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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 OSTI2018

Two-Level Least Squares Methods in Krylov Subspaces

Two-level least squares acceleration approaches are applied to the Chebyshev acceleration method and the restarted conjugate residual method in solving systems of linear algebraic equations with sparse unsymmetric coefficient matrices arising from finite volume or finite element approximations of boundary-value problems on irregular grids. Application of the proposed idea to other iterative restarted processes also is considered. The efficiency of the algorithms suggested is investigated numerically on a set of model Dirichlet problems for the convection-diffusion equation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Self-Consistent Field Methods for Excited States in Strong Magnetic Fields: A Comparison Between Energy - and Variance-based Approaches

An efficient implementation of geometrical derivatives at the Hartree–Fock (HF) and current-density functional theory (CDFT) levels is presented for the study of molecular structure in strong magnetic fields. The required integral derivatives are constructed using a hybrid McMurchie–Davidson and Rys quadrature approach, which combines the amenability of the former to the evaluation of derivative integrals with the efficiency of the latter for basis sets with high angular momentum. In addition to its application to evaluating derivatives of four-center integrals, this approach is also applied to gradients using the resolution-of-the-identity approximation, enabling efficient optimization of molecular structure for many-electron systems under a strong magnetic field. The CDFT contributions have been implemented for a wide range of density functionals up to and including the meta-GGA level with current-density dependent contributions and (range-separated) hybrids for the first time. Illustrative applications are presented to the OH and benzene molecules, revealing the rich and complex chemistry induced by the presence of an external magnetic field. Challenges for geometry optimization in strong fields are highlighted, along with the requirement for careful analysis of the resulting electronic structure at each stationary point. The importance of correlation effects is examined by comparison of results at the HF and CDFT levels. The present implementation of molecular gradients at the CDFT level provides a cost-effective approach to the study of molecular structure under strong magnetic fields, opening up many new possibilities for the study of chemistry in this regime.

Comparison↗
DOE OSTI2016

REPEATABILITY OF SPITZER/IRAC EXOPLANETARY ECLIPSES WITH INDEPENDENT COMPONENT ANALYSIS

The research of effective and reliable detrending methods for Spitzer data is of paramount importance for the characterization of exoplanetary atmospheres. To date, the totality of exoplanetary observations in the mid- and far-infrared, at wavelengths >3 μm, have been taken with Spitzer. In some cases, in past years, repeated observations and multiple reanalyses of the same data sets led to discrepant results, raising questions about the accuracy and reproducibility of such measurements. Morello et al. (2014, 2015) proposed a blind-source separation method based on the Independent Component Analysis of pixel time series (pixel-ICA) to analyze InfraRed Array Camera (IRAC) data, obtaining coherent results when applied to repeated transit observations previously debated in the literature. Here we introduce a variant to the pixel-ICA through the use of wavelet transform, wavelet pixel-ICA, which extends its applicability to low-signal-to-noise-ratio cases. We describe the method and discuss the results obtained over 12 eclipses of the exoplanet XO3b observed during the “Warm Spitzer” era in the 4.5 μm band. The final results are reported, in part, also in Ingalls et al. (2016), together with results obtained with other detrending methods, and over 10 synthetic eclipses that were analyzed for the “IRAC Data Challenge 2015.” Our results are consistent within 1σ with the ones reported in Wong et al. (2014) and with most of the results reported in Ingalls et al. (2016), which appeared on arXiv while this paper was under review. Based on many statistical tests discussed in Ingalls et al. (2016), the wavelet pixel-ICA method performs as well as or better than other state-of-art methods recently developed by other teams to analyze Spitzer/IRAC data, and, in particular, it appears to be the most repeatable and the most reliable, while reaching the photon noise limit, at least for the particular data set analyzed. Another strength of the ICA approach is its highest objectivity, as it does not use prior information about the instrument systematics, making it a promising method to analyze data from other observatories. The self-consistency of individual measurements of eclipse depth and phase curve slope over a span of more than three years proves the stability of Warm Spitzer/IRAC photometry within the error bars, at the level of 1 part in 10{sup 4} in stellar flux.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Abstract. As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer timescales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting model (WRF) 4.2.1 to different SST products. We first evaluate three different data sets: the Multiscale Ultrahigh Resolution (MUR25) SST analysis, a daily, 0.25∘ × 0.25∘ resolution product; the Operational Sea Surface Temperature and Ice Analysis (OSTIA), a daily, 0.054∘ × 0.054∘ resolution product; and SSTs from the Geostationary Operational Environmental Satellite 16 (GOES-16), an hourly, 0.02∘ × 0.02∘ resolution product. GOES-16 is not processed at the same level as OSTIA and MUR25; therefore, the product requires gap-filling using an interpolation method to create a complete map with no missing data points. OSTIA and GOES-16 SSTs validate markedly better against buoy observations than MUR25, so these two products are selected for use with model simulations, while MUR25 is at this point removed from consideration. We run the model for June and July of 2020 and find that for this time period, in the Mid-Atlantic, although OSTIA SSTs overall validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height (140 m) wind characterization performance on monthly timescales. Additionally, during hours-long flagged events (< 30 h each) that show statistically significant wind speed deviations between the two simulations, both simulations once again demonstrate similar validation performance (differences in bias, earth mover's distance, correlation, and root mean square error on the order of 10−1 or less), with GOES-16 winds validating nominally better than OSTIA winds. With a more refined GOES-16 product, which has been not only gap-filled but also assimilated with in situ SST measurements in the region, it is likely that hub-height winds characterized by GOES-16-informed simulations would definitively validate better than those informed by OSTIA SSTs.

