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At least 1,081 records · Page 60

Characterizing the Oscillatory Properties of Bulk Electric Systems

This paper presents a process for characterizing the oscillatory dynamics of a large bulk power system. As a demonstration, the process is applied to the Western Interconnection of North America. Several complementary analysis approaches, both new and existing, are employed to provide a comprehensive understanding of the oscillatory properties of the system. Established modal analysis techniques based on ringdown and mode-meter algorithms are utilized. In addition, we derive and apply methods based on spectral correlation analysis to identify modal frequencies, distinguish between modes that are closely spaced in frequency, and determine locations at which the modes are observable. Critical interarea modes are identified and characterized using actual-system synchrophasor measurements taken over several years of operation in concert with industry-standard simulation models. This includes 145 hours of PMU data and two planning base cases.

42 ENGINEERING↗

Improving I/O-aware Workflow Scheduling via Data Flow Characterization and trade-off Analysis

The scientific computing paradigm has transitioned from compute-intensive to I/O-intensive and memory-intensive in the past decade, especially when data-driven science has become common practice. Numerous empirical I/O-aware scheduling optimizations have been developed by incorporating I/O capacity and bandwidth as constraints into scheduling. Unfortunately, there is a lack of data flow (I/O) characterization tool and an understanding of trade-offs between concurrency, locality, and I/O bandwidth. To bridge the gap, this work 1) presents a set of descriptors to characterize, organize, and visualize I/O profiles, including flow size, I/O bandwidth, and operation count, which group data flows by I/O types, tasks, and files; 2) proposes an I/O Roofline model-based trade-off analysis to find the optimal trade-off between flow operational intensity, concurrency, and flow performance. The I/O descriptors generate useful insights into complicated I/O behaviors, suggesting distinct concurrency, storage, and scheduling to be used by types, tasks, and files. The proposed trade-off analysis guides scheduling decisions that generate resource assignment with the best flow parallelism. We evaluate our I/O-aware scheduling methodology on a highly I/O-intensive workflow–1000 Genomes. The experimental results demonstrate speedups of up to 2.4× compared to the state-of-the- art methods.

Guo, Luanzheng [BATTELLE (PACIFIC NW LAB)]↗

Characterization and Analysis of Insulated Metal Substrate-Based SiC Power Module for Traction Application

This paper presents the electrical characterization and drive cycle--based thermal analysis of an insulated metal substrate (IMS)--based silicon carbide power module for high-power traction inverters. The substrate was constructed using a thin layer of polymer--ceramic blend dielectric material with a thick copper core to improve transient thermal performance. The cooling performance of this module has already been validated with promising results. In this paper, an experimental test bed was set up to evaluate the dynamic and static electrical performance of the designed module under a wide range of operating conditions. The characterization results were then used to develop a drive cycle--based thermal model to validate the performance compared to the traditional direct bonded copper--based power module. The results indicate that the IMS-based power module is a suitable solution for high-power traction applications.

Chowdhury, Shajjad↗

Characterizing Minimum Admissible Separation Distances in Heavy Duty Vehicle Platoons

Heavy duty (HD) vehicles platooning under highway operating conditions, have been projected to provide significant fuel economy (FE) gains based on aerodynamic drag reduction of the platooning vehicles. Realizing these benefits under real-world operating conditions have several challenges. This paper (the second as part of series) quantifies the minimum admissible separation distance between platooning vehicles in HD commercial vehicle (CV) applications. This distance must be set up such that no collision occurs with leading vehicles under all deceleration conditions. In this paper key interactions to characterize this distance are demonstrated. These include, road surface conditions, grade, inter-vehicle communication delays, air-brake lag time, vehicle speed, weight, and aerodynamic drag. It is seen that characterizing the vehicle and road dynamics will be critical in establishing required deceleration or stopping distances for each vehicle in a platoon. The results of the study also necessitate vehicle control systems that can dynamically adjust not only the separation distances but also the braking reference points based on road conditions. The narrative provides application decision personnel with a well-defined set of admissible separation distance impact factors to consider as part of their architecture selection process.

