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DOE OSTI2024

Impact of reactor architecture and design parameters on the performance of microbial electrolysis cells revealed by the electrode potential slope analysis

Microbial electrolysis cells (MECs) are appealing for recovering the chemical energy contained in domestic and industrial liquid wastes as hydrogen gas. Despite several years of research in the field, there is still a lack of critical analysis of how the reactor architecture dictates the electrochemical performance of the cell. In this study, internal resistance and onset voltage from the electrode potential slope analysis (EPS) were used in combination with current density, hydrogen production rate, reactor packing density, electrode spacing, membrane type and composition from 23 different studies to identify the reactor design parameters that primarily govern electrochemical performance of MECs. Using anion exchange membranes resulted in smaller internal resistances (AEM R int = 41± 40 mΩ m 2 ) and larger current density (18 ± 14 A m −2 ) compared to single chamber reactors (SC R int = 68 ± 58 mΩ m 2 ; 22 ± 16 A m −2 ) or MECs with cation exchange membranes (CEM R int = 376 ± 280 mΩ m 2 ; 3.0 ± 2.1 A m −2 ). Higher electrochemical performance for AEM- and SC-MECs translated in larger hydrogen gas production rates (0.122 mL H 2 C −1 for AEM vs 0.117 mL H 2 C −1 for SC), but only when inhibitors against hydrogen scavengers were added in single chamber systems (0.080 mL H 2 C −1 for SC without inhibitors). Following membrane type and composition, maintaining a small electrode spacing was the most critical parameter to improve MEC performance, indicating that the low conductivity of the media primarily limit performance by increasing ohmic resistance. Here, reactor volume and electrode surface area negatively correlated with internal resistance and current density, indicating that better performance of scaled-up reactors can likely be obtained by stacking multiple smaller units rather than just increasing reactor size. Although challenges remain in the implementation of MECs for hydrogen production from liquid wastes, advances in electrochemical engineering of the reactors can facilitate scale up and performance prediction at scale.

Electrochemistry↗

DOE OSTI2024

Performance evaluation of finned tube heat exchanger using curved wavy delta winglet vortex generators with circular perforations

Vortex generation is recognized as an effective passive approach to improve the heat transfer rate in fin and tube heat exchangers (FTHEs). The current study proposed innovative designs of curved wavy delta winglet vortex generators (CWDWVGs), both without and with circular perforations, to improve the heat transfer efficiency of FTHEs. There is potential to increase heat transfer performance further through various CWDWVG designs. Here, this study explores seven unique CWDWVG configurations, from 1-wave to 7-wave. A 3-D computational numerical model is utilized to evaluate the Thermo-hydraulic performance of FTHEs fitted with these different CWDWVG configurations across Reynolds numbers from 400 to 2000. This comparative analysis of the Thermo-hydraulic performance of FTHEs featuring four parallel circular tube layouts assesses configurations both with and without vortex generators (VGs) and various hole configurations. The evaluation of Thermo-hydraulic performance involves different parameters, including the London area goodness factor (LAGF), Colburn factor (j), friction factor (f), pressure drop (?P), and Nusselt number (Nu). Results demonstrate that the various CWDWVG configurations and the number of holes in them substantially affect the efficiency, as evaluated by the dimensionless Performance Evaluation Criteria (PEC). Notably, the 7-wave CWDWVGs surpassed other configurations, and integrating circular punched perforations further improved the thermal-hydraulic performance of FTHE. Specifically, the 7-wave CWDWVGs without holes demonstrated superior performance over other configurations, showing a significant increase in Nusselt number by 75.18% and 85.16% at Reynolds numbers of 2000 and 400, respectively, alongside an increase in pressure drop by 216.38% to 224.96%. Meanwhile, the 7-wave CWDWVGs with eight holes, in comparison to those without holes, exhibited a Nusselt number increase of 0.85%, a pressure drop decrease of 7.31%, and a reduction in the friction factor by 5.82%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Optimization of catalyst-coated membranes for enhancing performance in proton exchange membrane electrolyzer cells

