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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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DOE OSTI2021

The CanBikeCO Mini Pilot: Preliminary Results and Lessons Learned

In fall 2020, the Colorado Energy Office, as part of the State of Colorado’s “Can Do Colorado” initiative, initiated a project aimed at encouraging energy-efficient transportation during the COVID-19 pandemic. The initial mini-pilot provided e-bikes to 13 low-income households under an individual ownership model. This report assesses the impact of providing this additional mobility option on the travel behavior of participants. It also outlines the lessons learned from deploying a continuous monitoring platform to track the travel behavior. These lessons will influence the evaluation component for the full pilot, which will cover multiple geographic regions, starting in summer 2021, and run for 2 years. The continuous data collection was enabled by a customized version of the open-source e-mission platform, called CanBikeCO, configured with a behavioral gamification feature. The Colorado Energy Office used this system to collect a unique data set consisting of 3 months of partially automated travel diaries, combining sensed and surveyed data and linked with demographic information, from 12 participants. The data collection process worked well overall: users generally liked the app, appreciated the game, and did not complain about battery life. The long tracking period introduced behavioral challenges in user engagement, which we plan to address using repeated patterns and automated status checks for the full pilot. The analysis results, based on the subset of trips with user-reported labels (68%), indicate that the e-bike was the dominant commute mode share (31%), in sharp contrast to the census bicycle commute mode share (<1%). E-bike trips primarily replaced single-occupancy vehicle (SOV) trips (28%), followed closely by walking (24%) and regular bike (20%). The nonmotorized mode replacement corresponds to lower travel time and increased productivity enabled by the program. The emissions impact analysis of the program, computed using trip-level energy intensity factors, indicates savings of 1,367 lbs. of CO 2 . Although the results are strongly positive, the narrow demographic profile of study participants, their limited mobility alternatives, and nonuniform labeling indicate caution in broader interpretation. These preliminary results do suggest that such programs, supported by real-time education and support from program managers, can simultaneously meet equity and sustainability goals. The planned full pilot, addressing the data collection challenges and broadening the geographic scope, will provide additional insights into the generality of this approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Polycyclic Aromatic Hydrocarbons with Aliphatic Sidegroups: Intensity Scaling for the C–H Stretching Modes and Astrophysical Implications

The so-called unidentified infrared emission (UIE) features at 3.3, 6.2, 7.7, 8.6, and 11.3 μm ubiquitously seen in a wide variety of astrophysical regions are generally attributed to polycyclic aromatic hydrocarbon (PAH) molecules. Astronomical PAHs may have an aliphatic component, as revealed by the detection in many UIE sources of the aliphatic C–H stretching feature at 3.4 μm. The ratio of the observed intensity of the 3.4 μm feature to that of the 3.3 μm aromatic C–H feature allows one to estimate the aliphatic fraction of the UIE carriers. This requires knowledge of the intrinsic oscillator strengths of the 3.3 μm aromatic C–H stretch (A{sub 3.3}) and the 3.4 μm aliphatic C–H stretch (A{sub 3.4}). Lacking experimental data on A{sub 3.3} and A{sub 3.4} for the UIE candidate materials, one often has to rely on quantum-chemical computations. Although the second-order Møller–Plesset (MP2) perturbation theory with a large basis set is more accurate than the B3LYP density functional theory, MP2 is computationally very demanding and impractical for large molecules. Based on methylated PAHs, we show here that, by scaling the band strengths computed at an inexpensive level (e.g., B3LYP/6-31G*), we are able to obtain band strengths as accurate as those computed at far more expensive levels (e.g., MP2/6-311+G(3df,3pd)). We calculate the model spectra of methylated PAHs and their cations excited by starlight of different spectral shapes and intensities. We find that (I{sub 3.4}/I{sub 3.3}){sub mod}, the ratio of the model intensity of the 3.4 μm feature to that of the 3.3 μm feature, is insensitive to the spectral shape and intensity of the exciting starlight. We derive a straightforward relation for determining the aliphatic fraction of the UIE carriers (i.e., the ratio of the number of C atoms in aliphatic units N{sub C,ali} to that in aromatic rings N{sub C,aro}) from the observed band ratios (I{sub 3.4}/I{sub 3.3}){sub obs}: N{sub C,ali}/N{sub C,aro}≈0.57×(I{sub 3.4}/I{sub 3.3}){sub obs} for neutrals and N{sub C,ali}/N{sub C,aro}≈0.26×(I{sub 3.4}/I{sub 3.3}){sub obs} for cations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

