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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 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantum-Accurate Multiscale Modeling of Shock Hugoniots, Ramp Compression Paths, Structural and Magnetic Phase Transitions, and Transport Properties in Highly Compressed Metals

Fully characterizing high energy density (HED) phenomena using pulsed power facilities (Z machine) and coherent light sources is possible only with complementary numerical modeling for design, diagnostic development, and data interpretation. The exercise of creating numerical tests, that match experimental conditions, builds critical insight that is crucial for the development of a strong fundamental understanding of the physics behind HED phenomena and for the design of next generation pulsed power facilities. The persistence of electron correlation in HED materials arising from Coulomb interactions and the Pauli exclusion principle is one of the greatest challenges for accurate numerical modeling and has hitherto impeded our ability to model HED phenomena across multiple length and time scales at sufficient accuracy. An exemplar is a ferromagnetic material like iron, while familiar and widely used, we lack a simulation capability to characterize the interplay of structure and magnetic effects that govern material strength, kinetics of phase transitions and other transport properties. Herein we construct and demonstrate the Molecular-Spin Dynamics (MSD) simulation capability for iron from ambient to earth core conditions, all software advances are open source and presently available for broad usage. These methods are multi-scale in nature, direct comparisons between high fidelity density functional theory (DFT) and linear-scaling MSD simulations is done throughout this work, with advancements made to MSD allowing for electronic structure changes being reflected in classical dynamics. Main takeaways for the project include insight into the role of magnetic spins on mechanical properties and thermal conductivity, development of accurate interatomic potentials paired with spin Hamiltonians, and characterization of the high pressure melt boundary that is of critical importance to planetary modeling efforts.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

Purpose: Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU’s relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors’ group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In the authors’ method, the sparse DDC matrix is first stored on a CPU in coordinate list format (COO). On the GPU side, this matrix is split into four submatrices according to beam angles, which are stored on four GPUs in compressed sparse row format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is accomplished using peer-to-peer access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein algorithm with a subspace step scheme is adopted here to solve the MP problem. A head and neck (H and N) cancer case is then used to validate the authors’ method. The authors also compare their multi-GPU implementation with three different single GPU implementation strategies, i.e., truncating DDC matrix (S1), repeatedly transferring DDC matrix between CPU and GPU (S2), and porting computations involving DDC matrix to CPU (S3), in terms of both plan quality and computational efficiency. Two more H and N patient cases and three prostate cases are used to demonstrate the advantages of the authors’ method. Results: The authors’ multi-GPU implementation can finish the optimization process within ∼1 min for the H and N patient case. S1 leads to an inferior plan quality although its total time was 10 s shorter than the multi-GPU implementation due to the reduced matrix size. S2 and S3 yield the same plan quality as the multi-GPU implementation but take ∼4 and ∼6 min, respectively. High computational efficiency was consistently achieved for the other five patient cases tested, with VMAT plans of clinically acceptable quality obtained within 23–46 s. Conversely, to obtain clinically comparable or acceptable plans for all six of these VMAT cases that the authors have tested in this paper, the optimization time needed in a commercial TPS system on CPU was found to be in an order of several minutes. Conclusions: The results demonstrate that the multi-GPU implementation of the authors’ column-generation-based VMAT optimization can handle the large-scale VMAT optimization problem efficiently without sacrificing plan quality. The authors’ study may serve as an example to shed some light on other large-scale medical physics problems that require multi-GPU techniques.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Modeling the nonlinear hysteretic response in DAE experiments of Berea sandstone: A case-study

Dynamic acousto-elasticity (DAE) allows probing the instantaneous state of a material while the latter slowly and periodically is changed by an external, dynamic source. In DAE investigations of geo-materials, hysteresis of the material's modulus defect displays intriguing features which have not yet been interpreted in terms of any specific mechanism occurring at atomic or mesoscale. Here, experimental results on dry Berea sandstone, which is the rock type best investigated by means of a DAE technique, are analyzed in terms of three rheological models providing simplified representations of mechanisms involving dislocations interacting with point defects which are distributed along the dislocations' core or glide planes, and microcracks with finite stiffness in compression. Constitutive relations linking macroscopic strain and stress are derived. From the latter, the modulus defect associated to each mechanism is recovered. These models are employed to construct a composite one which is capable of reproducing several of the main features observed in the experimental data. The limitations of the present approach and, possibly, of the current implementation of DAE are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Experimental study of shock-accelerated inclined heavy gas cylinder

