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DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ZIPPER: Zonal isolation with plug and perf in enhanced reservoirs (Final Technical Report)

Modern multistage hydraulic stimulation treatments in cased wellbores require an effective method of isolating stages between treatments. A commonly used method in the oil and gas industry, plug and perf, is not currently viable for Enhanced Geothermal Systems (EGS) due to limitations of a critical component of the system – the ball drop (flow through) frac plug. Commercially available ball drop frac plugs do not meet the temperature or wellbore diameter requirements needed for use in EGS stimulations. For example, one particular drillable bridge and frac plug line from a large tool developer is only rated to 175 °C, while another is only available up to casing sizes of 5 ½” and rated up to 204 °C. We developed, prototyped, and field tested an upgraded ball drop frac plug to meet the requirements of EGS stimulations, including high-temperature (225+ °C), differential pressure (6000+ psi), and wellbore diameters ranging from 6 5/8” to 10 5/8”. We focused on designs and materials to optimize drillability, a key cost driver in plug and perf systems. The primary objective was to upgrade a drillable frac plug (currently rated to a temperature of 175°C and a pressure of 10,000 psi in 7” casing) to withstand temperatures of 225+°C. The upgraded frac plug was tested in the laboratory as well as in a field trial at a geothermal field. Engineering design and fabrication of the new plug was completed in 2019. Lab testing, validation, and qualification of the plug was completed in 2020. Ultimately the plug was successfully run during a multistage stimulation treatment in a fully horizontal EGS well in 2022. Prior to this project, a ball-drop, flow-through stimulation plug had never been used in a geothermal well. At the conclusion of this project, three different types of zonal isolation plugs were evaluated under full-scale operating conditions across 16 stimulation treatment stages in a first-of-a-kind EGS project called Project Red. This trial included the newly designed high-temperature, large-diameter plug. The plugs met all of 5 the technical requirements for commercial viability. Project Red has since been fully commissioned and is generating electricity on the Nevada grid - the first EGS project to successfully deliver power to the grid in the US.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

High Throughput In-Line Coating Metrology Development for Solid Oxide Fuel Cell Manufacturing

Coatings play key roles in solid oxide fuel cell (SOFC) stack durability. For example, diffusion barrier coatings on Cr-containing interconnect and balance of plant (BOP) components protect electrodes from Cr poisoning over the long operational lifetimes (>10,000 hours) of the fuel cell stack. Common defects in coatings, such as cracks, pinholes, and porosity, result in a failure to protect the electrodes, resulting in shorter operational lifetime and thus higher cost. It is very unlikely, even in the best coating process, that all these defects can be mitigated, hence identifying critical defects in parts, and removing defective parts from production before they can damage the stack, becomes paramount. Furthermore, these quality control techniques must be operational in the production/assembly line (in-line), i.e., high throughput and non-destructive, and cost effective. Redox Power Systems, LLC (Redox) together with the National Renewable Energy Laboratory (NREL) developed much needed high throughput, in-line metrology techniques for protective coatings. The overall goal of the project is to lower cost while increasing robustness, reliability, and endurance of SOFC stacks. To accomplish this, we had several objectives, including: to identify key coating and substrate defects that lead to coating failure through the use of detailed characterization methods (e.g., microscopy, XRD, EDS, electrochemistry); to assess capabilities of in-line metrology techniques, e.g., optical profilometry (Redox) and thermography (NREL), to probe these defects, or evidence thereof; demonstrate long-term performance of “defect-free” protective coatings, as identified by in-line metrology, in solid oxide fuel cell (SOFC) stack operation. In the first part of this project, the ability to identify key defects expected to lead to coating and SOFC degradation using in-line metrology tools were evaluated. Coated interconnect samples with controlled defect types and populations were tested under conditions similar to SOFC operation, followed by detailed post-test analysis to reveal the defects responsible for observed degradation. In the second part of the project, the optimal in-line metrology techniques and methodologies were used to map the defect distribution in full-size interconnects with critical defects intentionally allowed to exist in some cases. These interconnects underwent SOFC testing for extended periods (up to ~3,000 hours) followed by post-test analysis to evaluate the effectiveness of in-line metrology techniques in mitigating MCO coating related degradation. Key accomplishments in this project included the following: Demonstrated ASR of < 0.05 ohm-cm 2 at 650 °C for 1,000 hours with low defect (determined by in-line metrology) interconnect samples (average ASR=37 milliohms-cm 2 after over 1,000 hours). Demonstrated that low defect coatings on interconnects (as screened using in-line metrology) have low volatilization of chromium at ~650 °C for 1,000 hours as detected using Cr-getter material (< 5 at% increase above baseline); 1022 hour duration tests under humidified, elevated temperature (750 °C rather than 650 °C) compared a base case against different coating thicknesses. Demonstrated capability to identify initial key defects of interest with in-line metrology techniques using up to 8 cm by 10 cm having coatings with and without intentional defects of interest using thermal imaging and optical profilometry. Correlated key defects identified using metrology techniques with observed coating performance (e.g., ASR and Cr volatility). Conducted several 4 cm by 4 cm cell tests using MCO-interconnects that were pre-screened using some of the metrology techniques developed in the project (e.g., optical profilometry). An analysis of ASR measurements were able to show that defect-free coatings resulted in the anticipated performance in the cell tests.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Development of fast-running eighth core nodal solution

