active center. This study facilitates further investigations of individual evolutionary steps that lead to a change of function in primordial proteins. In a second study (Seelig and Szostak, 2007), novel enzymes were generated that can join two pieces of RNA in a reaction for which no natural enzymes are known. Recently it was found that, as in the previous case, the proteins have a structure unknown among modern enzymes. In this case, in vitro evolution started from a small, non-enzymatic protein. A similar selection process initiated from a library of random polypeptides is in progress. These results not only allow for estimating the occurrence of function in random protein assemblies but also provide evidence for the possibility of alternative protein worlds. Extant proteins might simply represent a frozen accident in the world of possible proteins. Alternative collections of proteins, even with similar functions, could originate alternative evolutionary paths.

Seelig, Burchard↗
DOE OSTI2021

Radio frequency mixing modules for superconducting qubit room temperature control systems

As the number of qubits in nascent quantum processing units increases, the connectorized RF (radio frequency) analog circuits used in first generation experiments become exceedingly complex. The physical size, cost, and electrical failure rate all become limiting factors in the extensibility of control systems. We have developed a series of compact RF mixing boards to address this challenge by integrating I/Q quadrature mixing, intermediate frequency/LO (local oscillator)/RF power level adjustments, and direct current bias fine tuning on a 40 × 80 mm 2 four-layer printed circuit board with electromagnetic interference shielding. The RF mixing module is designed to work with RF and LO frequencies between 2.5 and 8.5 GHz. The typical image rejection and adjacent channel isolation are measured to be ~27 dBc and ~50 dB. By scanning the drive phase in a loopback test, the module short-term amplitude and phase linearity are typically measured to be 5 ×10 -4 (V pp /V mean ) and 1 ×10 -3 radian (pk-pk). The operation of the RF mixing board was validated by integrating it into the room temperature control system of a superconducting quantum processor and executing randomized benchmarking characterization of single and two qubit gates. We measured a single-qubit process infidelity of 9.3(3) × 10 -4 and a two-qubit process infidelity of 2.7(1) × 10 -2 .

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Unraveling the implications of finite specimen size on the interpretation of dynamic experiments for polycrystalline aluminum through direct numerical simulations

Normal and Pressure-shear plate impact (NPI and PSPI) experiments are popular experimental techniques for studying the mean-field macroscopic behavior of polycrystalline metals under high-rate dynamic loading. However, since both configurations rely upon geometry for subjecting the specimen to high strain rates, these experiments often involve a limited specimen size. Moreover, because of the inherent heterogeneities present within polycrystalline metals, it is difficult to ascertain if the size of the specimen and/or regions where measurements are made are sufficiently large for making representative inferences about the mean-field macroscopic properties from single-point velocity measurements. In the present study, we quantify the expected measurement variability on observable point measurements in NPI and PSPI experiments by carrying out direct numerical simulations (DNS) of statistically representative polycrystalline microstructures subjected to dynamic compression and compression-shear loading. In particular, we consider the role of specific material heterogeneities (e.g. the grain-to-grain difference in size, crystallographic orientation) on dispersion in the normal and transverse particle velocity records and on local fluctuations in key state variables (e.g. velocity, accumulated plastic strain) by incorporating these effects directly into a representative synthetic microstructure geometry and crystalline description of pure polycrystalline aluminum. The form of the present study is a large parametric investigation, consisting of ten ensembles of one hundred simulations. Each of the thousand simulations reflects a randomly realized synthetic microstructure in one of five cases of decreasing average grain size for the two loading configurations. Our analysis of the DNS results demonstrates that for both of these experimental configurations, the grain size directly correlates with the coefficient of variation (CV) in simulated point measurements, showing a convergent decrease in CV to zero (i.e. particle velocity record approaches the mean-field value) with decreasing grain size. Remarkably, the magnitude of variations in the particle velocity record is shown to be largest where the deviatoric stresses are most significant. In the case of NPI, this occurs at the elastic and plastic wavefront, whereas, in the case of PSPI, the magnitude of fluctuations are approximately constant throughout the experimental window time. The reasoning for the scatter in particle velocity due to the heterogeneous microstructure is demonstrated to be dependent on the mechanisms for accommodating deformation and on the interaction of reflection waves generated at sites of heterogeneities occurring at the scale of grains. Lastly, we develop a power-law description for the magnitude of scattering versus characteristic length, which provides a statistical framework for assessing the required number of grains per characteristic specimen dimension for minimizing scatter within these two experimental configurations (NPI, PSPI).

