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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-T-116: Analysis of Patient Specific VMAT QA Passing Rates with Delta4 for Matched Machines

Purpose: To test whether unified vendor specified beam conformance for matched machines implies volumetric modulated arc radiotherapy(VMAT) delivery consistency. Methods: Twenty-two identical patient QA plans, eleven 6MV and eleven 15MV, were delivered to the Delta{sup 4}(Scandidos, Uppsala, Sweden) on two Varian TrueBEAM matched machines. Sixteen patient QA plans, nine 6 MV and seven 10 MV, were delivered to Delta{sup 4} on two Elekta Agility matched machines. The percent dose deviation(%DDev), distance-to-agreement(DTA), and the gamma analysis(γ) were collected for all plans and the differences in measurements were tabulated between matched machines. A paired t-test analysis of the data with an alpha of 0.05 determines statistical significance. Power(P) was calculated to detect a difference of 5%; all data except Elekta %DDev sets were strong with above a 0.85 power. Results: The average differences for Varian machines (%DDev, DTA, and γ) are 6.4%, 1.6% and 2.7% for 6MV, respectively, and 8.0%, 0.6%, and 2.5% for 15MV. The average differences for matched Elekta machines (%DDev, DTA, and γ) are 10.2%, 0.6% and 0.9% for 6 MV, respectively, and 7.0%, 1.9%, and 2.8% for 10MV.A paired t-test shows for Varian the %DDev difference is significant for 6MV and 15MV(p-value6MV=0.019, P6MV=0.96; p-value15MV=0.0003, P15MV=0.86). Differences in DTA are insignificant for both 6MV and 15MV(p-value6MV=0.063, P6MV=1; p-value15MV=0.907, P15MV=1). Varian differences in gamma are significant for both energies(p-value6MV=0.025, P6MV=0.99; p-value15MV=0.013, P15MV=1). A paired t-test shows for Elekta the difference in %DDev is significant for 6MV but not 10MV(p-value6MV=0.00065, P6MV=0.68; p-value10MV=0.262, P10MV=0.39). Differences in DTA are statistically insignificant(p-value6MV=0.803, P6MV = 1; p-value10MV=0.269, P10MV=1). Elekta differences in gamma are significant for 10MV only(p-value6MV=0.094, P6MV=1; p-value10MV=0.011, P10MV=1). Conclusion: These results show vendor specified beam conformance across machines does not ensure equivalent patient specific QA pass rates. Gamma differences are statistically significant in three of the four comparisons for two pairs of vendor matched machines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Best Practices for NPT Transit Matching

Transit matching is the process for relating or matching reports of shipments and receipts submitted to the International Atomic Energy Agency (IAEA). Transit matching is a component used by the IAEA in drawing safeguards conclusions and performing investigative analysis. Transit matching is part of IAEA safeguards activities and the State evaluation process, and it is included in the annual Safeguards Implementation Report (SIR). Annually, the IAEA currently receives reports of ~900,000 nuclear material transactions, of which ~500,000 are for domestic and foreign transfers. Of these the IAEA software can automatically match (i.e., machine match) about 95% of the domestic transfers and 25% of the foreign transfers. Given the increasing demands upon IAEA resources, it is highly desirable for the machine-matching process to match as many transfers as possible. Researchers at Oak Ridge National Laboratory (ORNL) have conducted an investigation funded by the National Nuclear Security Administration through the Next Generation Safeguards Initiative to identify opportunities to strengthen IAEA transit matching. Successful matching, and more specifically machine matching, is contingent on quality data from the reporting States. In February 2016, ORNL hosted representatives from three States, the IAEA, and Euratom to share results from past studies and to discuss the processes, policies, and procedures associated with State reporting for transit matching. Drawing on each entity's experience and knowledge, ORNL developed a best practices document to be shared with the international safeguards community to strengthen transit matching. This paper shares the recommendations that resulted from this strategic meeting and the next steps being taken to strengthen transit matching.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2016