17 WIND ENERGY↗
DOE OSTI2014

MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm

Recovering individual genomes from metagenomic datasets allows access to uncultivated microbial populations that may have important roles in natural and engineered ecosystems. Understanding the roles of these uncultivated populations has broad application in ecology, evolution, biotechnology and medicine. Accurate binning of assembled metagenomic sequences is an essential step in recovering the genomes and understanding microbial functions. We have developed a binning algorithm, MaxBin, which automates the binning of assembled metagenomic scaffolds using an expectation-maximization algorithm after the assembly of metagenomic sequencing reads. Binning of simulated metagenomic datasets demonstrated that MaxBin had high levels of accuracy in binning microbial genomes. MaxBin was used to recover genomes from metagenomic data obtained through the Human Microbiome Project, which demonstrated its ability to recover genomes from real metagenomic datasets with variable sequencing coverages. Application of MaxBin to metagenomes obtained from microbial consortia adapted to grow on cellulose allowed genomic analysis of new, uncultivated, cellulolytic bacterial populations, including an abundant myxobacterial population distantly related to Sorangium cellulosum that possessed a much smaller genome (5 MB versus 13 to 14 MB) but has a more extensive set of genes for biomass deconstruction. For the cellulolytic consortia, the MaxBin results were compared to binning using emergent self-organizing maps (ESOMs) and differential coverage binning, demonstrating that it performed comparably to these methods but had distinct advantages in automation, resolution of related genomes and sensitivity. The automatic binning software that we developed successfully classifies assembled sequences in metagenomic datasets into recovered individual genomes. The isolation of dozens of species in cellulolytic microbial consortia, including a novel species of myxobacteria that has the smallest genome among all sequenced aerobic myxobacteria, was easily achieved using the binning software. This work demonstrates that the processes required for recovering genomes from assembled metagenomic datasets can be readily automated, an important advance in understanding the metabolic potential of microbes in natural environments. MaxBin is available at https://sourceforge.net/projects/maxbin/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Acute stress reduces population-level metabolic and proteomic variation

Abstract Background Variation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates. Results We demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins. Conclusions RVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Topical Report – Findings on Subtask 2.7 – Wet ESP and Aerosol Testing at Coal Creek Station