36 MATERIALS SCIENCE↗
DOE OSTI2016

TH-CD-209-05: Impact of Spot Size and Spacing On the Quality of Robustly-Optimized Intensity-Modulated Proton Therapy Plans for Lung Cancer

Purpose: To investigate how spot size and spacing affect plan quality, especially, plan robustness and the impact of interplay effect, of robustly-optimized intensity-modulated proton therapy (IMPT) plans for lung cancer. Methods: Two robustly-optimized IMPT plans were created for 10 lung cancer patients: (1) one for a proton beam with in-air energy dependent large spot size at isocenter (σ: 5–15 mm) and spacing (1.53σ); (2) the other for a proton beam with small spot size (σ: 2–6 mm) and spacing (5 mm). Both plans were generated on the average CTs with internal-gross-tumor-volume density overridden to irradiate internal target volume (ITV). The root-mean-square-dose volume histograms (RVH) measured the sensitivity of the dose to uncertainties, and the areas under RVH curves were used to evaluate plan robustness. Dose evaluation software was developed to model time-dependent spot delivery to incorporate interplay effect with randomized starting phases of each field per fraction. Patient anatomy voxels were mapped from phase to phase via deformable image registration to score doses. Dose-volume-histogram indices including ITV coverage, homogeneity, and organs-at-risk (OAR) sparing were compared using Student-t test. Results: Compared to large spots, small spots resulted in significantly better OAR sparing with comparable ITV coverage and homogeneity in the nominal plan. Plan robustness was comparable for ITV and most OARs. With interplay effect considered, significantly better OAR sparing with comparable ITV coverage and homogeneity is observed using smaller spots. Conclusion: Robust optimization with smaller spots significantly improves OAR sparing with comparable plan robustness and similar impact of interplay effect compare to larger spots. Small spot size requires the use of larger number of spots, which gives optimizer more freedom to render a plan more robust. The ratio between spot size and spacing was found to be more relevant to determine plan robustness and the impact of interplay effect than spot size alone. This research was supported by the National Cancer Institute Career Developmental Award K25CA168984, by the Fraternal Order of Eagles Cancer Research Fund Career Development Award, by The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, by Mayo Arizona State University Seed Grant, and by The Kemper Marley Foundation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

Background: Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU. Objective: We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. Methods: We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. Results: We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm. Conclusions: The novelty in our approach is that we built models to predict ICU length of stay and mortality with reasonable accuracy based on a combination of ML and the quantiles approach that utilizes only vital signs available from the patient’s profile without the need to use any external features. This approach is based on feature engineering of the vital signs by including their modified means, standard deviations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

