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DOE OSTI2012

Argonne Leadership Computing Facility 2011 annual report: Shaping future supercomputing

The ALCF's Early Science Program aims to prepare key applications for the architecture and scale of Mira and to solidify libraries and infrastructure that will pave the way for other future production applications. Two billion core-hours have been allocated to 16 Early Science projects on Mira. The projects, in addition to promising delivery of exciting new science, are all based on state-of-the-art, petascale, parallel applications. The project teams, in collaboration with ALCF staff and IBM, have undertaken intensive efforts to adapt their software to take advantage of Mira's Blue Gene/Q architecture, which, in a number of ways, is a precursor to future high-performance-computing architecture. The Argonne Leadership Computing Facility (ALCF) enables transformative science that solves some of the most difficult challenges in biology, chemistry, energy, climate, materials, physics, and other scientific realms. Users partnering with ALCF staff have reached research milestones previously unattainable, due to the ALCF's world-class supercomputing resources and expertise in computation science. In 2011, the ALCF's commitment to providing outstanding science and leadership-class resources was honored with several prestigious awards. Research on multiscale brain blood flow simulations was named a Gordon Bell Prize finalist. Intrepid, the ALCF's BG/P system, ranked No. 1 on the Graph 500 list for the second consecutive year. The next-generation BG/Q prototype again topped the Green500 list. Skilled experts at the ALCF enable researchers to conduct breakthrough science on the Blue Gene system in key ways. The Catalyst Team matches project PIs with experienced computational scientists to maximize and accelerate research in their specific scientific domains. The Performance Engineering Team facilitates the effective use of applications on the Blue Gene system by assessing and improving the algorithms used by applications and the techniques used to implement those algorithms. The Data Analytics and Visualization Team lends expertise in tools and methods for high-performance, post-processing of large datasets, interactive data exploration, batch visualization, and production visualization. The Operations Team ensures that system hardware and software work reliably and optimally; system tools are matched to the unique system architectures and scale of ALCF resources; the entire system software stack works smoothly together; and I/O performance issues, bug fixes, and requests for system software are addressed. The User Services and Outreach Team offers frontline services and support to existing and potential ALCF users. The team also provides marketing and outreach to users, DOE, and the broader community.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2015

Predictive Engineering Tools for Injection-Molded Long-Carbon-Fiber Thermoplastic Composites (FY 2015 Third Quarterly Report)

During the third quarter of FY 2015, the following technical progress has been made toward project milestones: 1) Magna oversaw the tool build and prepared the molding plan for the complex part of Phase II. 2) PlastiComp hosted a visit by Magna and Toyota on April 23rd to finalize the molding scope and schedule. The plan for molding trials including selection of molding parameters for both LFT and D-LFT for the U-shape complex part was established. 3) Toyota shipped the U-shape complex part tool to Magna on May 28th, 2015. 4) Plasticomp provided 30wt% LCF/PP and 30wt% LCF/PA66 compounded pellets to Magna for molding the complex part. 5) Magna performed preliminary molding trials on June 2nd, 2015 to validate wall thickness, fill profile, tool temperature and shot size requirements for the complex part. 6) Magna performed the first complex part run on June 16th and 17th, 2015 at Magna’s Composite Centre of Excellence in Concord, ON, Canada. Dale Roland of Plasticomp, and Umesh Gandhi of Toyota also attended the molding. 7) Magna discussed and finalized the plan with PNNL and the team for cutting samples from molded parts at selected locations for fiber orientation and length measurements. 8) Magna provided the computer-aided design (CAD) files of the complex parts with and without ribs to PNNL and Autodesk to build the corresponding ASMI models for injection molding simulations. Magna also provided the actual parameters used. 9) Plasticomp’s provided knowledge and experience of molding LCF materials essential to the successful molding of the parts including optimization of fill speed, tool temperatures, and plasticizing conditions for the 30wt% LCF/PP and 30wt% LCF/PA66 materials in both rib and non-rib versions. 10) Magna molded additional parts for evaluation of mechanical property testing including torsional stiffness on June 29th and 30th, 2015 at Magna’s Composite Center of Excellence. 11) Toyota began preparation for the torsion test of the specimens. Preparation of a computer-aided engineering (CAE) model to predict the performance is in progress. 12) Autodesk fixed an error in the implementation of the proper orthogonal decomposition (POD) calculation of fiber length that had caused the ASMI solution to crash and provided an updated build of ASMI containing the fix. 13)Autodesk reviewed and provided feedback for the complex part molding and measurement locations. 14) Autodesk provided support to set up the workflow for ASMI-ABAQUS® analysis, and provided a fix and workaround for a bug in the ASMI-ABAQUS® output command. 15) Autodesk helped build ASMI analysis models for the complex parts with and without ribs. 16) Autodesk worked on improving the orientation prediction accuracy in the shearing layer for 3D meshes based on comparison to measured data of the plaque moldings. 17) PNNL installed a new ASMI version received from Autodesk and performed comparative analyses to assess mid-plane versus 3D fiber length predictions using the full fiber length model and the reduced-order model (ROM) using POD. 18) PNNL presented the project scope, accomplishments, significant results and future plans to DOE and the USCAR Materials Tech Team on June 3rd, 2015. 19) PNNL discussed the cutting of samples from molded parts and finalized a plan with Magna and the team suggesting the sample size, locations and number of samples per location. 20) PNNL and Autodesk built ASMI models for the complex parts with and without ribs, and preliminary analyses of the part with ribs were conducted using the actual molding parameters received from Magna. 21) PNNL worked on a procedure to extract fiber orientation and length results from a 3D ASMI analysis to a 3D ABAQUS model. This procedure is essential to import ASMI fiber orientation and length to a 3D ABAQUS model of the part allowing future part structural analysis for weight reduction study.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Assessment of Core Physics Characteristics of Extended Enrichment and Higher Burnup LWR Fuels using the Polaris/PARCS Two-Step Approach. Vol. I: PWR Fuel

