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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-P-48: Evaluation of Intensity Modulated Radiotherapy (IMRT) with Three Different Commercial Planning Systems for the Treatment of Cervical Cancer

Purpose: To investigate the performances of three commercial treatment planning systems (TPS) for intensity modulated radiotherapy (IMRT) optimization regarding cervical cancer. Methods: For twenty cervical cancer patients, three IMRT plans were retrospectively re-planned: one with Pinnacle TPS,one with Oncentra TPS and on with Eclipse TPS. The total prescribed dose was 50.4 Gy delivered for PTV and 58.8 Gy for PTVnd by simultaneous integrated boost technique. The treatments were delivered using the Varian 23EX accelerator. All optimization schemes generated clinically acceptable plans. They were evaluated based on target coverage, homogeneity (HI) and conformity (CI). The organs at risk (OARs) were analyzed according to the percent volume under some doses and the maximum doses. The statistical method of the collected data of variance analysis was used to compare the difference among the quality of plans. Results: IMRT with Eclipse provided significant better HI, CI and all the parameters of PTV. However, the trend was not extension to the PTVnd, it was still significant better at mean dose, D50% and D98%, but plans with Oncentra showed significant better in the hight dosage volume, such as maximum dose and D2%. For the bladder wall, there were not notable difference among three groups, although Pinnacle and Oncentra systems provided a little lower dose sparing at V50Gy of bladder and rectal wall and V40Gy of bladder wall, respectively. V40Gy of rectal wall (p=0.037), small intestine (p=0.001 for V30Gy, p=0.010 for maximum dose) and V50Gy of right-femoral head (p=0.019) from Eclipse plans showed significant better than other groups. Conclusion: All SIB-IMRT plans were clinically acceptable which were generated by three commercial TPSs. The plans with Eclipse system showed advantages over the plans with Oncentra and Pinnacle system in the overwhelming majority of the dose coverage for targets and dose sparing of OARs in cervical cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

A linear, separable two-parameter model for dual energy CT imaging of proton stopping power computation

Purpose: To evaluate the accuracy and robustness of a simple, linear, separable, two-parameter model (basis vector model, BVM) in mapping proton stopping powers via dual energy computed tomography (DECT) imaging. Methods: The BVM assumes that photon cross sections (attenuation coefficients) of unknown materials are linear combinations of the corresponding radiological quantities of dissimilar basis substances (i.e., polystyrene, CaCl{sub 2} aqueous solution, and water). The authors have extended this approach to the estimation of electron density and mean excitation energy, which are required parameters for computing proton stopping powers via the Bethe–Bloch equation. The authors compared the stopping power estimation accuracy of the BVM with that of a nonlinear, nonseparable photon cross section Torikoshi parametric fit model (VCU tPFM) as implemented by the authors and by Yang et al. [“Theoretical variance analysis of single- and dual-energy computed tomography methods for calculating proton stopping power ratios of biological tissues,” Phys. Med. Biol. 55, 1343–1362 (2010)]. Using an idealized monoenergetic DECT imaging model, proton ranges estimated by the BVM, VCU tPFM, and Yang tPFM were compared to International Commission on Radiation Units and Measurements (ICRU) published reference values. The robustness of the stopping power prediction accuracy of tissue composition variations was assessed for both of the BVM and VCU tPFM. The sensitivity of accuracy to CT image uncertainty was also evaluated. Results: Based on the authors’ idealized, error-free DECT imaging model, the root-mean-square error of BVM proton stopping power estimation for 175 MeV protons relative to ICRU reference values for 34 ICRU standard tissues is 0.20%, compared to 0.23% and 0.68% for the Yang and VCU tPFM models, respectively. The range estimation errors were less than 1 mm for the BVM and Yang tPFM models, respectively. The BVM estimation accuracy is not dependent on tissue type and proton energy range. The BVM is slightly more vulnerable to CT image intensity uncertainties than the tPFM models. Both the BVM and tPFM prediction accuracies were robust to uncertainties of tissue composition and independent of the choice of reference values. This reported accuracy does not include the impacts of I-value uncertainties and imaging artifacts and may not be achievable on current clinical CT scanners. Conclusions: The proton stopping power estimation accuracy of the proposed linear, separable BVM model is comparable to or better than that of the nonseparable tPFM models proposed by other groups. In contrast to the tPFM, the BVM does not require an iterative solving for effective atomic number and electron density at every voxel; this improves the computational efficiency of DECT imaging when iterative, model-based image reconstruction algorithms are used to minimize noise and systematic imaging artifacts of CT images.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