SU-F-T-643: Feasibility of Performing Patient Specific VMAT QA On Single Linac for Plans Treated in Beam-Matched Elekta Agility Linacs

Purpose: The increasing application of VMAT demands a more efficient workflow and QA solution. This study aims to investigate the feasibility of performing VMAT QA measurements on one linac for plans treated on other beam-matched Elekta Agility linacs. Methods: A single model was used to create 24 clinically approved VMAT plans (12 head-and-neck and 12 prostate using 6MV and 10MV respectively) on Pinnacle v9.10 (Philips, Einhoven, Netherlands). All head-and-neck plans were delivered on three beam-matched machines while all prostate cases were delivered on two beam-matched 10MV Agility machines. All plans were delivered onto PTW Octavius 4D phantom with 1500 detector array (PTW, Freiburg, Germany). Reconstructed volume doses were then compared with the Pinnacle reference plans in Verisoft 6.1 under 3%/3mm gamma criteria at local dose. Plans were considered clinically acceptable if >90% of the voxels passing the gamma criteria. Results: All measurements were passed (3D gamma passing rate >90%) and the result shows that the mean difference of 3D gamma of 12 head-and-neck cases is 1.2% with standard deviation of 0.6%. While for prostate cases, the mean difference of 3D gamma is 0.9% with standard deviation of 0.7%. Maximum difference of 3D gamma of all measurements between beam-matched machines is less than 2.5%. The differences of passing rates between different machines were statistically insignificant (p>0.05). Conclusion. The result suggests that ther Conclusion: The result suggests that there exists a 3D gamma threshold, in our case 92.5%, above which the VMAT QA performed in any one of beam-matched machine will also pass in another one. Therefore, VMAT QA efficiency may be increased and phantom set up time can be saved by implementing such method. A constant performance across all beam matched machines must be maintained to make this QA approach feasible.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Measuring linac photon beam energy through EPID image analysis of physically wedged fields

Purpose: Electronic portal imaging devices (EPIDs) have proven to be useful tools for measuring several parameters of interest in linac quality assurance (QA). However, a method for measuring linac photon beam energy using EPIDs has not previously been reported. In this report, such a method is devised and tested, based on fitting a second order polynomial to the profiles of physically wedged beams, where the metric of interest is the second order coefficientα. The relationship between α and the beam quality index [percentage depth dose at 10 cm depth (PDD{sub 10})] is examined to produce a suitable calibration curve between these two parameters. Methods: Measurements were taken in a water-tank for beams with a range of energies representative of the local QA tolerances about the nominal value 6 MV. In each case, the beam quality was found in terms of PDD{sub 10} for 100 × 100 mm{sup 2} square fields. EPID images of 200 × 200 mm{sup 2} wedged fields were then taken for each beam and the wedge profile was fitted in MATLAB 2010b (The MathWorks, Inc., Natick, MA). α was then plotted against PDD{sub 10} and fitted with a linear relation to produce the calibration curve. The uncertainty in α was evaluated by taking five repeat EPID images of the wedged field for a beam of 6 MV nominal energy. The consistency of measuring α was found by taking repeat measurements on a single linac over a three month period. The method was also tested at 10 MV by repeating the water-tank crosscalibration for a range of energies centered approximately about a 10 MV nominal value. Finally, the calibration curve from the test linac and that from a separate clinical machine were compared to test consistency of the method across machines in a matched fleet. Results: The relationship betweenα and PDD{sub 10} was found to be strongly linear (R{sup 2} = 0.979) while the uncertainty in α was found to be negligible compared to that associated with measuring PDD{sub 10} in the water-tank (±0.3%). The repeat measurements over a three month period showed the method to be reasonably consistent (i.e., well within the limits defined by local QA tolerances). The measurements were repeated on a matched machine and the same linear relationship between α and PDD{sub 10} was observed. The results for both machines were found to be indistinguishable across the energy range of interest (i.e., across and close to the thresholds defined by local QA tolerances), hence a single relation could be established across a matched fleet. Finally, the experiment was repeated on both linacs at 10 MV, where the linear relationship between α and PDD{sub 10} was again observed. Conclusions: The authors conclude that EPID image analysis of physically wedged beam profiles can be used to measure linac photon beam energy. The uncertainty in such a measurement is dominated by that associated with measuring PDD{sub 10} in the water-tank; hence, the accuracies of these two methods are directly comparable. This method provides a useful technique for quickly performing energy constancy measurements while saving significant clinical downtime for QA.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Neutron-producing gas puff Z-pinch experiments on a fast, low-impedance, 0.5 MA linear transformer driver

