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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 OSTI2024

Solar, Wind, and Load Forecasting Dataset for MISO, NYISO, and SPP Balancing Areas

The Performance-based Energy Resource Feedback, Optimization, and Risk Management (PERFORM) program is an initiative intended to foster "a fundamental shift in grid management rooted in an understanding of asset risk and system risk" (ARPA-E 2020). Launched by the Advanced Research Projects Agency-Energy (ARPA-E), the program supports efforts to incorporate uncertainty in electric power decision making. In support of PERFORM, the National Renewable Energy Laboratory (NREL) has produced a set of time-coincident forecasts of solar, wind, and load profiles. As part of Phase I of the PERFORM effort, NREL created a dataset that consists of one year of time-coincident load, wind, and solar actuals and probabilistic forecasts based on data from the Electric Reliability Council of Texas (ERCOT) (Bryce et al. 2023). In Phase II, NREL developed similar datasets for three other U.S. Independent System Operators (ISO): the Midcontinent Independent System Operator (MISO), the New York Independent System Operator (NYISO), and the Southwest Power Pool (SPP).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Command of active and responsive elastomers by topological defects and patterns

The project resulted in the development of stimuli-responsive elastomer coatings formed by photopolymerized liquid crystal molecules. The molecular orientation of the liquid crystal elastomers is coupled to rubber-like elasticity. The project developed an approach to produce complex patterns of molecular orientation in elastomers by employing photopatterning technique based on plasmonic metamasks that convert unpolarized incoming light into a transmitted light beam with spatially-varying linear polarization. The polarization-modulated light beam aligns the film of azodye molecules, which serves as the substrate for liquid crystal elastomer coating. The alignment of the azodye molecules follows the polarization pattern with a phase shift of the alignment direction by 90 degrees. The azodye substrate imposes orientation of molecules in the adjacent liquid crystal layer which is preserved after the monomers are polymerized into a liquid crystal elastomer. By using differently aligned substrates one could produce various patterns of molecular orientation in the elastomer, which dictate the mechanical properties of the elastomers and their response to external stimuli such as temperature, humidity, and ultraviolet irradiation. In particular, the predesigned molecular orientation produces deterministically defined topography response of the liquid crystal coatings, in which their free surface changes from flat to either locally elevated or locally compressed, depending on whether the pre-inscribed molecular orientation is predesigned with a splay or bend deformation. The research established that the mechanism of this deterministic relation between the dynamic topography and pre-inscribed molecular orientation is rooted in the change of the tensorial order parameter that characterizes the degree of orientational order, and in the contraction/expansion of polymer molecules in response to the order parameter changes. In particular, lowering the order causes contraction of polymer globules. The work demonstrated that the dynamic topography of elastomer coatings with three-dimensional pattern of orientational order includes up and down motion of the material (along the normal to the coating) and also shifts in lateral directions. We demonstrate that the deterministic relationship between the orientational patterns and variations of coatings’ profile is caused by forces that are mathematically equivalent to active forces in the system of “pullers” or “pushers” in active matter, demonstrating universality of the out-of-equilibrium behavior. The project resulted in theoretical models that predictively describe the dynamic response of coatings. The topography of liquid crystal elastomers is sensitive to stimuli such as temperature, ultraviolet irradiation, and humidity. The applicability of the patterned approach to produce dynamic coatings was expanded from nematic elastomers to their smectic counterparts. The project combined a battery of imaging techniques to unveil the dynamic properties of the coatings, ranging from the state-of-the-art dynamic holographic microscopy to fluorescent confocal polarizing microscopy and polscope microscopy. The research uncovered potential applications of dynamic elastomer coatings, such as control placement of colloidal particles, engineering of living tissues with alignment patterns of cells that follow the orientational order of elastomer coatings. The approach developed in the project can be used to design programmable dynamic coatings with functionalities that mimic biological tissues such as skin.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Learning electric vehicle driver range anxiety with an initial state of charge-oriented gradient boosting approach

This manuscript focuses on the modeling of electric vehicle (EV) driver’s range anxiety, a fear that a vehicle does not have sufficient range, or state of charge (SOC) of the battery pack, to reach its destination and would strand its occupants. Despite numerous research studies on the modeling of charging behaviors, modeling efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To this end, an ensemble learning model based on gradient boosting is developed. The model sequentially fits new predictors to new residuals of the previous prediction and, then, minimizes the loss when adding the latest prediction. A total of 18 features are defined and extracted from the multisource data, which cover information on driver, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5-year’s charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. Here, the result shows the proposed model achieved a satisfactory result with a R square value of 0.54 and root mean square error of 0.14, both better than multiple linear regression model and random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users or that are further from residential areas.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Allometric relationships and trade‐offs in 11 common M editerranean‐climate grasses