An experimental study examines shock acceleration with an initially diffuse cylindrical column of sulfur hexafluoride surrounded by air and inclined with respect to the shock front. Three-dimensional vorticity deposition produces flow patterns whose evolution is captured with planar laser-induced fluorescence in two planes. Both planes are thus parallel to the direction of the shock propagation. The first plane is vertical and passes through the axis of the column. The second visualization plane is normal to the first plane and passes through the centerline of the shock tube. Vortex formation in the vertical and centerline planes is initially characterized by different rates and morphologies due to differences in initial vorticity deposition. In the vertical plane, the vortex structure manifests a periodicity that varies with Mach number. The dominant wavelength in the vertical plane can be related to the geometry and compressibility of the initial conditions. At later times, the vortex interaction produces a complex and irregular three-dimensional pattern suggesting transition to turbulence. We present highly repeatable experimental data for Mach numbers 1.13, 1.4, 1.7, and 2.0 at column incline angles of 0, 20, and 30 degrees for about 50 nominal cylinder diameters (30 cm) of downstream travel.

42 ENGINEERING↗
DOE OSTI2021

Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System

Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the powerflow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.

Dahale, Shweta↗
DOE OSTI2018

Influence of defects on the thermal conductivity of compressed LiF

We report defect formation in LiF, which is used as an observation window in ramp and shock experiments, has significant effects on its transmission properties. Given the extreme conditions of the experiments it is hard to measure the change in transmission directly. Using molecular dynamics, we estimate the change in conductivity as a function of the concentration of likely point and extended defects using a Green-Kubo technique with careful treatment of size effects. With this data, we form a model of the mean behavior and its estimated error; then, we use this model to predict the conductivity of a large sample of defective LiF resulting from a direct simulation of ramp compression as a demonstration of the accuracy of its predictions. Given estimates of defect densities in a LiF window used in an experiment, the model can be used to correct the observations of thermal energy through the window. Also, the methodology we develop is extensible to modeling, with quantified uncertainty, the effects of a variety of defects on the thermal conductivity of solid materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗
DOE OSTI2025

Dark Energy Survey Year 3 Results: Cosmological constraints from second- and third-order shear statistics

Here, we present a cosmological analysis of the third-order aperture mass statistic using Dark Energy Survey Year 3 (DES Y3) data. We perform a complete tomographic measurement of the three-point correlation function of the Y3 weak lensing shape catalog with the four fiducial source redshift bins. Building upon our companion methodology paper, we apply a pipeline that combines the two-point function ξ ± with the mass aperture skewness statistic ⟨ M ap 3 ⟩ , which is an efficient compression of the full shear three-point function. We use a suite of simulated shear maps to obtain a joint covariance matrix. By jointly analyzing ξ ± and ⟨ M ap 3 ⟩ measured from DES Y3 data with a Λ CDM model, we find S 8 = 0.780 ± 0.015 and Ω m = 0.26 6 - 0.040 + 0.039 , yielding 111% of figure-of-merit improvement in the Ω m - S 8 plane relative to ξ ± alone, consistent with expectations from simulated likelihood analyses. With a w CDM model, we find S 8 = 0.74 9 - 0.026 + 0.027 and w 0 = - 1.39 ± 0.31 , which gives an improvement of 22% on the joint S 8 - w 0 constraint. Our results are consistent with w 0 = - 1 . Our new constraints are compared to CMB data from the Planck satellite, and we find that with the inclusion of ⟨ M ap 3 ⟩ the existing tension between the datasets is at the level of 2.3 σ . We show that the third-order statistic enables us to self-calibrate the mean photometric redshift uncertainty parameter of the highest redshift bin with little degradation in the figure of merit. Our results demonstrate the constraining power of higher-order lensing statistics and establish ⟨ M ap 3 ⟩ as a practical observable for joint analyses in current and future surveys.