A study on the neutron production from single and double gas puff Z-pinches on the CESZAR linear transformer driver with ~0.45 MA current and 170 ns rise time is presented. Total neutron yield measurements made with a LaBr activation detector are compared for three configurations, using a double nozzle setup. When a single, hollow, deuterium gas shell was used, reliable implosions could only be attained at higher load mass than the optimal value to match implosion time with the driver rise time, with neutron yields of ~10 6 per pulse. The use of a double gas puff configuration with a deuterium center jet allowed a reduction in the shell density and operation closer to machine-matched conditions, recording up to (4.1 ± 0.3) × 10 7 neutrons/pulse when either Kr or D 2 was used in the shell. For a comparable mass and implosion time, using a higher atomic-number gas in the outer shell results in more unstable plasma surface and smaller plasma radius at the location of instability bubbles, which, however, do not seem to consistently correlate with a higher neutron yield. Comparing implosion dynamics with models and neutron yields with literature scaling suggests that the machine current is not well coupled to the plasma during the final stages of compression. Optimizing current and energy coupling to the pinched plasma is critical to improving performance, particularly in low-impedance drivers.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

SU-F-T-344: Commissioning Constant Dose Rate VMAT in the Raystation Treatment Planning System for a Varian Clinac IX

Purpose: To demonstrate the commissioning of constant dose rate volumetric modulated arc therapy (VMAT) in the Raystation treatment planning system for a Varian Clinac iX with Exact couch. Methods: Constant dose rate (CDR) VMAT is an option in the Raystation treatment planning system, enabling VMAT delivery on Varian linacs without a RapidArc upgrade. Raystation 4.7 was used to commission CDR-VMAT for a Varian Clinac iX. Raystation arc model parameters were selected to match machine deliverability characteristics. A Varian Exact couch model was added to Raystation 4.7 and commissioned for use in VMAT optimization. CDR-VMAT commissioning checks were performed on the linac, including patient-specific QA measurements for 10 test patients using both the ArcCHECK from Sun Nuclear Corporation and COMPASS from IBA Dosimetry. Multi-criteria optimization (MCO) in Raystation was used for CDR-VMAT planning. Results: Raystation 4.7 generated clinically acceptable and deliverable CDR-VMAT plans for the Varian Clinac. VMAT plans were optimized including a model of the Exact couch with both rails in the out positions. CDR-VMAT plans generated with MCO in Raystation were dosimetrically comparable to Raystation MCO-generated IMRT plans. Patient-specific QA measurements with the ArcCHECK on the couch showed good agreement with the treatment planning system prediction. Patient-specific, structure-specific, multi-statistical parameter 3D QA measurements with gantry-mounted COMPASS also showed good agreement. Conclusion: Constant dose rate VMAT was successfully modeled in Raystation 4.7 for a Varian Clinac iX, and Raystation’s multicriteria optimization generated constant dose rate VMAT plans which were deliverable and dosimetrically comparable to IMRT plans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Construction of accuracy-preserving surrogate for the eigenvalue radiation diffusion and/or transport problem