Abstract Biomass allocation in plants is the foundation for understanding dynamics in ecosystem carbon balance, species competition, and plant–environment interactions. However, existing work on plant allometry has mainly focused on trees, with fewer studies having developed allometric equations for grasses. Grasses with different life histories can vary in their carbon investment by prioritizing the growth of specific organs to survive, outcompete co‐occurring plants, and ensure population persistence. Further, because grasses are important fuels for wildfire, the lack of grass allocation data adds uncertainty to process‐based models that relate plant physiology to wildfire dynamics. To fill this gap, we conducted a greenhouse experiment with 11 common California grasses varying in photosynthetic pathway and growth form. We measured plant sizes and harvested above‐ and belowground biomass throughout the life cycle of annual species, while for the establishment stage of perennial grasses to quantify allometric relationships for leaf, stem, and root biomass, as well as plant height and canopy area. We used basal diameter as a reference measure of plant size. Overall, basal diameter is the best predictor for leaf and stem biomass, height, and canopy area. Including height as another predictor can improve model accuracy in predicting leaf and stem biomass and canopy area. Fine root biomass is a function of leaf biomass alone. Species vary in their allometric relationships, with most variation occurring for plant height, canopy area, and stem biomass. We further explored potential trade‐offs in biomass allocation across species between leaf and fine root, leaf and stem, and allocation to reproduction. Consistent with our expectation, we found that fast‐growing plants allocated a greater fraction to reproduction. Additionally, plant height and specific leaf area negatively influenced the leaf‐to‐stem ratio. However, contrary to our hypothesis, there were no differences in root‐to‐leaf ratio between perennial and annual or C 4 and C 3 plants. Our study provides species‐specific and functional‐type‐specific allometry equations for both above‐ and belowground organs of 11 common California grass species, enabling nondestructive biomass assessment in California grasslands. These allometric relationships and trade‐offs in carbon allocation across species can improve ecosystem model predictions of grassland species interactions and environmental responses through differences in morphology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Forecasting climate change impacts on plant populations over large spatial extents

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Soil evaporation and organic matter turnover in the Sub-Taiga and Forest-Steppe of southwest Siberia

Southwest Siberia encompasses the forest-steppe and sub-taiga climatic zones and has historically been utilized for agriculture. Coinciding with predicted changes in climate for the region is the pressure of agricultural development; however, a characterization of the soil water and carbon dynamics is lacking. We assessed current soil water properties and soil organic carbon turnover in forests and grasslands for two sites that span the forest steppe and sub-taiga bioclimatic zones. Soil evaporation was 0.62±0.17mm d-1 (mean±standard error) in grasslands and 0.45±0.08mm d-1 in the forests of the forest-steppe site. Evaporation at the sub-taiga site was 1.80±1.70mm d-1 in grasslands and 0.96±0.05mm d-1 in forest plots. Evaporation was significantly greater at the sub-taiga site than the forest-steppe site. The density of fine roots explained the soil water isotopic patterns between vegetation types and sites. We found soil organic matter turnover to be three times faster in the sub-taiga site than in the forest-steppe site. Our results show that while climate factors, in particular snow levels, between the two sites are drivers for water and carbon cycles, site level hydrology, soil characteristics, and vegetation directly interact to influence the water and carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Gearing up to handle the mosaic nature of life in the quest for orthologs

The Quest for Orthologs (QfO) is an open collaboration framework for experts in comparative phylogenomics and related research areas who have an interest in highly accurate orthology predictions and their applications. We here report highlights and discussion points from the QfO meeting 2015 held in Barcelona. Achievements in recent years have established a basis to support developments for improved orthology prediction and to explore new approaches. Central to the QfO effort is proper benchmarking of methods and services, as well as design of standardized datasets and standardized formats to allow sharing and comparison of results. Simultaneously, analysis pipelines have been improved, evaluated and adapted to handle large datasets. All this would not have occurred without the long-term collaboration of Consortium members. Meeting regularly to review and coordinate complementary activities from a broad spectrum of innovative researchers clearly benefits the community. Highlights of the meeting include addressing sources of and legitimacy of disagreements between orthology calls, the context dependency of orthology definitions, special challenges encountered when analyzing very anciently rooted orthologies, orthology in the light of whole-genome duplications, and the concept of orthologous versus paralogous relationships at different levels, including domain-level orthology. Furthermore, particular needs for different applications (e.g. plant genomics, ancient gene families and others) and the infrastructure for making orthology inferences available (e.g. interfaces with model organism databases) were discussed, with several ongoing efforts that are expected to be reported on during the upcoming 2017 QfO meeting.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

