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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were, respectively, 2.48 ± 2.19 and 8.32 ± 5.66 mm, whereas these distances were 1.66 ± 1.29 and 5.75 ± 4.88 mm between the segmentations by the reference reader and the independent observer, respectively. The automatic segmentations were also visually assessed by two trained CXR readers as “excellent,” “adequate,” or “insufficient.” The readers had good agreement in assessing the cavity outlines and 84% of the segmentations were rated as “excellent” or “adequate” by both readers. Conclusions: The proposed cavity segmentation technique produced results with a good degree of overlap with manual expert segmentations. The evaluation measures demonstrated that the results approached the results of the experienced chest radiologists, in terms of overlap measure and contour distance measures. Automatic cavity segmentation can be employed in TB clinics for treatment monitoring, especially in resource limited settings where radiologists are not available.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Energy efficient spark plasma sintering: Breaking the threshold of large dimension tooling energy consumption

An energy efficient spark plasma sintering method enabling the densification of large size samples assisted by very low electric current levels is developed. In this method, the electric current is concentrated in the graphite foils around the sample. High-energy dissipation is then achieved in this area enabling the heating and full densification of large (alumina) parts (Ø 40 mm) at relatively low currents (800 A). The electrothermal mechanical simulation reveals that the electric current needed to heat the large samples is 70% lower in the energy efficient configuration compared to the traditional configuration. The presence of thermal and densification gradients is also revealed for the larger size samples. Potential solutions for this problem are discussed. The experiments confirm the possibility of full densification (96%-99%) of large alumina samples. This approach allows using small (and low cost) SPS devices (generally limited to 10-15 mm samples) for large-size samples (40-50 mm). Finally, the developed technique enables also an optimized energy consumption by large-scale SPS systems.

36 MATERIALS SCIENCE↗
DOE OSTI2016

SU-G-TeP3-14: Three-Dimensional Cluster Model in Inhomogeneous Dose Distribution

Purpose: We aim to investigate 3D cluster formation in inhomogeneous dose distribution to search for new models predicting radiation tissue damage and further leading to new optimization paradigm for radiotherapy planning. Methods: The aggregation of higher dose in the organ at risk (OAR) than a preset threshold was chosen as the cluster whose connectivity dictates the cluster structure. Upon the selection of the dose threshold, the fractional density defined as the fraction of voxels in the organ eligible to be part of the cluster was determined according to the dose volume histogram (DVH). A Monte Carlo method was implemented to establish a case pertinent to the corresponding DVH. Ones and zeros were randomly assigned to each OAR voxel with the sampling probability equal to the fractional density. Ten thousand samples were randomly generated to ensure a sufficient number of cluster sets. A recursive cluster searching algorithm was developed to analyze the cluster with various connectivity choices like 1-, 2-, and 3-connectivity. The mean size of the largest cluster (MSLC) from the Monte Carlo samples was taken to be a function of the fractional density. Various OARs from clinical plans were included in the study. Results: Intensive Monte Carlo study demonstrates the inverse relationship between the MSLC and the cluster connectivity as anticipated and the cluster size does not change with fractional density linearly regardless of the connectivity types. An initially-slow-increase to exponential growth transition of the MSLC from low to high density was observed. The cluster sizes were found to vary within a large range and are relatively independent of the OARs. Conclusion: The Monte Carlo study revealed that the cluster size could serve as a suitable index of the tissue damage (percolation cluster) and the clinical outcome of the same DVH might be potentially different.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2026

Reference floating wind array designs for three representative regions

This work presents the systematic development of three open-source reference floating wind array designs. The designs are tailored to representative site conditions for three regions of the United States: Humboldt Bay off the coast of California, the Gulf of Maine, and the Gulf of America. We adopted existing reference designs for the individual 15 MW turbines, semisubmersible floating platforms, substations, mooring systems, and power cables – integrating and adapting them as needed for each location. We adapted existing dynamic cable designs to use larger conductor sizes to meet the arrays' power transmission requirements, and we set up redundant mooring systems for each substation. The layout of each array is a uniform-grid design optimized to approximately minimize the levelized cost of energy (LCOE) within a square lease area while satisfying spatial constraints. These constraints ensure adequate clearances between adjacent turbines and between underwater components during the layout optimization to prevent clashing and ensure that all components reside within the lease boundaries. Substations are included to allow accounting for intra-array cable costs. They are placed within the uniform grid to maintain the navigability of the arrays. For each feasible layout considered, annual energy production and cable routing costs are calculated and updated in the LCOE objective function. After the optimization, we refined the cable routing with a mix of algorithmic and manual methods to ensure that the cables avoid mooring system components and approach the substation with adequate clearances. We confirmed the suitability of each reference array's layout by comparing the wake losses at each wind heading angle to the wind rose, observing that the optimized layouts largely avoid wake losses in the predominant wind directions. These reference arrays provide open-source baseline designs to enable future research and innovation of floating wind technology at the array scale.

