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NASA NTRS2024

Launch Complex 39B, SWMU 009, 2023 Performance Monitoring and Air Sparge Expansion Construction Completion Report, Kennedy Space Center, Florida

The 2023 Performance Monitoring and Construction Completion Report (PM-CCR) presents the findings, observations, and results for Air Sparging (AS) operations and expansion activities, as well as sitewide groundwater monitoring for Launch Complex 39B (LC39B), Solid Waste Management Unit (SWMU) 009, at Kennedy Space Center (KSC), Florida. The reporting period for activities covered under this PM-CCR is from January 1, 2023, to December 31, 2023. At LC39B, AS operations began in 2017 in the area west of the launch pad, in the liquid oxygen (LOX) tank area located northwest of the launch pad, and in an area outside of the perimeter fence to protect nearby Outstanding Florida Waters (OFW). The LC39B AS system was installed with 279 AS wells to depths ranging from 23 to 60 feet below land surface (bls), including the sump, correlating to top of screen depths ranging from 20 to 57 feet bls. In December 2022, a total of 22 AS wells were abandoned to support launch pad crane operations, and in November 2023, the system was expanded with five additional AS wells installed to 13 or 17 feet bls near the LOX tank. The remedial objective of the LC39B AS Interim Measure (IM) is to actively decrease concentrations of contaminants of concern (COCs) in groundwater, specifically trichloroethene (TCE), cis-1,2-Dichloroethene (cDCE), and vinyl chloride (VC), to less than their respective Natural Attenuation Default Concentrations (NADCs), so LC39B can transition into a Long-Term Monitoring (LTM) program. This PM-CCR presents the following information for LC39B: • AS system operations and maintenance (O&M) (Year 7 of operation) from January 2023 to December 2023, to include AS trailer relocation in March 2023 and subsequent replacement and re-start in June 2023. • Construction completion details for AS system expansion, which included installation of five new AS wells and one new monitoring well in November 2023. As part of expansion activities, soil samples were also collected for petroleum analysis; no exceedances were identified, and no further investigation for petroleum is warranted. • Performance monitoring results for groundwater sampling events conducted in May/June 2023 (30 wells) and November 2023 (31 wells) in the AS IM area and in the Low Concentration Plume (LCP) areas located north and east of the launch pad for volatile organic compound (VOC) analysis. • Sampling results for one monitoring well, LOX-IW0012S, which is sampled for aluminum on an annual basis (May/June 2023). This well was resampled in November 2023 for both total and dissolved aluminum. Due to a communication error with the laboratory, the May/June 2023 sample was analyzed for total aluminum only. • Groundwater sampling results for per- and polyfluoroalkyl substances (PFAS) collected from seven monitoring wells during the May/June 2023 event to further investigate the Former Sewage Treatment Plant #6 and Percolation Pond area, west of the launch pad. O&M and performance monitoring results show that the AS system at LC39B is operating as designed and meeting performance criteria. Overall runtime was 45 percent (%) during the reporting period (January to December), but the operational runtime was 78% during the timeframe when the system could run (June to December). The most significant downtime contributor was post-launch crane operations following the Artemis launch on November 16, 2022, which lasted until June 2023. During that timeframe, the AS trailer at LC39B was relocated to another KSC remediation site (Wilson Corners) and was subsequently replaced with the AS trailer from the Paint & Oil Locker (POL) remediation site at KSC to resume AS system operations. Performance monitoring results in the AS IM and LCP areas continue to show reduction in COC concentrations over time when compared to baseline levels. In 2023, only one monitoring well (MW0048) detected a COC exceeding its NADC (VC at 740 micrograms per liter [µg/L]), which marks the baseline result for this new well installed during system expansion. Across the rest of