The construction of surrogate models for high fidelity models is now considered an important objective in support of all engineering activities which require repeated execution of the simulation, such as verification studies, validation exercises, and uncertainty quantification. The surrogate must be computationally inexpensive to allow its repeated execution, and must be computationally accurate in order for its predictions to be credible. This manuscript introduces a new surrogate construction approach that reduces the dimensionality of the state solution via a range-finding algorithm from linear algebra. It then employs a proper orthogonal decomposition-like approach to solve for the reduced state. The algorithm provides an upper bound on the error resulting from the reduction. Different from the state-of-the-art, the new approach allows the user to define the desired accuracy a priori which controls the maximum allowable reduction. We demonstrate the utility of this approach using an eigenvalue radiation diffusion model, where the accuracy is selected to match machine precision. Results indicate that significant reduction is possible for typical reactor assembly models, which are currently considered expensive given the need to employ very fine mesh many group calculations to ensure the highest possible fidelity for the downstream core calculations. Given the potential for significant reduction in the computational cost, we believe it is possible to rethink the manner in which homogenization theory is currently employed in reactor design calculations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS

With recent developments in computational techniques, Data-driven Machine Learning Models (DMLs) have shown great potential in simulating streamflow and capturing the rainfall-runoff relationship in given watersheds, which are traditionally fulfilled by Process-based Hydrologic Models (PHMs). There are debates on whether the DMLs can outperform and possibly replace the classical PHMs for streamflow simulation and river forecasting, but no clear conclusions have been made. This study aims to investigate whether the newer DMLs have any potential in further improving the simulation accuracy of classical PHMs, and vice versa. To do this, we compared a few popular PHMs and DMLs over four watersheds across the Continental US (CONUS) that are associated with different input, climate, and regional conditions. A total of five hydrologic models were chosen, including (1) two classical lumped models, i.e., the Sacramento Soil Moisture Accounting (SAC-SMA) and Xinanjiang (XAJ); (2) one modern distributed model, termed Coupled Routing and Excess Storage (CREST); (3) and two DMLs including an Artificial Neural Networks (ANN) and a deep learning model, termed Long Short Term Memory (LSTM). Our results demonstrated that the DMLs still significantly biased when using the baseline input scenario with the PHMs. However, the DMLs fed with delayed input scenarios had great potential and can reach high simulation accuracy. The DMLs, especially the ANN, outperformed other employed models under the rainfall-runoff relationship in which rainfall dominantly drives. Furthermore, the DMLs also showed better performance in the high-flow regime, while the PHMs had a better performance for the low-flow regime, implying both PHMs and DMLs have their own merits and are worthy of joint development. In general, our study indicated a great potential of using DMLs to simulate streamflow, but further studies are still needed to verify the transferability and scalability of DMLs in large-scale experiments, such as the Distributed Model Intercomparison Projects 1&2 conducted by National Weather Services but to compare modern DMLs and PHMs.

58 GEOSCIENCES↗
DOE OSTI2022

Technical and Economic Evaluation of the First Ever Polymer Flood Field Pilot to Enhance the Recovery of Heavy Oils on Alaska's North Slope via Machine Assisted History Matching