AI-assisted detector design for the EIC (AID(2)E)

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using G EANT 4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Smart trigger logic for focal plane arrays

An electronic device includes a memory configured to receive data representing light intensity values from pixels in a focal plane array and a processor that analyzes the received data to determine which light values correspond to triggered pixels, where the triggered pixels are those pixels that meet a predefined set of criteria, and determines, for each triggered pixel, a set of neighbor pixels for which light intensity values are to be stored. The electronic device also includes a buffer that temporarily stores light intensity values for at least one previously processed row of pixels, so that when a triggered pixel is identified in a current row, light intensity values for the neighbor pixels in the previously processed row and for the triggered pixel are persistently stored, as well as a data transmitter that transmits the persistently stored light intensity values for the triggered and neighbor pixels to a data receiver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets

This dataset contains turbine- and plant-level power outputs for 252,500 cases of diverse wind plant layouts operating under a wide range of yawing and atmospheric conditions. The power outputs were computed using the Gaussian wake model in NREL's FLOw Redirection and Induction in Steady State (FLORIS) model, version 2.3.0. The 252,500 cases include 500 unique wind plants generated randomly by a specialized Plant Layout Generator (PLayGen) that samples randomized realizations of wind plant layouts from one of four canonical configurations: (i) cluster, (ii) single string, (iii) multiple string, (iv) parallel string. Other wind plant layout parameters were also randomly sampled, including the number of turbines (25-200) and the mean turbine spacing (3D-10D, where D denotes the turbine rotor diameter). For each layout, 500 different sets of atmospheric conditions were randomly sampled. These include wind speed in 0-25 m/s, wind direction in 0 deg.-360 deg., and turbulence intensity chosen from low (6%), medium (8%), and high (10%). For each atmospheric inflow scenario, the individual turbine yaw angles were randomly sampled from a one-sided truncated Gaussian on the interval 0 deg.-30 deg. oriented relative to wind inflow direction. This random data is supplemented with a collection of yaw-optimized samples where FLORIS was used to determine turbine yaw angles that maximize power production for the entire plant. To generate this data, a subset of cases were selected (50 atmospheric conditions from 50 layouts each for a total of additional 2,500 cases) for which FLORIS was re-run with wake steering control optimization. The IEA onshore reference turbine, which has a 130 m rotor diameter, a 110 m hub height, and a rated power capacity of 3.4 MW was used as the turbine for all simulations. The simulations were performed using NREL's Eagle high performance computing system in February 2021 as part of the Spatial Analysis for Wind Technology Development project funded by the U.S. Department of Energy Wind Energy Technologies Office. The data was collected, reformatted, and preprocessed for this OEDI submission in May 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub repository under explore_wind_plant_data_h5.ipynb.

AI↗
DOE OSTI2020

Electronic Bottleneck Suppression in Next‐Generation Networks with Integrated Photonic Digital‐to‐Analog Converters

Digital‐to‐analog converters (DAC) are indispensable functional units in signal processing instrumentation and wide‐band telecommunication links for both civil and military applications. As photonic systems are capable of high data throughput and low latency, an increasingly found system limitation stems from the required domain crossing such as digital to analog and electronic to optical. A photonic DAC implementation, in contrast, enables a seamless signal conversion with respect to both energy efficiency and short signal delay, often requiring bulky discrete optical components and electric–optic transformation, hence introducing inefficiencies. Herein, a novel coherent parallel photonic DAC concept along with a 4‐bit experimental prototype capable of performing this DAC without optic–electric–optic domain crossing is introduced. This new paradigm guarantees a linear intensity weighting among bits when operating at high sampling rates (50 GHz), featuring an exceptional sampling efficiency (> 100 GS ) and small footprint (≈1 mm 2 ) in an 8‐bit implementation. Importantly, this photonic DAC enables seamless interfaces of next‐generation data processing hardware with high relevance in data centers, task‐specific compute accelerators such as neuromorphic engines, and network edge processing applications.