the site, VC concentrations have declined or remained stable during the 2023 sampling events. Excluding MW0048, the highest VC result in 2023 was during the May/June sampling event with a concentration of 63 µg/L at MW0032, which is located near MW0048 and the AS expansion area by the LOX tank. TCE was detected in select monitoring wells in the IM area in 2023, but only two locations exceeded the State of Florida Groundwater Cleanup Target Level (GCTL): MW0032 (21 µg/L in May/June 2023 and 9.1 µg/L in November 2023) and MW0036 (5.0 µg/L in November 2023). MW0036 is also located near the LOX tank, on the north side, where the AS system is still operational (Zone Z4). cDCE and trans-1,2-dichloroethene concentrations were less than laboratory method detection limits or their respective GCTLs in all wells sampled in 2023. Near the OFW located northwest of the launch complex, all COC concentrations were less than laboratory method detection limits from monitoring wells (MW0039, MW0040, and LOXTA0002S) sampled in 2023. Aluminum results from LOX-IW0012S, which has been sampled routinely since 2006, detected a total aluminum concentration of 3,900 µg/L during the May/June 2023 sampling event. Results from the November 2023 event detected 5,700 µg/L for total aluminum and 5,500 µg/L for dissolved aluminum. These concentrations slightly decreased from the previous year but remain relatively consistent with historical detections. Aluminum will continue to be sampled on an annual basis at this well as results still exceed the GCTL of 200 µg/L and the Upper Limit of the KSC Background Concentration of 280 µg/L. PFAS results detected nine different PFAS compounds (out of 32 analyzed) from seven wells sampled. Two PFAS compounds, perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA), currently have FDEP Provisional GCTLs of 70 nanograms per liter (ng/L). All seven samples collected resulted in concentrations less than the FDEP Provisional GCTLs for both PFAS compounds; no exceedances were observed. PFOS and PFOA also have assigned United States Environmental Protection Agency (USEPA) Maximum Contaminant Levels (MCLs) of 4 nanograms per liter (ng/L). None of the PFOS results exceeded the USEPA MCLs. PFOA was detected in two samples above the USEPA MCL at concentrations of 5.8 ng/L (ECS-IW0009I) and 5.5 ng/L (ECS-IW0009S). Three other PFAS compounds, perfluorohexanesulfonic acid (PFHxS), perfluoro-n-nonanoic acid (PFNA), and hexafluoropropylene oxide dimer acid (GenX), currently have USEPA MCLs of 10 ng/L. PFHxS, PFNA, and GenX were not detected at concentrations greater than their respective USEPA MCLs in any of the seven wells. PFAS compounds without FDEP Provisional GCTLs or USEPA MCLs were screened against USEPA RSLs. No other detections exceeded their respective USEPA RSLs. Additional PFAS sampling will be conducted as part of a future PFAS Site Assessment. Based on O&M activities and performance monitoring, the following is recommended for LC39B: • Continue with Year 8 AS system operation within Zone Z4, which includes the AS expansion area. Zone Z3, which has been off since 2018, should remain off as no rebound has been observed. Zones Z1 and Z2, which were turned off at the end of 2022, will remain shut down as monitoring well results have consistently been below GCTLs or have low-level detections with stable or decreasing trends (Meeting Minute 2402-M10, Decision 2402-D30). • Continue with performance monitoring in 2024 with the same monitoring well network as 2023, except with the addition of MW0048 in both semi-annual events. Baseline concentrations for this well were collected during the November 2023 performance monitoring event. Semi-annual sampling should be planned for the May 2024 and November 2024 timeframes (Meeting Minute 2402-M10, Decision 2402-D31). • Continue sampling monitoring well, LOX-IW0012S, for aluminum (total and dissolved) on an annual basis in May 2024. It is also recommended to re-develop this well prior to the next sampling event (Meeting Minute 2402-M10, Decision 2402-D32). The above recommendations for LC39B were presented at the February 2024 KSCRT Meeting, with Team consensus reached on the path forward. The contents of this PM-CCR were also presented at this meeting.