A fast-running, eighth-core solution technique has been developed for the quick core calculations in ANC, the Westinghouse core analysis code based on the nodal expansion method, and the BEACON{sup TM} Core Monitoring System (BEACON). This technique, referred to as 'Fast Eighth' (Fast8), enables users to obtain satisfactory macroscopic core calculation results such as reactivity, axial offset and assembly power much more quickly than calculations performed in the standard full-core or quarter-core geometry. Fast8 uses three techniques: folding the radial geometry into eighth-core geometry, reducing the axial geometry into a coarse axial mesh geometry, and smearing the spacer grids into nodes on-the-fly after the first calculation in the standard geometry. These geometry adjustments in Fast8 contribute to the reduction of the number of neutronic and material nodes while maintaining the reaction rates in each node. The axial mesh condition dependency in Fast8 is investigated in a xenon transient calculation in single assembly geometry, and the Fast8 maximum axial mesh size is determined based on these results. Fast8 is then applied to the load swing calculation, and the results are compared with the reference, full-core geometry, solution. The critical boron concentration and axial offset of Fast8 show good agreement with the reference solutions, and that Fast8 is a good technique for obtaining macroscopic core calculation results in significantly shorter time. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Unraveling the Structural Sensitivity of Metal Catalysts in Ethylene Hydroformylation: Insights from Theory and Experiments

Here, in this study, we combined experimental and theoretical methods to investigate the structural sensitivity of metal catalysts in the ethylene hydroformylation reaction. Among Rh, Pt, Ir, Ni, Au, Ag, Pd, and Cu catalysts studied using experimental and theoretical methods, Rh showed the highest selectivity toward the C-C coupling product from CO and C 2 H 5 (i.e., C 2 H 5 CHO). The results from DFT and microkinetic simulations revealed that the activation energy barrier for C-C coupling is lowest on the Rh nanocluster, which explains the experimentally observed highest C 2 H 5 CHO selectivity on the Rh catalyst. Furthermore, DFT results demonstrated that the sites located on the flat surfaces of nanoparticles primarily promote the hydrogenation reaction, leading to the formation of undesired C 2 H 6 . In contrast, undercoordinated edge and corner sites of the nanocluster promote the C-C coupling reaction. Thus, our results illustrate that the selectivity toward C 3 oxygenates in ethylene hydroformylation reaction can be steered by tuning the size of Rh nanoparticles (the best-performing catalyst) to optimize the active (edge and corner) sites that preferentially promote the C-C coupling reaction.

