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At least 847 records · Page 47

Aerosol Vertical Profiling at Oliktok Point (AVPOP) Field Campaign Report

Aerosols are an important component of the atmospheric system through their various impacts on climate. Notably, the largest uncertainty of the energy budget (i.e., radiative forcing estimate) is the aerosol indirect effect. Aerosol-induced microphysical modifications influence cloud lifetime and albedo. However, constraining aerosol-cloud impacts in climate models, specifically in regions such as the Arctic, remains a significant challenge due to limited observations. The efficacy of an aerosol to impact the arctic energy budget largely depends on its concentration, composition, and source. While ground-based measurements will provide a consistent, high-resolution time series of aerosol observations, the arctic atmosphere can be stratified, and thus what is observed on the ground may not be representative of the aerosol aloft. A key gap in arctic aerosol research is routine vertical profiling of abundance and source characterization. Previous work outside of the Arctic has focused on using launched or tethered balloon systems (LBS and TBS, respectively) to measure vertical profiles of aerosol size distributions and has demonstrated the utility of such observations (e.g., Hofmann et al. 1989; Hofmann 1993; Iwasaka et al. 2003; Kim et al. 2003; Tobo et al. 2007). More specifically, particle spectrometers have also been deployed via TBS systems (de Boer et al. 2018; Greenberg et al. 2009; Maletto et al. 2003; Renard et al. 2016; Wehner et al. 2007; Siebert et al. 2004) affording information on aerosol layer locations and evolution by means of multiple profiles. Filter samples for post-processing of aerosol composition and ice nucleation properties have also been conducted in mid-latitudes (Creamean et al. 2018). However, while these studies present novel measurements of aerosol vertical profiling via balloon systems, few exist to date in the Arctic. The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility TBS is composed of a variety of winches, balloons, and instrumentation that flies within ARM’s Restricted Airspace (R-2204) at the third ARM Mobile Facility (AMF3) at Oliktok Point, Alaska (Figure 1). The system payload comprises instruments that can simultaneously collect in situ data on horizontal winds, ice microphysics, turbulence, thermodynamic state, aerosols, and the cloud-top environment (https://www.arm.gov/capabilities/instruments/tbs). Recent campaigns at AMF3 have involved the use of the ARM helikite, which is a balloon-kite hybrid that uses lighter-than-air principles to obtain its initial lift and a kite to achieve stability and dynamic lift. Additional information on the specifications of the helikite TBS can be found in the ARM TBS handbook (Dexheimer 2018).

Oliktok Point, North Slope of Alaska,, tethered ba↗

OpenACC Profiling Support for Clang and LLVM using Clacc and TAU

Since its launch in 2010, OpenACC has evolved into one of the most widely used portable programming models for accelerators on HPC systems today. Clacc is a project funded by the US Exascale Computing Project (ECP) to bring OpenACC support for C and C++ to the popular Clang and LLVM compiler infrastructure. In this paper, we describe Clacc's support for the OpenACC Profiling Interface, a critical component of the OpenACC specification that standardizes an interface that profiling tools and libraries can depend upon across OpenACC implementations. As part of Clacc's general strategy to build OpenACC support upon OpenMP, we describe how Clacc builds OpenACC Profiling Interface support upon an extended version of OMPT. We then describe how a major profiling and tracing toolkit within ECP, the TAU Performance System, takes advantage of this support. We also describe TAU's selective instrumentation support for OpenACC. Finally, using Clacc and TAU, we present example visualizations for several SPEC ACCEL OpenACC benchmarks running on an IBM AC922 node, and we show that the associated performance overhead is negligible.