A New Source Biasing Approach in ADVANTG

The ADVANTG code has been developed at Oak Ridge National Laboratory to generate biased sources and weight window maps for MCNP using the CADIS and FW-CADIS methods. In preparation for an upcoming RSICC release, a new approach for generating a biased source has been developed. This improvement streamlines user input and improves reliability. Previous versions of ADVANTG generated the biased source from ADVANTG input, writing an entirely new general fixed-source definition (SDEF). Because volumetric sources were translated into SDEF-format as a finite set of points, the user had to perform a convergence study to determine whether the number of source points used accurately represented the source region. Further, the large number of points that must be written in SDEF-format made the MCNP input and output files excessively long and difficult to debug. ADVANTG now reads SDEF-format distributions and generates corresponding source biasing cards, eliminating the need for a convergence study. Many problems of interest use complicated source regions that are defined using cell rejection. In cell rejection, the source distribution in space is defined using an arbitrarily complex cell and a simple bounding region. Source positions are sampled within the bounding region but accepted only if they fall within the cell; otherwise, the position is resampled entirely. When biasing in space is applied to sources that use rejection sampling, current versions of MCNP do not account for the rejection in setting the source weight of histories, resulting in an 'unfair game'. This problem was circumvented in previous versions of ADVANTG by translating volumetric sources into a finite set of points, which does not alter the mean history weight ({bar w}). To use biasing parameters without otherwise modifying the original cell-rejection SDEF-format source, ADVANTG users now apply a correction factor for {bar w} in post-processing. A stratified-random sampling approach in ADVANTG is under development to automatically report the correction factor with estimated uncertainty. This study demonstrates the use of ADVANTG's new source biasing method, including the application of {bar w}.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Removal of spurious data in Bragg coherent diffraction imaging: an algorithm for automated data preprocessing

Bragg coherent diffraction imaging (BCDI) provides a powerful tool for obtaining high-resolution structural information from nanocrystalline materials. Here a BCDI sample consisting of a large number of randomly oriented nanoscale crystals is considered. Ideally, only one crystal is oriented to produce a Bragg peak on the detector. However, diffraction from other crystals often produces additional signals on the detector. Before the measured diffraction patterns can be processed into structural images, scientists routinely need to manually identify and remove the `alien' intensities from sources other than the intended crystal. With the development of modern high-coherence storage rings, such as the upgraded Advanced Photon Source (APS), the already slow process of manual preprocessing will be untenable for the large volumes of data that will be produced. An automated method of identifying and deleting alien intensities is proposed. This method exploits the fact that BCDI of a perfect crystal produces diffraction data with inversion symmetry around the Bragg peak. This approach uses the machine learning clustering method DBSCAN to distinguish between diffraction from multiple sources, and then calculates cluster size and inversion symmetry to assess whether clusters of intensity belong to desired data or alien signals. This approach can dramatically reduce the amount of time spent manually processing data, allowing BCDI data processing capabilities to keep pace with the technological advances of fourth-generation synchrotron light sources.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantitative Evaluation of Top Coal Caving Methods at the Working Face of Extra‐Thick Coal Seams Based on the Random Medium Theory

Adopting an effective top coal caving method is the key to enhancing coal recovery and reducing gangue content for the fully mechanized top coal caving working face with extra‐thick coal seams. In this study, the movement of coal particles generated during top coal caving is considered to follow a normal distribution. Then, the caving body and coal‐rock settlement along the working face during the caving process are studied based on both the random media theory and probability theory. Accordingly, the optimal caving interval and caving sequences are determined, and a novel interval symmetrical coal caving method is proposed. The proposed method is systematically verified with results from physical similarity tests, and different caving methods are assessed by field tests. The results show the following: (1) The coal‐rock settlement and the caving body demonstrate clear axial symmetrical features along the working face; the size of the caving body increases as the caving height grows and its shape turns progressively from semicircular to semielliptical with a lower foot of the coal‐rock settlement. (2) The caving interval is derived using the sum of the radii of the coal‐rock settlement curves formed by the two largest caving bodies. (3) The symmetrical caving approach provides a symmetrical space for the subsequent movement of the broken top coal, which enables a uniform development of the caving body. (4) Compared with the traditional sequential coal caving method with the same number of supports, the interval symmetrical caving method results in a 21.7% of coal production increase, 17% caving rate promotion, and a shortened caving time by 23.4%. (5) The interval symmetrical caving method is found to improve the controllability of the caving process at the fully mechanized top coal caving working face. In general, this work presents a theoretical approach to select the optimal caving methods for the fully mechanized caving working face in extra‐thick coal seams for an improved production efficiency of the work face. The results of this study can also provide theoretical significance and referencing value for quantitative analyses of the coal caving methods for work faces with similar geological conditions.