Meng, Jiawei↗
DOE OSTI2024

When Machine Learning Meets 2D Materials: A Review

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

2D materials↗
DOE OSTI2014

CAST: Contraction Algorithm for Symmetric Tensors

Tensor contractions represent the most compute-intensive core kernels in ab initio computational quantum chemistry and nuclear physics. Symmetries in these tensor contractions makes them difficult to load balance and scale to large distributed systems. In this paper, we develop an efficient and scalable algorithm to contract symmetric tensors. We introduce a novel approach that avoids data redistribution in contracting symmetric tensors while also avoiding redundant storage and maintaining load balance. We present experimental results on two parallel supercomputers for several symmetric contractions that appear in the CCSD quantum chemistry method. We also present a novel approach to tensor redistribution that can take advantage of parallel hyperplanes when the initial distribution has replicated dimensions, and use collective broadcast when the final distribution has replicated dimensions, making the algorithm very efficient.

distributed memory↗
DOE OSTI2014

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

Purpose: 4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. Methods: The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is invented to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. Results: The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3–0.5 mm for patients 1–3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1–1.5 min per phase. Conclusions: High-quality 4D-CBCT imaging based on the clinically standard 1-min 3D CBCT scanning protocol is feasible via the proposed hybrid reconstruction algorithm.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Efficient Binary Static Code Data Flow Analysis Using Unsupervised Learning

The ever increasing need to ensure that code is reliably, efficiently and safely constructed has fueled the evolution of popular static binary code analysis tools. In identifying potential coding flaws in binaries, tools such as IDA Pro are used to disassemble the binaries into an opcode/assembly language format in support of manual static code analysis. Because of the highly manual and resource intensive nature involved with analyzing large binaries, the probability of overlooking potential coding irregularities and inefficiencies is quite high. In this paper, a light-weight data, unsupervised data flow methodology is described which uses highly-correlated data flow graph (CDFGs) to identify coding irregularities such that analysis time and required computing resources are minimized. Such analysis accuracy and efficiency gains are achieved by using a combination of graph analysis and unsupervised machine learning techniques which allows an analyst to focus on the most statistically significant flow patterns while performing binary static code analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Exploring OpenSNAPI Use Cases and Evolving Requirements [Slides]

Emerging system architectures are rapidly transforming in order to meet shifting requirements. Motivated by expanding data volumes, energy efficiency concerns, and the omnipresent need to improve performance, architectures are increasingly adopting a data-centric approach. At the core of this concept is the goal of minimizing data motion and instead processing data in-situ to the greatest degree possible. Therefore, data-centric designs, in contrast to conventional CPU-centric models, typically distribute compute capabilities throughout the architecture. As part of this paradigm shift, a novel class of devices known as data processing units (DPUs), alongside CPUs and GPUs, are quickly forming a third pillar of data-centric systems. These devices, which include smart network adapters and switches, seek to offload computation on data at the network edge as well as in-flight within the network fabric. The Open Smart Network API (OpenSNAPI) project seeks to develop a unified API for DPU devices. In our previous talks, we introduced the OpenSNAPI project and detailed our investigations regarding the viability of offloading compute intensive kernels to BlueField DPUs. In contrast, in this talk we detail our efforts to offload application-level file I/O to the DPU. We also discuss plans and early efforts to explore in-network compute capabilities. Finally, we describe our observations with respect to the evolving design of OpenSNAPI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks

Focal Area: This white paper responds to Focal area III by exploring data fusion, learning and explainable AI methods in characterizing hydrological extremes and interconnections. It also addresses Focal area II by using probabilistic AI and ensemble ML for predicting extremes and compound extremes. Science Challenge: A key question associated with the integrated water (or hydrological) cycle grand challenge in the Earth and Environmental Systems Sciences Division (EESSD) strategic plan, is how the frequency and intensity of hydrological events will change. Prediction of the tail behavior (extremes) of the hydrological cycle is especially challenging, because of their stochasticity and low probability. These extreme events and their compound impacts have significant societal and economic consequences. It is anticipated for the next-generation Earth System models (ESMs), that model predictability of the water cycle will improve with increased resolution (e.g., regionally refined E3SM), advanced software and computational architectures, and improved model physics based on the data from ARM measurements and high-fidelity models. However, the challenges for predictability of low-probability high-impact extreme events will unlikely be alleviated with conventional modeling and data-driven approaches, as ESMs are calibrated largely for capturing the high-frequency mean climate states. Recent AI and ML applications have shown great potential in quantifying well-defined climate extremes (e.g., supervised learning of tropical cyclones/atmospheric rivers by ClimateNet1) but few efforts are dedicated to compound events, extreme drivers and uncertainty estimation. We envision the opportunity to develop and apply ML and interpretable AI methods extended on the existing efforts, specifically, for: (1) identification of compound extremes, (2) diagnosing drivers of extremes, (3) bias correction in extreme predictions and (4) probabilistic modeling of extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