17 WIND ENERGY↗
DOE OSTI2019

Scaling and Quality of Modularity Optimization Methods for Graph Clustering

Real-world graphs exhibit structures known as “communities” or “clusters” consisting of a group of vertices with relatively high connectivity between them, as compared to the rest of the vertices in the network. Graph clustering or community detection is a fundamental graph operation used to analyze real-world graphs occurring in the areas of computational biology, cybersecurity, electrical grids, etc. Similar to other graph algorithms, owing to irregular memory accesses and inherently sequential nature, current algorithms for community detection are challenging to parallelize. However, in order to analyze large networks, it is important to develop scalable parallel implementations of graph clustering that are capable of exploiting the architectural features of modern supercomputers. In response to the 2019 Streaming Graph Challenge, we present quality and performance analysis of our distributed- memory community detection using Vite, which is our distributed memory implementation of the popular Louvain method, on the ALCF Theta supercomputer. Clustering methods such as Louvain that rely on modularity maximization are known to suffer from the resolution limit problem, preventing identification of clusters of certain sizes. Hence, we also include quality analysis of our shared-memory implementation of the Fast-tracking Resistance method, in comparison with Louvain on the challenge datasets. Furthermore, we introduce an edge-balanced graph distribution for our distributed memory implementation, that significantly reduces communication, offering up to 80% improvement in the overall execution time. In addition to performance/quality analysis, we also include details on the power/energy consumption, and memory traffic of the distributed-memory clustering implementation using real-world graphs with over a billion edges.

Ghosh, Sayan↗
DOE OSTI2014

WE-D-BRF-01: FEATURED PRESENTATION - Investigating Particle Track Structures Using Fluorescent Nuclear Track Detectors and Monte Carlo Simulations

Purpose: To report on the efforts funded by the AAPM seed funding grant to develop the basis for fluorescent nuclear track detector (FNTD) based radiobiological experiments in combination with dedicated Monte Carlo simulations (MCS) on the nanometer scale. Methods: Two confocal microscopes were utilized in this study. Two FNTD samples were used to find the optimal microscope settings, one FNTD irradiated with 11.1 MeV/u Gold ions and one irradiated with 428.77 MeV/u Carbon ions. The first sample provided a brightly luminescent central track while the latter is used to test the capabilities to observe secondary electrons. MCS were performed using TOPAS beta9 version, layered on top of Geant4.9.6p02. Two sets of simulations were performed, one with the Geant4-DNA physics list and approximating the FNTDs by water, a second set using the Penelope physics list in a water-approximated FNTD and a aluminum-oxide FNTD. Results: Within the first half of the funding period, we have successfully established readout capabilities of FNTDs at our institute. Due to technical limitations, our microscope setup is significantly different from the approach implemented at the DKFZ, Germany. However, we can clearly reconstruct Carbon tracks in 3D with electron track resolution of 200 nm. A second microscope with superior readout capabilities will be tested in the second half of the funding period, we expect an improvement in signal to background ratio with the same the resolution.We have successfully simulated tracks in FNTDs. The more accurate Geant4-DNA track simulations can be used to reconstruct the track energy from the size and brightness of the observed tracks. Conclusion: We have achieved the goals set in the seed funding proposal: the setup of FNTD readout and simulation capabilities. We will work on improving the readout resolution to validate our MCS track structures down to the nanometer scales.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

Fricke, Sophia N.↗
DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Tuning successive linear programming to solve AC optimal power flow problem for large networks