Sujan, Vivek↗

Frequency-domain Flexibility Characterization of Heterogeneous End-use Loads for Grid Services

Demand response, based on flexible loads, has been a novel and important proposal for smart grids. A population of small loads can be aggregated to provide ancillary services to the grid and generate value for the owners. In this paper, we investigate the flexibility of such populations from the frequency-domain perspective. We propose a methodology to characterize the frequency-dependent flexibility of heterogeneous populations of loads considering practical constraints. The resultant frequency-domain characterization can help flexible load aggregators assess the capability of contracted populations. System operators can also benefit from a better understanding of the capabilities of the service providers. The effectiveness of the methodology is demonstrated by simulations.

Wang, Dexin↗

Viscoelastic Material Characterization and Modeling of Photovoltaic Module Packaging Materials for Direct Finite-Element Method Input

Numerical tools, such as the finite-element method, are increasingly used to design and evaluate the photovoltaic (PV) modules, providing for the reduction of development time and improved performance and reliability. However, high-fidelity material models are necessary to accurately model the complex structural behavior of the involved packaging materials. A common simplification used in recent years is to model the polymer materials (i.e., encapsulant and backsheet) as linear elastic, which will lead to inaccurate results. Therefore, in this work, we present a thorough characterization of the time- and temperature-dependent mechanical response of predominant PV module encapsulant and backsheet materials. Based on this material characterization, we developed and experimentally validated generalized Maxwell models to describe each material's viscoelastic response. In addition, we included measurements of the coefficient of thermal expansion and presented all material models in such a fashion for direct input into commercial finite-element method modeling software.

FEM↗

Nanomechanical and Fluorescence Characterizations of Weathered PV Module Encapsulation

Nanoindentation and fluorescence spectroscopy provide spatially resolved mechanical and chemical characterization for degradation of poly (ethylene-co-vinyl acetate) (EVA) encapsulants aged under accelerated weathering and in field deployed photovoltaic (PV) modules. For the accelerated weathering tests, two different EVA formulations in the glass/EVA/glass-laminated coupons were exposed to ultraviolet (UV) light for 180 days at three different temperatures (40, 60, and 80 °C) and UV intensities (0.4, 2.2, and 2.9 W/m2/nm at 340 nm). The fluorescence spectra and moduli were uniform through the cross section of both EVA formulations at the lowest temperature and UV intensity; however, under the highest temperature and UV intensity, the UV irradiated sides showed lower moduli and higher fluorescence compared to the opposite sides. For EVA obtained from PV modules deployed 27 years in Sacramento, CA, USA, changes in fluorescence intensity across the thickness were relatively greater than changes in modulus. On the other hand, for EVA obtained from the same type of module stored in the shed for 27 years, no fluorescence was observed, and a uniform modulus was seen through the EVA layer, but with substantially higher values than those obtained from the field-deployed modules. The different extent of changes between laboratory and field exposures suggests the importance of considering effects of temperature and UV intensity for designing a reliable accelerated testing. This study uniquely demonstrates the capability of microscale characterization using nanoindentation and fluorescence microscopy to monitor mechanical and chemical degradation of EVA encapsulants in PV modules.

14 SOLAR ENERGY↗

Characterization of Field-Exposed Photovoltaic Modules Featuring Signs of Contact Degradation

Here, this work investigates several photovoltaic (PV) modules that have shown signs of metal contact corrosion due to field exposure in a hot and humid climate. This includes two multicrystalline silicon aluminum back surface field systems with 10 and 14 years of exposure and one monocrystalline silicon passivated emitter and rear cell system with four years of exposure. A comprehensive, multiscale characterization process is used to evaluate these PV modules in great detail. Current–voltage (I−V), Suns-V OC measurements, electroluminescence imaging, infrared imaging, and ultraviolet fluorescence imaging were performed, and locations of interest were cored and analyzed using cross-sectional scanning electron microscopy (SEM). A rigorous, quantitative analysis procedure for the cross-sectional SEM images is proposed and implemented. Careful characterization does reveal that some of these PV modules do indeed exhibit the same classic signs of acetic-acid-based corrosion of the glass frit that is present at the silver/silicon interface, which have been observed previously in PV modules exposed to damp heat in an environmental chamber.

