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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Optimization Algorithms for Hierarchical Problems, with Applications to Nanoporous Materials

The aim of the overall project was research to formulate and solve multi-scale optimization problems for energy storage applications. More specifically, we formulated multi-scale models for optimizing the internal structure of nanoporous materials, with the goal of developing models for related energy applications associated with sustainable sources of energy. The applications include supercapacitors, hydrogen storage, catalytic beds, and filters. The optimization algorithms we have developed can be applied to general hierarchical design problems arising from complex systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Statistical Properties of Magnetic Structures and Energy Dissipation during Turbulent Reconnection in the Earth's Magnetotail

Here, we present the first statistical study of magnetic structures and associated energy dissipation observed during a single period of turbulent magnetic reconnection, by using the in situ measurements of the Magnetospheric Multiscale mission in the Earth's magnetotail on 26 July 2017. The structures are selected by identifying a bipolar signature in the magnetic field and categorized as plasmoids or current sheets via an automated algorithm which examines current density and plasma flow. The size of the plasmoids forms a decaying exponential distribution ranging from subelectron up to ion scales. The presence of substantial number of current sheets is consistent with a physical picture of dynamic production and merging of plasmoids during turbulent reconnection. The magnetic structures are locations of significant energy dissipation via electric field parallel to the local magnetic field, while dissipation via perpendicular electric field dominates outside of the structures. Significant energy also returns from particles to fields.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Statistical properties of magnetic structures and energy dissipation during turbulent reconnection in the Earth's magnetotail

We present the first statistical study of magnetic structures and associated energy dissipation observed during a single period of turbulent magnetic reconnection, by using the in situ measurements of the Magnetospheric Multiscale mission in the Earth's magnetotail on 26 July 2017. The structures are selected by identifying a bipolar signature in the magnetic field and categorized as plasmoids or current sheets via an automated algorithm which examines current density and plasma flow. The size of the plasmoids forms a decaying exponential distribution ranging from subelectron up to ion scales. The presence of substantial number of current sheets is consistent with a physical picture of dynamic production and merging of plasmoids during turbulent reconnection. The magnetic structures are locations of significant energy dissipation via electric field parallel to the local magnetic field, while dissipation via perpendicular electric field dominates outside of the structures. Significant energy also returns from particles to fields.

Magnetotail↗
DOE OSTI2024

Demonstration of the rodeo algorithm on a quantum computer

The rodeo algorithm is an efficient algorithm for eigenstate preparation and eigenvalue estimation for any observable on a quantum computer. This makes it a promising tool for studying the spectrum and structure of atomic nuclei as well as other fields of quantum many-body physics. The only requirement is that the initial state has sufficient overlap probability with the desired eigenstate. While it is exponentially faster than well-known algorithms such as phase estimation and adiabatic evolution for eigenstate preparation, it has yet to be implemented on an actual quantum device. In this work, we apply the rodeo algorithm to determine the energy levels of a random one-qubit Hamiltonian, resulting in a relative error of 0.08% using mid-circuit measurements on the IBM Q device Casablanca. This surpasses the accuracy of directly-prepared eigenvector expectation values using the same quantum device. We take advantage of the high-accuracy energy determination and use the Hellmann-Feynman theorem to compute eigenvector expectation values for a different random one-qubit observable. For the Hellmann-Feynman calculations, we find a relative error of 0.7%. Here, we conclude by discussing possible future applications of the rodeo algorithm for multi-qubit Hamiltonians.