SU-F-T-113: Inherent Functional Dependence of Spinal Cord Doses of Variable Irradiated Volumes in Spine SBRT

Purpose: Spinal cord tolerance for SBRT has been recommended for the maximum point dose level or at irradiated volumes such as 0.35 mL or 10% of contoured volumes. In this study, we investigated an inherent functional relationship that associates these dose surrogates for irradiated spinal cord volumes of up to 3.0 mL. Methods: A hidden variable termed as Effective Dose Radius (EDR) was formulated based on a dose fall-off model to correlate dose at irradiated spinal cord volumes ranging from 0 mL (point maximum) to 3.0 mL. A cohort of 15 spine SBRT cases was randomly selected to derive an EDR-parameterized formula. The mean prescription dose for the studied cases was 21.0±8.0 Gy (range, 10–40Gy) delivered in 3±1 fractions with target volumes of 39.1 ± 70.6 mL. Linear regression and variance analysis were performed for the fitting parameters of variable EDR values. Results: No direct correlation was found between the dose at maximum point and doses at variable spinal cord volumes. For example, Pearson R{sup 2} = 0.643 and R{sup 2}= 0.491 were obtained when correlating the point maximum dose with the spinal cord dose at 1 mL and 3 mL, respectively. However, near perfect correlation (R{sup 2} ≥0.99) was obtained when corresponding parameterized EDRs. Specifically, Pearson R{sup 2}= 0.996 and R{sup 2} = 0.990 were obtained when correlating EDR (maximum point dose) with EDR (dose at 1 mL) and EDR(dose at 3 mL), respectively. As a result, high confidence level look-up tables were established to correlate spinal cord doses at the maximum point to any finite irradiated volumes. Conclusion: An inherent functional relationship was demonstrated for spine SBRT. Such a relationship unifies dose surrogates at variable cord volumes and proves that a single dose surrogate (e.g. point maximum dose) is mathematically sufficient in constraining the overall spinal cord dose tolerance for SBRT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

MAVTgsa: An R Package for Gene Set (Enrichment) Analysis

Gene set analysis methods aim to determine whether an a priori defined set of genes shows statistically significant difference in expression on either categorical or continuous outcomes. Although many methods for gene set analysis have been proposed, a systematic analysis tool for identification of different types of gene set significance modules has not been developed previously. This work presents an R package, called MAVTgsa, which includes three different methods for integrated gene set enrichment analysis. (1) The one-sided OLS (ordinary least squares) test detects coordinated changes of genes in gene set in one direction, either up- or downregulation. (2) The two-sided MANOVA (multivariate analysis variance) detects changes both up- and downregulation for studying two or more experimental conditions. (3) A random forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes. MAVTgsa computes the P values and FDR (false discovery rate) q -value for all gene sets in the study. Furthermore, MAVTgsa provides several visualization outputs to support and interpret the enrichment results. This package is available online.

Chien, Chih-Yi↗
DOE OSTI2015

Ppb-level formaldehyde detection using a CW room-temperature interband cascade laser and a miniature dense pattern multipass gas cell

A ppb-level formaldehyde (H 2 CO) sensor was developed using a thermoelectrically cooled (TEC), continuous-wave (CW) room temperature interband cascade laser (ICL) emitting at 3.59 μm and a miniature dense pattern multipass gas cell with >50 m optical path length. Performance of the sensor was investigated with two measurement schemes: direct absorption (DAS) and wavelength modulation spectroscopy (WMS). With an integration time of less than 1.5 second, a detection limit of ~3 ppbv for H 2 CO measurement with precision of 1.25 ppbv for DAS and 0.58 ppbv for WMS, respectively, was achieved without zero air based background subtraction. An Allan-Werle variance analysis indicated that the precisions can be further improved to 0.26 ppbv @ 300s for DAS and 69 pptv @ 90 s for WMS, respectively. Finally, a side-by-side comparison between two measurement schemes is also discussed in detail.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