SU-F-T-328: Real-Time in Vivo Dosimetry of Prostate SBRT Boost Treatments Using MOSkin Detectors

Purpose: To provide in vivo measurements of dose to the anterior rectal wall during prostate SBRT boost treatments using MOSFET detectors. Methods: Dual MOSkin detectors were attached to a Rectafix rectal sparing device and inserted into patients during SBRT boost treatments. Patients received two boost fractions, each of 9.5–10 Gy and delivered using 2 VMAT arcs. Measurements were acquired for 12 patients. MOSFET voltages were read out at 1 Hz during delivery and converted to dose. MV images were acquired at known frequency during treatment so that the position of the gantry at each point in time was known. The cumulative dose at the MOSFET location was extracted from the treatment planning system at in 5.2° increments (FF beams) or at 5 points during each delivered arc (FFF beams). The MOSFET dose and planning system dose throughout the entirety of each arc were then compared using root mean square error normalised to the final planned dose for each arc. Results: The average difference between MOSFET measured and planning system doses determined over the entire course of treatment was 9.7% with a standard deviation of 3.6%. MOSFETs measured below the planned dose in 66% of arcs measured. Uncertainty in the position of the MOSFET detector and verification point are major sources of discrepancy, as the detector is placed in a high dose gradient region during treatment. Conclusion: MOSkin detectors were able to provide real time in vivo measurements of anterior rectal wall dose during prostate SBRT boost treatments. This method could be used to verify Rectafix positioning and treatment delivery. Further developments could enable this method to be used during high dose treatments to monitor dose to the rectal wall to ensure it remains at safe levels. Funding has been provided by the University of Newcastle. Kimberley Legge is the recipient of an Australian Postgraduate Award.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Expanding the role of reactive transport models in critical zone processes

We report that models test our understanding of processes and can reach beyond the spatial and temporal scales of measurements. Multi-component Reactive Transport Models (RTMs), initially developed more than three decades ago, have been used extensively to explore the interactions of geothermal, hydrologic, geochemical, and geobiological processes in subsurface systems. Driven by extensive data sets now available from intensive measurement efforts, there is a pressing need to couple RTMs with other community models to explore non-linear interactions among the atmosphere, hydrosphere, biosphere, and geosphere. Here we briefly review the history of RTM development, summarize the current state of RTM approaches, and identify new research directions, opportunities, and infrastructure needs to broaden the use of RTMs. In particular, we envision the expanded use of RTMs in advancing process understanding in the Critical Zone, the veneer of the Earth that extends from the top of vegetation to the bottom of groundwater. We argue that, although parsimonious models are essential at larger scales, process-based models offer tools to explore the highly nonlinear coupling that characterizes natural systems. We present seven testable hypotheses that emphasize the unique capabilities of process-based RTMs for (1) elucidating chemical weathering and its physical and biogeochemical drivers; (2) understanding the interactions among roots, micro-organisms, carbon, water, and minerals in the rhizosphere; (3) assessing the effects of heterogeneity across spatial and temporal scales; and (4) integrating the vast quantity of novel data, including “omics” data (genomics, transcriptomics, proteomics, metabolomics), elemental concentration and speciation data, and isotope data into our understanding of complex earth surface systems. In conclusion, with strong support from data-driven sciences, we are now in an exciting era where integration of RTM framework into other community models will facilitate process understanding across disciplines and across scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Influence of mechanical alloying and extrusion conditions on the microstructure and tensile properties of Low-Cr ODS FeCrAl alloys