FrESCO: Framework for Exploring Scalable Computational Oncology

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information from electronic health records. This data is then used to create summary statistics about cancer incidence and mortality to facilitate population health monitoring. Extracting phenotypic information from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating the information extraction process from cancer pathology reports has the potential to improve data quality by extracting information in a consistent manner across registries. It can also improve patient outcomes by reducing the time from diagnosis, enabling rapid case ascertainment for clinical trials. Here we present FrESCO, a modular deep-learning natural language processing (NLP) library initially designed for extracting pathology information from clinical text documents. This repository is not solely limited to clinical medical text, but may also be used by researchers just getting started with NLP methods and those looking for a robust solution for their classification problems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Metal hydride differential scanning calorimetry as an approach to compositional determination of mixtures of hydrogen isotopologues and helium

Gaseous mixtures of diatomic hydrogen isotopologues and helium are often encountered in the nuclear energy industry and in analytical chemistry. Compositions of stored mixtures can vary due to interactions with storage and handling materials. When tritium is present, it decays to form ions and helium-3, both of which can lead to further compositional variation. Monitoring of composition is typically achieved by mass spectrometry, a method that is bulky and energy-intensive. Mass spectrometers disperse sample material through vacuum pumps, which is especially troublesome if tritium is present. Moreover, our ultimate goal is to create a compact, fast, low-power sensor that can determine composition with minimal gas consumption and waste generation, as a complement to mass spectrometry that can be instantiated more widely. We propose calorimetry of metal hydrides as an approach to this, due to the strong isotope effect on gas absorption, and demonstrate the sensitivity of measured heat flow to atomic composition of the gas. Peak shifts are discernible when mole fractions change by at least 1%. A mass flow restriction results in a unique dependence of the measurement on helium concentration. We present a mathematical model as a first step toward prediction of the peak shapes and positions. The model includes a useful method to compute estimates of phase diagrams for palladium in the presence of arbitrary mixtures of hydrogen isotopologues. As a result, we expect that this approach can be used to deduce unknown atomic compositions from measured calorimetric data over a useful range of partial pressures of each component.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Theoretical level energies, radiative lifetimes and transitions in W IX

The atomic states of the W IX (W{sup 8+}) tungsten ion lying below the W{sup 9+} ionisation threshold have been studied theoretically, employing the multiconfiguration Dirac–Hartree–Fock method with configuration interaction. The level electronic structures and their energies are presented. The electric dipole (E1), magnetic dipole (M1), electric quadrupole (E2), and magnetic quadrupole (M2) radiative transitions have been computed in order to calculate the radiative lifetimes of given states. Transition wavelengths, energies, and decay rates are also presented for selected high-intensity E1 transitions. The configuration interaction method was applied to estimate electron correlation effects. The aim of the present research was to fill a lack of atomic data for low-charged tungsten ions, which may be useful in low-temperature plasma diagnostics and may form the base for collisional–radiative modelling of spectra for low-charged tungsten ions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Sparse modeling of space- and time-varying diffraction response of a progressively loaded aluminum alloy

Highlights: • Plasticity induces crystal orientation and lattice spacing heterogeneity. • High energy x-ray diffraction spot broadening typically accompanies plasticity. • Method quantifies spot broadening in difficult, mixed microstructure data. • Algorithm finds sparse combination of entries from dictionary of peak functions. • Approach showed asymmetric plastic response in preprocessed AA7075 T651 sample. - Abstract: High energy X-ray diffraction data collected in situ during loading experiments permits probing of the crystal structure of a plastically deforming material sample. An elastoplastic deformation is associated with heterogeneity in both crystal orientation and lattice spacing—each manifesting as azimuthal broadening and radial broadening of diffraction peaks respectively. Quantifying the spreading effect is challenging, especially in cases where the sample has a granularity between that of a single crystal and fine grain or powder material. The approach developed in this paper begins by modeling the intensity signal in the vicinity of a Debye-Scherrer ring as a sparse, nonnegative superposition of Gaussian basis functions drawn from an over-complete dictionary. Processing automatically selects from the dictionary basis functions whose widths and amplitudes are representative of the data. The chosen basis functions are used to compute scalar measures of radial and azimuthal broadening for the set of diffraction peaks composing a Debye-Scherrer ring. To demonstrate the utility of the approach, we show that analysis of diffraction peak shape information from an aluminum alloy 7075 T651 (AA7075 T651) sample provides insight into the origin of material state that would have otherwise have been difficult if not impossible to extract using conventional methods. Furthermore, the extracted information revealed a deformation response linked to the processing history of the sample.