58 GEOSCIENCES↗
DOE OSTI2016

TU-H-209-00: Planning and Delivering HDR APBI Treatments

Learnings Objectives: Although brachytherapy is the oldest form of radiation therapy, the rapid advancement of the methods of dose calculation, treatment planning and treatment delivery pushes us to keep updating our knowledge and experience to new procedures all the time. Our purpose is to present the newest applicators used in Accelerated Partial Breast Irradiation (APBI) and the techniques of using them for a maximum effective treatment. Our objective will be to get the user familiar with the Savi, Contura and ML Mammosite from the detailed description and measurements to cavity eval and choice or size, to acceptance tests and use of each. At the end of the session the attendants will be able to assist at the scanning of the patient for the first treatment, decide on the proper localization and immobilization devices, import the scans in the treatment planning system, perform the structure segmentation, reconstruct the catheters and develop a treatment plan using inverse planning (IPSA) or volume optimization. The attendant should be able to evaluate the quality of a treatment plan according to the ABS protocols and B39 after this session. Our goal is that all the attendants to gain knowledge of all the quality assurance procedures required to be performed prior to a treatment, at the beginning of a treatment day, weekly, monthly and annualy on the remote afterloader, the treatment planning system and the secondary check system. We will provide tips for a consistent treatment delivery of the 10 fractions in a BID (twice daily) regimen.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Review—Development of Highly Active and Stable Catalyst Supports and Platinum–Free Catalysts for PEM Fuel Cell

Metal-free, metal-containing, and template-assisted metal-containing nitrogen-modified carbon-based catalysts carbon composite supports, and highly active and electrochemically stable hybrid cathode catalysts for oxygen reduction reaction are reviewed in this manuscript. Here, novel procedures are developed for the synthesis of (i) highly catalytically active PGM-free catalyst and stable carbon composite catalyst supports. The carbon composite catalyst supports are engineered with optimized BET surface area and pore size distribution and with a well-defined kinetic and mass transfer region during the reaction and their performance is dicussed in detail in this review. The surface activation results in increasing carbon graphitization and inclusion of non-metallic active sites on the support surface. The USC catalysts exhibit the initial performance of 0.91 V and a maximum power density of 177 mW cm –2 , with well-defined kinetic and mass-transfer regions and ~2.5% H 2 O 2 production. Activated carbon composite support (ACCS) is modified to optimize its kinetic activity and its electrochemical stability shows excellent thermal stability and support stability under simulated start-up/shut-down operating conditions. The stabilities of various supports developed in this study are compared with those of a commercial Pt/C catalyst. The active sites for the ORR identified with electrochemical and physicochemical methods are pyridinic-N and quaternary-N.

25 ENERGY STORAGE↗
DOE OSTI2019

Roll-to-Roll Advanced Materials Manufacturing DOE Laboratory Collaboration - FY2018 Final Report