algorithm↗
DOE OSTI2016

Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) Science Plan

With their extensive coverage, low clouds greatly impact global climate. Presently, low clouds are poorly represented in global climate models (GCMs), and the response of low clouds to changes in atmospheric greenhouse gases and aerosols remains the major source of uncertainty in climate simulations. The poor representations of low clouds in GCMs are in part due to inadequate observations of their microphysical and macrophysical structures, radiative effects, and the associated aerosol distribution and budget in regions where the aerosol impact is the greatest. The Eastern North Atlantic (ENA) is a region of persistent but diverse subtropical marine boundary-layer (MBL) clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. Boundary-layer aerosol in the ENA region is influenced by a variety of sources, leading to strong variations in cloud condensation nuclei (CCN) concentration and aerosol optical properties. Recently a permanent ENA site was established by the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility on Graciosa Island in the Azores, providing invaluable information on MBL aerosol and low clouds. At the same time, the vertical structures and horizontal variabilities of aerosol, trace gases, cloud, drizzle, and atmospheric thermodynamics are critically needed for understanding and quantifying the budget of MBL aerosol, the radiative properties, precipitation efficiency, and lifecycle of MBL clouds, and the cloud response to aerosol perturbations. Much of this data can be obtained only through aircraft-based measurements. In addition, the interconnected aerosol and cloud processes are best investigated by a study involving simultaneous in situ aerosol, cloud, and thermodynamics measurements. Furthermore, in situ measurements are also necessary for validating and improving ground-based retrieval algorithms at the ENA site. This project is motivated by the need for comprehensive in situ characterizations of boundary-layer structure, and associated vertical distributions and horizontal variabilities of low clouds and aerosol over the Azores. ARM Aerial Facility (AAF) Gulfstream-1 (G-1) aircraft will be deployed at the ENA site during two intensive operational periods (IOPs) of early summer (June to July) of 2017 and winter (January to February) of 2018, respectively. Deployments during both seasons allow for examination of key aerosol and cloud processes under a variety of representative meteorological and cloud conditions. The science themes for the deployments include: 1) Budget of MBL CCN and its seasonal variation; 2) Effects of aerosol on cloud and precipitation; 3) Cloud microphysical and macrophysical structures, and entrainment mixing; 4) Advancing retrievals of turbulence, cloud, and drizzle; and 5) Model evaluation and processes studies. A key advantage of the deployments is the strong synergy between the measurements onboard the G-1 and the routine measurements at the ENA site, including state-of-the-art profiling and scanning radars. The 3D cloud structures provided by the scanning radars will put the detailed in situ measurements into mesoscale and cloud lifecycle contexts. On the other hand, high quality in situ measurements will enable validation and improvements of ground-based retrieval algorithms at the ENA site, leading to high-quality and statistically robust data sets from the routine measurements. The deployments, combined with the routine measurements at the ENA site, will have a long lasting impact on the research and modeling of low clouds and aerosols in the remote marine environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

A Measure-Theoretic Algorithm for Estimating Bottom Friction in a Coastal Inlet: Case Study of Bay St. Louis during Hurricane Gustav (2008)

The majority of structural damage and loss of life during a hurricane is due to storm surge, thus it is important for communities in hurricane-prone regions to understand their risk due to surge. Storm surge in particular is largely influenced by coastal features such as topography/bathymetry and bottom roughness. Bottom roughness determines how much resistance there is to the flow. Manning’s formula can be used to model the bottom stress with the Manning’s n coefficient, a spatially dependent field. Given a storm surge model and a set of model outputs, an inverse problem may be solved to determine probable Manning’s n fields to use for predictive simulations. The inverse problem is formulated and solved in a measure-theoretic framework using the state-of-the-art Advanced Circulation (ADCIRC) storm surge model. The use of measure theory requires minimal assumptions and involves the direct inversion of the physics-based map from model inputs to output data determined by the ADCIRC model. Thus, key geometric relationships in this map are preserved and exploited. By using a recently available subdomain implementation of ADCIRC that significantly reduces the computational cost of forward model solves, the authors demonstrate the method on a case study using data obtained from an ADCIRC hindcast study of Hurricane Gustav (2008) to quantify uncertainties in Manning’s n within Bay St. Louis. However, the methodology is general and could be applied to any inverse problem that involves a map from model input to output quantities of interest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Development and Validation of Quantitative Model-Based Image Reconstruction for NDE of Reinforced Steel-Lined Concrete Structures: Kal-El FY 2020

This project studied the capabilities of Model-Based Image Reconstruction (MBIR) and Machine Learning (ML) algorithms in the imaging and estimation of rebar corrosion from ultrasound signals. The application was challenging due to the introduction of a steel liner between the sensor and the concrete containing the rebar. The study focused in a synthetic specimen with a 6.35 mm thick steel liner, and a 19.05 mm diameter rebar at a depth of 44.5 mm and with four levels of corrosion; 0%, 20%, 50%, and 100% corrosion level. The corrosion was applied uniformly around the rebar, generating a ring in the perimeter of the rebar with different acoustic density. In order to generate a realistic synthetic dataset, we added random texture to the concrete of the synthetic specimens. This texture will mimic the variations encountered in real concrete specimens.We adapted our MBIR algorithm for the application and enhanced the algorithm to integrate in the physical model known specimen features, such as the steel liner thickness. The new MBIR method was able to cancel out reverberation from the steel liner and properly image the rebar. In particular, corrosion for the 50% and 100% cases were easily visible and quantitative metrics showed a slight separation for the 0% and 20% levels. The quantitative results were in agreement with the qualitative assessment. We also developed a ML XGBoost model to estimate corrosion level from the ultrasound signals. By combining the ML method with MBIR, we can pinpoint in the ultrasound signals the section that corresponds to the echoes from the rebar. The extract signals are processed by the XGBoost model for prediction. From each system scan, we obtain 15 predictions. The median prediction value is used as the final prediction. The True Acceptance Rate for the method is over 99%.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-E-T-91: Accuracy of Dose Calculation Algorithms for Patients Undergoing Stereotactic Ablative Radiotherapy