Coti, Camille↗

CROCUS Sodar Measurements of Lower Atmospheric Wind Profiles at Argonne Testbed for Multiscale Observational Science (ATMOS) Site

The Scintec MFAS Sodar (Multiple-Frequency Acoustic Sounder) is an autonomous, ground-based acoustic remote sensing system designed to measure vertical profiles of horizontal wind speed, wind direction, and vertical velocity in the lower atmosphere. The instrument transmits sequences of acoustic pulses and detects the Doppler-shifted sound waves backscattered by small-scale temperature and velocity fluctuations caused by atmospheric turbulence. From these Doppler shifts, the system derives three-dimensional wind vectors by combining radial velocities from multiple beam orientations.The MFAS Sodar operates with a first usable range gate beginning at approximately 30 m above ground level and a configurable vertical resolution of 10 m. Under favorable acoustic conditions, the system provides wind profiles extending up to 600 m above ground level. Measurements are processed into 15-minute averaged profiles containing wind speed, direction, vertical velocity, and diagnostic quantities such as signal-to-noise ratio and echo strength.This dataset was collected at the Argonne Testbed for Multiscale Observational Science (ATMOS) facility in Lemont, Illinois, as part of DOE's CROCUS Urban Integrated Field Laboratory (UIFL) initiative. The purpose of these observations is to characterize the vertical wind structure and boundary-layer evolution across the urban–suburban gradient of the greater Chicago region. In particular, these data are intended to improve understanding of how local meteorology, such as lake-breeze penetration, nocturnal jets, and daytime mixing, varies between the densely built urban core and the suburban periphery. The MFAS observations provide critical context for evaluating high-resolution model simulations and for integrating with complementary lidar, radar, and in-situ meteorological measurements within the broader CROCUS UIFL network.All data are archived in NetCDF (Network Common Data Form) format and include wind and diagnostic parameters. The files can be accessed and analyzed using standard software that supports NetCDF, such as Python (e.g., xarray, netCDF4), MATLAB, R (e.g., ncdf4, raster), or Panoply (NASA’s NetCDF visualization application).

54 ENVIRONMENTAL SCIENCES↗

The Convolutional Multiple Whole Profile (CMWP) Fitting Method, a Global Optimization Procedure for Microstructure Determination

The analysis of line broadening in X-ray and neutron diffraction patterns using profile functions constructed on the basis of well-established physical principles and TEM observations of lattice defects has proven to be a powerful tool for characterizing microstructures in crystalline materials. These principles are applied in the convolutional multiple-whole-profile (CMWP) procedure to determine dislocation densities, crystallite size, stacking fault and twin boundary densities, and intergranular strains. The different lattice defect contributions to line broadening are separated by considering the hkl dependence of strain anisotropy, planar defect broadening and peak shifts, and the defect dependent profile shapes. The Levenberg–Marquardt (LM) peak fitting procedure can be used successfully to determine crystal defect types and densities as long as the diffraction patterns are relatively simple. However, in more complicated cases like hexagonal materials or multiple-phase patterns, using the LM procedure alone may cause uncertainties. Here, we extended the CMWP procedure by including a Monte Carlo statistical method where the LM and a Monte Carlo algorithm were combined in an alternating manner. The updated CMWP procedure eliminated uncertainties and provided global optimized parameters of the microstructure in good correlation with electron microscopy methods.

36 MATERIALS SCIENCE↗

Optimal control of the electron temperature profile in DIII-D using machine learning surrogate models

The viability of the tokamak as a potential fusion reactor depends on the ability to keep the plasma in a stable regime while achieving temperatures, densities, and confinement times that are as high as possible. Tokamak scenario development attempts to find plasma regimes that achieve all of these conditions and are accessible with a given set of hardware constraints. This requires the ability to control plasma properties such as the normalized beta, the internal inductance, safety factor, rotation, etc. One property that has received less attention than some of the others, but is no less critical to achieving high performance, is the electron temperature (T e ) profile. In this work, Linear Quadratic Integral (LQI) control is used to develop a controller for the electron temperature profile in DIII-D. The controller is based on a linearized model derived from the transport equation that describes the evolution of the electron temperature, and includes contributions from the neural network surrogate models NubeamNet and MMMnet. Furthermore, the controller is tested in simulation using COTSIM, and is proven capable of tracking a target T e profile.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Modeling the impact of radiation-enhanced diffusion on implanted ion profiles