Growing concerns over the impact of CO 2 emissions from combustion sources on global climate change have prompted numerous research and development projects aimed at developing cost-effective technologies for CO 2 capture. One family of technologies being demonstrated at pilot and full scale globally is postcombustion carbon capture (PCCC) systems that employ amine-based solvents. The captured CO 2 can be compressed and permanently stored underground or used for enhanced oil recovery. The proximity of North Dakota’s lignite-fired fleet of power plants to potential CO 2 storage options creates a unique atmosphere for PCCC within the state. However, the unique components present in lignite flue gas present a challenge for large-scale PCCC at North Dakota power plants by contributing to aerosol formation. Aerosols can negatively impact the long-term performance of amine-based solvents for CO 2 capture. Amine-based solvents are volatile, and flue gas particulate provides nucleation sites where amine vapors can condense as aerosols. Because aerosols cannot be easily captured at the column outlet using conventional technologies, the amine-laden aerosols escape the system and lead to accelerated solvent losses. Moreover, particulate components can chemically react with amines to form degradation products that can permanently deactivate the amine, cause fouling, and lead to hazardous emissions. Many of the elements that have been shown to catalyze solvent degradation are present in lignite coals and can exacerbate solvent replacement economics. Understanding this issue is critical to the implementation of solvent-based CO 2 capture systems as applied to lignite-fired generation systems. The Energy & Environmental Research Center (EERC) designed and carried out this project to fully characterize aerosol behavior with various control technologies installed to better optimize aerosol mitigation technology for CO 2 capture. To meet the goal of this project, the following objectives were identified: Determine the effectiveness of a wet electrostatic precipitator (WESP) on mitigating formation of problematic aerosols at Great River Energy’s Coal Creek Station, upstream of the PCCC system. Determine the effectiveness of the Mitsubishi Heavy Industries (MHI) proprietary amine emission reduction unit (AERU) as a postcapture solvent recovery system for reducing aerosol emissions and extending solvent life downstream of the PCCC system. Determine the impact of aerosols on the efficiency and degradation products of both commercial and advanced solvents within the PCCC system. Work was conducted at Coal Creek Station Unit 1 using a slipstream of flue gas from the outlet of the plant’s flue gas desulfurization (FGD) unit. Flue gas was routed through a pilot-scale FGD unit to remove SO 2 to very low levels (~1 ppm) and then through a direct contact cooler (DCC) to further cool the gas and to remove moisture. The gas exiting the DCC was then optionally routed through a WESP before passing to the CO 2 absorber columns. The MHI solvent was used to scrub CO 2 from the slipstream through a set of two absorber columns. The rich solvent was regenerated in a stripper column by heating to drive off captured CO 2 . Flue gas exiting the absorber column was routed to MHI’s proprietary AERU to recover entrained solvent. The system operated using a catch-and-release method where the CO 2 was separated to provide data on the process, but the captured CO 2 was released back into the host site stack. Particulate was measured, collected, and analyzed from multiple locations throughout the pilot-scale system. Unlike the performance observed in prior work, the inlet FGD and DCC did not remove significant particulate matter from the flue gas. This appears to be due to a difference in the nature of the particulate. The DCC seemed to increase particulate size and count, most likely owing to water condensing onto the surfaces of fly ash particles. When the WESP was operated, it achieved >95% particulate capture. Very little particulate matter or indications of solvent were detected at the AERU outlet. When operating with advanced KS 21 solvent, the particulate material at the AERU outlet was even further decreased. Solvent analysis showed that some species derived from flue gas and ash were slowly concentrating in the solvent over the duration of the test. The levels observed were reported to be within expected ranges and were not of concern to MHI. A high-level techno-economic assessment of installing CO 2 capture at Coal Creek Station suggested that, when using a standard monoethanolamine (MEA)-based solution with simple heat integration, the energy penalty to net generation would be 34%. The bulk of this was due to steam losses for regenerating solvent, followed by parasitic electrical demand for CO 2 compression and then by increased parasitic load for pushing flue gas through the absorber column. These demands could be decreased with a more advanced solvent that exhibits lower heat of regeneration and lower pressure drop than does a simple MEA solution. Further energy could be saved with more thorough heat integration to recover useful energy from the steam used for solvent regeneration. Installing a WESP was predicted to increase the cost of electricity by nearly $5/MWh. This would become cost-effective if solvent losses were roughly 10 times the baseline estimate when not using a WESP but could be returned to baseline by installing the WESP. Piping CO 2 for storage in more favorable geology could help with carbon capture and storage economics. Although storing off-site would necessitate construction of a CO 2 transport pipeline, the cost of this pipeline might be more than offset by reducing the number of wells required, the depths of the wells required, and the electrical demand for the CO 2 compressor. Additional factors that favor off-site storage costs include smaller expected CO 2 plume sizes, which translates to less monitoring and fewer landowner agreements. More detailed assessment of the specific geology in the region under and around Coal Creek Station would be needed to accurately assess the costs and benefits of different storage site options. This subtask was cofunded through the Energy & Environmental Research Center–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission.