Deborah M Wilson↗
NASA NTRS2024

Launch Complex 34, SWMU CC054 2023 DNAPL Source Zone Operations, Maintenance, and Monitoring, Site-Wide Long-Term Monitoring, and Hot Spot 6 Air Sparge System Annual Performance Monitoring and Phase Two Expansion Construction Completion Report Cape Canaveral Space Force Station, Florida

This Annual Performance Monitoring Report (PMR) for the Dense Non-Aqueous Phase Liquid (DNAPL) Source Zone (DSZ), Site-Wide Long-Term Monitoring (LTM), and Hot Spot 6 (HS 6) Air Sparge (AS) System presents the results of Year 14 operations and performance monitoring of the hydraulic containment (HC) Interim Measure (IM), details associated with construction and implementation of the HS 6 AS system expansion (Phase Two), and the results of operations and performance sampling of the HS 6 AS IM at Launch Complex 34 (LC34), located at Cape Canaveral Space Force Station (CCSFS), Florida. The timeframe for activities documented in this PMR extends from April 1, 2023 to March 31, 2024. LC34 has been designated Solid Waste Management Unit CC054 under the Kennedy Space Center (KSC) Resource Conservation and Recovery Act Corrective Action Program. The objective of the HC IM at LC34 is to contain the shallow and deep DSZ and surrounding dissolved-phase trichloroethene (TCE) high concentration plume via operation of a hydraulic containment system (HCS). The pre-IM design 300 micrograms per liter (μg/L) TCE groundwater contour was used to establish the deep zone capture area for deep recovery wells, and the shallow zone capture area was defined by the DSZ. The system began operating in 2010, and in 2015, the system was expanded to provide HC for areas within the 300 μg/L TCE groundwater isocontours of HS 3 and 4. In 2018 and 2019, an investigation was conducted to recharacterize the DSZ, which included investigating TCE mass in Layer 7. This data was subsequently used to optimize the pumping rates of the HCS and install additional recovery wells in Layer 7 to more adequately capture residual contaminant mass. The operational period for Year 14 of the HCS was from April 1, 2023 to March 31, 2024. Operational runtime for the system was 94 percent during Year 14, with downtime events attributed to planned maintenance, system repairs, and power outages. As of March 31, 2024, a total of 344,849,634 cumulative gallons of groundwater containing 94,656 pounds of chlorinated volatile organic compounds (CVOCs) have been removed by the HCS. During the reporting period covered under this report, the HCS recovered 31,176,393 gallons and approximately 6,319 pounds of CVOC mass. Total combined influent concentrations of TCE have decreased since startup from approximately 280,000 µg/L (January 2010) to 25,000 µg/L (March 2024). During the reporting period, all effluent concentrations from the HCS (aqueous and vapor) were below regulatory reporting limits, indicating the system continues to operate as intended. Performance monitoring was conducted in January 2024 within the DSZ to evaluate TCE contamination. Groundwater samples were collected via DPT at nine locations, consistent with previous events between 2017 and 2022. Full vertical profile sampling was completed at each DPT from 8 to 98 feet below land surface (bls), at 5-foot intervals. The DPT performance monitoring results are summarized in this PMR. The results revealed TCE remains at concentrations greater than 11,000 µg/L in the DSZ (1-percent solubility, indicative of DNAPL) at eight of the nine DPT locations and at depths ranging from 28 to 98 feet bls. An overall decreasing trend of TCE concentrations was observed in DPT samples during this reporting period, which is a reduction from the previous event (December 2022) and the peak event in December 2021, where TCE percentages appeared to increase in all depth zones because several recovery wells were turned off during the AS pilot study in the DSZ. The maximum TCE concentration in January 2024 was 1,600,000 µg/L in the 53 feet bls depth interval at DPT594 (previous maximum result in 2022 was 1,800,000 µg/L in the 48 feet bls depth interval at DPT599). This maximum concentration in the 53 feet bls depth interval is in the deep capture zone. During the January 2024 DPT event, the largest portion of TCE mass was observed in the 48 feet bls interval above/within Layer 4. This trend remains consistent with previous years and appears to indicate continued mass discharge from Layer 4 (fine-grained unit). In addition to DPT sampling, annual groundwater samples were collected from 11 deep monitoring wells in the DSZ area (Layers 7 and 8) in December 2023 to verify vertical and horizontal delineation. Three of the wells were also sampled biweekly to evaluate operations of recovery well RW21D (screened 86 to 106 feet bls), which was installed in January 2023. Of the Layer 7/8 monitoring sampled only annually, results were non-detect or less than groundwater cleanup target levels GCTLs in December 2023, with the exception of one well, IW45D2, which had a cis-1,2-dichloroethene (cDCE), detection greater than the GCTL. Of the three wells sampled biweekly during the operational period, the well located closest to Layer 7 recovery well RW21D (IW44D2, screened 105 to 115 feet bls) had concentrations of TCE, cDCE and vinyl chloride (VC) greater than GCTLs throughout the operational period, but displayed a decreasing trend since the peak concentrations in September 2023. The maximum TCE concentration during this operational period was 190,000 µg/L at IW44D2 in September 2023, but reduced to 700 µg/L in March 2024, indicating the HCS is still effectively removing mass from the source area. Expansion of the HCS and addition of new recovery wells is ongoing and will continue to be evaluated as the groundwater recovery scheme is optimized. Details of the expansion and optimization will be provided in a future PMR. The HS 6 AS IM was initiated in 2018 with 160 AS wells and expanded in 2019 with another 140 AS wells. An additional expansion of the HS 6 AS IM was completed during the reporting period covered under this report and details of the construction implementation and startup of the expansion are detailed in Section III of this report. The new expansion, referred to as Phase Two, was implemented between August 17, 2022 and August 28, 2023, and included the installation of 190 air sparge wells to treat an additional 11.2 acres. The original configuration (referred to as Phase One) operated until Phase Two came online, then all but 52 AS wells were turned off so the components could be moved and utilized in the Phase Two area. The 52 AS wells that remain on are in a barrier configuration preventing contaminated groundwater from impacting the treated area. The HS 6 AS system (both Phase One and Two) operated normally during the reporting period covered under this report. Semi-annual performance monitoring of the Phase One configuration was conducted in April and November 2023, consistent with previous years. For the Phase Two configuration, 21 new monitoring wells were installed and sampled quarterly, with a baseline event in July 2023, and quarterly events in November 2023 and February 2024 summarized in this report. Semi-annual monitoring results collected in April and October 2023 show concentrations of contaminants of concern (cDCE, trans-1,2-dichloroethene, and VC) have decreased to less than GCTLs in nearly all wells and not impacting the surface water drainage canal, indicating the HS 6 IM continues to meet objectives. The baseline and quarterly sampling for the Phase Two configuration indicate generally decreasing concentrations in wells within and around the perimeter of the treatment area. At least two more quarters of monitoring will be conducted and once those results are evaluated a reduced the sampling frequency may be considered. Overall, the tasks associated with Year 14 operation of the HC IM and operation of the HS 6 AS IM were performed in accordance with recommendations included in the previous 2022 LC34 (Year 13) PMR. Evaluation of results from the HC IM and HS 6 IM show that these systems are operating as designed and meeting performance objectives.