We report that ion irradiations are a valuable research tool for exploring radiation effects in materials. However, it is well recognized that the implanted ions can artificially modify the radiation damage evolution, e.g., enhancing amorphization processes at low irradiation temperatures and suppressing void swelling at elevated temperatures. Therefore, the implanted ion region should be avoided for most studies of ion irradiated materials. Due to increased interest in high damage, high temperature ion irradiations studying radiation effects in materials for proposed high dose Generation IV fission and fusion energy applications, it is crucial to quantify the extent of diffusional broadening of the implanted ion profile for a variety of temperatures, irradiation fluxes, and sink strengths. The present study summarizes computational analyses of thermal and depth-dependent radiation enhanced difusion (RED) on diffusion broadening of the implanted ion profiles in Fe and Ni for a variety of irradiation conditions. For a low assumed RED coefficient (10 -20 -10 -19 m 2 /s and 10 -4 and 10 -3 peak dpa/s, respectively) or high flux, broadening of the as-implanted ion profile is very small and a suitable artifact-free midrange region for analysis exists for ion energies above 5-6 MeV at 100 peak dpa. At high RED coefficients (10 -19 -10 -18 m 2 /s and 10 -4 and 10 -3 peak dpa/s, respectively) broadening is much more significant, and no valid region for investigation exists below 6-8 MeV ion energies at any damage level >50 dpa. While increasing flux decreases irradiation time, it also increases the RED coefficient; these effects mostly offset except under recombination-dominant conditions. For typical high dose irradiation studies of void swelling that exceed ~100 displacement per atom (dpa), ion energies > 6-8 MeV must be employed in order to achieve suitable artifact-free midrange irradiation analysis regions, depending on the material system. Lastly, analysis of amorphization/precipitation require even higher energies (>10 MeV).

07 ISOTOPE AND RADIATION SOURCES↗

Synapse: Synthetic application profiler and emulator

Abstract Motivated by the need to emulate workload execution characteristics on high- performance and distributed heterogeneous resources, we introduce Synapse. Synapse is used as a proxy application (or "representative application") for real workloads, with the advantage that it can be tuned in different ways and dimensions, and also at levels of granularity that are not possible with real applications. Synapse has a platform-independent application profiler, and has the ability to emulate profiled workloads on a variety of resources. Experiments show that the automated profiling performed using Synapse captures an application's characteristics with high fidelity. The emulation of an application using Synapse can reproduce the application's execution behavior in the original run- time environment, and can also reproduce those behaviors on different run-time environments.

97 MATHEMATICS AND COMPUTING↗

Control of plasma profile in microwave discharges via inverse-problem approach

In the manufacturing process of semiconductors, plasma processing is an essential technology, and the plasma used in the process is required to be of high density, low temperature, large diameter, and high uniformity. This research focuses on the microwave-excited plasma that meets these needs, and the research target is a spatial profile control. Two novel techniques are introduced to control the uniformity; one is a segmented slot antenna that can change radial distribution of the radiated field during operation, and the other is a hyper simulator that can predict microwave power distribution necessary for a desired radial density profile. The control system including these techniques provides a method of controlling radial profiles of the microwave plasma via inverse-problem approach, and is investigated numerically and experimentally.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Rapid measurement of charged particle beam profiles using a current flux grating

The principle and physics issues of charged particle beam diagnostics using a current flux grating are presented. Unidirectional array of conducting channels with interstitial insulating layers of spacing d is placed in the beam path to capture flux of charge and electronically reproduce an exact beam current profile with density variation. The role of secondary electrons due to the impinging particle beam (both electron and ion) on the probe is addressed and a correction factor is introduced. A 2-dimensional profile of the electron beam is obtained by rotating the probe about the beam axis. Finally, a comparison of measured beam profile with a Gaussian is presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Effects of the plasma profiles on photon and pair production in ultrahigh intensity laser solid interaction