Jiulin, Shi (ORCID:0000000155819062)↗
DOE OSTI2017

Speed and Memory Improvements to MCNP6 Delayed-Gamma Line Treatment

The delayed-gamma (DG) line-emission (line) sampling was first introduced into MCNPX in 2005. At the time, storage demands for problems with large numbers of DG lines was a major concern. In order to accommodate large amounts of line data while keeping the memory footprint of such a problem from exceeding memory limits, complicated logic was included to sift through various storage options. In mid-2015 concerns arose over the adequacy of the MCNP6 DG source option (inherited from MCNPX) with line sampling for modeling high-fidelity problems. One indication that there was a problem came when studying the MCNP6 produced DG spectra of U-238 and its progeny using high-fidelity spectroscopic tallies when compared to DG line amplitudes directly from ENDF/B-VII.1 emission data. Despite producing a correct integral number of gammas, the comparison to ENDF data showed a broadening in the energy lines and a reduction in the amplitude of more prominent lines. It was later found that the cause of this broadening was one of the memory-saving techniques used to store DG line data when problems required many DG lines. Specifically, the DG lines were integrated into fine energy bins for sampling. When a DG line was to be sampled, a random number would be used to choose which energy bin to sample based on emission probability. Once chosen, a second random number was used to perform nearest-line sampling in that bin. The integration of lines led to an oversampling of low-probability lines, especially when in close proximity to high-probability lines. This treatment also caused a similar issue with the time-dependent behavior of DG line emission which occurred when low-probability lines were sampled near high probability lines with the former decay constant being used. The effect on time-dependent gamma production was demonstrated in analytic benchmarks performed by Weldon et al.. To address these issues a new gamma line storage and sampling treatment was introduced in 2015. This new treatment, stores gamma emission data in a line-by-line CDF which is sampled directly, thereby resolving both the line-broadening issue, and the time-dependence issue as was demonstrated by Tutt el al.. In addition, the new DG sampling treatment was written to use dynamically dimensioned storage arrays as opposed to large fixed-dimensioned arrays, as in the former treatment, and also makes more efficient use of line data that is stored by reducing or eliminating repeated data. As a result, storage arrays are generated only for DG data relevant to the problem being executed. The result should be a reduction in both memory requirements and runtime. However, these reductions could not be realized until the logic for the old treatment could be removed. This paper provides an update on recent work which has focused on the removal of the old DG sampling logic and attempts to quantify the memory reduction and speed up of selected problems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2000

A Parallel Genetic Algorithm for Automated Electronic Circuit Design

Parallelized versions of genetic algorithms (GAs) are popular primarily for three reasons: the GA is an inherently parallel algorithm, typical GA applications are very compute intensive, and powerful computing platforms, especially Beowulf-style computing clusters, are becoming more affordable and easier to implement. In addition, the low communication bandwidth required allows the use of inexpensive networking hardware such as standard office ethernet. In this paper we describe a parallel GA and its use in automated high-level circuit design. Genetic algorithms are a type of trial-and-error search technique that are guided by principles of Darwinian evolution. Just as the genetic material of two living organisms can intermix to produce offspring that are better adapted to their environment, GAs expose genetic material, frequently strings of 1s and Os, to the forces of artificial evolution: selection, mutation, recombination, etc. GAs start with a pool of randomly-generated candidate solutions which are then tested and scored with respect to their utility. Solutions are then bred by probabilistically selecting high quality parents and recombining their genetic representations to produce offspring solutions. Offspring are typically subjected to a small amount of random mutation. After a pool of offspring is produced, this process iterates until a satisfactory solution is found or an iteration limit is reached. Genetic algorithms have been applied to a wide variety of problems in many fields, including chemistry, biology, and many engineering disciplines. There are many styles of parallelism used in implementing parallel GAs. One such method is called the master-slave or processor farm approach. In this technique, slave nodes are used solely to compute fitness evaluations (the most time consuming part). The master processor collects fitness scores from the nodes and performs the genetic operators (selection, reproduction, variation, etc.). Because of dependency issues in the GA, it is possible to have idle processors. However, as long as the load at each processing node is similar, the processors are kept busy nearly all of the time. In applying GAs to circuit design, a suitable genetic representation 'is that of a circuit-construction program. We discuss one such circuit-construction programming language and show how evolution can generate useful analog circuit designs. This language has the desirable property that virtually all sets of combinations of primitives result in valid circuit graphs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm and circuit simulation software, we present experimental results as applied to three analog filter and two amplifier design tasks. For example, a figure shows an 85 dB amplifier design evolved by our system, and another figure shows the performance of that circuit (gain and frequency response). In all tasks, our system is able to generate circuits that achieve the target specifications.