Nuclear fuel with extended enrichment (235U enrichment within 5-8 wt%) is one of the evolutionary changes that have been pursued in recent years by commercial light water reactor operators and fuel vendors to improve the fuel cycle economy and operation performance of a nuclear plant. This work assesses the performance of the Polaris/PARCS two-step approach in core physics modeling of the pressurized water reactor cores with extended enrichment fuel, referred to as “LEU+” in this report. A representative LEU+ core with a 24-month fuel cycle developed by Southern Nuclear Company (SNC) was modeled using this two-step approach. A representative LEU core with an 18-month fuel cycle was also modeled to provide a reference for the LEU+ core. As expected, significantly more burnable poison absorbers were used in the LEU+ core to accomodate its higher fuel enrichment. Nine different fuel assembly types were modeled using Polaris for each core to generate the assembly cross sections, which were then processed by GenPMAX to prepare the cross-section data for PARCS. The average specific powers of each fuel batch in each core were derived from VERA results and higher specific powers in fresh assemblies were found in the LEU core due to its less total uranium loading included in the VERA LEU model, given that the total core power was assumed to be the same for both cores. PARCS models were developed to simulate the steady-state operations of both cores. PARCS results on the LEU+ core were first compared with the VERA results for verification purpose; good agreements were seen in soluble boron and burnup distribution results, indicating that the Polaris/PARCS modeling and simulation were correctly implemented. Core physics parameters calculated by PARCS, at zero power physics tests, beginning of cycle (BOC), and end of cycle conditions (EOC), were compared between the LEU+ core and the LEU core, including soluble boron concentration, burnup distributions, assembly and pin power peaking factors, fuel temperature reactivity coefficients, moderator temperature and density reactivity coefficients, control rod worth, and shut down margin. The main differences in PARCS results between the LEU+ and the LEU cores are summarized below: 1)The critical boron concentrations were found to be much higher in the LEU+ core than in the LEU core (1582 vs. 1335 ppm for peak values). 2)Higher assembly radial power peaking factors (1.4 vs. 1.3 for peak values), 2D pin peaking factors (1.53 vs. 1.42 for peak values), and 3D pin peaking factors (1.89 vs. 1.81 for peak values) were found in the LEU+ core than in the LEU core. 3)Significantly higher reactivity coefficients of moderator temperature (and density) were found in LEU+ than in LEU.4)Significantly lower control rod worth at EOC were found in LEU+ than LEU for all but one control banks.5)Significantly lower shut down margins were found in the LEU+ core than in the LEU core. The Polaris/GenPMAX/PARCS code suite was found to be capable of modeling the LEU+ PWR core for steady-state operations and no unexpected results in core physics parameters were observed, in spite of that a) several bugs in PARCS were identified and workarounds were used; b) several features were found lacking in the current version of PARCS that would be useful for core modeling. A list of requests for bug fixes and feature upgrades for PARCS originated from this work were transmitted to the code developers. The assessments on the performance of the Polaris/PARCS two-step approach in core modeling for boiling water reactor with LEU+ fuel is ongoing.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Combined Mesonet and Tracker

Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (°C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a “0” if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the “time warp” period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503–2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100–103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278–283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]

54 ENVIRONMENTAL SCIENCES↗