To achieve large-scale application of proton exchange membrane electrolyzer cells (PEMECs) for hydrogen production, it is highly desirable to reduce the manufacturing cost while enhancing cell performance. In the PEMPECs, a catalyst-coated membrane (CCM) is the vital component where electrochemical reactions and mass transport mainly occur. The fabrication methods and catalyst layer (CL) structure can significantly affect the cell performance. Herein, for the first time, a comparative study of CCM fabrications with decal transfer and direct spray deposition methods have been conducted by both ex-situ materials characterization and in-situ performance testing in PEMECs. It is found CCMs that are fabricated with a direct spray deposition method display enhanced cell performance compared to CCMs fabricated with a decal transfer method, mainly due to the largely reduced ohmic resistance and improved mass transport. More importantly, cell performance can be greatly enhanced by simply regulating the Nafion ionomer content at the anode CL. The optimal Nafion ionomer content of 10 wt% gives the best cell performance at 80 °C with a low cell voltage of 1.887 V at 2 A cm -2 , outperforming the commercial CCM and most other previous publications. Our study provides a valuable guidance for fabrication and optimization of CCMs with significantly enhanced performance and reduced cost for practical application of the PEMECs.

08 HYDROGEN↗

DOE OSTI2022

Methane foam performance evaluation in water-wet unconsolidated porous media: A systematic experimental investigation at elevated pressure and temperature conditions

In the petroleum industry, enhanced oil recovery (EOR) techniques employ foam extensively to establish conformance control in heterogeneous and fractured reservoirs in order to increase the sweep efficiency. In such applications, foam performance evaluation under complex subsurface conditions is pivotal for the effective and optimized deployment of foam treatment. However, there is a scarcity of hydrocarbon gas foam generation and evaluation studies that examine the relationships between foam performance and the critical foam parameters at high-pressure and high-temperature conditions. Here, this study aims at methodically investigating the effects of several foam parameters on methane foam performance in water-wet proppant packs under harsh operating conditions. This is in relation to the technical needs of hydrocarbon foam injection into hydraulically-induced, propped fractures in unconventional oil reservoirs. To this end, a state-of-the-art experimental foam generation apparatus was designed, fabricated, and commissioned. We performed a large number of foam flow experiments on proppant packs using methane gas and different foaming agents at 3500 psi and 115°C. Anionic and amphoteric surfactants were employed to probe the effect of their ionic nature on foam performance. Foam performance sensitivities to various foam generation parameters and operating conditions, such as surfactant concentration, gas fraction, total injection rate, operating pressure, salinity, and proppant pack length were investigated. To this end, steady-state pressure drops across the proppant packs during foam generation and foam's apparent viscosity were measured to quantify the foam performance of surfactants. The results were then analyzed to determine optimum values of the foam parameters and the interplay between these parameters are discussed here. The systematic results achieved from this work are in agreement with the trends available in the literature and provide new insights into complexities of in situ foam generation in water-wet, unconsolidated porous media at extreme reservoir conditions.

03 NATURAL GAS↗

DOE OSTI2023

Irradiation performance of a U-7Mo in Al-Si matrix dispersion full-size fuel plate assembly