Gomes, R. C. H. [University of Pennsylvania] (ORCI↗
DOE OSTI2015

Role of Aquaporin 0 in lens biomechanics

Maintenance of proper biomechanics of the eye lens is important for its structural integrity and for the process of accommodation to focus near and far objects. Several studies have shown that specialized cytoskeletal systems such as the beaded filament (BF) and spectrin-actin networks contribute to mammalian lens biomechanics; mutations or deletion in these proteins alters lens biomechanics. Aquaporin 0 (AQP0), which constitutes ∼45% of the total membrane proteins of lens fiber cells, has been shown to function as a water channel and a structural cell-to-cell adhesion (CTCA) protein. Our recent ex vivo study on AQP0 knockout (AQP0 KO) mouse lenses showed the CTCA function of AQP0 could be crucial for establishing the refractive index gradient. However, biomechanical studies on the role of AQP0 are lacking. The present investigation used wild type (WT), AQP5 KO (AQP5{sup −/−}), AQP0 KO (heterozygous KO: AQP0{sup +/−}; homozygous KO: AQP0{sup −/−}; all in C57BL/6J) and WT-FVB/N mouse lenses to learn more about the role of fiber cell AQPs in lens biomechanics. Electron microscopic images exhibited decreases in lens fiber cell compaction and increases in extracellular space due to deletion of even one allele of AQP0. Biomechanical assay revealed that loss of one or both alleles of AQP0 caused a significant reduction in the compressive load-bearing capacity of the lenses compared to WT lenses. Conversely, loss of AQP5 did not alter the lens load-bearing ability. Compressive load-bearing at the suture area of AQP0{sup +/−} lenses showed easy separation while WT lens suture remained intact. These data from KO mouse lenses in conjunction with previous studies on lens-specific BF proteins (CP49 and filensin) suggest that AQP0 and BF proteins could act co-operatively in establishing normal lens biomechanics. We hypothesize that AQP0, with its prolific expression at the fiber cell membrane, could provide anchorage for cytoskeletal structures like BFs and together they help to confer fiber cell shape, architecture and integrity. To our knowledge, this is the first report identifying the involvement of an aquaporin in lens biomechanics. Since accommodation is required in human lenses for proper focusing, alteration in the adhesion and/or water channel functions of AQP0 could contribute to presbyopia. - Highlights: • AQP0 aids in lens biomechanics. • AQP0 provides lens stiffness. • AQP0 is critical for lens transparency. • AQP0 could play a significant role in lens accommodation in human. • Alteration in the function(s) of lens AQP0 could lead to presbyopia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

A set of parallel, implicit methods for a reconstructed discontinuous Galerkin method for compressible flows on 3D hybrid grids

A set of implicit methods are proposed for a third-order hierarchical WENO reconstructed discontinuous Galerkin method for compressible flows on 3D hybrid grids. An attractive feature in these methods are the application of the Jacobian matrix based on the P1 element approximation, resulting in a huge reduction of memory requirement compared with DG (P2). Also, three approaches -- analytical derivation, divided differencing, and automatic differentiation (AD) are presented to construct the Jacobian matrix respectively, where the AD approach shows the best robustness. A variety of compressible flow problems are computed to demonstrate the fast convergence property of the implemented flow solver. Furthermore, an SPMD (single program, multiple data) programming paradigm based on MPI is proposed to achieve parallelism. The numerical results on complex geometries indicate that this low-storage implicit method can provide a viable and attractive DG solution for complicated flows of practical importance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

In this work, we present Lift & Learn, a physics-informed method for learning low-dimensional models for large-scale dynamical systems. The method exploits knowledge of a system’s governing equations to identify a coordinate transformation in which the system dynamics have quadratic structure. This transformation is called a lifting map because it often adds auxiliary variables to the system state. The lifting map is applied to data obtained by evaluating a model for the original nonlinear system. This lifted data is projected onto its leading principal components, and low-dimensional linear and quadratic matrix operators are fit to the lifted reduced data using a least-squares operator inference procedure. Analysis of our method shows that the Lift & Learn models are able to capture the system physics in the lifted coordinates at least as accurately as traditional intrusive model reduction approaches. This preservation of system physics makes the Lift & Learn models robust to changes in inputs. Numerical experiments on the FitzHugh–Nagumo neuron activation model and the compressible Euler equations demonstrate the generalizability of our model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Physical control oriented model of large scale refrigerators to synthesize advanced control schemes. Design, validation, and first control results