The extreme ultraviolet variability of quasars

We study the extreme ultraviolet (EUV) variability (rest frame wavelengths 500–920 Å) of high-luminosity quasars using Hubble Space Telescope (HST) (low to intermediate redshift sample) and Sloan Digital sky Survey (SDSS) (high redshift sample) archives. The combined HST and SDSS data indicates a much more pronounced variability when the sampling time between observations in the quasar rest frame is >2×10{sup 7} s compared to <1.5×10{sup 7} s. Based on an excess variance analysis, for time intervals <2×10{sup 7} s in the quasar rest frame, 10% of the quasars (4/40) show evidence of EUV variability. Similarly, for time intervals >2×10{sup 7} s in the quasar rest frame, 55% of the quasars (21/38) show evidence of EUV variability. The propensity for variability does not show any statistically significant change between 2.5×10{sup 7} s and 3.16×10{sup 7} s (1 year). The temporal behavior is one of a threshold time interval for significant variability as opposed to a gradual increase on these timescales. A threshold timescale can indicate a characteristic spatial dimension of the EUV region. We explore this concept in the context of the slim disk models of accretion. We find that for rapidly spinning black holes, the radial infall time to the plunge region of the optically thin surface layer of the slim disk that is responsible for the preponderance of the EUV flux emission (primarily within 0–7 black hole radii from the inner edge of the disk) is consistent with the empirically determined variability timescale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Variance-Based Sensitivity Analysis to Support Simulation-Based Design Under Uncertainty

Sensitivity analysis plays a critical role in quantifying uncertainty in the design of engineering systems. A variance-based global sensitivity analysis is often used to rank the importance of input factors, based on their contribution to the variance of the output quantity of interest. However, this analysis assumes that all input variability can be reduced to zero, which is typically not the case in a design setting. Distributional sensitivity analysis (DSA) instead treats the uncertainty reduction in the inputs as a random variable, and defines a variance-based sensitivity index function that characterizes the relative contribution to the output variance as a function of the amount of uncertainty reduction. This paper develops a computationally efficient implementation for the DSA formulation and extends it to include distributions commonly used in engineering design under uncertainty. Application of the DSA method to the conceptual design of a commercial jetliner demonstrates how the sensitivity analysis provides valuable information to designers and decision-makers on where and how to target uncertainty reduction efforts.

42 ENGINEERING↗
DOE OSTI2014

ANALYSIS OF VARIANCE-BASED MIXED MULTISCALE FINITE ELEMENT METHOD AND APPLICATIONS IN STOCHASTIC TWO-PHASE FLOWS

The stochastic partial differential systems have been widely used to model physical processes, where the inputs involve large uncertainties. Flows in random and heterogeneous porous media is one of the cases where the random inputs (e.g., permeability) are often modeled as a stochastic field with high-dimensional random parameters. To treat the high dimensionality and heterogeneity efficiently, model reduction is employed in both stochastic space and physical space. An analysis of variance (ANOVA)-based mixed multiscale finite element method MsFEM) is developed to decompose the high-dimensional stochastic problem into a set of lower-dimensional stochastic subproblems, which require much less computational complexity and significantly reduce the computational cost in stochastic space, and the mixed MsFEM can capture the heterogeneities on a coarse grid to greatly reduce the computational cost in the spatial domain. In addition, to enhance the efficiency of the traditional ANOVA method, an adaptive ANOVA method based on a new adaptive criterion is developed, where the most active dimensions can be selected to greatly reduce the computational cost before conducting ANOVA decomposition. This novel adaptive criterion is based on variance-decomposition method coupled with sparse-grid probabilistic collocation method or multilevel Monte Carlo method. The advantage of this adaptive criterion lies in its much lower computational overhead for identifying the active dimensions and interactions. A number of numerical examples in two-phase stochastic flows are presented and demonstrate the accuracy and performance of the adaptive ANOVA-based mixed Ms-FEM.