Low-chromium (<10%Cr) high strength oxide dispersion strengthened (ODS) FeCrAl alloys are considered promising candidates for accident tolerant fuel cladding in light water fission reactors. These alloys are designed to combine the beneficial high temperature mechanical properties of ODS materials with the exceptionally high temperature oxidation resistance of FeCrAl in comparison to conventional Zr-containing cladding materials, while also providing good mechanical and aqueous corrosion behavior under normal light water reactor operating conditions. The initial (1st generation) ODS FeCrAl alloys combined gas atomized FeCrAl powders with yttria and other oxides for alloying element additions using the mechanical alloying approach. These alloys exhibited low ductility but excellent high temperature tensile strength while maintaining good oxidation resistance at temperatures up to 1400 °C. In an attempt to improve alloy ductility for accident tolerant fuel cladding applications, new low-Cr ODS FeCrAl alloys with decreased oxygen content were developed with the Zr alloying solute already gas atomized into the powder prior to mechanical alloying. The resultant Fe-10Cr-6.1Al-0.3Zr+0.3Y 2 O 3 (106ZY) powders were ball milled for 10, 20, and 40hr followed by consolidation via hot extrusion at temperatures ranging from 900 to 1050 °C. Increasing the mechanical alloying time decreased the resultant grain size and improved high temperature tensile properties. Decreasing the extrusion temperature refined the grain size and subsequently strengthened the ODS FeCrAl 106ZY alloys while lowering the ductility. Scanning transmission electron microscopy (STEM) and energy dispersive spectroscopy (EDS) demonstrated the Zr solute addition effectively sequestered impurity C and N within the matrix. The root mean square (RMS) hardening superposition model for yield strength shows good agreement with experimental results. Errors between predicted and experimental values are discussed within the scope of processing parameters. As a result, these 2nd generation 106ZY alloys show a distinct increase in alloy ductility without sacrificing the high temperature tensile properties characteristic of legacy or 1st generation ODS FeCrAl alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Application of a Density Law via Python for Aqueous Plutonium Nitrate

A predictive density tool has been developed in Python to reduce bias and uncertainty in nuclear criticality safety calculations for plutonium nitrate systems. The Pitzer Method and an empirical method were implemented into the tool, allowing for plutonium nitrate density calculations. Additionally, the Python tool can generate atom densities for a MCNP6.2 material card using the density from the selected method and directly the densities into a prepared MCNP6 input text file. The material card and density are calculated based on customizable user inputs of plutonium content, nitric acid content, temperature, plutonium isotope weight percentages and impurity concentrations. The Python tool has been validated and verified against the International Handbook of Evaluated Criticality Safety Benchmark Experiments to predict densities within a root mean square error of 1.0% for the Pitzer method and 1.8% for the empirical method. These errors in density were shown to lead to a ±0.5% error in MCNP6.2 calculated k effective for the Pitzer method and a ±1.7% error for the Empirical method. Simultaneous work is also being done at the University of New Mexico and Los Alamos National Laboratory to create a similar tool for plutonium chloride solutions, which aims to provide the accreditation of the chlorine absorption. These capabilities will not only provide more accurate models but also lead the way towards a better understanding of solution systems and potential relaxation in the conservatism of the current aqueous plutonium processing limits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Measured and estimated performance of a fleet of shaded photovoltaic systems with string and module-level inverters

Shade obstructions can significantly impact the performance of photovoltaic (PV) systems. Although there are many models for partially shaded PV arrays, there is a lack of information available regarding their accuracy and uncertainty when compared with actual field performance. This work assesses the recorded performance of 46 residential PV systems, equipped with either string-level or module-level inverters, under a variety of shading conditions. We compare their energy production data to annual PV performance predictions, with a focus on the practical models developed here for National Renewable Energy Laboratory's system advisor model software. This includes assessment of shade extent on each PV system by using traditional onsite surveys and newer 3D obstruction modelling. The electrical impact of shade is modelled by either a nonlinear performance model or assumption of linear impact with shade extent, depending on the inverter type. When applied to the fleet of residential PV systems, performance is predicted with median annual bias errors of 2.5% or less, for systems with up to 20% estimated shading loss. The partial shade models are not found to add appreciable uncertainty to annual predictions of energy production for this fleet of systems but do introduce a monthly root-mean-square error of approximately 4%-9% due to seasonal effects. Here the use of a detailed 3D model results in similar or improved accuracy over site survey methods, indicating that, with proper description of shade obstructions, modelling of partially shaded PV arrays can be done completely remotely, potentially saving time and cost.