Purpose: The purpose of this study was to investigate and quantify the variation in dose distributions in small field lung cancer radiotherapy using seven different dose calculation algorithms. Methods: The study was performed in 21 lung cancer patients who underwent Stereotactic Ablative Body Radiotherapy (SABR). Two different methods (i) Same dose coverage to the target volume (named as same dose method) (ii) Same monitor units in all algorithms (named as same monitor units) were used for studying the performance of seven different dose calculation algorithms in XiO and Eclipse treatment planning systems. The seven dose calculation algorithms include Superposition, Fast superposition, Fast Fourier Transform ( FFT) Convolution, Clarkson, Anisotropic Analytic Algorithm (AAA), Acurous XB and pencil beam (PB) algorithms. Prior to this, a phantom study was performed to assess the accuracy of these algorithms. Superposition algorithm was used as a reference algorithm in this study. The treatment plans were compared using different dosimetric parameters including conformity, heterogeneity and dose fall off index. In addition to this, the dose to critical structures like lungs, heart, oesophagus and spinal cord were also studied. Statistical analysis was performed using Prism software. Results: The mean±stdev with conformity index for Superposition, Fast superposition, Clarkson and FFT convolution algorithms were 1.29±0.13, 1.31±0.16, 2.2±0.7 and 2.17±0.59 respectively whereas for AAA, pencil beam and Acurous XB were 1.4±0.27, 1.66±0.27 and 1.35±0.24 respectively. Conclusion: Our study showed significant variations among the seven different algorithms. Superposition and AcurosXB algorithms showed similar values for most of the dosimetric parameters. Clarkson, FFT convolution and pencil beam algorithms showed large differences as compared to superposition algorithms. Based on our study, we recommend Superposition and AcurosXB algorithms as the first choice of algorithms in lung cancer radiotherapy involving small fields. However, further investigation by Monte Carlo simulation is required to confirm our results.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Geometric electrostatic particle-in-cell algorithm on unstructured meshes

We present a geometric particle-in-cell (PIC) algorithm on unstructured meshes for studying electrostatic perturbations with frequency lower than electron gyrofrequency in magnetized plasmas. In this method, ions are treated as fully kinetic particles and electrons are described by the adiabatic response. The PIC method is derived from a discrete variational principle on unstructured meshes. To preserve the geometric structure of the system, the discrete variational principle requires that the electric field is interpolated using Whitney 1-forms, the charge is deposited using Whitney 0-forms and the electric field is computed by discrete exterior calculus. The algorithm has been applied to study the ion Bernstein wave (IBW) in two-dimensional magnetized plasmas. The simulated dispersion relations of the IBW in a rectangular region agree well with theoretical results. In a two-dimensional circular region with fixed boundary condition, the spectrum and eigenmode structures of the IBW are obtained from simulations. We compare the energy conservation property of the geometric PIC algorithm derived from the discrete variational principle with that of previous PIC methods on unstructured meshes. The comparison shows that the new PIC algorithm significantly improves the energy conservation property.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

CASM — A software package for first-principles based study of multicomponent crystalline solids

CASM is a software package that enables first-principles based studies of crystalline materials. It has been designed to treat coupled chemical, mechanical, vibrational, and magnetic degrees of freedom to determine ground state and finite temperature properties of crystals. The symmetry of the underlying parent crystal structure is used to enumerate perturbations of the parent crystal structure and generate derivative structures which can be input to first-principles calculations in order to explore the ground state energy landscape. CASM algorithmically constructs cluster expansions that fully couple discrete and continuous degrees of freedom, and generates highly efficient code to evaluate the cluster expansion basis functions. Widely used machine learning methods are integrated for fitting expansion coefficients to first-principle calculations. The fully parameterized cluster expansions can be combined with (kinetic) Monte Carlo methods to calculate finite temperature thermodynamic and kinetic properties. CASM Alloy Manager identifies distinct parent crystal structures in an alloy system, creating individual projects for each, and integrating the results. The integrated infrastructure facilitates the linkage between first-principles statistical mechanics predictions with higher length scale computational methods, such as phase field simulations. In conclusion, CASM projects are designed to be easy to share in repositories, to re-use, and to extend to include additional chemical species or types of degrees of freedom.