Long, Jason D.↗
NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗
DOE OSTI2019

Experimental investigation of cross flow mixing in a randomly packed bed and streamwise vortex characteristics using particle image velocimetry and proper orthogonal decomposition analysis

The study of flow and heat transfer through porous media or randomly packed beds is important as these configurations are widely used in many engineering applications, for example, heat energy storage, chemical catalytic reactors, and nuclear reactors. The flow mixing characteristics in a cross-flow plane of a facility with randomly packed spheres at an aspect ratio of 6.3 were experimentally investigated. The velocity fields at several regions of the cross-flow plane located in the vicinity of the wall and in the pores between spheres were obtained by applying the matched-index-of-refraction and time-resolved particle image velocimetry (TR-PIV) techniques for Reynolds numbers ranging from 700 to 1700. The TR-PIV results revealed various flow patterns in the transverse plane of the packed spheres, including swirling flow structures aligned with the axial flow direction, a strong bypass flow near the enclosure wall, and a circulation region created when the bypass flow ejected into a large spatial gap. When the Reynolds number was increased, the peaks of root-mean-square fluctuating velocities, $u'_{rms}$ and $v'_{rms}$, were found to increase approximately at the same ratio as the increase in Reynolds number, and the magnitude of the Reynolds stress increased considerably. In addition, the characteristics of flow mixing in different flow regions were investigated via the two-point cross-correlation of fluctuating velocities. Using Taylor’s hypothesis, the vorticity iso-surfaces were constructed. Thus, constructed iso-surfaces showed that shear layers generated from the bypass flow gaps were stretched, broken into smaller flow structures, and then evolved as vortex pairs when entering the neighboring gaps. The results obtained by applying proper orthogonal decomposition (POD) analysis to the velocity fields showed that the statistically dominant flow structures had approximately the same size and shape as those depicted by Taylor’s hypothesis. Vortex characteristics, such as populations, spatial distributions, and strengths, for various spatial regions and Reynolds numbers were obtained by a combination of POD analysis and vortex identification.

42 ENGINEERING↗
NASA NTRS1993

New perspectives for high accuracy SLR with second generation geodesic satellites

This paper reports on the accuracy limitations imposed by geodesic satellite signatures, and on the potential for achieving millimetric performances by means of alternative satellite concepts and an optimized 2-color system tradeoff. Long distance laser ranging, when performed between a ground (emitter/receiver) station and a distant geodesic satellite, is now reputed to enable short arc trajectory determinations to be achieved with an accuracy of 1 to 2 centimeters. This state-of-the-art accuracy is limited principally by the uncertainties inherent to single-color atmospheric path length correction. Motivated by the study of phenomena such as postglacial rebound, and the detailed analysis of small-scale volcanic and strain deformations, the drive towards millimetric accuracies will inevitably be felt. With the advent of short pulse (less than 50 ps) dual wavelength ranging, combined with adequate detection equipment (such as a fast-scanning streak camera or ultra-fast solid-state detectors) the atmospheric uncertainty could potentially be reduced to the level of a few millimeters, thus, exposing other less significant error contributions, of which by far the most significant will then be the morphology of the retroreflector satellites themselves. Existing geodesic satellites are simply dense spheres, several 10's of cm in diameter, encrusted with a large number (426 in the case of LAGEOS) of small cube-corner reflectors. A single incident pulse, thus, results in a significant number of randomly phased, quasi-simultaneous return pulses. These combine coherently at the receiver to produce a convolved interference waveform which cannot, on a shot to shot basis, be accurately and unambiguously correlated to the satellite center of mass. This paper proposes alternative geodesic satellite concepts, based on the use of a very small number of cube-corner retroreflectors, in which the above difficulties are eliminated while ensuring, for a given emitted pulse, the return of a single clean pulse with an adequate cross-section.