14 SOLAR ENERGY↗
DOE OSTI2015

Multipinhole SPECT helical scan parameters and imaging volume

Purpose: The authors developed SPECT imaging capability on an animal PET scanner using a multiple-pinhole collimator and step-and-shoot helical data acquisition protocols. The objective of this work was to determine the preferred helical scan parameters, i.e., the angular and axial step sizes, and the imaging volume, that provide optimal imaging performance. Methods: The authors studied nine helical scan protocols formed by permuting three rotational and three axial step sizes. These step sizes were chosen around the reference values analytically calculated from the estimated spatial resolution of the SPECT system and the Nyquist sampling theorem. The nine helical protocols were evaluated by two figures-of-merit: the sampling completeness percentage (SCP) and the root-mean-square (RMS) resolution. SCP was an analytically calculated numerical index based on projection sampling. RMS resolution was derived from the reconstructed images of a sphere-grid phantom. Results: The RMS resolution results show that (1) the start and end pinhole planes of the helical scheme determine the axial extent of the effective field of view (EFOV), and (2) the diameter of the transverse EFOV is adequately calculated from the geometry of the pinhole opening, since the peripheral region beyond EFOV would introduce projection multiplexing and consequent effects. The RMS resolution results of the nine helical scan schemes show optimal resolution is achieved when the axial step size is the half, and the angular step size is about twice the corresponding values derived from the Nyquist theorem. The SCP results agree in general with that of RMS resolution but are less critical in assessing the effects of helical parameters and EFOV. Conclusions: The authors quantitatively validated the effective FOV of multiple pinhole helical scan protocols and proposed a simple method to calculate optimal helical scan parameters.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Embryogenic cell suspensions for high-capacity genetic transformation and regeneration of switchgrass (Panicum virgatum L.)

Abstract Background Switchgrass ( Panicum virgatum L.), a North American prairie grassland species, is a potential lignocellulosic biofuel feedstock owing to its wide adaptability and biomass production. Production and genetic manipulation of switchgrass should be useful to improve its biomass composition and production for bioenergy applications. The goal of this project was to develop a high-throughput stable switchgrass transformation method using Agrobacterium tumefaciens with subsequent plant regeneration. Results Regenerable embryogenic cell suspension cultures were established from friable type II callus-derived inflorescences using two genotypes selected from the synthetic switchgrass variety ‘Performer’ tissue culture lines 32 and 605. The cell suspension cultures were composed of a heterogeneous fine mixture culture of single cells and aggregates. Agrobacterium tumefaciens strain GV3101 was optimum to transfer into cells the pANIC-10A vector with a hygromycin-selectable marker gene and a pporRFP orange fluorescent protein marker gene at an 85% transformation efficiency. Liquid cultures gave rise to embryogenic callus and then shoots, of which up to 94% formed roots. The resulting transgenic plants were phenotypically indistinguishable from the non-transgenic parent lines. Conclusion The new cell suspension-based protocol enables high-throughput Agrobacterium -mediated transformation and regeneration of switchgrass in which plants are recovered within 6–7 months from culture establishment.

Ondzighi-Assoume, Christine A.↗
DOE OSTI2020

Post-Synthetic Reduction of Pectin Methylesterification Causes Morphological Abnormalities and Alterations to Stress Response in Arabidopsis thaliana

Pectin is a critical component of the plant cell wall, supporting wall biomechanics and contributing to cell wall signaling in response to stress. The plant cell carefully regulates pectin methylesterification with endogenous pectin methylesterases (PMEs) and their inhibitors (PMEIs) to promote growth and protect against pathogens. We expressed Aspergillus nidulans pectin methylesterase (AnPME) in Arabidopsis thaliana plants to determine the impacts of methylesterification status on pectin function. Plants expressing AnPME had a roughly 50% reduction in methylester content compared with control plants. AnPME plants displayed a severe dwarf phenotype, including small, bushy rosettes and shorter roots. This phenotype was caused by a reduction in cell elongation. Cell wall composition was altered in AnPME plants, with significantly more arabinose and significantly less galacturonic acid, suggesting that plants actively monitor and compensate for altered pectin content. Cell walls of AnPME plants were more readily degraded by polygalacturonase (PG) alone but were less susceptible to treatment with a mixture of PG and PME. AnPME plants were insensitive to osmotic stress, and their susceptibility to Botrytis cinerea was comparable to wild type plants despite their compromised cell walls. This is likely due to upregulated expression of defense response genes observed in AnPME plants. These results demonstrate the importance of pectin in both normal growth and development, and in response to biotic and abiotic stresses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Thermodynamics of Highly Concentrated Aqueous Electrolytes: Based on Boltzmann's eponymous equation