In this paper, a physical method to obtain control-oriented dynamical models of large scale cryogenic refrigerators is proposed, in order to synthesize model-based advanced control schemes. These schemes aim to replace classical user experience designed approaches usually based on many independent PI controllers. This is particularly useful in the case where cryoplants are submitted to large pulsed thermal loads, expected to take place in the cryogenic cooling systems of future fusion reactors such as the International Thermonuclear Experimental Reactor (ITER) or the Japan Torus-60 Super Advanced Fusion Experiment (JT-60SA). Advanced control schemes lead to a better perturbation immunity and rejection, to offer a safer utilization of cryoplants. The paper gives details on how basic components used in the field of large scale helium refrigeration (especially those present on the 400W @1.8K helium test facility at CEA-Grenoble) are modeled and assembled to obtain the complete dynamic description of controllable subsystems of the refrigerator (controllable subsystems are namely the Joule-Thompson Cycle, the Brayton Cycle, the Liquid Nitrogen Precooling Unit and the Warm Compression Station). The complete 400W @1.8K (in the 400W @4.4K configuration) helium test facility model is then validated against experimental data and the optimal control of both the Joule-Thompson valve and the turbine valve is proposed, to stabilize the plant under highly variable thermals loads. This work is partially supported through the European Fusion Development Agreement (EFDA) Goal Oriented Training Program, task agreement WP10-GOT-GIRO.

42 ENGINEERING↗
DOE OSTI2014

Significant enhancement of thermoelectric properties and metallization of Al-doped Mg{sub 2}Si under pressure

We report results of investigations of electronic transport properties and lattice dynamics of Al-doped magnesium silicide (Mg{sub 2}Si) thermoelectrics at ambient and high pressures to and beyond 15 GPa. High-quality samples of Mg{sub 2}Si doped with 1 at. % of Al were prepared by spark plasma sintering technique. The samples were extensively examined at ambient pressure conditions by X-ray diffraction studies, Raman spectroscopy, electrical resistivity, magnetoresistance, Hall effect, thermoelectric power (Seebeck effect), and thermal conductivity. A Kondo-like feature in the electrical resistivity curves at low temperatures indicates a possible magnetism in the samples. The absolute values of the thermopower and electrical resistivity, and Raman spectra intensity of Mg{sub 2}Si:Al dramatically diminished upon room-temperature compression. The calculated thermoelectric power factor of Mg{sub 2}Si:Al raised with pressure to 2–3 GPa peaking in the maximum the values as high as about 8 × 10{sup −3} W/(K{sup 2}m) and then gradually decreased with further compression. Raman spectroscopy studies indicated the crossovers near ∼5–7 and ∼11–12 GPa that are likely related to phase transitions. The data gathered suggest that Mg{sub 2}Si:Al is metallized under moderate pressures between ∼5 and 12 GPa.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Effect of pressure on the structural properties of Li[Li{sub 0.1}Ni{sub 0.35}Mn{sub 0.55}]O{sub 2}

Li-rich cathode material Li[Li{sub 0.1}Ni{sub 0.35}Mn{sub 0.55}]O{sub 2} was synthesized by a sol-gel method. The morphology and a-NaFeO{sub 2} type structure of the material at ambient conditions were characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). The high-pressure behavior of the material was studied at pressure up to 19.7 GPa by using angle dispersive synchrotron X-ray powder diffraction (ADXD). The studies affirmed that the a-NaFeO{sub 2} type structure existed up to at least 19.7 GPa. Rietveld refinement analysis indicated that the c/a lattice parameter ratio decreased and the Li-O bond length shrunk with the increasing pressure, however, the Li{sup +}/Ni{sup 2+} ions exchange increased under elevated pressure. The pressure-volume data of the material were fitted to the third-order Birch-Murnaghan equation of state and bulk modulus obtained upon compression from the fitting was 117 (1) GPa.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Plasma physical parameters along CME-driven shocks. II. Observation–simulation comparison

In this work, we compare the spatial distribution of the plasma parameters along the 1999 June 11 coronal mass ejection (CME)-driven shock front with the results obtained from a CME-like event simulated with the FLIPMHD3D code, based on the FLIP-MHD particle-in-cell method. The observational data are retrieved from the combination of white-light coronagraphic data (for the upstream values) and the application of the Rankine–Hugoniot equations (for the downstream values). The comparison shows a higher compression ratio X and Alfvénic Mach number M{sub A} at the shock nose, and a stronger magnetic field deflection d toward the flanks, in agreement with observations. Then, we compare the spatial distribution of M{sub A} with the profiles obtained from the solutions of the shock adiabatic equation relating M{sub A}, X, and θ{sub Bn} (the angle between the upstream magnetic field and the shock front normal) for the special cases of parallel and perpendicular shock, and with a semi-empirical expression for a generically oblique shock. The semi-empirical curve approximates the actual values of M{sub A} very well, if the effects of a non-negligible shock thickness δ{sub sh} and plasma-to magnetic pressure ratio β{sub u} are taken into account throughout the computation. Moreover, the simulated shock turns out to be supercritical at the nose and sub-critical at the flanks. Finally, we develop a new one-dimensional Lagrangian ideal MHD method based on the GrAALE code, to simulate the ion-electron temperature decoupling due to the shock transit. Two models are used, a simple solar wind model and a variable-γ model. Both produce results in agreement with observations, the second one being capable of introducing the physics responsible for the additional electron heating due to secondary effects (collisions, Alfvén waves, etc.).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Revision of Faraday rotation measure constraints on the primordial magnetic field using the IllustrisTNG simulation