Lund, Glenn↗
DOE OSTI2024

Enhanced Resistance Pines for Improved Renewable Biofuel and Chemical Production (Technical Report)

We completed phenotyping constitutive and inducible oleoresin flow across two seasons, constitutive resin canal number and density and wood terpene content in our ADEPT2 and CCLONES populations. We completed genetic association between 19 oleoresin phenotypes and a total of 523,192 SNP markers from ADEPT2 and 13,883 SNP markers in CCLONES using four mixed linear models. A total of 293 significant SNPs (FDR = 0.20) were identified. We used the MENTOR tool to mine mechanistic connections from a multiplex network constructed from poplar multi-omic data to construct a conceptual model for a subset of these significant SNPs. Our model contains 6 transcriptional regulators in addition to 3 monoterpene synthases. To generate more lines of evidence for these significant SNPs, we completed a time course RNAseq experiment after inducing vascular zone cells to differentiate into new resin canals with a methyl jasmonate treatment, a single nuclei RNAseq that identified differentiating resin canal epithelial cells and are completing analysis for a QTL study in a hybrid pine population. The time course identified 4634 significantly down and 1890 significantly up regulated transcripts after treatment with methyl jasmonate, an inducer of new resin canal formation in the vascular cambial meristem. To analyze this large set of differentially regulated genes, we created a predictive expression network and analyzed it with random walk restart using 6 seed genes coding for transcription factors regulating xylem differentiation in poplar. Of the top ranked 200 transcripts, 119 transcripts were significant differentially expressed supporting these transcripts as potential candidates regulating resin canal formation. Analysis of single nuclei sequencing of shoot tips that contain differentiating resin canals, identified 10 clusters. One cluster was highly enriched in transcripts coding for 9 of the enzymes in the MEP pathway 3 prenyl synthetases, and 3 monoterpene synthases strongly suggesting that this cluster represents resin canal epithelial cells. We are mining the additional transcripts to create a trajectory analysis. In summary, we have identified > 10 novel genes that are strongly supported candidates for further analysis in breeding lines and for genetic engineering over- and under- expressing lines to increase wood terpene content to improve resistance to insect and fungal pathogens while simultaneously increasing terpene supplies for renewable chemicals and biofuels.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Parameters Impacting Columnated Granular Soil Pneumatic Seal Performance