The Korea Atomic Energy Research Institute (KAERI) is leading the Ki-Jang Research Reactor (KJRR) project with the intent to develop a new reactor for medical isotope production and other nuclear research purposes. The KJRR core is designed to use high density fuel system where uranium alloyed with 7 wt% molybdenum (U-7Mo) particles are dispersed in a matrix of aluminum alloyed with 5 wt% silicon (Al-5Si) and clad in aluminum alloy 6061 (Al-6061) to form fuel plates. KAERI developed a fabrication facility to construct KJRR fuel assemblies and partnered with the Idaho National Laboratory (INL) to irradiate a full-size fuel assembly, with 21 total fuel plates, in the Advanced Test Reactor (ATR). Irradiation testing and subsequent Post Irradiation Exam (PIE) campaigns were performed successfully over a multi-year project. Monte Carlo neutronic calculations, coupling with a depletion code, were performed based on ATR’s as-run power history which showed that the highest power plate (plate 20) reached 83.1 % end-of-life (EOL) local burnup based on initial 235 U content. Additionally, finite element thermal modeling was performed based as-run power history which showed a beginning-of-life (BOL) peak local heat flux of 184 W/cm 2 . No anomalous fuel performance was observed during the irradiation and target test conditions were achieved. PIE showed favorable performance of the fuel assembly regarding all important phenomena. This paper describes the KJRR fuel assembly irradiation conditions and PIE data to support the conclusion that it performed well, without evidence of unexpected or problematic fuel performance, within an irradiation test designed to bound the KJRR design environment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Impact of Selected High-Performance Fuel Blends on Three-Way Catalyst Light Off under Synthetic Spark-Ignition Engine-Exhaust Conditions

The U.S. Department of Energy funded Co-Optimization of Fuels and Engines initiative aims to simultaneously develop advanced engines along with high-performance fuels to reduce petroleum consumption. The engine exhaust of spark-ignited light-duty vehicles contains pollutants such as nitrogen oxides, carbon monoxide (CO), and non-methane organic gases. When operated above their “light-off” temperature, three-way catalysts (TWCs) efficiently control the emissions of these pollutants from the vehicle exhaust. However, below the catalyst light-off temperature, during cold start, the TWCs are not effective. Thus, the stringent environmental regulations necessitate cold-start compliance of advanced engines operating on novel fuels for commercialization. Exhaust composition strongly impacts the effectiveness of TWCs. Hence, ensuring that the high-performance fuels under consideration do not have detrimental effects on current emissions control technology is necessary. To mitigate cold-start emissions, a low light-off temperature of the fuel on the TWC is desirable. As real-world fuels are multicomponent blends, we conducted investigations into the light-off behavior of representative fuel mixtures on a three-mode redox-aged commercial TWC under synthetic engine-exhaust conditions. The high-performance fuels in this study included 10–30% volumetric levels of ethanol, isobutanol, diisobutylene, and an aromatic mixture. Each of these high-performance fuel components was mixed into a gasoline surrogate blendstock for oxygenate blending (BOB). Our results showed that aromatics and alkenes in the surrogate BOB inhibit low-temperature reactivity of alkanes, alcohols, and CO on the TWC and dominate the blend light-off behavior. All the high-performance fuel blends had a very similar light-off behavior to the surrogate gasoline BOB, indicating that blending up to 30% (by vol.) of high-performance blendstocks in a gasoline base fuel can potentially reduce greenhouse gas emissions through improved engine efficiency and petroleum displacement without jeopardizing the ability to meet emissions regulations. While some high-performance blendstocks demonstrated lower light-off temperatures than a surrogate gasoline blend, taking advantage of the higher catalytic reactivity of these blendstocks to reduce cold-start emissions would require reducing the aromatic content in petroleum-based market fuels.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Effects of Ink Formulation on Construction of Catalyst Layers for High-Performance Polymer Electrolyte Membrane Fuel Cells