Successive linear programming (SLP) is a practical approach for solving large-scale nonlinear optimization problems. Alternating current optimal power flow (ACOPF) is no exception, particularly the large size of real-world networks. However, in order to achieve tractability, it is essential to tune the SLP algorithm presented in the literature. This paper presents a modified SLP algorithm to solve the ACOPF problem, specified by the U.S. Department of Energy’s (DOE) Grid Optimization (GO) Competition Challenge 1, within strict time limits. The algorithm first finds a near-optimal solution for the relaxed problem (i.e., Stage 1). Then, it finds a feasible solution in the proximity of the near-optimal solution (i.e., Stage 2 and Stage 3). The numerical experiments on test cases ranging from 500-bus to 30,000-bus systems show that the algorithm is tractable. Here the results show that our proposed algorithm is tractable and can solve more than 80% of test cases faster than the well-known Interior Point Method while significantly reduce the number of iterations required to solve ACOPF. The number of iterations is considered an important factor in the examination of tractability which can drastically reduce the computational time required within each iteration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

The effect of self-interaction error on electrostatic dipoles calculated using density functional theory

Spurious electron self-interaction in density functional approximations (DFAs) can lead to inaccurate predictions of charge transfer in heteronuclear molecules that manifest as errors in calculated electrostatic dipoles. Here we show the magnitude of these errors on dipoles computed for a diverse set of 47 molecules taken from the recent benchmark study of Hait and Head-Gordon. We compare results of Perdew-Wang local spin density approximation (PW92), Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA), and strongly constrained and appropriatedly normed (SCAN) meta-GGA dipole calculations, along with those of their respective self-interaction-corrected (SIC) counterparts, to reference values from accurate wave function-based methods. The SIC calculations were carried out using the Fermi-Löwdin orbital (FLO-SIC) approach. We find that correcting for self-interaction generally increases the degree of charge transfer, thereby increasing the size of calculated dipole moments. The FLO-SIC-PW92 and FLO-SIC-PBE dipoles are in better agreement with reference values than their uncorrected DFA counterparts, particularly for strongly ionic molecules where significant improvement is seen. Applying FLO-SIC to SCAN does not improve dipole values overall. In addition, we show that removing self-interaction improves the description of the dipole for stretched-bond geometries and recovers the physically correct separated atom limit of zero dipole. Finally, we find that the best agreement between the FLO-SIC-DFA and reference dipoles occurs when the molecular geometries are optimized using the FLO-SIC-DFA.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Quantum solver for single-impurity Anderson models with particle-hole symmetry

Quantum embedding methods, such as dynamical mean-field theory (DMFT), provide a powerful framework for investigating strongly correlated materials. A central computational bottleneck in DMFT is in solving the Anderson impurity model (AIM), whose exact solution is classically intractable for large bath sizes. In this work, we benchmark a quantum-classical hybrid solver tailored for particle-hole symmetric AIMs, using the variational quantum eigensolver to prepare the ground state of the model with shallow quantum circuits. The solver uses shallow quantum ansätze and one set of variational parameters to prepare the ground state and its particle and hole excitations, enabling the construction of the impurity Green’s function through a continued-fraction expansion. We evaluate the performance of this approach across a few bath sizes and interaction strengths under noisy, shot-limited conditions. We compare three optimization routines (COBYLA, Adam, and L-BFGS-B) in terms of convergence and fidelity, assess the benefits of estimating a quantum-computed moment correction to the variational energies, and benchmark the approach by comparing the density of states computed from the impurity Green’s function against that obtained using a classical pipeline. Our results demonstrate the feasibility of Green’s function construction on near-term devices and establish practical benchmarks for quantum impurity solvers embedded within self-consistent DMFT loops.

Karabin, Mariia [ORNL]↗
DOE OSTI2023

Technoeconomic assessment of distillation heat transfer intensification using aeroelastically fluttering reeds

Dry cooling, where forced air is the heat transfer medium, is a preferable cooling method in arid locations lacking readily available process water. However, such locations often experience high ambient temperatures that limit the effectiveness of air cooling. The objective of this study is to quantify the economic and energetic benefits of heat transfer intensification via the implementation of aeroelastically fluttering reeds to the air-cooled condenser of a methanol distillation column. Condenser size and performance, regarding recovered methanol and required fan power, is evaluated across condenser operating temperatures (T cond ) from 60 to 62°C and heat transfer coefficients (U) U base –2U base for a range of inlet air temperatures based on ambient temperature data from Yuma, Arizona. Under typical design sizing, condenser capital cost was reduced by 6%–35% (1.3U base –2U base ) and nominal methanol recovery was increased from 0.26% to 0.38% (T cond = 62–60°C). At optimized condenser size, all enhanced U and T cond pairs increase methanol recovery and reduce fan power costs compared to the optimal Ubase reference. Overall, using enhanced heat transfer to maintain condenser temperature under a wider range of inlet conditions, rather than to reduce operation temperature, produces more favorable performance. Methanol price is not a determining factor in which pairs are profitable. Analysis was repeated for a global warming scenario, revealing more valuable improvements under elevated temperatures. Energy savings from condenser improvement to a methanol production system are not significant with respect to an optimized conventional system. Furthermore, unit economic and energetic incentives suggest implementation of fluttering reeds may be justified in other applications.