groundwater remediation↗
DOE OSTI2020

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific applications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application-level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher prediction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗
NASA NTRSDate not supplied

Effect of Carbon Dioxide Exposure on Physical and Cognitive Performance in a Simulated Spaceflight Contingency Scenario

Introduction: Carbon dioxide (CO2) produced by astronauts inside space suits can accumulate to levels that affect health and performance. The human health and performance risks associated with different levels of CO2 exposure in the flight environment remain an area of debate. The purpose of this study is to characterize the limit of acceptable performance (cognitive and physical) decrements and symptom severity for mission operations when subjected to elevated inspired CO2 levels in the spacesuit during contingency EVA scenarios. Methods: This study will create a simulation of a 1-hour contingency EVA walk back to the habitat, incorporating a passive treadmill and a fully immersive virtual reality (VR) simulation of a lunar EVA. Subjects will be asked to walk on a treadmill while breathing partial pressures of CO2 of 0, 5, 10, 15, 20, 25, 30mmHg for 1 hour at a time. Cognitive performance will be quantified using validated cognitive tests and measures of functional task performance embedded within the VR environment. Test subject symptoms and self-assessment of performance will be evaluated via survey. Results: This study currently has approval from NASA’s Institutional Review Board and has completed the Test Readiness Review process. Next steps for this research study include test subject recruitment, data collection, and analysis. Data collection is planned for calendar year 2022 and 2023. Discussion: This study will provide valuable information regarding how various partial pressures of CO2 exposure impact acute health, as well as cognitive and physical performance during simulated lunar EVA. This information will be vital in the assessment of overall risk associated with current hardware (vehicle and suit) design for upcoming exploration missions. It will also be invaluable for informing future standards and requirements.

carbon dioxide↗
DOE OSTI2024

Performance Characterization and Provenance of Distributed Task-based Workflows on HPC Platforms

Understanding performance and provenance of task-based workflows poses significant challenges, particularly in distributed configurations where resources are shared by multiple applications. Task-based workflow management systems further complicate performance predictability because of their dynamicity that subtly alters task execution order from run to run. In this paper we propose a layered characterization framework for performance and task provenance for Dask.distributed workflows running on high-performance computing (HPC) platforms. It collects data from jobs, the workflow management system, and the operating system to aid in understanding the performance of these workflows. Our approach encompasses three main contributions: first, an extension of Dask.distributed to capture high-fidelity task provenance using Mochi data services; second, the adaptation of the established HPC I/O characterization tool Darshan to gather high-fidelity I/O data, thereby enhancing the granularity of our analysis; and third, a framework to combine and process the collected data and provide helpful insights into performance characterization and reproducibility, alongside our lessons learned.

Dask↗
DOE OSTI2022

Performance and power modeling and prediction using MuMMI and 10 machine learning methods

Energy-efficient scientific applications require insight into how high performance computing system features impact the applications' power and performance. This insight can result from the development of performance and power models. Here, in this article, we use the modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and 10 machine learning methods to model and predict performance and power consumption and compare their prediction error rates. We use an algorithm-based fault-tolerant linear algebra code and a multilevel checkpointing fault-tolerant heat distribution code to conduct our modeling and prediction study on the Cray XC40 Theta and IBM BG/Q Mira at Argonne National Laboratory and the Intel Haswell cluster Shepard at Sandia National Laboratories. Our experimental results show that the prediction error rates in performance and power using MuMMI are less than 10% for most cases. By utilizing the models for runtime, node power, CPU power, and memory power, we identify the most significant performance counters for potential application optimizations, and we predict theoretical outcomes of the optimizations. Based on two collected datasets, we analyze and compare the prediction accuracy in performance and power consumption using MuMMI and 10 machine learning methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ExaCA: A performance portable exascale cellular automata application for alloy solidification modeling