Rational design of catalyst layers in a membrane electrode assembly (MEA) is crucial for achieving high-performance polymer electrolyte membrane fuel cells. Establishing a clear understanding of the property (catalyst ink)–structure (catalyst layer)–performance (MEA) relationship lays the foundation for this rational design. Here, a synergistic approach was taken to correlate the ink formulation, the microstructure of catalyst layers, and the resulting MEA performance to establish such a property–structure–performance relationship. The solvent composition (n-PA/H 2 O mixtures) demonstrated a strong influence on the performance of the MEA fabricated with an 830-EW (Aquivion) ionomer, especially polarization losses of cell activation and mass transport. The performance differences were studied in terms of how the solvent composition affects the catalyst/ionomer interface, ionomer network, and pore structure of the resulting catalyst layers. The ionomer aggregates mainly covered the surface of catalyst aggregates acting as oxygen reduction reaction active sites, and the aggregate sizes of the ionomer and catalyst (revealed by ultrasmall angle X-ray scattering and cryo-transmission electron microscopy) were dictated by tuning the solvent composition, which in turn determined the catalyst/ionomer interface (available active sites). In n-PA/H 2 O mixtures with 50~90 wt % H 2 O, the catalyst agglomerates could be effectively broken up into small aggregates, leading to enhanced kinetic activities. The boiling point of the mixed solvents determined the pore structure of ultimate catalyst layers, as evidenced by mercury porosimetry and scanning electron microscopy. For mixed solvents with a higher boiling point, the catalyst–ionomer aggregates in the ink tend to agglomerate during the solvent evaporation process and finally form larger catalyst–ionomer aggregates in the ultimate catalyst layer, resulting in more secondary pores and thus lower mass transport resistance. Both the enlarged catalyst/ionomer interface and appropriate pore structure were achieved with the catalyst layer fabricated from an n-PA/H 2 O mixture with 90 wt % H 2 O, leading to the best MEA performance.

25 ENERGY STORAGE↗

DOE OSTI2023

Evaluating performance and portability of high-level programming models: Julia, Python/Numba, and Kokkos on exascale nodes

We explore the performance and portability of the high-level programming models: the LLVM-based Julia and Python/Numba, and Kokkos on high-performance computing (HPC) nodes: AMD Epyc CPUs and MI250X graphical processing units (GPUs) on Frontier’s test bed Crusher system and Ampere’s Arm-based CPUs and NVIDIA’s A100 GPUs on the Wombat system at the Oak Ridge Leadership Computing Facilities. We compare the default performance of a hand-rolled dense matrix multiplication algorithm on CPUs against vendor-compiled C/OpenMP implementations, and on each GPU against CUDA and HIP. Rather than focusing on the kernel optimization per-se, we select this naive approach to resemble exploratory work in science and as a lower-bound for performance to isolate the effect of each programming model. Julia and Kokkos perform comparably with C/OpenMP on CPUs, while Julia implementations are competitive with CUDA and HIP on GPUs. Performance gaps are identified on NVIDIA A100 GPUs for Julia’s single precision and Kokkos, and for Python/Numba in all scenarios. We also comment on half-precision support, productivity, performance portability metrics, and platform readiness. We expect to contribute to the understanding and direction for high-level, high-productivity languages in HPC as the first-generation exascale systems are deployed.

Godoy, William↗

DOE OSTI2022

Understanding Strong Scaling on GPUs Using Empirical Performance Saturation Size

The roofline model provides a concise overview of the maximum performance capabilities of a given computer system through a combination of peak memory bandwidth and compute performance rates. The increasing complexity of scheduling and cache in recent GPUs, however, has introduced complicated performance variability that is not captured by arithmetic intensity alone. This work examines the effect of problem size and GPU launch configurations on roofline performance for V100, A100, MI100, and MI250X graphics processing units. We introduce an extended roofline model that takes problem size into account, and find that strong scaling on GPUs can be characterized by saturation problem sizes as additional key metrics. Saturation problem sizes break up a plot of GPU performance vs. problem size into three distinct performance regimes– size-limited, cache-bound, and DRAM-bound. With our extended roofline model, we are able to provide a robust view of these performance regimes across recent GPU architectures.

Eberius, David↗

DOE OSTI2022

Performance Debugging and Tuning of Flash-X with Data Analysis Tools

State-of-the-art multiphysics simulations running on large scale leadership computing platforms have many variables contributing to their performance and scaling behavior. We recently encountered an interesting performance anomaly in Flash-X, a multiphysics multicomponent simulation software, when characterizing its performance behavior on several large-scale HPC platforms. The anomaly was tracked down to the interaction between the use of dynamic allocation of scratch data and data locality in the cache hierarchy. In this paper we present the details of unexpected performance variability of Flash-X, its extensive analysis using the performance measurement tool TAU to collect the data and Python data analysis libraries to explore the data, and our insights from this experience. In this process, we discovered and removed or mitigated two additional performance limiting bottlenecks for performance tuning.