Strege, Joshua↗
DOE OSTI2017

Groundwater recharge estimation in arid hardrock-alluvium aquifers using combined water-table fluctuation and groundwater balance approaches

This article proposes an approach to estimate groundwater recharge using an optimization-based water-table fluctuation method combined with a groundwater balance model in an arid hardrock-alluvium region, located at the Oman–United Arab Emirates border. Here, we introduce an “effective hardrock thickness” term to identify the percentage of the considered hardrock thickness in which effective groundwater flow takes place. The proposed method is based upon a Thiessen polygon zoning approach. The method includes subpolygons to represent specific geologic units and to enhance the confidence of the estimated groundwater recharge. Two linear and 1 nonlinear submodels were developed to evaluate the model components for the calibration (October 1996 to September 2008) and validation (October 2008 to September 2013) periods. Long-term annual groundwater recharge from rainfall and return flow over the model domain are estimated as 24.62 and 5.71 Mm 3 , respectively, while the effective groundwater flow circulation is found to occur in the upper 7% of the known hardrock thickness (42 m), confirming conclusions of previous field studies. Considering a total difference in groundwater levels between eastern and western points of the study area of the order of 220 m and a 12-year monthly calibration period, a weighted root mean squared error in predicted groundwater elevation of 2.75 m is considered quite reasonable for the study area characterized by remarkable geological and hydrogeological diversity. The proposed approach provides an efficient and robust method to estimate groundwater recharge in regions with a complex geological setting in which interaction between fractured and porous media cannot be easily assessed.

58 GEOSCIENCES↗
DOE OSTI2025

A fully ro-vibrationally resolved corona model for the molecular hydrogen Fulcher-𝛼 system

A ro-vibrationally resolved corona model for the molecular hydrogen Fulcher-𝛼 transition based on the Yacora solver is introduced. The model couples 1365 ro-vibrational levels of the 𝑋 1 ⁢𝛴$^+_𝑔$, 𝑑 3⁢ 𝛱 𝑢 and 𝑎 3 ⁢𝛴$^+_𝑔$ states via electron impact excitation from the ground state into the 𝑑 3⁢ 𝛱 𝑢 state and subsequent spontaneous emission into the 𝑎 3 ⁢𝛴$+_𝑔$ state. For the process of electron impact excitation a set of 45260 fully ro-vibrationally resolved cross sections calculated with the molecular convergent close-coupling (MCCC) method in the adiabatic-nuclei formulation is applied. The MCCC cross sections are compared with results of a scaling method used in other works, demonstrating the need for dedicated ro-vibrationally resolved cross sections. By post-processing the model output, entire Fulcher spectra can be simulated. These spectra are benchmarked with measurements from an inductively coupled plasma discharge at a pressure between 1.1–10 Pa and a RF power of 700 W. The model results agree very well with the experiment both in relative shape and in absolute value. A non-ro-vibrationally resolved, purely electronically resolved collisional radiative model for molecular hydrogen (including further states and reaction channels) is applied to investigate the validity of the corona approximation for the benchmark plasmas and the relevance of possible process extensions of the corona model. Furthermore, the influence of collisional quenching, autoionization and predissociation is discussed.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Real-time coupled-cluster approach for the cumulant Green's function

Green’s function methods within many-body perturbation theory provide a general framework for treating electronic correlations in excited states. Here we investigate the cumulant form of the one-electron Green’s function based on the coupled-cluster equation of motion approach in an extension of our previous study. The approach yields a non-perturbative expression for the cumulant in terms of the solution to a set of coupled first order, non-linear differential equations. The method thereby adds non-linear corrections to traditional cumulant methods linear in the self energy. The approach is applied to the core-hole Green’s function and illustrated for a number of small molecular systems. For these systems we find that the non-linear contributions lead to significant improvements both for quasiparticle properties such as core-level binding energies, as well as the satellites corresponding to inelastic losses observed in photoemission spectra.