Modeling the as-solidified grain structures that form during alloy processing is a critical component in understanding process-property relationships, particularly for additive manufacturing (AM) where grain structure is very sensitive to processing conditions. While cellular automata (CA)-based models have proven able to predict aspects of microstructure for several alloys and AM process conditions, long run times and large resource sets required limit the utility and the problem size to which existing CA models can be applied. As part of the ExaAM project, an initiative within the Exascale Computing Project (ECP) to develop, test, and optimize an exascale-capable coupled and self-consistent model of AM parts, we developed ExaCA (https://github.com/LLNL/ExaCA) for the liquid–solid phase transformation in the wake of AM melt pools. The CA-based code is parallelized using MPI and the Kokkos programming model, the latter enabling simulation on both CPUs and GPUs within a single-source implementation. Here, we detail the steps taken to transform a baseline, MPI-based CA code into one that is performant on CPUs and GPUs. Performance testing of ExaCA on Summit (a pre-exascale machine at Oak Ridge National Laboratory) was used to quantify CPU–GPU speedup comparing with equal numbers of nodes. Testing showed comparable CPU performance to the MPI-only CA code and a 5-20x speedup when running AM-based test problems using GPUs. The improved performance of CA through GPU utilization and the performance portable nature of ExaCA will enable accurate part-scale modeling by harnessing the power of current and future generations of high performance computing resources. Future work will include improving the strong scaling of ExaCA on GPUs by reducing load imbalance associated with the locality of the problem, and continuing performance optimization across exascale hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Design-time performance modeling of compositional parallel programs

Performance models are powerful instruments for understanding the performance of parallel systems and uncovering their bottlenecks. Already during system design, performance models can help ponder alternative development options. However, creating a performance model – whether theoretically or empirically – for an entire application that does not exist yet is challenging. In this paper, we propose to generate performance models of full programs from performance models of their components using formal composition operators derived from parallel design patterns. As long as the design of the overall system follows such a pattern, its performance model can be predicted with reasonable accuracy without an actual implementation. In conclusion, we demonstrate our approach with design patterns of varying complexity, including pipeline, task pool, and eventually MapReduce, which is representative of a broad class of data-analytics applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Design and full core fuel performance assessment of high burnup cores for 4-loop PWRs

Increasing the fuel discharge burnup of current light water reactors (LWRs) promises reductions in fuel cycle and/or operations costs. By assuming a constant core power density, the economic gain is enabled by better fuel utilization and/or an increased capacity factor. In this effort to investigate greater than 62 MWd/kgU maximum rod average burnup for 110+ kW/l core power density, two core designs have been developed for a standard 17x17, 193 fuel assemblies pressurized water reactor (PWR). The levelized unit cost methodology is employed to evaluate fuel cycle, operation and maintenance, and capital cost impacts and to examine the economic viability of both core design pathways. Core design and optimization are performed using the commercial STUDSVIK code package. Fuel performance analysis is realized in full core configuration via auditing FRAPCON4.1, FAST1.2, and the high-fidelity code BISON. To provide a realistic assessment, the core design process takes into consideration best practices in current PWR core design. It features acceptable performance in terms of various core design constraints on maximum allowable peaking and boron concentration. Gadolinia (Gd2O3) is used as a burnable poison with a maximum of 9 wt% concentration while feeding 89 or 77 fuel assemblies in a 3-batch refueling scheme. Full core fuel performance simulation, which allows for characterization of relevant fuel temperatures, plenum pressures, stresses, and strains, is performed with respect to two bounding burnup levels. Such performance is potentially licensable for the 18-month high burnup core (<68 MWd/kgU peak pin), while it is more challenging for the 24-month high burnup core design pathway (<75 MWd/kgU peak pin). Maximum rod plenum pressure is identified as the most limiting fuel performance parameter. Here, while the scope of the present study focuses on the steady-state plus overpower conditions, the acceptability of the new discharge burnup has to be further assessed by considering uncertainties and impacts under accident scenarios in the future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Overview of interpretive modelling of fusion performance in JET DTE2 discharges with TRANSP