Huck, Kevin↗

DOE OSTI2022

Automatic Generation of High-Performance Convolution Kernels on ARM CPUs for Deep Learning

In this work, we present FastConv, a template-based code auto-generation open source library that can automatically generate high-performance deep learning convolution kernels of arbitrary matrices/tensors shapes. FastConv is based on the Winograd algorithm, which is reportedly the highest performing algorithm for the time-consuming convolution layers of convolutional neural networks. ARM CPUs cover a wide range designs and specifications, from embedded devices to HPC-grade CPUs. The leads to the dilemma of how to consistently optimize Winograd-based convolution solvers for convolution layers of different shapes. FastConv addresses this problem by using templates to auto-generate multiple shapes of tuned kernels variants suitable for skinny tall matrices. As a performance portable library, FastConv transparently searches for the best combination of kernel shapes, cache tiles, scheduling of loop orders, packing strategies, access patterns, and online/offline computations. Auto-tuning is used to search the parameter configuration space for the best performance for a given target architecture and problem size. The experiments with layer-wise evaluation on the VGG--16 model confirms a 1.25x performance gains is got by tuning the Winograd library. Integrated comparison results shows 1.02x to 1.40x, 1.14x to 2.17x, and 1.22x and 2.48x speedup is achieved over NNPACK, Arm NN, and FeatherCNN on the Kunpeng 920 beside few cases. Furthermore, problem size performance portability experiments with various convolution shapes shows that FastConv achieves 1.2x to 1.7x speedup and 2x to 22x speedup over NNPACK and ARM NN inference engine using Winograd on Kunpeng 920 . CPU performance portability evaluation on the VGG--16 show an average speedup over NNPACK of 1.42x, 1.21x, 1.26x, 1.37x, 2.26x, and 11.02x is observed on Kunpeng 920, Snapdragon 835, 855, 888, Apple M1, and AWS Graviton2, respectively.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Editors’ Choice—Examining Performance and Durability of Anion Exchange Membrane Fuel Cells with Novel Spirocyclic Anion Exchange Membranes

A series of spirocyclic copolymer membranes with varying ion exchange capacities (IECs) were investigated to probe the impact of polymer properties on in situ fuel cell performance and stability. In-situ electrochemical tests and post-mortem electron microscopy analysis of cross-sectioned membrane electrode assemblies (MEAs) have been combined with voltage loss breakdown analysis to evaluate the performance and degradation of different MEAs, and to probe the catalyst morphology and electrode structure at different stages of operation. Voltage loss breakdown results show that membrane degradation and kinetic losses played only a minor role in observed performance degradation and that performance losses were primarily related to increasing mass transport losses. From microscopy studies, carbon corrosion and Pt nanoparticle growth were identified at both the cathode and anode although more pronounced on the cathode resulting in significant structural changes. The membrane with the lowest IEC (1.3 mmolg −1 ) demonstrated the lowest peak power density ~ 1.16 W cm −2 , however, it showed the most stable performance (constant 0.6 A cm −2 hold) with ~ 5% degradation over 540 h. Isolation of performance losses and microscopic analysis of electrodes for anion exchange membrane fuel cells has not been reported previously, and these results help identify critical performance degradation concerns.

25 ENERGY STORAGE↗

DOE OSTI2020

COVID-19 global pandemic planning: Performance and electret charge of N95 respirators after recommended decontamination methods