Vila, Fernando D.↗
DOE OSTI2024

IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems such as cell signaling and epigenetic regulation. Precisely defining the stoichiometry of PTMs has been challenging because, in the widely used bottom-up proteomics methods, the detection occurs at the peptide level and thus the link between peptides and their specific modification site is lost, resulting in proteoform ambiguity. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Proteins with positional isomers refers to proteoforms with identical total mass and set of modifications but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for relative quantification of PI within a single tool. We benchmarked IsoForma’s performance against two existing workflows and highlight the similarity of the results and improvements in speed. Overall, IsoForma provides a streamlined process, reduces the time of conducting isoform-based analyses, and offers an essential framework for developing customized proteoform analysis workflows. Finally, the software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Coupled cluster evaluation of the frequency dispersion of the first and second hyperpolarizabilities of water, methanol, and dimethyl ether

The static and dynamic first (β{sub ‖}) and second (γ{sub ‖}) hyperpolarizabilities of water, methanol, and dimethyl ether have been evaluated within the response function approach using a hierarchy of coupled cluster levels of approximation and doubly augmented correlation consistent atomic basis sets. For the three compounds, the electronic β{sub ‖} and γ{sub ‖} values calculated at the CCSD and CC3 levels are in good agreement with gas phase electric field-induced second harmonic generation (EFISHG) measurements. In addition, for dimethyl ether, the frequency dispersion of both properties follows closely recent experimental values [V. W. Couling and D. P. Shelton, J. Chem. Phys. 143, 224307 (2015)] demonstrating the reliability of these methods and levels of approximation. This also suggests that the vibrational contributions to the EFISHG responses of these molecules are small.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Model Residuals as Shields: A Two-Level Formulation to Defend Smart Grids From Poisoning Attacks

The advancement of smart grids presents both vast opportunities and heightened cybersecurity risks. Data-driven defense mechanisms, though designed as a shield against these threats, can fall prey to poisoning attacks. We delve into regression settings, underscoring the imperative to fortify defenses against a spectrum of poison ratios, notably those above 0.5—an issue scarcely addressed in prior studies. Recognizing the susceptibilities of smart grids and their manipulable sensors, we exploit the very intent of poisoning attacks, compromising model accuracy, as our defense mechanism. Our proposed two-level optimization framework discerns between poisoned and authentic data based on model residuals, outperforming or matching existing methods in 72% to 77% of precision and 75% to 80% of recalls across various poisoning attacks, poison ratios, and datasets. Once the authentic data are identified, the trained model is adaptable for a variety of applications. Comprehensive evaluations on different smart grid datasets, pitted against myriad poisoning schemes, validate our methodology’s edge over existing methods. Here, we also shed light on the implications of model misspecification originating from temporal auto-correlation, a common feature in Internet of Things and smart grid data.

Adversarial machine learning (ML)↗
DOE OSTI2018

A Solar Time-based Analog Ensemble Method for Regional Solar Power Forecasting

This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Further, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American Mesoscale Forecast System, the Global Forecast System, and the Short-Range Ensemble Forecast, for both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. In conclusion, the normalized root mean square error (NRMSE) has been reduced by 13.80%-61.21% when compared with three tested baselines.

14 SOLAR ENERGY↗
DOE OSTI2018

Breeding progress and preparedness for mass‐scale deployment of perennial lignocellulosic biomass crops switchgrass, miscanthus, willow and poplar

Abstract Genetic improvement through breeding is one of the key approaches to increasing biomass supply. This paper documents the breeding progress to date for four perennial biomass crops (PBCs) that have high output–input energy ratios: namely Panicum virgatum (switchgrass), species of the genera Miscanthus (miscanthus), Salix (willow) and Populus (poplar). For each crop, we report on the size of germplasm collections, the efforts to date to phenotype and genotype, the diversity available for breeding and on the scale of breeding work as indicated by number of attempted crosses. We also report on the development of faster and more precise breeding using molecular breeding techniques. Poplar is the model tree for genetic studies and is furthest ahead in terms of biological knowledge and genetic resources. Linkage maps, transgenesis and genome editing methods are now being used in commercially focused poplar breeding. These are in development in switchgrass, miscanthus and willow generating large genetic and phenotypic data sets requiring concomitant efforts in informatics to create summaries that can be accessed and used by practical breeders. Cultivars of switchgrass and miscanthus can be seed‐based synthetic populations, semihybrids or clones. Willow and poplar cultivars are commercially deployed as clones. At local and regional level, the most advanced cultivars in each crop are at technology readiness levels which could be scaled to planting rates of thousands of hectares per year in about 5 years with existing commercial developers. Investment in further development of better cultivars is subject to current market failure and the long breeding cycles. We conclude that sustained public investment in breeding plays a key role in delivering future mass‐scale deployment of PBCs.