The model of photon and pair production in strong field quantum electrodynamics is implemented into our 1D3V particle-in-cell code with Monte Carlo algorithm. Using this code, the evolution of the particles in ultrahigh intensity laser (∼10{sup 23} W/cm{sup 2}) interaction with aluminum foil target is observed. Four different initial plasma profiles are considered in the simulations. The effects of initial plasma profiles on photon and pair production, energy spectra, and energy evolution are analyzed. The results imply that one can set an optimal initial plasma profile to obtain the desired photon distributions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Beam profile and coherence properties of synchrotron beams after reflection on modified multilayer mirrors

Multilayer mirrors present an attractive alternative for reflective hard X-ray monochromators due to their increased bandwidth compared with crystal-based systems. An issue remains the strong modulations in the reflected beam profile, i.e. an irregular stripe pattern. This is a major problem for micro-imaging applications, where multilayer-based monochromators are frequently employed to deliver higher photon flux density. A subject of particular interest is how to overcome beam profile modifications, namely the stripe patterns, induced by the reflection on a multilayer. For multilayer coatings in general it is known that the substrate and its surface quality significantly influence the performance of such kind of mirrors as the coating reproduces to a certain degree roughness and shape of the substrate. Our studies have shown that modified coatings can significantly change the impact of the multilayer reflection on the beam profile. We will present recent results as well as a critical review.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Faraday-effect polarimetry for current profile measurement in the tokamak plasma edge

Toroidal current profile measurements in the tokamak plasma edge are critical for fusion plasma physics research and model validation. A three-wave Faraday-effect polarimeter-interferometer with a sub-centimeter spatial resolution is proposed on the DIII-D tokamak to determine the edge current profile via Abel inversion. By using probe beams with 316 µm wavelength, a low-field-side, vertical-view, single-pass optical layout covering the plasma edge region (R = 2.15–2.27 m) is assessed. Measurements with no greater than 0.1° polarimetric systematic uncertainty, no greater than 0.01° polarimetric root-mean-square noise (1 kHz bandwidth), and a 0.8 cm radial chord spacing are considered feasible based on the achieved performance of existing systems using similar wavelengths on fusion devices. Furthermore, synthetic diagnostic calculations taking various factors into account, such as diagnostic uncertainty and quality of magnetic flux surfaces, find that the edge current profile can be determined with up to 0.12 MA/m 2 uncertainty, or about 10% of the peak current density in the pedestal of an investigated high-confinement plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Runaway electron beam dynamics at low plasma density in DIII-D: energy distribution, current profile, and internal instability

Parameters of the post-disruption runaway electron (RE) beam in the collisionless background plasma achieved after deuterium injection are investigated in DIII-D. The spatially resolved RE energy distribution function is measured for the first time during the RE plateau stage by inverting hard X-ray bremsstrahlung spectra. It has maximum energy up to 20 MeV and a non-monotonous feature at 5{6 MeV observed only in the core of the beam supporting the possibility of kinetic instabilities. The RE current profile is reconstructed for the first time using the spatially resolved RE energy distribution. It is found to be more peaked than the pre-disruption plasma current, with higher internal inductance, suggesting preferential formation of REs in the core plasma or potentially a radially inward motion of the REs. The accessed relatively low-current (180 kA) RE beam is found stable, likely due to its elevated q profile. From this base stable equilibrium, instability is accessed by ramping the solenoid current. Under a large applied accelerating loop voltage an internal kink mode is observed. The kink mode leads to sawtooth-like relaxation of the RE current profile, but drives no RE loss. Under conditions of a large decelerating voltage, chirping Alfvénic instabilities driven by REs during the RE plateau stage are observed for the first time. The low-frequency (0.1{7 MHz) Alfvénic instabilities, supposedly compressional Alfvén eigenmodes, are found to be correlated with a RE loss signal. Finally, this provides novel plasma conditions to study these instabilities and investigate opportunities to utilize them for RE control. Bifurcated electron cyclotron emission (ECE) spectrum is measured during the collisionless RE plateau, with a break point at ≈100 GHz suggesting resonant absorption of the ECE at low frequencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Single-shot measurement of longitudinal phase space beam profile in an electron storage ring