36 MATERIALS SCIENCE↗
DOE OSTI2014

SU-E-J-151: Dosimetric Evaluation of DIR Mapped Contours for Image Guided Adaptive Radiotherapy with 4D Cone-Beam CT

Purpose: To estimate dosimetric errors resulting from using contours deformably mapped from planning CT to 4D cone beam CT (CBCT) images for image-guided adaptive radiotherapy of locally advanced non-small cell lung cancer (NSCLC). Methods: Ten locally advanced non-small cell lung cancer (NSCLC) patients underwent one planning 4D fan-beam CT (4DFBCT) and weekly 4DCBCT scans. Multiple physicians delineated the gross tumor volume (GTV) and normal structures in planning CT images and only GTV in CBCT images. Manual contours were mapped from planning CT to CBCTs using small deformation, inverse consistent linear elastic (SICLE) algorithm for two scans in each patient. Two physicians reviewed and rated the DIR-mapped (auto) and manual GTV contours as clinically acceptable (CA), clinically acceptable after minor modification (CAMM) and unacceptable (CU). Mapped normal structures were visually inspected and corrected if necessary, and used to override tissue density for dose calculation. CTV (6mm expansion of GTV) and PTV (5mm expansion of CTV) were created. VMAT plans were generated using the DIR-mapped contours to deliver 66 Gy in 33 fractions with 95% and 100% coverage (V66) to PTV and CTV, respectively. Plan evaluation for V66 was based on manual PTV and CTV contours. Results: Mean PTV V66 was 84% (range 75% – 95%) and mean CTV V66 was 97% (range 93% – 100%) for CAMM scored plans (12 plans); and was 90% (range 80% – 95%) and 99% (range 95% – 100%) for CA scored plans (7 plans). The difference in V66 between CAMM and CA was significant for PTV (p = 0.03) and approached significance for CTV (p = 0.07). Conclusion: The quality of DIR-mapped contours directly impacted the plan quality for 4DCBCT-based adaptation. Larger safety margins may be needed when planning with auto contours for IGART with 4DCBCT images. Reseach was supported by NIH P01CA116602.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

MO-C-17A-13: Uncertainty Evaluation of CT Image Deformable Registration for H and N Cancer Adaptive Radiotherapy

Purpose: To evaluate uncertainties of organ specific Deformable Image Registration (DIR) for H and N cancer Adaptive Radiation Therapy (ART). Methods: A commercial DIR evaluation tool, which includes a digital phantom library of 8 patients, and the corresponding “Ground truth Deformable Vector Field” (GT-DVF), was used in the study. Each patient in the phantom library includes the GT-DVF created from a pair of CT images acquired prior to and at the end of the treatment course. Five DIR tools, including 2 commercial tools (CMT1, CMT2), 2 in-house (IH-FFD1, IH-FFD2), and a classic DEMON algorithms, were applied on the patient images. The resulting DVF was compared to the GT-DVF voxel by voxel. Organ specific DVF uncertainty was calculated for 10 ROIs: Whole Body, Brain, Brain Stem, Cord, Lips, Mandible, Parotid, Esophagus and Submandibular Gland. Registration error-volume histogram was constructed for comparison. Results: The uncertainty is relatively small for brain stem, cord and lips, while large in parotid and submandibular gland. CMT1 achieved best overall accuracy (on whole body, mean vector error of 8 patients: 0.98±0.29 mm). For brain, mandible, parotid right, parotid left and submandibular glad, the classic Demon algorithm got the lowest uncertainty (0.49±0.09, 0.51±0.16, 0.46±0.11, 0.50±0.11 and 0.69±0.47 mm respectively). For brain stem, cord and lips, the DVF from CMT1 has the best accuracy (0.28±0.07, 0.22±0.08 and 0.27±0.12 mm respectively). All algorithms have largest right parotid uncertainty on patient #7, which has image artifact caused by tooth implantation. Conclusion: Uncertainty of deformable CT image registration highly depends on the registration algorithm, and organ specific. Large uncertainty most likely appears at the location of soft-tissue organs far from the bony structures. Among all 5 DIR methods, the classic DEMON and CMT1 seem to be the best to limit the uncertainty within 2mm for all OARs. Partially supported by research grant from Elekta.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-I-82: Improving CT Image Quality for Radiation Therapy Using Iterative Reconstruction Algorithms and Slightly Increasing Imaging Doses