In the paper we present an overview of interpretive modelling of a database of JET-ILW 2021 D-T discharges using the TRANSP code. The main aim is to assess our capability of computationally reproducing the fusion performance of various D-T plasma scenarios using different external heating and D-T mixtures, and to understand the performance driving mechanisms. We find that interpretive simulations confirm a general power-law relationship between increasing external heating power and fusion output, which is supported by absolutely calibrated neutron yield measurements. A comparison of measured and computed D-T neutron rates shows that the calculations' discrepancy depends on the absolute neutron yield. The calculations are found to agree well with measurements for higher performing discharges with external heating power above ~20 MW, while low-neutron shots display an average discrepancy of around +40% compared to measured neutron yields. A similar trend is found for the ratio between thermal and beam-target fusion, where larger discrepancies are seen in shots with dominant beam-driven performance. We compare the observations to studies of JET-ILW D discharges, to find that on average the fusion performance is well modelled over a range of heating power, although an increased unsystematic deviation for lower-performing shots is observed. The ratio between thermal and beam-induced D-T fusion is found to be increasing weakly with growing external heating power, with a maximum value of ≳1 achieved in a baseline scenario experiment. An evaluation of the fusion power computational uncertainty shows a strong dependence on the plasma scenario type and fusion drive characteristics, varying between ±25% and 35%. D-T fusion alpha simulations show that the ratio between volume-integrated electron and ion heating from alphas is ≲10 for the majority of analysed discharges. Alphas are computed to contribute between ~15% and 40% to the total electron heating in the core of highest performing D-T discharges. An alternative workflow to TRANSP was employed to model JET D-T plasmas with the highest fusion yield and dominant non-thermal fusion component because of the use of fundamental radio-frequency heating of a large minority in the scenario, which is calculated to have provided ~10% to the total fusion power.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

SYMBIOSYS: A Methodology for Performance Analysis of Composable HPC Data Services

Microservices are a powerful new way of building, customizing, and deploying distributed services owing to their flexibility and maintainability. Several large-scale distributed platforms have emerged to serve the growing needs of data-centric workloads and services in commercial computing. Concurrently, high-performance computing (HPC) systems and software are rapidly evolving to meet the demands of diversified applications and heterogeneity. The interplay of hardware factors, software configuration parameters, and the flexibility offered with a microservice architecture makes it nontrivial to estimate the optimal service instantiation for a given application workload. Further, this problem is exacerbated when considering that these services operate in a dynamic and heterogeneous HPC environment. An optimally integrated service can be vastly more performant than a haphazardly integrated one. Existing performance tools for HPC either fail to understand the request-response model of communication inherent to microservices or they operate within a narrow scope, limiting the insight that can be gleaned from employing them in isolation. We propose a methodology for integrated performance analysis of HPC microservices frameworks and applications called SYMBIOSYS. We describe its design and implementation within the context of the Mochi framework. This integration is achieved by combining distributed callpath profiling and tracing with a performance data exchange strategy that collects fine-grained, low-level metrics from the RPC communication library and network layers. The result is a portable, low-overhead performance analysis setup that provides a holistic profile of the dependencies among microservices and how they interact with the Mochi RPC software stack. Using HEPnOS, a production-quality Mochi data service, we demonstrate the low-overhead operation of SYMBIOSYS at scale and use it to identify the root causes of poorly performing service configurations.

microservices↗
DOE OSTI2025

Meta‐Analysis and Regression Modeling of the Impacts of Four Indoor Environmental Quality Metrics on Office Performance

Awareness of how buildings interact with the occupant experience—especially human performance—is becoming more prevalent, as seen by increasing interest and investment in healthy built environments. However, there is a need to synthesize the wide array of existing indoor environmental assessment and performance research in a way that can translate directly to building design and operation. Existing research in this area typically focuses on a single isolated metric and has not focused on making the results utilizable by building practitioners. The aim of this research is to investigate existing office performance literature through meta‐analyses and produce regression models for four indoor environmental quality (IEQ) metrics to support critical decision‐making for building operation and renovation. To reach this aim, a literature review was conducted to identify studies that measure the impact of changing ventilation rate, temperature, horizontal illuminance, and noise level in offices on occupant task performance. This repository of field and laboratory studies was analyzed to visualize the trends between the selected IEQ metrics and task performance. The temperature, ventilation rate, and horizontal illuminance regression models showed clear improvement potential when modifying indoor conditions toward the defined high‐performance range, while the regression model for noise level was inconclusive. The discussion notes the importance of designing holistically for all components of these IEQ categories to utilize the results, for example, good filtration on outdoor air for quantifying ventilation impact and uniform overhead lighting with low contrast for quantifying horizontal illuminance impact. The novelty of this work is in considering multiple facets of the indoor environment under a single, unified analysis schema and producing IEQ‐based performance gains that can directly inform cost‐benefit analyses of building design and renovation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Evaluation of ASCE 4-16 and AISC 43-18 (Draft) for use in the Risk-Informed Performance-Based Seismic Design of Nuclear Power Plant Structures, Systems, and Components