Shortages of N95 respirators for use by medical personnel have driven consideration of novel conservation strategies, including decontamination for reuse and extended use. Decontamination methods listed as promising by the Centers for Disease Control and Prevention (CDC) (vaporous hydrogen peroxide (VHP), wet heat, ultraviolet irradiation (UVI)) and several methods considered for low resource environments (bleach, isopropyl alcohol and detergent/soap) were studied for two commonly used surgical N95 respirators (3M™ 1860 and 1870+ Aura™). Although N95 filtration performance depends on the electrostatically charged electret filtration layer, the impact of decontamination on this layer is largely unexplored. As such, respirator performance following decontamination was assessed based on the fit, filtration efficiency, and pressure drop, along with the relationship between (1) surface charge of the electret layer, and (2) elastic properties of the straps. Decontamination with VHP, wet heat, UVI, and bleach did not degrade fit and filtration performance or electret charge. Isopropyl alcohol and soap significantly degraded fit, filtration performance, and electret charge. Pressure drop across the respirators was unchanged. Modest degradation of N95 strap elasticity was observed in mechanical fatigue testing, a model for repeated donnings and doffings. CDC recommended decontamination methods including VHP, wet heat, and UV light did not degrade N95 respirator fit or filtration performance in these tests. Extended use of N95 respirators may degrade strap elasticity, but a loss of face seal integrity should be apparent during user seal checks. NIOSH recommends performing user seal checks after every donning to detect loss of appropriate fit. Decontamination methods which degrade electret charge such as alcohols or detergents should not be used on N95 respirators. The loss of N95 performance due to electret degradation would not be apparent to a respirator user or evident during a negative pressure user seal check.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Performance and community structure dynamics of microbial electrolysis cells operated on multiple complex feedstocks

Microbial electrolysis is a promising technology for converting aqueous wastes into hydrogen. However, substrate adaptability is an important feature, seldom documented in microbial electrolysis cells (MECs). In addition, the correlation between substrate composition and community structure has not been well established. This study used an MEC capable of producing over 10 L/L-day of hydrogen from a switchgrass-derived bio-oil aqueous phase and investigated four additional substrates, tested in sequence on a mature biofilm. The additional substrates included a red oak-derived bio-oil aqueous phase, a corn stover fermentation product, a mixture of phenol and acetate, and acetate alone. The MECs fed with the corn stover fermentation product resulted in the highest performance among the complex feedstocks, producing an average current density of 7.3 ± 0.51 A/m 2 , although the acetate fed MECs outperformed complex substrates, producing 12.3 ± 0.01 A/m 2 . 16S rRNA gene sequencing showed that community structure and community diversity were not predictive of performance, and replicate community structures diverged despite identical inoculum and enrichment procedure. The trends in each replicate, however, were indicative of the influence of the substrates. Geobacter was the most dominant genus across most of the samples tested, but its abundance did not correlate strongly to current density. High-performance liquid chromatography (HPLC) showed that acetic acid accumulated during open circuit conditions when MECs were fed with complex feedstocks and was quickly degraded once closed circuit conditions were applied. The largest net acetic acid removal rate occurred when MECs were fed with red oak bio-oil aqueous phase, consuming 2.93 ± 0.00 g/L-day. Principal component analysis found that MEC performance metrics such as current density, hydrogen productivity, and chemical oxygen demand removal were closely correlated. Net acetic acid removal was also found to correlate with performance. However, no bacterial genus appeared to correlated to these performance metrics strongly, and the analysis suggested that less than 70% of the variance was accounted for by the two components. This study demonstrates the robustness of microbial communities to adapt to a range of feedstocks and conditions without relying on specific species, delivering high hydrogen productivities despite differences in community structure. The results indicate that functional adaptation may play a larger role in performance than community composition. Further investigation of the roles each microbe plays in these communities will help MECs to become integral in the 21st-century bioeconomy to produce zero-emission fuels.