36 MATERIALS SCIENCE↗
DOE OSTI2014

SU-E-T-63: Carotid Sparing Tomohelical Three Dimensional Conformal Radiotherapy for T1N0 Glottic Cancer

Purpose: We investigated the dosimetric benefit and treatment efficiency of carotid-sparing TomoHelical (TH) three-dimensional conformal radiotherapy (3DCRT) for early glottic cancer. Methods: Computed tomography (CT) simulation was performed for 10 patients with early-stage (T1N0M0) glottic squamous cell carcinoma. The clinical target volume, planning target volume (PTV), carotid artery (CA), and spinal cord (SP) were delineated for each CT data set. Two-field 3DCRT (2F-3DCRT), three-field intensity-modulated radiation therapy (IMRT) (3F-IMRT), TomoHelical-IMRT (TH-IMRT), and TH-3DCRT plans were generated, with a total prescribed dose of 67.5 Gy in 30 fractions to the PTV for each patient. In order to evaluate plan quality, dosimetric characteristics were compared in terms of the conformity index (CI) and homogeneity index (HI) for the PTV, V35, V50, and V63 for the CAs and in terms of the maximum dose for the SP. Additionally, treatment planning and delivery times were compared to evaluate treatment efficiency. Results: The CIs for 3F-IMRT (0.650±0.05), TH-IMRT (0.643±0.03), and TH-3DCRT (0.631±0.03) were much better than that for 2F-3DCRT (0.318±0.03). The HIs for TH-IMRT (1.053±0.01) and TH-3DCRT (1.055±0.01) were slightly better than those for 2F-3DCRT (1.062±0.01) and 3F-IMRT (1.091±0.007). 2F-3DCRT showed poor CA sparing in terms of the V35, V50, and V63 compared to 3F-IMRT, TH-IMRT, and TH-3DCRT (p<0.05), whereas there was no significant dose difference between 3F-IMRT, TH-IMRT, and TH-3DCRT (p>0.05). The maximum dose to the SP with all plans was below 45 Gy. The treatment planning times for 2F-3DCRT (5.9±0.66 min) and TH-3DCRT (7.32±0.94 min) were much lower than those for 3F-IMRT (45.51±2.76 min) and TH-IMRT (35.58±4.41 min), whereas the delivery times with all plans was below 3 minutes. Conclusion: TH-3DCRT showed excellent carotid sparing capability, comparable to that with TH-IMRT, with high treatment efficiency and short planning and treatment times, comparable to those for 2F-3DCRT, while maintaining good PTV coverage. This work was supported by the Technology Innovation Program, 10040362, Development of an integrated management solution for radiation therapy funded by the Ministry of Knowledge Economy (MKE, Korea)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Oncentra brachytherapy planning system

In modern cancer management, treatment planning has progressed as a contemporary tool with all the advances in computing power in recent years. One of the advanced planning tools uses 3-dimensional (3D) data sets for accurate dose distributions in patient prescription. Among these planning processes, brachytherapy has been a very important part of a successful cancer management program, offering clinical benefits with specific or combined treatments with external beam therapy. In this chapter, we mainly discussed the Elekta Oncentra planning system, which is the main treatment planning tool for high-dose rate (HDR) modality in our facility and in many other facilities in the United States. HDR is a technically advanced form of brachytherapy; a high-intensity radiation source (3.6 mm in length) is delivered with step motor in submillimeter precision under computer guidance directly into the tumor areas while minimizing injury to surrounding normal healthy tissue. Oncentra planning is the key component to generate a deliverable brachytherapy procedure, which is executed on the microSelectron V3 remote afterloader treatment system. Creating a highly conformal plan can be a time-consuming task. The development of Oncentra software (version 4.5.3) offers a variety of useful tools that facilitate many of the clinical challenging tasks for planning, such as contouring and image reconstruction, as well as rapid planning calculations with dose and dose volume histogram analysis. Oncentra Brachy module creates workflow and optimizes the planning accuracy for wide varieties of clinical HDR treatments, such as skin, gynecologic (GYN), breast, prostate, and many other applications. The treatment file can also be transferred to the afterloader control station for speedy delivery. The design concept, calculation algorithms, and optimization modules presented some key characteristics to plan and treat the patients effectively and accurately. The dose distribution and accuracy of several clinical sample cases were discussed to illustrate the effectiveness and clinical efficacy. The American Association of Physicists in Medicine brachytherapy reports of TG-43 and TG-186 were also described and compared in evaluations of fundamental calculation methodologies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