This report describes the assessment of ASCE Standards 4-16 and 43-18 (Draft) for use in the Risk-Informed Performance-Based (RIPB) seismic design of structures, systems, and components (SSCs) at nuclear power plants. This work was performed for the U.S. Nuclear Regulatory Commission (NRC) Office of Regulatory Research (RES), to support potential endorsement of these industry standards for the design of nuclear power plants based on the RIPB approach. Currently, the NRC endorses a deterministic design approach for demonstrating the design adequacy of SSCs based on the Standard Review Plan (NUREG-0800) and NRC Regulatory Guides. In the RIPB approach, the design criteria are developed to achieve a target performance goal, which is defined by the annual frequency of occurrence of the design basis earthquake (i.e., Seismic Design Category) and the acceptable level of structural performance (i.e., Limit State) for the SSCs. ASCE 4-16 provides methods for performing a seismic analysis of structures to obtain the seismic response of these structures (e.g., building displacements, accelerations, in-structure response spectra) which are used in the design of the SSCs. It also provides methods for performing seismic analysis of SSCs to determine the seismic demands (e.g. member forces and displacements) needed to design individual SSCs. ASCE 43-18 (Draft) provides the criteria for the seismic design of SSCs using the seismic demands developed in ASCE 4-16. The use of ASCE Standard 43-18 (Draft), along with ASCE 4-16, provides the criteria for the seismic design of the SSCs. ASCE 43-18 (DRAFT) relies on other consensus codes and standards such as ACI 349 for reinforced concrete, AISC/N690 for steel structures, ASME Section III for pressure-retaining mechanical components and Containments, and IEEE-344 for Class 1E equipment. The goal of this technical review was to assess the adequacy of the provisions in these standards for use by the NRC in developing regulatory guidance for design of SSCs in nuclear power plants, based on the RIPB approach. The research reported herein describes the basis for acceptance of the new standards and identifies areas where additional staff guidance is needed for the seismic design of SSCs at nuclear power plants. This technical review has determined that ASCE 4-16 and ASCE 43-18 (Draft) provide an appropriate framework for the seismic design of SSCs at nuclear power plants using a Risk-Informed Performance-Based approach. However, some of the criteria therein warrant exceptions, qualifications, and/or clarifications.

42 ENGINEERING↗
DOE OSTI2023

Supplement to M&V Guidelines: Measurement and Verification for Performance-Based Contracts Version 4.0

Measurement and verification (M&V) are key to determining and confirming the continued operation of, and savings associated with, performance-based contracts. The Federal Energy Management Program (FEMP) updates these guidelines to reflect the evolution of M&V to incorporate industry best practices and ensure that savings from these performance-based projects are properly planned, communicated, and realized. FEMP released M&V Guidelines: Measurement and Verification for Federal Energy Projects Version 3.0 in 2008. This document was developed with input from industry-government working groups, previous M&V guideline authors, and national laboratory subject matter experts. The document provided the methods and guidelines, with a focus on federal energy savings performance contracts (ESPCs). When FEMP released M&V Guidelines: Measurement and Verification for Performance-Based Contracts Version 4.0 in 2015, FEMP expanded the intended audience to include anyone (energy managers, procurement officers, and contractors) involved in implementing performance-based contracts. The streamlined M&V Guidelines Version 4.0 provided the procedures and guidelines needed to quantify savings resulting from installing energy-efficient equipment, water conservation, improved operation and maintenance (O&M), renewable energy, and cogeneration projects with a performance-based contract; however, it removed some of the associated details on topics such as developing regression models and sampling plans. The details and information provided in M&V Guidelines v. 3.0 are still pertinent to M&V for performance-based contracts. The objective is to keep FEMP M&V version 4.0 condensed, providing the reference document for specifying M&V methods and procedures and a resource for developing project-specific M&V plans; this supplement provides additional details. The content of this supplement has been updated to include pertinent revisions between M&V version 3.0 and M&V version 4.0 by providing details and updating the content to ensure relevancy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Sex comparisons in physiological and cognitive performance during hypoxic challenge

Within the tactical aviation community, human performance research lags in considering potential psychophysiological differences between male and female aviators due to little inclusion of females during the design and development of aircraft systems. A poor understanding of how male and female aviators differ with respect to human performance results in unknown potential sex differences on aeromedically relevant environmental stressors, perchance leading to suboptimal performance, safety, and health guidelines. For example, previous hypoxia studies have excluded female participants or lacked a sizeable sample to examine sex comparisons. As such, progress toward sensor development and improving hypoxia familiarization training are stunted due to limited knowledge of how individual differences, including sex, may or may not underlie hypoxia symptoms and performance impairment. Investigating sex differences bridges the gap between aerospace medicine and operational health, and addressing hypoxia is one of many facets yet to be studied. In the current study, we retrospectively examined N = 6 hypoxia studies with male-female participant samples (total, N = 189; male, n = 118; female, n = 71). We explored sex as a predictor of physiological response, sensory deficits, the severity of cognitive performance declines, and symptom manifestation via linear and binary logistic regression models. We found that the female sex predicted lower peripheral oxygen saturation and the likelihood of headache reporting in response to hypoxic challenge, yet explained little variance when combined with age and body mass index. The sensory and cognitive performance models did not converge, suggesting high intra-individual variability. Together, sex, age, and body mass index were not the most robust predictors in responses to hypoxic challenge; we cannot infer this for sensory deficits and cognitive performance within an experimentally induced hypoxic environment. The findings have implications for improving hypoxia familiarization training, monitoring sensor development, and emergency response and recovery protocols in case of a hypoxia occurrence suitable for all aircrew. We recommend continuing to elucidate the impact of sex and intrapersonal differences in hypoxia and other aeromedically relevant stressors in tactical aviation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Effects of Coating on the Electrochemical Performance of a Nickel-Rich Cathode Active Material