09 BIOMASS FUELS↗
DOE OSTI2016

SU-G-JeP2-12: Quantification of 3D Geometric Distortion for 1.5T and 3T MRI Scanners Used for Radiation Therapy

Purpose: To quantify the magnitude of geometric distortion for MRI scanners and provide recommendations for MRI imaging for radiation therapy Methods: A novel phantom, QUASAR MRID3D [Modus Medical Devices Inc.], was scanned to evaluate the level of 3D geometric distortion present in five MRI scanners used for radiation therapy in our department. The phantom was scanned using the body coil with 1mm image slice thickness to acquire 3D images of the phantom body. The phantom was aligned to its geometric center for each scan, and the field of view was set to visualize the entire phantom. The dependence of distortion magnitude with distance from imaging isocenter and with magnetic field strength (1.5T and 3T) was investigated. Additionally, the characteristics of distortion for Siemens and GE machines were compared. The image distortion for each scanner was quantified in terms of mean, standard deviation (STD), maximum distortion, and skewness. Results: The 3T and 1.5T scans show a similar absolute distortion with a mean of 1.38mm (0.33mm STD) for 3T and 1.39mm (0.34mm STD) for 1.5T for a 100mm radius distance from isocenter. Some machines can have a distortion larger than 10mm at a distance of 200mm from the isocenter. The distortions are presented with plots of the x, y, and z directional components. Conclusion: The results indicate that quantification of MRI image distortion is crucial in radiation oncology for target and organ delineation and treatment planning. The magnitude of geometric distortion determines the margin needed for target contouring which is usually neglected in treatment planning process, especially for SRS/SBRT treatments. Understanding the 3D distribution of the MRI image distortion will improve the accuracy of target delineation and, hence, treatment efficacy. MRI imaging with proper patient alignment to the isocenter is vital to reducing the effects of MRI distortion in treatment planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

White Box Access to Quantum Testbeds for Co-Design

At Lawrence Livermore National Laboratory (LLNL), we operate and maintain the Quantum Device and Integration Testbed (QuDIT) facility, a small quantum testbed that supports about 10 active research teams (including our own) and over 50 internal and external collaborators. This testbed is designed to give remote white box access to users for research, training, and outreach. A guiding principle behind the development of our testbed infrastructure, software and user interfaces is to empower users to perform experiments at the cutting edge of quantum information science at any level of abstraction, from materials studies, device physics and control and characterization techniques to algorithm development and quantum operating system design. Our testbed targets a multilevel quantum system (qudit) to expand the accessible Hilbert space of a simple-to-manufacture quantum device and focuses on quantum simulation, typically implemented through custom gates designed with quantum optimal control methods, rather than on a universal computing framework with a fixed gate set. We leverage the Lab’s high-performance computing (HPC) program and related expertise to simulate quantum systems, develop hybrid algorithms, and generate gates optimized for given simulations. Additionally, we have adopted a co-design philosophy from the HPC community in designing new hardware, so that the systems we develop are optimized for the specific physics simulations we plan to use them for.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

A Two-Fold Structural Classification Method for Determining the Accurate Ensemble of Protein Structures

Atomic-level structural characterization of flexible proteins, such as intrinsically disordered proteins and multi-domain proteins connected by flexible linkers, is challenging as they possess distinct conformations in physiological conditions. Significant efforts have been made to develop integrated approaches by combining small angle neutron/X-ray scattering experiments with molecular simulations to reveal the distinct atomic structures and the corresponding populations for these flexible proteins. One widely used method, the basis-set supported ensemble method, classifies the simulation-generated protein conformations into a set of structural basis and then derives the corresponding populations by fitting to the experimental data. This method makes an implicit assumption that protein conformations of similar structures have similar small angle scattering profiles.The present work demonstrates that, for various protein systems ranging from compact globular proteins and flexible multidomain proteins through to intrinsically disordered proteins, this method provides inaccurate assessment of the structural ensemble of the protein molecules due to the breakdown of the assumption made. To alleviate this problem, a two-fold-clustering method is developed to cluster the simulation-generated protein structures using information on both 3D structure and scattering profiles. As benchmarked by both simulation and experimental results, this new method yields much more accurate populations of structural basis of protein molecules.

59 BASIC BIOLOGICAL SCIENCES↗