High-value R2R processing is used to support a wide range of products in applications which span many industrial business sectors. The overall R2R process technology can be considered as “mature” as the process methodology and has been in use for decades. This continuous processing technique traditionally involves deposition of material(s) onto moving webs or carriers or other continuous R2R, belt-fed, or conveyor-based processes that enable successive steps to build a final version which serves to support the deposited materials. Current process technologies, which typify “roll to roll”, include tape casting, silk-screen printing, reel-to-reel vacuum deposition/coating and R2R lithography. Products supported by R2R manufacturing include micro-electronics, electro-chromic window films, PVs, fuel cells for energy conversion, battery electrodes for energy storage, and barrier materials. Due to innovation in materials and process equipment, high-quality yet very low-cost multi-layer technologies can be manufactured on a very cost-competitive basis. To move energy-related products from high-cost niche applications to the commercial sector, a means must be available to enable manufacture of these products in a cost-competitive manner that is affordable by the general consumer. Fortunately, products such as fuel cells, thin- and mid-film PVs, batteries, electrochromic and piezoelectric films, water separation membranes, and other energy saving technologies readily lend themselves to manufacture using R2R approaches.Within the DOE EERE AMO, it was recognized that establishment of a program supported at the DOE National Laboratories, along with their immense design of materials and equipment modelling capabilities enabled with use of high-performance computing, could take advantage of available R2R infrastructure to manufacture new technologies. In FY 2016, a R2R Consortium was established and provided with initial “seed” funding to take an approach that was envisioned to be supportable by advanced manufacturing R2R processes. This collaborative approach was designed to foster identification and development of materials and processes related to R2R for clean-energy product development. Using computational and experimental capabilities by acknowledged subject matter experts within the supported National Laboratory system, this collaborative project would leverage the capabilities and expertise at each of four laboratories to further the development of an enabling high-volume cost-competitive platform technology. The collaboration team that is comprised of ORNL, ANL, NREL and LBNL, coordinating with EPB and other selected industry partners, was formed in April 2016 to initially address enhancing battery electrode performance and R2R manufacturing challenges. The research efforts were to predict and measure changes and results in electrode morphology and performance based on process condition changes; to evaluate mixed, active, particle size deposition and drying for novel electrode materials; to model various process condition changes and the resulting morphology and electrode performance; and to develop and validate NDE techniques for in-line measurement of battery electrode material properties. These efforts carried through FY 2017 and completed at the end of FY 2018.The approach was to look at compositions of materials with different particle sizes to make electrode samples using a R2R manufacturing process. The shape, size, and morphology of the materials, the chemistry of the formulation, the nature of slurries, their coating rate, the rate of drying all play a role in determining the final coating architecture, quality, and performance. A commercial cathode material was selected to make a series of cathodes and anodes by single pass, dual pass and slot die methods. Analysis of all the compiled results for this battery electrode development were that the best performing cathodes included: dual-pass electrode with large particles near the foil, mixed small and large particles, and small particles only. Whereas, the best performing anodes were with a mix of small and large graphite particles. An additional core project was added in FY 2017 to conduct studies of fuel cell materials that can be produced using R2R processes. The goal of this project is to explore, understand and optimize material and process parameters to support increased throughput, increased quality, and reduced cost for high volume production of gas-diffusion electrodes (GDEs) for PEMFCs. Project work in FY 2018 were to use a R2R process to fabricate electrodes without ionomer overlayer that can produce the equivalent mass activity as spray-coated electrodes with an ionomer overlayer. Oxygen-limiting current measurements were utilized to optimize oxygen mass transport. Alcohol-rich solvents were investigated, and results were that the alcohol and water ratio can be tuned to control ionomer distribution. Water-rich solvents produce a more dispersed ink which results in better high-current density performance.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Integration of Control Methods and Digital Twins for Advanced Nuclear Reactors

Advanced nuclear reactors offer a new set of features to energy generation, due to their ability to adapt to variable energy demand, operate autonomously, be deployed in rural locations and monitored remotely, afford compact size and lower power ratings, and rely on novel technologies to achieve safer operations. Thus, a requirement for the success of these reactors is the use of intelligent forms of control to track changing power demands, make autonomous decisions, and reduce the need for human involvement. Regulatory requirements pertaining to control of nuclear reactors could be met via historical means of control; however, these are not expected to enable the level of highly autonomous operations desired in advanced nuclear reactors. Historical control methods rely on both logical and high-performance (HP) control. These two types of control are usually used separately, with a human element being introduced whenever decisions are cascaded from one science to another. AI/ML control, on the other hand, can replace the human element in the current U.S. fleet of nuclear power plants (NPPs) by acting as a supervisory optimizer that understands the plant internal/external variables in order to make control decisions, and can easily handle non-linear and multi-input/multi out (MIMO) decisions—another requirement for advanced nuclear reactors that could be difficult to handle via logical and HP control. Because of the harsh operating environments produced in advanced reactors, resulting in the frequent failure of sensors and other types of equipment, and considering the lack of operating history for advanced nuclear reactors, control of advanced nuclear reactors would necessitate relying on a model that can track and adapt to the actual process (i.e., a digital twin). This digital twin can make approximations when knowledge and data are unavailable and would evolve as more knowledge is gained. The reactor control must also be risk-informed to account for the high-consequence nature of advanced reactors. This report introduces a high-level (i.e., not method- or process-specific) integration of the three different control and digital twinning methods able to meet the requirements for advanced nuclear reactors. These methods could be applied during both the operational and design stages of these reactors. The aim is to demonstrate how each method interfaces with and highlights enabling solutions necessitated by the unique features of advanced nuclear reactors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