Due to their safety and high power density, one of the most promising types of all-solid-state lithium batteries is the one made with the argyrodite solid electrolyte (ASE). Although substantial efforts have been made toward the commercialization of this battery, it is still challenged by some technical issues. One of these issues is to prevent the side reactions at the interface of the ASE and the cathode active material (CAM). A solution to address this issue is to coat the CAM particles with a material that is compatible with both ASE and CAM. Prior studies show that the lithium niobate, LiNbO 3 , (LNO) is a promising material for coating CAM particles to reduce the interfacial side reactions. However, no systematic study is available in the literature to show the effect of coating LNO on CAM performance. This paper aims to quantify the effect of LNO coating on the electrochemical performance of a nickel-rich CAM. The electrochemical performance parameters that are studied are the capacity, cycling performance, and rate performance of the coated-CAM; and the effectiveness of the coating to prevent the side reactions at the ASE and CAM interface is out of the scope of this study. To eliminate the effect of side reactions at the ASE and CAM interface, we conduct all tests in the organic liquid electrolyte (OLE) cells to solely present the effect of coating on the CAM performance. For this purpose, 0.5 wt.% and 1 wt.% LNO are used to coat the LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC-60) CAM through two synthesizing methods. Consequently, the effects of the synthesizing method and the coating weight percentage on the NMC-60 performance are presented.

10 SYNTHETIC FUELS↗
NASA NTRS1962

EFFECTS OF HIGH G CONDITIONS ON PILOT PERFORMANCE

The general development trend in space vehicle design suggests the desirability of maximally using the occupant to both control capsule attitude and to monitor vehicle systems during the boost (and reentry) acceleration phases, as well as during orbital flight. Consequently, much more information is needed concerning man's ability to perform certain control functions under conditions in which he is exposed to accelerations which approach not only his physiological tolerance limits, but also his performance tolerance limits. In addition to the need for more data concerning the acceleration stress that man can endure and still retain the ability to perform control functions, there is a need to know specifically the nature of performance errors which can arise, not only as the direct result of acceleration, but also as secondary effects of acceleration inter acting with other conditions such as the type of control task, the type of control device, the damping and stability parameters, and the pilot's physiological endurance8 . Current concern over the performance capabilities of the human pilot immersed in certain acceleration environments is well founded since there are very few experimental reports describing the effects of these conditions on performance. The present paper attempts to summarize some of the results of recent studies conducted at the Aviation Medical Acceleration Laboratory (AMAL) in which specific pilot performance capabilities were studied under several conditions of acceleration stress.

Gravity↗
NASA NTRS1988

Fault tolerant system performance modeling

With the proliferation of complex digital systems on aircraft, the need to accurately predict system performance early in the system design cycle becomes imperative. In the past, system designers have relied on ad hoc methods for evaluating performance issues. This has produced systems that have not always worked as originally intended. To alleviate these design deficiencies, formal methods, with supporting tools, must be adhered to during the system design process. The use of performance modeling tools is becoming widely accepted as a way to address timing considerations of system design. An additional incentive for the use of these tools is that they allow the system architect to analyze system component interactions (i.e., bus contention, contention of functions for a processing site, and system repair activity on application performance). This inherent flexibility can result in an explicit specification of the system architecture. This paper addresses a method that supports performance modeling of fault tolerant systems using a discrete event simulation tool. An additional focus is on lessons learned from analyzing these classes of problems. The methodology and supporting work provide system architects with the capability to specify candidate architectures and accurately predict their performance in the early stages of design, where changes to system design is most cost effective. The work has been supported under NASA contract NAS1-18099. Integrated Airframe Propulsion Control System Architecture (IAPSA II). This contract addresses methodology, analysis, and detailed design of integrated control system architectures suitable for high-performance aircraft of the 1990's.

Discrete event simulation↗