Polymer flooding has become globally established as a potential enhanced oil recovery method for heavy oils. To determine whether this technology may be useful in developing the substantial heavy oil resources on the Alaska North Slope, a polymer flood field pilot commenced at the Milne Point Unit in August 2018. This study seeks to evaluate the results of the field pilot on a technical and economic basis. A reservoir simulation model is constructed and calibrated to predict the oil recovery performance of the pilot through machine-assisted reservoir simulation techniques. To replicate the early water breakthrough observed during waterflooding, transmissibility contrasts are introduced into the simulation model, forcing viscous fingering effects. In the ensuing polymer flood, these transmissibility contrasts are reduced to replicate the restoration of injection conformance during polymer flooding. Transmissibility contrasts are later reinstated to replicate fracture overextension interpreted in one of the producing wells. The calibrated simulation models produced at each stage of the history matching process are used to forecast oil recovery. These forecasts are used as input for economic analysis, incremental to waterflooding expectations. The simulation forecasts indicate that polymer flooding significantly increases the heavy oil production for this field pilot compared to waterflooding alone, yielding attractive project economics. However, meaningful variations between simulation scenarios demonstrate that a simulation model is only valid for prediction if flow behavior in the reservoir remains consistent with that observed during the history matched period. Critically, this means that a simulation model calibrated for waterflooding may not fully capture the technical and economic benefits of an enhanced oil recovery process such as polymer flooding. Subsequently, the simulation model and economic model are used in conjunction to conduct a sensitivity analysis for polymer flood design parameters, from which recommendations are provided for both the continued operation of the current field pilot and future polymer flood designs. The results demonstrate that a higher polymer concentration can be injected due to the development of fractures in the reservoir. The throughput rate should remain high without exceeding operating constraints. A calculated point-forward polymer utilization parameter demonstrates the decreasing efficiency of the polymer flood at later times in the pattern life. Future projects will benefit from starting polymer injection earlier in the pattern life. A pattern with tighter horizontal well spacing will observe a greater incremental benefit from polymer flooding.

Keith, Cody↗
DOE OSTI2021

A machine-learning inverse model framework for rapid forecasting and history matching in unconventional reservoirs

Model-based optimization for real-time forecasting in unconventional reser-voirs requires novel methods and work?ows since the strategies and work?ows used in conventional reservoirs are either inapplicable, or prohibitively expen-sive and time-consuming. Insu?cient site data and computational expense of high-?delity simulations mean that work?ows with high-?delity simulations are not ideal for usage in comprehensive uncertainty quanti?cation stud-ies that require 1000s of forward model runs. We present an alternative, novel work?ow for unconventional reservoirs, based on the interplay between reduced-order models and machine-learning. Our physics-informed machine-learning (PIML) work?ow addresses the challenges to real-time reservoir management in uncoventionals, namely lack of data (the time-frame for which the wells have been producing), and computational expense of high-?delity modeling. We use the machine-learning paradigm of transfer-learning to bind together fast but less accurate reduced-order models with slow, but accurate high-?delity models and circumvent the di?culties inherent in the current state-of-the-art for unconventionals. Such a PIML work?ow, grounded in physics, is a viable candidate for real-time history matching and production forecasting in a fractured shale gas reservoir. The signi?cance of our approach is that while it is developed for a particu-lar well and site in the Marcelus Shale gas reservoir of the Appalachian basin (MSEEL), it is not wedded to it. We expect the same work?ow can be ap-plied to other shale formations (e.g., Woodford, Barnett, Utica, EagleFord) should site-data become available, using the same set of machine-learning techniques from transfer learning. Some ?ne-tuning (or minimal retraining of the neural networks) will be required to transfer knowledge across shale gas sites/formations but it is a clearly superior alternative to developing a new machine-learning model altogether when considering a di?erent site.

Srinivasan, Shriram↗
DOE OSTI2018

Estimating residential energy consumption in metropolitan areas: A microsimulation approach

Prior research has shown that land use patterns and the spatial configurations of cities have a significant impact on residential energy demand. Given the pressing issues surrounding energy security and climate change, there is renewed interest in developing and retrofitting cities to make them more energy efficient. Yet deriving micro-scale residential energy footprints of metropolitan areas is challenging because high resolution data from energy providers is generally unavailable. In this study, a bottom-up model is proposed to estimate residential energy demand using datasets that are commonly available in the United States. The model applies novel machine learning methods to match records in the Residential Energy Consumption Survey with Public Use Microdata samples. This matching and machine learning produce a synthetic household energy distribution at a neighborhood scale. The model was tested and validated with data from the Atlanta metropolitan region to demonstrate its application and promise.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Text-based Analytics for Biosurveillance

The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related to biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when). The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related to biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when).