09 BIOMASS FUELS↗

DOE OSTI2025

Multi-contrast machine learning improves schistosomiasis diagnostic performance

Schistosomiasis currently affects over 250 million people and remains a public health burden despite ongoing global control efforts. Conventional microscopy is a practical tool for diagnosis and screening ofSchistosoma haematobium, but identification of eggs requires a skilled microscopist. Here we present a machine learning (ML)-based strategy for automated detection ofS. haematobiumthat combines two imaging contrasts, brightfield (BF) and darkfield (DF), to improve diagnostic performance. We collected BF and DF images of urine samples, many of them containingS. haematobiumeggs, during two different field studies in Côte d’Ivoire using a mobile phone-based microscope, the SchistoScope. We then trained separate egg-detection ML models and compared the patient-level performance of BF and DF models alone to combinations of BF and DF models, using annotations from trained microscopists as the gold standard. We found that models trained on DF images, and almost all BF and DF combinations, performed significantly better than models trained on BF images only. When models were trained on images from the first field study (n = 349 patients, 748 images of each contrast), patient-level classification performance on patient images from the second study (n = 375 patients, 752 images of each contrast) met the WHO Diagnostic Target Product Profile (TPP) sensitivity and specificity for the monitoring and evaluation use case (sensitivity for all models and combinations was >75% when evaluated at a confidence score threshold that resulted in specificity >96.5%). When we used images from both field studies for the training set, performance of the models was improved. Overall, this work shows that the use of DF and BF increases the performance of ML models on images from devices with low-cost optics, while retaining the portability, power, and time-to-results of the WHO’s diagnostic TPP. DF requires no additional sample preparation and does not increase the complexity of the imaging system. It thus offers a practical means to improve performance of automated diagnostics forS. haematobiumas well as other microscopy-based diagnostics.

Infectious Diseases↗

DOE OSTI2021

Evaluation of “Tight Oil” Well Performance and Completion Practices in the Powder River Basin - “Time Slice” Analysis

The objective of this paper is to assess how well completion practices and well performance have evolved over time (“time slices”) in the tight sands and shales of the Powder River Basin (PRB). This information can provide a foundation for helping operators define more effective well completion practices and thus optimize well performance in an emerging “tight oil” basin. To start, the authors assembled a “database” containing well completion practices, production data, and geologic information for more than 800 horizontal (Hz) wells targeting three “tight oil” formations -- Turner, Frontier, and Mowry--placed on production in the past dozen years. To control for the impact of geologic and reservoir properties on well performance, the authors defined 12 geologically distinct areas (“partitions”) for the Turner, Frontier, and Mowry Shale in the Powder River Basin (4 partitions in each formation). This paper discusses the methodology for establishing “time slices” for each partition within each of the three “tight oil” formations, including (1) taking out, to the extent practical, the effects of geology by partitioning the three “tight oil” formations based on their geologic parameters; (2) using type curves and estimated ultimate recoveries (EURs) to establish a reliable measure of well performance; and (3) rigorously inspecting the production and well completion data to assure a quality dataset. Partition #2, a high thermal maturity area of the Frontier Sandstone in the western Powder River Basin, helps illustrate the results of the study. Using a dataset of 55 Hz wells, the study found that well performance has steadily improved, with oil EURs increasing from 180 MBbl in 2012-13 to 290 MBbl in 2018-19. Much of this improvement was due to the use of longer Hz laterals, increasing notably from 3,765 ft in 2012-13 to 10,260 ft in 2018-19. More intensive completion practices contributed, as well. For example, the number of frac stages more than doubled, from 17 to 35, and proppant concentrations increased from 1,060 lbs/ft to 1,320 lbs/ft. Finally, the data show diminishing returns to longer laterals and more intensive completion practices. For example, the key performance measure of oil EUR per 1,000 ft of lateral is decreasing. For 2012-13, Hz wells recovered 50 MBbl of oil per 1,000 ft of lateral while the more recent 2018-19 Hz wells only provide 30 MBbl of oil per 1,000 ft of lateral. Considerable insight can be gained by using “time slices” and geologic partitioning to better understand the relationship between changes in well drilling and completion practices and changes in well performance in emerging “tight oil” plays. The results from this study can also serve as a foundation for subsequent, more intense efforts involving data analytics for defining more optimum well completion practices targeting specified geologic settings and formations.