14 SOLAR ENERGY↗

Characterization of Quantum Frequency Processors

Frequency-bin qubits possess unique synergies with wavelength-multiplexed lightwave communications, suggesting valuable opportunities for quantum networking with the existing fiber-optic infrastructure. Although the coherent manipulation of frequency-bin states requires highly controllable multi-spectral-mode interference, the quantum frequency processor (QFP) provides a scalable path for gate synthesis leveraging standard telecom components. Here, we summarize the state of the art in experimental QFP characterization. Distinguishing between physically motivated “open box” approaches that treat the QFP as a multiport interferometer, and “black box” approaches that view the QFP as a general quantum operation, we highlight the assumptions and results of multiple techniques, including quantum process tomography of a tunable beamsplitter—to our knowledge the first full process tomography of any frequency-bin operation. Our findings should inform future characterization efforts as the QFP increasingly moves beyond proof-of-principle tabletop demonstrations toward integrated devices and deployed quantum networking experiments.

42 ENGINEERING↗

Characterizing Machine Learning I/O Workloads on Leadership Scale HPC Systems

High performance computing (HPC) is no longer solely limited to traditional workloads such as simulation and modeling. With the increase in the popularity of machine learning (ML) and deep learning (DL) technologies, we are observing that an increasing number of HPC users are incorporating ML methods into their workflow and scientific discovery processes, across a wide spectrum of science domains such as biology, earth science, and physics. This gives rise to a diverse set of I/O patterns than the traditional checkpoint/restart-based HPC I/O behavior. The details of the I/O characteristics of such ML I/O workloads have not been studied extensively for large-scale leadership HPC systems. This paper aims to fill that gap by providing an in-depth analysis to gain an understanding of the I/O behavior of ML I/O workloads using darshan - an I/O characterization tool designed for lightweight tracing and profiling. We study the darshan logs of more than 23, 000 HPC ML I/O jobs over a time period of one year running on Summit - the second-fastest supercomputer in the world. This paper provides a systematic I/O characterization of ML I/O jobs running on a leadership scale supercomputer to understand how the I/O behavior differs across science domains and the scale of workloads, and analyze the usage of parallel file system and burst buffer by ML I/O workloads.

Paul, Arnab↗

Translating Material-Level Characterization of Carbon-Nanotube-Reinforced Composite Gridlines to Module-Level Degradation

Cell cracks in PV modules caused by poor handling during shipping and installation as well as from extreme weather events can lead to gradual or immediate power degradation. To directly address cell-crack-induced degradation, we have formulated a carbon nanotube additive for commercial screen printed silver pastes. We have shown in previous work that these metal matrix composites have little to no effect on the cell's efficiency while enhancing the metallization's fracture toughness and electrical gap-bridging capability. In this work, we focus on translating materials level characterization techniques to module level degradation. We found that we get conflicting results from two different methods of measuring the metallization's ability to electrically bridge gaps in cracked solar cells. Mini-module stress testing is currently underway to determine which materials characterization correlates well with the min-module degradation characteristics.

carbon nanotubes↗

In-Situ Characterization Tools for Evaluating Radiation Tolerance and Elemental Migration in Perovskites