42 ENGINEERING↗
DOE OSTI2019

Optimization of turbine design in wind farms with multiple hub heights, using exact analytic gradients and structural constraints

Wind farms are generally designed with turbines of all the same hub height. If wind farms were designed with turbines of different hub heights, wake interference between turbines could be reduced, lowering the cost of energy (COE). This paper demonstrates a method to optimize onshore wind farms with two different hub heights using exact, analytic gradients. Gradient-based optimization with exact gradients scales well with large problems and is preferable in this application over gradient-free methods. Our model consisted of the following: a version of the FLOw Redirection and Induction in Steady-State wake model that accommodated three-dimensional wakes and calculated annual energy production, a wind farm cost model, and a tower structural model, which provided constraints during optimization. Structural constraints were important to keep tower heights realistic and account for additional mass required from taller towers and higher wind speeds. We optimized several wind farms with tower height, diameter, and shell thickness as coupled design variables. Our results indicate that wind farms with small rotors, low wind shear, and closely spaced turbines can benefit from having two different hub heights. A nine-by-nine grid wind farm with 70-meter rotor diameters and a wind shear exponent of 0.08 realized a 4.9% reduction in COE by using two different tower sizes. If the turbine spacing was reduced to 3 diameters, the reduction in COE decreased further to 11.2%. Allowing for more than two different turbine heights is only slightly more beneficial than two heights and is likely not worth the added complexity.

17 WIND ENERGY↗
DOE OSTI2014

Fabrication and microstructure of cerium doped lutetium aluminum garnet (Ce:LuAG) transparent ceramics by solid-state reaction method

Highlights: • We fabricate Ce doped lutetium aluminum garnet ceramics by solid-state method. • The raw materials include Lu{sub 2}O{sub 3} nanopowders synthesized by co-precipitation method. • The density of the transparent ceramics reach 99.7% of the theoretical value. • The optical transmittance of the bulk ceramic at 550 nm was 57.48%. • Some scattering centers decrease the optical characteristic of the ceramic. - Abstract: Polycrystalline Ce{sup 3+} doped lutetium aluminum garnet (Ce:LuAG) transparent ceramics fabricated by one step solid-state reaction method using synthetic nano-sized Lu{sub 2}O{sub 3}, commercial α-Al{sub 2}O{sub 3} and CeO{sub 2} powders were investigated in this paper. The green compacts shaped by the mixed powders were successfully densified into Ce:LuAG transparent ceramics after vacuum sintering at 1750 °C for 10 h. The in-line optical transmittance of the Ce:LuAG ceramic made by home-made Lu{sub 2}O{sub 3} powders could reach 57.48% at 550 nm, which was higher than that of the ceramic made by commercial Lu{sub 2}O{sub 3} powders (22.96%). The microstructure observation showed that light scattering centers caused by micro-pores, aluminum segregation and refraction index inhomogeneities induced the decrease of optical transparency of the Ce:LuAG ceramics, which should be removed and optimized in the future work.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Accelerating the Mining of Influential Nodes in Complex Networks through Community Detection

Computing the set of influential nodes with a given size to ensure maximal spread of influence on a complex network is a challenging problem impacting multiple applications. A rigorous approach to influence maximization involves utilization of optimization routines that comes with a high computational cost. In this work, we propose to exploit the existence of communities in complex networks to accelerate the mining of influential seeds. We provide intuitive reasoning to explain why our approach should be able to provide speedups without significantly degrading the extent of the spread of influence when compared to the case of influence maximization without using the community information. Additionally, we have parallelized the complete workflow by leveraging an existing parallel implementation of the Louvain community detection algorithm. We then conduct a series of experiments on a dataset with three representative graphs to first verify our implementation and then demonstrate the speedups. Our method achieves speedups ranging from 3x - 28x for graphs with small number of communities while nearly matching or even exceeding the activation performance on the entire graph. Complexity analysis reveals that dramatic speedups are possible for larger graphs that contain a correspondingly larger number of communities. In addition to the speedups obtained from the utilization of the community structure, scalability results show up to 6.3x speedup on 20 cores relative to the baseline run on 2 cores. Finally, current limitations of the approach are outlined along with the planned next steps.