NSTX-U National Research Program: White Paper in Response to Call from FESAC Sub-Committee

Both scientific and technical innovation is needed for the realization of an attractive engineering solution for a timely and cost-effective Pilot Plant, the design and construction of which is the overarching recommendation of the FESAC Long Range Plan, and the 2021 NASEM Pilot Plant reports, which underpin the Bold Decadal Vision. The two most significant plasma physics gaps to close for a Compact Pilot Plant (CPP) are core confinement improvement and heat flux mitigation, neither of which have been closed in an integrated fashion for any planned fusion power production device. High core confinement and stability are essential for producing majority self-driven plasmas in CPPs with reduced size and auxiliary heating power requirements, with an improvement in confinement being the major driver for cost reduction of a CPP. The National Spherical Tokamak Experiment - Upgrade (NSTX-U) is a unique low aspect ratio research facility that will address the fundamental challenge of developing the science and technology basis for a CPP design that integrates high core and edge confinement with the ability to mitigate very high incident heat fluxes. NSTX-U capabilities will enable the high performance, already achieved on NSTX, to extend into physics regimes much closer to those anticipated in Spherical Tokamak (ST)-based CPPs. These confinement and stability properties will be assessed by a full complement of diagnostics and analysis tools, which will also aid in the development of the underlying theory and predictive models needed for further optimization. Both conventional and transformative heat flux mitigation methods, such as liquid lithium plasma-facing components, will be developed and tested in-situ in NSTX-U at incident heat fluxes of ~100 MW/m 2 , and will inform plans and reduce risk for a subsequent major upgrade to the device to fully heated, high-Z wall and full liquid lithium divertor capability, a technology that potentially could then be implemented on any magnetic confinement device at any aspect ratio. NSTX-U research is fully complementary to programs performed on other STs, nationally and internationally. Furthermore, NSTX-U research has a direct connection to the private sector by informing design choices for future power production facilities being developed by these companies. The NSTX-U program will operate as a national User Facility, with collaborating researchers, engineers, and graduate students from 19 outside institutions, and open to participation and experiments led by researchers from both public and private entities. The research program will advance workforce development through training of young scientists, engineers, and technicians, and it will also serve for further diagnostic innovation, especially for high heat flux and high-Z wall environments, and implementation of advanced artificial intelligence (AI) for plasma and heat flux control.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Efficient sensitivity analysis of the thermal profile in powder bed fusion of metals using hypercomplex automatic differentiation finite element method