This paper discusses the in-situ characterization tools designed to assess radiation tolerance and elemental migration in perovskite materials. With the increasing use of perovskites in various technological applications, understanding their response to radiation exposure is paramount. Ion Beam Induced Charge (IBIC) emerges as a powerful tool for investigating the radiation tolerance of perovskites at the microscale. By employing focused ion beams, IBIC allows for the spatial mapping of charge carriers, offering insights into the material's electronic response to radiation-induced defects. This technique enables researchers to pinpoint areas of enhanced or suppressed charge collection, providing valuable information on the perovskite's intrinsic properties under irradiation. Rutherford Backscattering Spectrometry (RBS) complements the study by offering a quantitative analysis of elemental migration in perovskite materials. Through the precise measurement of backscattered ions, RBS provides a detailed understanding of the elemental composition and distribution within the perovskite lattice after radiation exposure. The integration of IBIC and RBS techniques in in-situ experiments enhances the comprehensive characterization of radiation effects on perovskites.

ion beams↗

Scalable quantum processor noise characterization

Measurement fidelity matrices (MFMs) (also called error kernels) are a natural way to characterize state preparation and measurement errors in near-term quantum hardware. They can be employed in post processing to mitigate errors and substantially increase the effective accuracy of quantum hardware. However, the feasibility of using MFMs is currently limited as the experimental cost of determining the MFM for a device grows exponentially with the number of qubits. In this work we present a scalable way to construct approximate MFMs for many-qubit devices based on cumulant expansions. Our method can also be used to characterize various types of correlation error.

Hamilton, Kathleen↗

A Flang Plugin for Fortran Feature Characterization

As new compute systems are developed, there is still a need to compile and execute codes authored in Fortran on these leading edge systems. In order to achieve this, development of compilers that support the latest hardware is continuously under development. Though the specification of Fortran is extensive, it is helpful to compiler authors to be able to prioritize the development of key features in order to get certain codes deemed important, e.g., applications of interest to leadership computing facilities, executable on leading edge compute systems. Identifying key features though is largely done through querying software experts or users of the Fortran applications of interest, who then manually report what features are and are not present. This exercise can both time consuming and error prone. To automate this process, we present a compiler plugin to Flang, the Fortran frontend for LLVM. This plugin is a tool that operates on the parse tree representation generated by Flang and detects key features based on walking parse tree nodes that correspond to features of interest. We show the result of our tool on four applications, three of which were manually profiled by software experts. We show the discrepancies between our tool and the manual characterization of the three applications, as well as generate a characterization for an application not yet profiled. We intend to open-source our tool in order to invite the community to benefit from the tool and make contributions for other features.

Cabrera, Anthony [ORNL]↗

Preload Characterization of Short Models of MQXF the Nb 3 Sn Low-β Quadrupole for the Hi-Lumi LHC

MQXF is the Nb3Sn Low-β Quadrupole magnet that the HL-LHC project is planning to install in the LHC interaction regions in 2026 as part of an upgrade to increase the LHC inte-grated luminosity by about a factor of ten. The magnet will be fab-ricated in two different lengths: 4.2 m for MQXFA, built in the US by the Accelerator Upgrade Project (AUP), and 7.15 m for MQXFB, fabricated by CERN. In order to qualify the magnet de-sign and characterize its performance with different conductors, cable geometries and pre-load configuration, five short model magnets, called MQXFS, were fabricated, assembled and tested. The latest model, MQXFS6, uses a new powder-in-tube (PIT) su-perconducting wire, featuring a bundle barrier surrounding the filaments. The coil and the support structure were equipped with strain gauges and optical fibres to monitor strain during assembly, cool-down and excitation. In this paper we further develop the conventional azimuthal preload analysis and introduce a new set of tools for MQXF coil pack characterization which we use to an-alyse the behaviour of MQXFS6 room temperature preload and to reanalyse all the short models tested at CERN. A comparison is made between all the studied magnets revealing new characteriz-ing preload parameters.