Biomedical NLP↗
DOE OSTI2018

Machine Learning Algorithms for Matching Theories, Simulations, and Observations in Cosmology (Final Project)

The search for simple and elegant equations to explain and predict observable natural phenomena has been a driving force in scientific theory for several centuries. The great successes of Newton's laws, Maxwell's equations, the Schrodinger equation, and the theory of General Relativity have encouraged this perspective, and perhaps a beautiful equation comprising a “theory of everything” remains to be discovered. But now scientists are grappling with systems of ever-increasing complexity, fraught with nonlinearity, and with the quantities of interest often only indirectly observed. Nature may resist a simple description, and the most important discoveries of the next century may be complex theories with countless variables and parameters. The era of big data opens up a promising new approach to scientific discovery in this setting. The predictions of modern theories, even if complex or nonlinear, can be examined through detailed and computationally-intensive simulations that take days or weeks to run and fill petabytes of disk space. The upcoming generation of scientific instruments will provide petabytes of observations at unprecedented resolution and depth. The challenge is to compare data to simulations so as to test theories and identify those that best match the observations. The data are numerous and rich but are subject to noise and various systematic biases. The simulations are large and detailed but are so costly that the effective sample size is small. Moreover, the mapping from data to simulation to the parameters of interest is typically complicated and ill-conditioned. Although these challenges, and the solutions we propose, apply across the sciences, we will ground our efforts by focusing on cosmology. Here, we are truly entering the era of big data in several senses. Ever larger experiments survey larger and larger cosmological volumes, resulting in enormous amounts of pixel data to process and extract the information of cosmological interest. These include large imaging surveys, which collect all the light in broad wavelength ranges over significant fractions of the sky, and spectroscopic surveys, which measure the light as a function of wavelength to map out 3D distances. These experiments are designed to shed light on the big unanswered questions in cosmology. Distilling the petabytes of data into observed quantities for comparison with theory is a daunting task. But these large experiments do not only result in large amounts of data to analyze: if we wish to compare to theories of physics, then we need increasingly extensive predictions for the observed quantities as well, which requires ever-larger cosmological simulations that take substantial -- and at times prohibitive -- computational resources to produce. Our goal is to develop statistical and machine learning methods for using observed and simulated data to advance machine learning with applications cosmology. In particular, we focus our research efforts on the following tasks: The challenge for automated science is that it is computationally impossible and statistically dangerous to consider every possible model in order to find the best one. We will develop Bayesian Optimization based active-learning methods that accelerate both the execution of the simulations and the search for best-fitting parameters. The key idea is to make the simulations adaptive -- across resolutions, time, and parameters -- using the data to search as the simulation runs; Most ML methods operate on simple finite dimensional feature vectors. However, many cosmology and other science applications require ML methods that can operate on more complex objects as inputs or outputs such as functions, distributions, or sets and point clouds. Our goal is to develop efficient ML methods for this problem and demonstrate their applicability in Cosmology, Astrophysics, and other science problems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks

Next generation High Performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power. Achieving reliable disruption prediction for a device's HP operation based on its Low Performance (LP) data is a key to its success. In this letter, through explorative data analysis and dedicated numerical experiments on multiple existing tokamaks, we demonstrate how the operational regimes of tokamaks can affect the power of a trained disruption predictor. First, our results suggest data-driven disruption predictors trained on abundant LP discharges work poorly on the HP regime of the same tokamak, which is a consequence of the distinct distributions of the tightly correlated signals related to disruptions in these two regimes. Second, we find that matching operational parameters among tokamaks strongly improves cross-machine accuracy which implies our model learns from the underlying scalings of dimensionless physics parameters like q 95 , β p and confirms the importance of these parameters in disruption physics and cross machine domain matching from the data-driven perspective. Finally, our results show in the absence of HP data from the target devices, the best predictivity of the HP regime for the target machine can be achieved by combining LP data from the target with HP data from other machines. Furthermore, these results provide a possible disruption predictor development strategy for next generation tokamaks, such as ITER and SPARC, and highlight the importance of developing baseline scenario discharges of future tokamaks on existing machines to collect more relevant disruptive data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Charged Wellbore Casing Controlled Source Electromagnetics (CWC-CSEM) for Reservoir Imaging and Monitoring (Final report)