Rapid cyclic temperature fluctuation occurring in powder bed fusion of metals using a laser beam (PBF-LB/M) influences the formation of flaws in printed parts. Consequently, there is a pressing need to enhance the quality of printed parts by developing innovative methodologies that can predict thermal histories and help uncover the intricate relationships between process parameters and thermal profiles. Sensitivity Analysis (SA) emerges as an essential tool for this, offering the potential for process optimization and enhanced quality control. Nonetheless, conventional SA methodologies often incur in excessive computational costs and potential numerical approximation errors. Here, to address this technical challenge, we present a novel method for SA that integrates the HYPercomplex-based Automatic Differentiation (HYPAD) technique with transient thermal simulations conducted via the finite element method (FEM). Leveraging this methodology, we efficiently and accurately perform SA for PBF-LB/M processes in a post-processing step. Compared to traditional methods like Finite Differences (FD), HYPAD-FEM required 96 % less computational time for obtaining sensitivities for 22 process parameters, under a comparative study conducted within the context of the 2018–02 AM benchmark of the National Institute of Standards and Technology. In summary, HYPAD-FEM offers superior efficiency and accuracy in SA over conventional methods, delivering the best sensitivity of a model without the need for step-size selection and problem or parameter-based implementations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An analytical method for identifying synergies between behind-the-meter battery and thermal energy storage

Electric utilities build generation capacity to meet the highest demand period, and they often pass on the costs associated with these peaking generators to building owners through demand charges. Building owners can minimize these demand charges by shifting energy use away from peak periods with behind-the-meter storage. This storage can include batteries, which can directly shift the metered load, or thermal energy storage, which can shift thermal-driven electric loads like air conditioning. However, there is a lack of research on how best to combine battery and thermal energy storage. In this study, we develop an analytical sizing method to calculate the potential demand reduction and annualized cost savings for different combinations of thermal and battery energy storage sizes. We show that adding batteries to a thermal energy storage system can increase the total system's load shaving potential. This is particularly true when the building has onsite photovoltaic generation or electric vehicle charging, which add significant variability to the load shape. We also show that for a given total storage size, selecting a higher fraction of thermal energy storage can significantly lower the cycling of the battery, and therefore extend the battery life. This, combined with the expected lower first cost of thermal energy storage materials compared to batteries, shows that hybrid energy storage systems can outperform a standalone battery or standalone thermal storage system. Assuming the thermal storage has a capital cost 6x lower than the battery, our analysis shows that the optimal system is 71% thermal energy storage and 29% battery energy storage for a scenario with electric vehicle charging. The annualized cost savings for this system are $48.6 k/yr, whereas an equivalently sized standalone thermal energy storage system would provide annualized cost savings of $28.5 k/yr and a standalone battery would lead to savings of $8.72 k/yr. The hybrid system also reduces battery cycling by 52% compared to a standalone battery, extending battery lifetime.

25 ENERGY STORAGE↗
DOE OSTI2016

SU-G-TeP1-08: LINAC Head Geometry Modeling for Cyber Knife System

Purpose: Knowledge of the LINAC head information is critical for model based dose calculation algorithms. However, the geometries are difficult to measure precisely. The purpose of this study is to develop linac head models for Cyber Knife system (CKS). Methods: For CKS, the commissioning data were measured in water at 800mm SAD. The measured full width at half maximum (FWHM) for each cone was found greater than the nominal value, this was further confirmed by additional film measurement in air. Diameter correction, cone shift and source shift models (DCM, CSM and SSM) are proposed to account for the differences. In DCM, a cone-specific correction is applied. For CSM and SSM, a single shift is applied to the cone or source physical position. All three models were validated with an in-house developed pencil beam dose calculation algorithm, and further evaluated by the collimator scatter factor (Sc) correction. Results: The mean square error (MSE) between nominal diameter and the FWHM derived from commissioning data and in-air measurement are 0.54mm and 0.44mm, with the discrepancy increasing with cone size. Optimal shift for CSM and SSM is found to be 9mm upward and 18mm downward, respectively. The MSE in FWHM is reduced to 0.04mm and 0.14mm for DCM and CSM (SSM). Both DCM and CSM result in the same set of Sc values. Combining all cones at SAD 600–1000mm, the average deviation from 1 in Sc of DCM (CSM) and SSM is 2.6% and 2.2%, and reduced to 0.9% and 0.7% for the cones with diameter greater than 15mm. Conclusion: We developed three geometrical models for CKS. All models can handle the discrepancy between vendor specifications and commissioning data. And SSM has the best performance for Sc correction. The study also validated that a point source can be used in CKS dose calculation algorithms.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-DE-BRA-10: Development of a Novel Scanning Beam Low-Energy Intraoperative Radiation Therapy (SBIORT) System for Pancreatic Cancer