Wei, Jia↗
DOE OSTI2016

Sensitivity analysis of simulated SOA loadings using a variance-based statistical approach: SENSITIVITY ANALYSIS OF SOA

We investigate the sensitivity of secondary organic aerosol (SOA) loadings simulated by a regional chemical transport model to 7 selected tunable model parameters: 4 involving emissions of anthropogenic and biogenic volatile organic compounds, anthropogenic semi-volatile and intermediate volatility organics (SIVOCs), and NOx, 2 involving dry deposition of SOA precursor gases, and one involving particle-phase transformation of SOA to low volatility. We adopt a quasi-Monte Carlo sampling approach to effectively sample the high-dimensional parameter space, and perform a 250 member ensemble of simulations using a regional model, accounting for some of the latest advances in SOA treatments based on our recent work. We then conduct a variance-based sensitivity analysis using the generalized linear model method to study the responses of simulated SOA loadings to the tunable parameters. Analysis of SOA variance from all 250 simulations shows that the volatility transformation parameter, which controls whether particle-phase transformation of SOA from semi-volatile SOA to non-volatile is on or off, is the dominant contributor to variance of simulated surface-level daytime SOA (65% domain average contribution). We also split the simulations into 2 subsets of 125 each, depending on whether the volatility transformation is turned on/off. For each subset, the SOA variances are dominated by the parameters involving biogenic VOC and anthropogenic SIVOC emissions. Furthermore, biogenic VOC emissions have a larger contribution to SOA variance when the SOA transformation to non-volatile is on, while anthropogenic SIVOC emissions have a larger contribution when the transformation is off. NOx contributes less than 4.3% to SOA variance, and this low contribution is mainly attributed to dominance of intermediate to high NOx conditions throughout the simulated domain. The two parameters related to dry deposition of SOA precursor gases also have very low contributions to SOA variance. This study highlights the large sensitivity of SOA loadings to the particle-phase transformation of SOA volatility, which is neglected in most previous models.

Shrivastava, Manish↗
DOE OSTI2014

WE-D-BRE-07: Variance-Based Sensitivity Analysis to Quantify the Impact of Biological Uncertainties in Particle Therapy

Purpose: In particle therapy, treatment planning and evaluation are frequently based on biological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2). In the context of the linear-quadratic model, these quantities depend on biological parameters (α, β) for ions as well as for the reference radiation and on the dose per fraction. The needed biological parameters as well as their dependency on ion species and ion energy typically are subject to large (relative) uncertainties of up to 20–40% or even more. Therefore it is necessary to estimate the resulting uncertainties in e.g. RBE or EQD2 caused by the uncertainties of the relevant input parameters. Methods: We use a variance-based sensitivity analysis (SA) approach, in which uncertainties in input parameters are modeled by random number distributions. The evaluated function is executed 10{sup 4} to 10{sup 6} times, each run with a different set of input parameters, randomly varied according to their assigned distribution. The sensitivity S is a variance-based ranking (from S = 0, no impact, to S = 1, only influential part) of the impact of input uncertainties. The SA approach is implemented for carbon ion treatment plans on 3D patient data, providing information about variations (and their origin) in RBE and EQD2. Results: The quantification enables 3D sensitivity maps, showing dependencies of RBE and EQD2 on different input uncertainties. The high number of runs allows displaying the interplay between different input uncertainties. The SA identifies input parameter combinations which result in extreme deviations of the result and the input parameter for which an uncertainty reduction is the most rewarding. Conclusion: The presented variance-based SA provides advantageous properties in terms of visualization and quantification of (biological) uncertainties and their impact. The method is very flexible, model independent, and enables a broad assessment of uncertainties. Supported by DFG grant WI 3745/1-1 and DFG cluster of excellence: Munich-Centre for Advanced Photonics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Surrogate-driven Variance-based Sensitivity Analysis of Thermal Storage Tanks in Integrated Energy Systems