This sharply focused book invites the reader to explore the chemical thermodynamics of highly concentrated aqueous electrolytes from a different vantage point than traditional methods. The book's foundation is deeply rooted in Ludwig Boltzmann's eponymous equation. The pathway from micro to macro thermodynamics is explained heuristically, in a step-by-step approach. Concepts and mathematical formalism are explained in detail to captivate and maintain interest as the algebra twists and turns. Every significant result is derived in a lucid and piecemeal fashion. Application of the theory is exemplified with examples. It is amazing to realize that Boltamann's simple equation contains sufficient information from which such an elaborate theory can emerge. This book is suitable for undergraduate and graduate level classes in chemical engineering, chemistry, geochemistry, environmental sciences, and those studying aerosol particles in the troposphere. Students interested in understanding how thermodynamic theories may be developed would be inspired by the methodology. The author wishes that readers get as much excitement reading this book as he did writing it.

Ally, Moonis↗
DOE OSTI2015

Efficient traveltime solutions of the acoustic TI eikonal equation

Numerical solutions of the eikonal (Hamilton–Jacobi) equation for transversely isotropic (TI) media are essential for imaging and traveltime tomography applications. Such solutions, however, suffer from the inherent higher-order nonlinearity of the TI eikonal equation, which requires solving a quartic polynomial for every grid point. Analytical solutions of the quartic polynomial yield numerically unstable formulations. Thus, it requires a numerical root finding algorithm, adding significantly to the computational load. Using perturbation theory we approximate, in a first order discretized form, the TI eikonal equation with a series of simpler equations for the coefficients of a polynomial expansion of the eikonal solution, in terms of the anellipticity anisotropy parameter. Such perturbation, applied to the discretized form of the eikonal equation, does not impose any restrictions on the complexity of the perturbed parameter field. Therefore, it provides accurate traveltime solutions even for models with complex distribution of velocity and anisotropic anellipticity parameter, such as that for the complicated Marmousi model. The formulation allows for large cost reduction compared to using the direct TI eikonal solver. Furthermore, comparative tests with previously developed approximations illustrate remarkable gain in accuracy in the proposed algorithm, without any addition to the computational cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

TH-CD-207A-07: Prediction of High Dimensional State Subject to Respiratory Motion: A Manifold Learning Approach

Purpose: The development of high dimensional imaging systems (e.g. volumetric MRI, CBCT, photogrammetry systems) in image-guided radiotherapy provides important pathways to the ultimate goal of real-time volumetric/surface motion monitoring. This study aims to develop a prediction method for the high dimensional state subject to respiratory motion. Compared to conventional linear dimension reduction based approaches, our method utilizes manifold learning to construct a descriptive feature submanifold, where more efficient and accurate prediction can be performed. Methods: We developed a prediction framework for high-dimensional state subject to respiratory motion. The proposed method performs dimension reduction in a nonlinear setting to permit more descriptive features compared to its linear counterparts (e.g., classic PCA). Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where low-dimensional prediction is performed. A fixed-point iterative pre-image estimation method is applied subsequently to recover the predicted value in the original state space. We evaluated and compared the proposed method with PCA-based method on 200 level-set surfaces reconstructed from surface point clouds captured by the VisionRT system. The prediction accuracy was evaluated with respect to root-mean-squared-error (RMSE) for both 200ms and 600ms lookahead lengths. Results: The proposed method outperformed PCA-based approach with statistically higher prediction accuracy. In one-dimensional feature subspace, our method achieved mean prediction accuracy of 0.86mm and 0.89mm for 200ms and 600ms lookahead lengths respectively, compared to 0.95mm and 1.04mm from PCA-based method. The paired t-tests further demonstrated the statistical significance of the superiority of our method, with p-values of 6.33e-3 and 5.78e-5, respectively. Conclusion: The proposed approach benefits from the descriptiveness of a nonlinear manifold and the prediction reliability in such low dimensional manifold. The fixed-point iterative approach turns out to work well practically for the pre-image recovery. Our approach is particularly suitable to facilitate managing respiratory motion in image-guide radiotherapy. This work is supported in part by NIH grant R01 CA169102-02.

60 APPLIED LIFE SCIENCES↗