Purpose: Developing a compact collimator system and validating a 3D surface imaging module for a scanning beam low-energy x-ray radiation therapy (SBIORT) system that enables delivery of non-uniform radiation dose to targets with irregular shapes intraoperatively. Methods: SBIORT consists of a low energy x-ray source, a custom compact collimator module, a robotic arm, and a 3D surface imaging module. The 3D surface imaging system (structure sensor) is utilized for treatment planning and motion monitoring of the surgical cavity. SBIORT can deliver non-uniform dose distributions by dynamically moving the x-ray source assembly along optimal paths with various collimator apertures. The compact collimator utilizes a dynamic shutter mechanism to form a variable square aperture. The accuracy and reproducibility of the collimator were evaluated using a high accuracy encoder and a high resolution camera platform. The dosimetrical characteristics of the collimator prototype were evaluated using EBT3 films with a Pantak Therapax unit. The accuracy and clinical feasibility of the 3D imaging system were evaluated using a phantom and a cadaver cavity. Results: The SBIORT collimator has a compact size: 66 mm diameter and 10 mm thickness with the maximum aperture of 20 mm. The mechanical experiment indicated the average accuracy of leaf position was 0.08 mm with a reproducibility of 0.25 mm at 95% confidence level. The dosimetry study indicated the collimator had a penumbra of 0.35 mm with a leaf transmission of 0.5%. 3D surface scans can be acquired in 5 seconds. The average difference between the acquired 3D surface and the ground truth is 1 mm with a standard deviation of 0.6 mm. Conclusion: This work demonstrates the feasibility of the compact collimator and 3D scanning system for the SBIORT. SBIORT is a way of delivering IORT with a compact system that requires minimum shielding of the procedure room. This research is supported by the University of Iowa Internal Funding Initiatives.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Performance of Photovoltaic Systems Recorded by Open Solar Performance and Reliability Clearinghouse (oSPARC)

This article analyzes data from 2,200 photovoltaic systems collected through the Open Solar Performance and Reliability Clearinghouse (oSPARC, 2018) to draw conclusions about the measured performance of the systems, how it compares to expectations, and how wide the variation is. The systems range in size from 2 kW to 1,200 kW, with an average size of 229 kW, and were activated in oSPARC between March 13, 2007 and Dec. 16, 2016. Data regarding co-incident insolation and ambient temperature are provided through the oSPARC platform for the population of systems and summarized here. Results indicate that - after correcting for measured insolation, derating for temperature, and correcting for balance-of-system efficiency and degradation as a result of system age - the performance ratio of these 2,200 systems averages 91.7%. Adjusting for known parameters such as age and efficiency, the ratio would ideally be 100%. Thus, the 'underperformance' of this system sample is measured at 9.3% and suggests that as much as 9.3% could be recovered if the systems were caused to perform as expected through optimal operation and maintenance. Efforts to correlate the performance ratio with environmental conditions resulted in very low coefficients of regression, but the method already normalizes for actual site insolation and temperature, which are responsible for much of the site-specific variation. An exemption is air-quality nonattainment areas, where the measured performance of plants averages 86.9% and is consistently lower than the average of 95.9% in areas that are in compliance with air quality standards, likely because of particulate matter such as diesel soot exacerbating the soiling rate.