A novel scheme to measure the longitudinal emittance and phase space profile in an electron storage ring by using correlations between time and the vertical coordinate, and between energy and the horizontal coordinate, is proposed. This longitudinal profile measurement scheme will help to demonstrate recent results of advanced studies for manipulating the longitudinal beam profile and for investigating beam instability in an electron storage ring.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Improving the accuracy and resolution of neutron crystallographic data by three-dimensional profile fitting of Bragg peaks in reciprocal space

Neutron crystallography is a powerful technique for directly visualizing the locations of H atoms in biological macromolecules. This information has provided key new insights into enzyme mechanisms, ligand binding and hydration. However, despite the importance of this information, the application of neutron crystallography in biology has been limited by the relatively low flux of available neutron beams and the large incoherent neutron scattering from hydrogen, both of which contribute to weak diffraction data with relatively low signal-to-background ratios. A method has been developed to fit weak data based on three-dimensional profile fitting of Bragg peaks in reciprocal space by an Ikeda–Carpenter function with a bivariate Gaussian. When applied to data collected from three different proteins, three-dimensional profile fitting yields intensities with higher correlation coefficients (CC 1/2 ) at high resolutions, decreased R free factors, extended resolutions and improved nuclear density maps. Importantly, additional features are revealed in nuclear density maps that may provide additional scientific information. These results suggest that three-dimensional profile fitting will help to extend the capabilities of neutron macromolecular crystallography.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Transfer Learning Trained LSTM Models for Household Load Profile Forecasting

Grid edge renewable energy resources, such as rooftop solar photovoltaics, closely interact with consumer load profiles. Therefore, forecasting future electricity demand, ideally at the individual household level, is indispensable. In this paper, we present a transfer learning enhanced household load profile forecasting method. First, we tune a long short-term memory forecasting model to perform day-ahead prediction of household electricity load profiles. Then we improve these individualized models using transfer learning, and we use k-means clustering to create optimal source data sets. We find average improvements of 4.38% (largest improvement of 10.71%) when the entire data set was used to train the source model and 2.45% (largest improvement of 11.57%) in the mean absolute error when households were first clustered and used to train separate source models for each cluster. We find that transfer learning with clustered data can effectively boost the forecasting performance of the LSTM models. We use realistic household power measurements for 148 real residential households in Austin, Texas.

deep learning↗

A Data-Driven Framework for Residential Electric Vehicle Charging Load Profile Generation

Residential electric vehicle charging load profile is indispensable to achieve reliable control strategies for mitigating negative effects on power distribution system due to emerging electrified transportation. This paper introduces a data-driven framework of charging load profile generation for residential plug-in electric vehicles. Real world historical residential charging behavior data is utilized to construct empirical charging decision making model by using machine learning algorithm. A multiple channels method with kernel density estimation is proposed to construct probability density functions for estimating charging duration based on parking duration. A generation algorithm considering parking time and travel demand dependency is introduced to generate residential charging behaviors. This framework is extensible to generate various charging load profiles and simulate varied residential charging scenarios under different number of households and charging rates. This will be crucial for designing and validating residential charging control strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Profiling and Predicting Application Performance on the Cloud

Cloud providers continue to expand and diversify their collection of leasable resources to meet the needs of an increasingly wide range of applications. While this flexibility is a key benefit of the cloud, it also creates a complex landscape in which users are faced with many resource choices for a given application. Suboptimal selections can both degrade performance and increase costs. Given the rapidly evolving pool of resources, it is infeasible for users alone to select instance types; instead, automated methods are needed to simplify and guide resource provisioning. Here we present a method for the automatic prediction of application performance on arbitrary cloud instances. We combine offline and online profiling approaches, using historical data gathered from non-cloud environments and targeted profiling runs on cloud environments to create a composite application model that can predict run times on a given cloud instance type for a given input data size. We demonstrate average error of 17.2% across nine applications used in production bioinformatics workflows. Finally, we evaluate an experiment design approach to explore the trade-off between the cost of profiling and the accuracy of our models. Using this approach, with no prior knowledge, we show that using 4 selectively chosen experiments we can achieve performance within 30% of a model trained using all instance types.

Cloud profiling↗