Purpose: Iterative reconstruction (IR) algorithms are developed to improve CT image quality (IQ) by reducing noise without diminishing spatial resolution or contrast. For CT in radiation therapy (RT), slightly increasing imaging dose to improve IQ may be justified if it can substantially enhance structure delineation. The purpose of this study is to investigate and to quantify the IQ enhancement as a result of increasing imaging doses and using IR algorithms. Methods: CT images were acquired for phantoms, built to evaluate IQ metrics including spatial resolution, contrast and noise, with a variety of imaging protocols using a CT scanner (Definition AS Open, Siemens) installed inside a Linac room. Representative patients were scanned once the protocols were optimized. Both phantom and patient scans were reconstructed using the Sinogram Affirmed Iterative Reconstruction (SAFIRE) and the Filtered Back Projection (FBP) methods. IQ metrics of the obtained CTs were compared. Results: IR techniques are demonstrated to preserve spatial resolution as measured by the point spread function and reduce noise in comparison to traditional FBP. Driven by the reduction in noise, the contrast to noise ratio is doubled by adopting the highest SAFIRE strength. As expected, increasing imaging dose reduces noise for both SAFIRE and FBP reconstructions. The contrast to noise increases from 3 to 5 by increasing the dose by a factor of 4. Similar IQ improvement was observed on the CTs for selected patients with pancreas and prostrate cancers. Conclusion: The IR techniques produce a measurable enhancement to CT IQ by reducing the noise. Increasing imaging dose further reduces noise independent of the IR techniques. The improved CT enables more accurate delineation of tumors and/or organs at risk during RT planning and delivery guidance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Isobaric Reconstruction of the Baryonic Acoustic Oscillation

In this Letter, we report a significant recovery of the linear baryonic acoustic oscillation (BAO) signature by applying the isobaric reconstruction algorithm to the nonlinear matter density field. Assuming only the longitudinal component of the displacement being cosmologically relevant, this algorithm iteratively solves the coordinate transform between the Lagrangian and Eulerian frames without requiring any specific knowledge of the dynamics. For dark matter field, it produces the nonlinear displacement potential with very high fidelity. The reconstruction error at the pixel level is within a few percent and is caused only by the emergence of the transverse component after the shell-crossing. As it circumvents the strongest nonlinearity of the density evolution, the reconstructed field is well described by linear theory and immune from the bulk-flow smearing of the BAO signature. Therefore, this algorithm could significantly improve the measurement accuracy of the sound horizon scale s. For a perfect large-scale structure survey at redshift zero without Poisson or instrumental noise, the fractional error Δs/s is reduced by a factor of ∼2.7, very close to the ideal limit with the linear power spectrum and Gaussian covariance matrix.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays

Molecular initiating events (MIEs) are key events in adverse outcome pathways (AOPs) that link molecular chemistry to target biology. As they are based in chemistry, these interactions are excellent targets for computational chemistry approaches to in silico modelling. In this work, we aim to link ligand chemical structure to MIEs for androgen receptor (AR) and glucocorticoid receptor (GR) binding using ToxCast data. This has been done using an automated computational algorithm to perform maximal common substructure searches on chemical binders for each target from the ToxCast dataset. The models developed show a high level of accuracy, correctly assigning 87.20% of AR binders and 96.81% of GR binders in a 25% test set using holdout cross-validation. The 2D structural alerts developed can be used as in silico models to predict these MIEs, and as guidance for in vitro ToxCast assays to confirm hits. These models can target such experimental work, reducing the number of assays to be performed to gain required toxicological insight. Development of these models has also allowed some structural alerts to be identified as predictors for agonist or antagonist behavior at the receptor target. This work represents a first step in using computational methods to guide and target experimental approaches.