14 SOLAR ENERGY↗
DOE OSTI2016

SU-F-P-34: Commission of Enhanced Dynamic Wedge of Varian Truebeam Linac System with Feature Study

Purpose: To evaluate an Enhanced Dynamic Wedge (EDW) as part of machine commission process with feature study. Methods: The EDW system in this study was from a Truebeam, which is the Linear accelerator manufactured by Varian Medical Systems. The EDW feature vectors includes selected elements. These elements were dosimetric output spots check, field size, wedge angles, dose rate, collimator orientation, and different energy settings. Point dose measurement was done by a PTW farmer chamber, and profiles were measured by Gafchromic EBT2 films positing at different depths of the Solidwater based on the study elements. The output spot measurements were done with PTW farmer chamber with Solidwater setting for all orientation and wedge angles in the EDW system. The profiles comparisons were done by IMRT measurement function in RIT software at version 6.3. And the films were scanned by Vidar scanner. Dosimetry calculation were done by using the same Solidwater scanned by GE LightSpeed CT in Eclipse Treatment Planning System (TPS). Then measurements were compared to simulation results in TPS. Results: The energy average percentage difference between chamber measurement and TPS was 0.16% with standard deviation (SD) at 0.93%. For selected features, the average percentage difference between film measurement and computation was 0.93% with SD at 1.55% in horizontal profiles, and 1.18% with SD at 0.98% at vertical profiles. The average gamma difference for film measurement and TPS computing results was at 0.924 with SD at 0.314. Conclusion: A feature vector was developed to describe the commission of EDW, and developing a complete set of features for sufficiency of commission of a LINAC function could provide optimal commission instance with acceptable confident level of clinical application of the machine. Given the institution specific vector pattern and big data process, it could provide wide range clinical outcome comparison information in application of EDW.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Carbon nanotube supported PdAg nanoparticles for electrocatalytic oxidation of glycerol in anion exchange membrane fuel cells

Electro-oxidation of alcohol is the key reaction occurring at the anode of a direct alcohol fuel cell (DAFC), in which both reaction kinetics (rate) and selectivity (to deep oxidation products) need improvement to obtain higher power density and fuel utilization for a more efficient DAFC. We recently found that a PdAg bimetallic nanoparticle catalyst is more efficient than Pd for alcohol oxidation: Pd can facilitate deprotonation of alcohol in a base electrolyte, while Ag can promote intermediate aldehyde oxidation and cleavage of C-single bondC bond of C 3 species to C 2 species. Furthermore, a combination of the two active sites (Pd and Ag) with two different functions, can simultaneously improve the reaction rates and deeper oxidation products of alcohols. In this continuing work, Pd, Ag mono, and bimetallic nanoparticles supported on carbon nanotubes (Ag/CNT, Pd/CNT, Pd 1 Ag 1 /CNT, and Pd 1 Ag 3 /CNT) were prepared using an aqueous-phase reduction method; they served as working catalysts for studying electrocatalytic oxidation of glycerol in an anion-exchange membrane-based direct glycerol fuel cell. Combined XRD, TEM, and HAADF-STEM analyses performed to fully characterize as-prepared catalysts suggested that they have small particle sizes: 2.0 nm for Pd/CNT, 2.3 nm for PdAg/CNT, 2.4 nm for PdAg 3 /CNT, and 13.9 nm for Ag/CNT. XPS further shows that alloying with Ag results in more metal state Pd presented on the surface, and this may be related to their higher direct glycerol fuel cell (DGFC) performances. Single DGFC performance and product analysis results show that PdAg bimetallic nanoparticles can not only improve the glycerol reaction rate so that higher power output can be achieved, but also facilitate deep oxidation of glycerol so that a higher faradaic efficiency and fuel utilization can be achieved along with optimal reaction conditions (increased base-to-fuel ratio). Half-cell electrocatalytic activity measurement and single fuel cell product analysis of different glycerol oxidation intermediates, including C 3 : glycerate, tartronate, mesoxalate, and lactate; C 2 : glycolate and oxalate, over PdAg/CNT catalyst was further conducted and produced deeper insight into the synergistic effects and reaction pathways of bimetallic PdAg catalysts in glycerol electrocatalytic oxidation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