This project addresses the needs of the U.S. Department of Energy (DOE) to develop advanced monitoring technologies and protocols to track the fate of subsurface carbon dioxide (CO2) plumes for carbon storage. Specifically, the project seeks to develop and test a unique and novel system of technologies consisting of electromagnetic data acquisition, coupled multiphysics imaging, and reservoir model enhancement to understand the migration and long-term distribution of CO2 in the subsurface. The overarching objective is to develop an integrated approach for long term monitoring of carbon storage. The two main components of the project include the methodology development and the test of the method at a field site. The methodology component consists of 1) developing the field procedure and protocol for collecting time-lapse controlled-source electromagnetic (CSEM) data with source electric current injected into the subsurface through wellbore casings; 2) building of background 3D electrical conductivity utilizing multiple sources of data such as supplemental surface transient EM (TEM) surveys, well-logs, and seismic structural information, for enhancing CSEM signal from reservoir depths; and 3) coupled multiphysics simulations and inversion of CSEM data constrained by production data and by structural information from seismic imaging of the reservoir and overlying formations. The testing component used the field site of Bell Creek Oil Field, which served both as a field laboratory for the method development as well as a test site to evaluate the CSEM signal strengths and the methodology developed in this research project. We have accomplished all the proposed tasks and developed the methodology as planned. These include the procedure for time-lapse CSEM data acquisition, data processing techniques, integration with 3D conductivity model building, fast reservoir simulation for history matching using machine learning, and interpreting CSEM data with coupling to the reservoir modeling. Collectively, the outcome of these tasks form a coherent workflow that can be applied to monitor dedicated carbon storage in saline reservoirs. The testing component evaluated the applicability and limitations of the method, and concluded that the method would be ideal for monitoring dedicated carbon storage sites utilizing saline reservoirs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SU-E-T-585: Commissioning of Electron Monte Carlo in Eclipse Treatment Planning System for TrueBeam

Purpose: To commission electron Monte Carlo (eMC) algorithm in Eclipse Treatment Planning System (TPS) for TrueBeam Linacs, including the evaluation of dose calculation accuracy for small fields and oblique beams and comparison with the existing eMC model for Clinacs. Methods: Electron beam percent-depth-dose (PDDs) and profiles with and without applicators, as well as output factors, were measured from two Varian TrueBeam machines. Measured data were compared against the Varian TrueBeam Representative Beam Data (VTBRBD). The selected data set was transferred into Eclipse for beam configuration. Dose calculation accuracy from eMC was evaluated for open fields, small cut-out fields, and oblique beams at different incident angles. The TrueBeam data was compared to the existing Clinac data and eMC model to evaluate the differences among Linac types. Results: Our measured data indicated that electron beam PDDs from our TrueBeam machines are well matched to those from our Varian Clinac machines, but in-air profiles, cone factors and open-filed output factors are significantly different. The data from our two TrueBeam machines were well represented by the VTBRBD. Variations of TrueBeam PDDs and profiles were within the 2% /2mm criteria for all energies, and the output factors for fields with and without applicators all agree within 2%. Obliquity factor for two clinically relevant applicator sizes (10×10 and 15×15 cm{sup 2}) and three oblique angles (15, 30, and 45 degree) were measured for nominal R100, R90, and R80 of each electron beam energy. Comparisons of calculations using eMC of obliquity factors and cut-out factors versus measurements will be presented. Conclusion: eMC algorithm in Eclipse TPS can be configured using the VTBRBD. Significant differences between TrueBeam and Clinacs were found in in-air profiles and open field output factors. The accuracy of the eMC algorithm was evaluated for a wide range of cut-out factors and oblique incidence.

07 ISOTOPE AND RADIATION SOURCES↗