community detection↗
DOE OSTI2026

Roadmap to Advance Heliostat Technologies for High Temperature Solar-Thermal Systems

Since its establishment, the Heliostat Consortium (HelioCon) has made substantial progress toward closing many of the gaps in concentrating solar power (CSP) research. Numerous techno-economic studies have been performed, investigating topics ranging from the trade-off between size and temperature for industrial process heat applications to optimization of the heliostat design itself for various applications. Significant improvements have been made in optical metrology techniques, with first steps toward in situ measurement of heliostat fields. Several standards have been, and continue to be, developed with the coordination of an international group of CSP industry participants. Training programs have been developed, with universities including CSP in their engineering curricula, and many public webinars have been held to provide broad access to the latest CSP research. Improved CSP components such as mirror facets and wireless communication systems have been developed, and the solar tower at Sandia National Laboratories has been upgraded with a testbed for closed-loop controls research and development. Field deployment challenges involving heliostat foundations and sensitive wildlife habitats have been explored, with progress made toward methods for streamlining project development and permitting. Additional knowledge has been added to the body of work on wind behavior of heliostats and arrays of heliostats, with progress made toward a holistic understanding of wind design methods. Finally, techniques have been developed and demonstrated for assessing soiling conditions at a proposed project site, with predictive models for the soiling rate showing good results. Taking these results together, HelioCon has contributed greatly to the global CSP research and development effort over the past several years.

14 SOLAR ENERGY↗
DOE OSTI2014

Seniority-based coupled cluster theory

Doubly occupied configuration interaction (DOCI) with optimized orbitals often accurately describes strong correlations while working in a Hilbert space much smaller than that needed for full configuration interaction. However, the scaling of such calculations remains combinatorial with system size. Pair coupled cluster doubles (pCCD) is very successful in reproducing DOCI energetically, but can do so with low polynomial scaling (N{sup 3}, disregarding the two-electron integral transformation from atomic to molecular orbitals). We show here several examples illustrating the success of pCCD in reproducing both the DOCI energy and wave function and show how this success frequently comes about. What DOCI and pCCD lack are an effective treatment of dynamic correlations, which we here add by including higher-seniority cluster amplitudes which are excluded from pCCD. This frozen pair coupled cluster approach is comparable in cost to traditional closed-shell coupled cluster methods with results that are competitive for weakly correlated systems and often superior for the description of strongly correlated systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

SU-E-T-299: Dosimetric Characterization of Small Field in Small Animal Irradiator with Radiochromic Films

Purpose: The small animal irradiator has been used with small animals to optimize new radiation therapy as preclinical studies. The small animal was irradiated by whole- or partial-body exposure. In this study, the dosimetric characterizations of small animal irradiator were carried out in small field using Radiochromic films Material & Methods: The study was performed in commercial animal irradiator (XRAD-320, Precision x-ray Inc, North Brantford) with Radiochromic films (EBT2, Ashland Inc, Covington). The calibration curve was generated between delivery dose and optical density (red channel) and the films were scanned by and Epson 1000XL scanner (Epson America Inc., Long Beach, CA).We evaluated dosimetric characterization of irradiator using various filter supported by manufacturer in 260 kV. The various filters were F1 (2.0mm Aluminum (HVL = about 1.0mm Cu) and F2 (0.75mm Tin + 0.25mm Copper + 1.5mm Aluminum (HVL = about 3.7mm Cu). According to collimator size (3, 5, 7, 10 mm, we calculated percentage depth dose (PDD) and the surface –source distance(SSD) was 17.3 cm considering dose rate. Results: The films were irradiated in 260 kV, 10mA and we increased exposure time 5sec. intervals from 5sec. to 120sec. The calibration curve of films was fitted with cubic function. The correlation between optical density and dose was Y=0.1405 X{sup 3}−2.916 X{sup 2}+25.566 x+2.238 (R{sup 2}=0.994). Based on the calibration curve, we calculated PDD in various filters depending on collimator size. When compared PDD of specific depth (3mm) considering animal size, the difference by collimator size was 4.50% in free filter and F1 was 1.53% and F2 was within 2.17%. Conclusion: We calculated PDD curve in small animal irradiator depending on the collimator size and the kind of filter using the radiochromic films. The various PDD curve was acquired and it was possible to irradiate various dose using these curve.

60 APPLIED LIFE SCIENCES↗