02 PETROLEUM↗

DOE OSTI2020

Cesium Removal Performance Comparisons of Crystalline Silicotitanate Media Batches with Savannah River Site Waste Simulant

The Tank Closure Cesium Removal (TCCR) system uses ion exchange columns filled with Crystalline Silicotitanate (CST) media to process radioactive waste solutions for the removal of Cs137. TCCR currently focuses on dissolving Savannah River Site (SRS) Tank 10H waste (primarily sodium salt cake solids) within the tank followed by at-tank ion exchange column treatment. Plans are underway to prepare and install a second TCCR unit at SRS. Capacity and particle size differences exist between archived (IE-911) and more recently-prepared CST media batches (9120- B and 9140-B). Side-by-side comparison testing was performed to evaluate the cesium removal performance of each batch to aid in selecting the preferred CST batch and media characteristics to load into the second TCCR unit. Batch contact equilibrium and flow-through column tests have been conducted with three CST batches using an SRS Average Simulant. Simulant batch contact equilibrium cesium loading results for the three CST batches (including two lots of one batch) are provided in Table ES-1. The 35 °C data indicates that the archived IE-911 batch has a higher cesium capacity than recently-prepared CST media and that the minor TCCR CST 9120-B media lot (2099000035) has similar cesium removal performance to the major TCCR lot (2099000034). Tests conducted at 25 ºC for the archived IE-911 CST batch indicated lower cesium removal performance with this simulant batch than was observed recently with a different SRS Average Simulant batch. A dilution factor (DF) is typically utilized when modeling engineered CST media cesium loading performance to account for mass contributions from the binder material. In cases where CST performance is lower than expected, this factor includes corrections for the binder and for low performance. The ZAM (Zheng, Anthony, and Miller) Isotherm Model DF values are provided in Table ES-1 for each simulant batch contact result. DF values near 0.5 were determined for the 9120-B and 9140-B CST batch contact tests while DF values near 0.6 (20% higher) were determined for the tests with IE-911. These dilution factors are lower than recently observed with a different SRS Average Simulant batch (9140-B DF = 0.68; IE-911 DF = 1.0). Based on these results, it appears that some component in the simulant solution used for equilibrium testing may have resulted in reduced cesium loading on the CST media.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Improving the performance of medical imaging applications using SYCL

As opposed to the Open Computing Language (OpenCL) programming model, in which host and device codes are generally written in different programming languages, SYCL can combine host and device codes for an application in a type-safe way to improve development productivity and performance portability. Hence, this report shows the experimental results of applying the SYCL programming model to medical imaging applications for a study on performance portability and programming productivity between OpenCL and SYCL. Rodinia is a widely used open-source benchmark suite for heterogeneous computing. We choose two medical imaging applications (Heart Wall and Particle Filter) in the benchmark suite, migrate the OpenCL implementations of the applications to the SYCL implementations, and evaluate their performance and productivity on Intel® microprocessors that contain a central processing unit (CPU) and an integrated graphics processing unit (GPU). The maturing SYCL compilers, which are based on a conformant implementation of the SYCL 1.2.1 Khronos specification, have been actively optimized for Intel® computing platforms. The experimental results are promising in terms of the raw performance and productivity. Although the SYCL implementation of the Heart Wall application does not execute successfully on a CPU, the SYCL implementations of the application can achieve comparable or better performance than the OpenCL implementation on Intel® integrated GPUs. For the Particle Filter application, the performance difference between the SYCL and OpenCL implementations is comparable on the GPUs for most cases, but the SYCL implementations are on average 4X faster than the OpenCL implementations on Intel® Xeon® CPUs. For programming productivity, we arguably use lines of code as a way to measure programming productivity in software. The SYCL programs reduce the lines of code of the OpenCL programs by 52% and 38% for the Heart Wall and Particle Filter, respectively. The results indicate that SYCL is a promising programming model for heterogeneous computing with the maturing compilers. We organize the remainder of the report as follows. Section II introduces the SYCL programming model, compares the major differences between an OpenCL program and a SYCL program, and describes the characteristics of the two applications. Section III describes the SYCL programming model in more details, and shows the SYCL implementation of a kernel function in the Particle Filter application as an example. In Section IV, we evaluate the performance of the applications on the CPUs and GPUs. Section V concludes the report.

59 BASIC BIOLOGICAL SCIENCES↗