The ability of a column of loose granular soil to form a pneumatic seal was investigated by varying the diameter of the soil column, the effective column height, and the level of compaction in the soil. The soil column diameter was tested at three levels using pipes with inner diameters measuring 5.08, 10.16, and 15.24 centimeters (2, 4, and 6 inches). Soil was filled in each pipe to form 15, 30, and 45 centimeter (6, 12, and 18 inch) tall soil columns. Each diameter/height configuration was also tested at three levels of soil compaction, compared by calculating the bulk density of the soil with mass and volume measurements. GRC-1a simulant was used, with approximate low/medium/high bulk densities of 1.6, 1.75, and 1.9 g/cc achieved with a combination of vibration and tamping. The order of tests for a given column diameter was randomized and repeated three times. With the top of the soil open to atmosphere at room temperature, compressed air was injected through a small diffuser at the column base with several small downward-facing holes. The number and size of these holes was scaled such that a constant total inlet orifice area to column cross sectional area ratio was maintained for each column diameter. Inlet air pressure was slowly increased via a precision regulator to preserve quasi-static equilibrium in the soil column to minimize the impact of dynamics. Air pressure was increased all the way through the static and bubbling regimes until slugging or turbulent behavior was observed in the soil to ensure that the entire static regime had been captured during data collection. A three-factor, three-level analysis of variance (ANOVA) statistical analysis was performed on the resulting data to determine the extent to which each physical parameter impacted the soil column seal performance. It was concluded that there is statistically significant evidence that column height, and the interaction between column height and diameter impact soil seal performance. In all other cases there was insufficient data to identify a statistically significant causal relationship. Additionally, plots were generated comparing experimental data to the predictive formula developed by Ogino et al. for fluidized beds in 1993. Because this model was developed for industrial spouted fluidized beds, the accuracy of its output prior to fluidization in the static seal ‘edge case’ is unknown, especially considering in this application the working gas was diffused across the column base rather than being injected through a spout. Further, the fidelity of the model had not yet been tested with lunar soil simulants. Plotting the Ogino et al. model alongside test data allows for a more intuitive sense of the impact of test parameters on soil seal performance, as well as providing a quick means to further tune this predictive model for more accurate use with static seals across granular lunar soil simulants. The model provided by Ogino et al. was further tuned using test data to determine the degree to which a spouted bed model could be applied to a slightly modified set of testing conditions: lunar soil simulants and a more diffuse, homogeneous application of pneumatic pressure. A Matlab script was created to test different values for the leading coefficient and exponents in Ogino’s formula. The script swept preset ranges, then iterated with higher resolutions across narrower ranges to converge on the optimal value for each parameter. The resulting adjusted model was compared to the original, as well as test data with noticeable improvements across the entire test domain.

Jack Stewart↗
DOE OSTI2024

Droplet Growth or Evaporation Does Not Buffer the Variability in Supersaturation in Clean Clouds

Abstract Water vapor supersaturation in clouds is a random variable that drives activation and growth of cloud droplets. The Pi Convection–Cloud Chamber generates a turbulent cloud with a microphysical steady state that can be varied from clean to polluted by adjusting the aerosol injection rate. The supersaturation distribution and its moments, e.g., mean and variance, are investigated for varying cloud microphysical conditions. High-speed and collocated Eulerian measurements of temperature and water vapor concentration are combined to obtain the temporally resolved supersaturation distribution. This allows quantification of the contributions of variances and covariances between water vapor and temperature. Results are consistent with expectations for a convection chamber, with strong correlation between water vapor and temperature; departures from ideal behavior can be explained as resulting from dry regions on the warm boundary, analogous to entrainment. The saturation ratio distribution is measured under conditions that show monotonic increase of liquid water content and decrease of mean droplet diameter with increasing aerosol injection rate. The change in liquid water content is proportional to the change in water vapor concentration between no-cloud and cloudy conditions. Variability in the supersaturation remains even after cloud droplets are formed, and no significant buffering is observed. Results are interpreted in terms of a cloud microphysical Damköhler number (Da), under conditions corresponding to , i.e., the slow-microphysics regime. This implies that clouds with very clean regions, such that is satisfied, will experience supersaturation fluctuations without them being buffered by cloud droplet growth. Significance Statement The saturation ratio (humidity) in clouds controls the growth rate and formation of cloud droplets. When air in a turbulent cloud mixes, the humidity varies in space and time throughout the cloud. This is important because it means cloud droplets experience different growth histories, thereby resulting in broader size distributions. It is often assumed that growth and evaporation of cloud droplets buffers out some of the humidity variations. Measuring these variations has been difficult, especially in the field. The purpose of this study is to measure the saturation ratio distribution in clouds with a range of conditions. We measure the in-cloud saturation ratio using a convection cloud chamber with clean to polluted cloud properties. We found in clouds with low concentrations of droplets that the variations in the saturation ratio are not suppressed.

54 ENVIRONMENTAL SCIENCES↗