Sensitivity analysis and uncertainty quantification are essential steps for enhancing the accuracy of computational models by identifying and mitigating uncertainties. This study focuses on these steps for the Thermal Energy Delivery System at Idaho National Laboratory, specifically targeting the thermocline tank. Using a Modelica/Dymola simulation model, the study perturbed various design parameters and boundary conditions, including shape factor, porosity, outlet temperature, inlet mass flow rate, and system pressure, to predict and quantify uncertainty in the tank’s ax- ial temperature. A dataset of over 1,000 simulations was generated, and surrogate models were developed using the pyMAISE (Michigan Artificial Intelligence Standard Environment) library, which is an Automatic Machine Learning library for nuclear engineering applications. The optimal model, a feedforward neural network with two hidden layers, achieved an R2 score above 0.99 and a mean absolute error below 1 Kelvin. Sensitivity analyses using Sobol indices and Fourier amplitude sensitivity testing methods on this surrogate model revealed that the inlet mass flow rate at initial timestamps and porosity significantly impacts predicted temperatures across all sensors and time steps.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

The cluster decomposition of the configurational energy of multicomponent alloys

Abstract The cluster expansion method (CEM) is a widely used lattice-based technique in the study of multicomponent alloys. Despite its prevalent use, a clear understanding of expansion terms is lacking. We present a modern mathematical formalism of the CEM and introduce thecluster decomposition—a unique and basis-independent decomposition for functions of the atomic configuration in a crystal. We identify the cluster decomposition as an invariant ANOVA decomposition; and demonstrate how functional analysis of variance and sensitivity analysis can be used to interpret interactions among species. Furthermore, we show how the mathematical structure of the cluster decomposition enables numerical evaluation that scales with the number of clusters and is independent of the number of species. Overall, our work enables rigorous interpretations of interactions among species, provides opportunities to explore parameter estimation beyond linear regression, introduces a numerical efficient implementation, and enables analysis of cluster expansions based on established mathematical and statistical principles.

Chemistry↗
DOE OSTI2019

UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras

The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led to the widespread use of this technology, especially for precision agriculture and plant phenotyping. There are several thermal camera systems in the market that are available at a low cost. However, their efficacy and accuracy in various applications has not been tested. In this study, three commercially available UAV thermal cameras, including ICI 8640 P-series (Infrared Cameras Inc., USA), FLIR Vue Pro R 640 (FLIR Systems, USA), and thermoMap (senseFly, Switzerland) have been tested and evaluated for their potential for forest monitoring, vegetation stress detection, and plant phenotyping. Mounted on multi-rotor or fixed wing systems, these cameras were simultaneously flown over different experimental sites located in St. Louis, Missouri (forest environment), Columbia, Missouri (plant stress detection and phenotyping), and Maricopa, Arizona (high throughput phenotyping). Thermal imagery was calibrated using procedures that utilize a blackbody, handheld thermal spot imager, ground thermal targets, emissivity and atmospheric correction. A suite of statistical analyses, including analysis of variance (ANOVA), correlation analysis between camera temperature and plant biophysical and biochemical traits, and heritability were utilized in order to examine the sensitivity and utility of the cameras against selected plant phenotypic traits and in the detection of plant water stress. In addition, in reference to quantitative assessment of image quality from different thermal cameras, a non-reference image quality evaluator, which primarily measures image focus that is based on the spatial relationship of pixels in different scales, was developed. Our results show that (1) UAV-based thermal imaging is a viable tool in precision agriculture and (2) the three examined cameras are comparable in terms of their efficacy for plant phenotyping. Overall, accuracy, when compared against field measured ground temperature and estimating power of plant biophysical and biochemical traits, the ICI 8640 P-series performed better than the other two cameras, followed by FLIR Vue Pro R 640 and thermoMap cameras. Our results demonstrated that all three UAV thermal cameras provide useful temperature data for precision agriculture and plant phenotying, with ICI 8640 P-series presenting the best results among the three systems. Cost wise, FLIR Vue Pro R 640 is more affordable than the other two cameras, providing a less expensive option for a wide range of applications.

47 OTHER INSTRUMENTATION↗