Allen, Timothy H.↗
DOE OSTI2017

Efficient block preconditioned eigensolvers for linear response time-dependent density functional theory

We present two efficient iterative algorithms for solving the linear response eigen- value problem arising from the time dependent density functional theory. Although the matrix to be diagonalized is nonsymmetric, it has a special structure that can be exploited to save both memory and floating point operations. In particular, the nonsymmetric eigenvalue problem can be transformed into a product eigenvalue problem that is self-adjoint with respect to a K-inner product. This product eigenvalue problem can be solved efficiently by a modified Davidson algorithm and a modified locally optimal block preconditioned conjugate gradient (LOBPCG) algorithm that make use of the K-inner product. The solution of the product eigenvalue problem yields one component of the eigenvector associated with the original eigenvalue problem. However, the other component of the eigenvector can be easily recovered in a postprocessing procedure. Therefore, the algorithms we present here are more efficient than existing algorithms that try to approximate both components of the eigenvectors simultaneously. The efficiency of the new algorithms is demonstrated by numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

TH-E-17A-06: Anatomical-Adaptive Compressed Sensing (AACS) Reconstruction for Thoracic 4-Dimensional Cone-Beam CT

Purpose: The Feldkamp-Davis-Kress (FDK) algorithm currently used for clinical thoracic 4-dimensional (4D) cone-beam CT (CBCT) reconstruction suffers from noise and streaking artifacts due to projection under-sampling. Compressed sensing theory enables reconstruction of under-sampled datasets via total-variation (TV) minimization, but TV-minimization algorithms such as adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS) often converge slowly and are prone to over-smoothing anatomical details. These disadvantages can be overcome by incorporating general anatomical knowledge via anatomy segmentation. Based on this concept, we have developed an anatomical-adaptive compressed sensing (AACS) algorithm for thoracic 4D-CBCT reconstruction. Methods: AACS is based on the ASD-POCS framework, where each iteration consists of a TV-minimization step and a data fidelity constraint step. Prior to every AACS iteration, four major thoracic anatomical structures - soft tissue, lungs, bony anatomy, and pulmonary details - were segmented from the updated solution image. Based on the segmentation, an anatomical-adaptive weighting was applied to the TV-minimization step, so that TV-minimization was enhanced at noisy/streaky regions and suppressed at anatomical structures of interest. The image quality and convergence speed of AACS was compared to conventional ASD-POCS using an XCAT digital phantom and a patient scan. Results: For the XCAT phantom, the AACS image represented the ground truth better than the ASD-POCS image, giving a higher structural similarity index (0.93 vs. 0.84) and lower absolute difference (1.1*10{sup 4} vs. 1.4*10{sup 4}). For the patient case, while both algorithms resulted in much less noise and streaking than FDK, the AACS image showed considerably better contrast and sharpness of the vessels, tumor, and fiducial marker than the ASD-POCS image. In addition, AACS converged over 50% faster than ASD-POCS in both cases. Conclusions: The proposed AACS algorithm was shown to reconstruct thoracic 4D-CBCT images more accurately and with faster convergence compared to ASD-POCS. The superior image quality and rapid convergence makes AACS promising for future clinical use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Scanning ARM Cloud Radars. Part II: Data Quality Control and Processing

The Scanning ARM Cloud Radars (SACR’s) are the primary instruments for documenting the four-dimensional structure and evolution of clouds within a 20-30 km radius from the ARM fixed and mobile sites. Here, the post-processing of the calibrated SACR measurements is discussed. First, a feature mask algorithm that objectively determines the presence of significant radar returns is described. The feature mask algorithm is based on the statistical properties of radar receiver noise. It accounts for atmospheric emission and is applicable even for SACR profiles with few or no signal-free range gates. Using the nearest-in-time atmospheric sounding, the SACR radar reflectivities are corrected for gaseous attenuation (water vapor and oxygen) using a line-by-line absorption model. Despite having a high pulse repetition frequency, the SACR has a narrow Nyquist velocity limit and thus Doppler velocity folding is commonly observed. An unfolding algorithm that makes use of a first guess for the true Doppler velocity using horizontal wind measurements from the nearest sounding is described. The retrieval of the horizontal wind profile from the HS-RHI SACR scan observations and/or nearest sounding is described. The retrieved horizontal wind profile can be used to adaptively configure SACR scan strategies that depend on wind direction. Several remaining challenges are discussed, including the removal of insect and second-trip echoes. The described algorithms significantly enhance SACR data quality and constitute an important step towards the utilization of SACR measurements for cloud research.

climate↗