ABSTRACT Previously derived Faraday rotation constraints on the volume-filling intergalactic magnetic field (IGMF) have used analytical models that made a range of simplifying assumptions about magnetic field evolution in the intergalactic medium and did not consider the effect of baryonic feedback on large-scale structures. In this work, we revise existing Faraday rotation constraints on the IGMF using a numerical model of the intergalactic medium from the IllustrisTNG cosmological simulation that includes a sophisticated model of the baryonic feedback. We use the IllustrisTNG model to calculate the rotation measure and compare the resulting mean and median of the absolute value of the rotation measure with data from the NRAO VLA Sky Survey (NVSS). The numerical model of the intergalactic medium includes a full magnetohydrodynamic model of the compressed primordial magnetic field as well as a model of the regions where the magnetic field is not primordial, but is rather produced by the process of baryonic feedback. Separating these two types of regions, we are able to assess the influence of the primordial magnetic field on the Faraday rotation signal. We find that by correcting for regions of compressed primordial field and accounting for the fact that part of the intergalactic medium is occupied by magnetic fields spread by baryonic feedback processes rather than by the primordial field relaxes the Faraday rotation bound by a factor of ≃3. This results in B0 < 1.8 × 10−9 G for large correlation length IGMFs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Compression Freezing Kinetics of Water to Ice VII

Time-resolved x-ray diffraction (XRD) of compressed liquid water shows transformation to ice VII in 6 nsec, revealing crystallization rather than amorphous solidification during compression freezing. Application of classical nucleation theory indicates heterogeneous nucleation and one-dimensional (e.g., needlelike) growth. In conclusion, these first XRD data demonstrate rapid growth kinetics of ice VII with implications for fundamental physics of diffusion-mediated crystallization and thermodynamic modeling of collision or impact events on ice-rich planetary bodies.

36 MATERIALS SCIENCE↗
DOE OSTI2014

SU-D-12A-06: A Comprehensive Parameter Analysis for Low Dose Cone-Beam CT Reconstruction

Purpose: There is always a parameter in compressive sensing based iterative reconstruction (IR) methods low dose cone-beam CT (CBCT), which controls the weight of regularization relative to data fidelity. A clear understanding of the relationship between image quality and parameter values is important. The purpose of this study is to investigate this subject based on experimental data and a representative advanced IR algorithm using Tight-frame (TF) regularization. Methods: Three data sets of a Catphan phantom acquired at low, regular and high dose levels are used. For each tests, 90 projections covering a 200-degree scan range are used for reconstruction. Three different regions-of-interest (ROIs) of different contrasts are used to calculate contrast-to-noise ratios (CNR) for contrast evaluation. A single point structure is used to measure modulation transfer function (MTF) for spatial-resolution evaluation. Finally, we analyze CNRs and MTFs to study the relationship between image quality and parameter selections. Results: It was found that: 1) there is no universal optimal parameter. The optimal parameter value depends on specific task and dose level. 2) There is a clear trade-off between CNR and resolution. The parameter for the best CNR is always smaller than that for the best resolution. 3) Optimal parameters are also dose-specific. Data acquired under a high dose protocol require less regularization, yielding smaller optimal parameter values. 4) Comparing with conventional FDK images, TF-based CBCT images are better under a certain optimally selected parameters. The advantages are more obvious for low dose data. Conclusion: We have investigated the relationship between image quality and parameter values in the TF-based IR algorithm. Preliminary results indicate optimal parameters are specific to both the task types and dose levels, providing guidance for selecting parameters in advanced IR algorithms. This work is supported in part by NIH (1R01CA154747-01)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