43 PARTICLE ACCELERATORS↗

Characterization of Amplification Properties of the Superconducting-Ferromagnetic Transistor

Here we report on the measurement results of the superconducting-ferromagnetic transistors (SFTs). The devices were made at Northwestern University and Hypres (SeeQC), Inc. (Nevirkovets et al., 2014; 2015). SFT is a multiterminal device with the SIS'FIFS (or SFIFS'IS) structure (where S, I, and F denote a superconductor, an insulator, and a ferromagnetic material, respectively) exploiting intense quasiparticle injection in order to modify the nonlinear I-V curve of a superconducting tunnel junction. SFT is capable of providing voltage, current, and power amplification while having good input/output isolation. We characterized the devices using different measurement techniques. We measured S parameters of the single- and double-acceptor devices at frequencies up to 5 MHz. Importantly, we confirmed that the isolation between the input and output of the device is quite good. However, the techniques typically employed to characterize semiconductor devices do not allow for revealing the full potential of our low-resistive SFT devices, especially those having two acceptors. In the latter case, we also tested the devices using the battery-powered current sources with floating grounds. Analyzing double-acceptor I-V curves recorded at different levels of injection currents, for an optimal load, we deduced a small-signal voltage gain of 33 and a power gain of 2.4. We suggest that further improvement of the SFT device parameters is possible in optimized devices, so that the device potentially may serve as a preamplifier for readout of output signals of cryogenic detectors and be useful as an element of other superconductor-based circuits. In addition, we used scanning transmission electron microscopy to identify some problems in the fabrication of the devices without any planarization.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Characterization of a Small-Scale Prototype Detector With Wide Dynamic Range for Time-Resolved High-Energy X-Ray Applications

We present characterization measurements of a fast-framing, wide-dynamic-range x-ray area detector intended for high-energy applications (≥20 keV photons). The MM-PAD-2.1 combines an integrating pixel front-end with a charge-removal mechanism to extend the maximum measurable signal to > 107 20-keV ph/pixel/frame. The charge-removal mechanism is dead-time-less (i.e., incoming signal continues to be integrated by the front-end while charge removal is taking place) up to an incoming photon rate of > 109 20-keV ph/pix/s. A 16×16 pixel prototype was fabricated and characterized using both laboratory and synchrotron x-ray sources, allowing for measurements from single-photon to high-flux conditions. High-flux measurements show linear performance up to ~1010 ph/s in a spot spanning approximately 2×1.5 pixels (FWHM). The onset of temporary radiation damage in the detector was observed during these measurements, manifesting as failure of the digital readout from the detector. These effects were seen to reverse after annealing at 30° C for several hours.

47 OTHER INSTRUMENTATION↗

Topological Characterization and Uncertainty Visualization of Atmospheric Rivers

Atmospheric rivers (ARs) are long, narrow regions of water vapor in the Earth's atmosphere that transport heat and moisture from the tropics to the mid-latitudes. ARs are often associated with extreme weather events in North America and contribute significantly to water supply and flood risk. However, characterizing ARs has been a major challenge due to the lack of a universal definition and their structural variations. Existing AR detection tools (ARDTs) produce distinct AR boundaries for the same event, making the risk assessment of ARs a difficult task. Understanding these uncertainties is crucial to improving the predictability of AR impacts, including their landfall areas and associated precipitation, which could cause catastrophic flooding and landslides over the coastal regions. In this work, we develop an uncertainty visualization framework that captures boundary and interior uncertainties, i.e., structural variations, of an ensemble of ARs that arise from a set of ARDTs. We first provide a statistical overview of the AR boundaries using the contour boxplots of Whitaker et al. that highlight the structural variations of AR boundaries based on their nesting relationships. We then introduce the topological skeletons of ARs based on Morse complexes that characterize the interior variation of an ensemble of ARs. We propose an uncertainty visualization of these topological skeletons, inspired by MetroSets of Jacobson et al. that emphasizes the agreements and disagreements across the ensemble members. Through case studies and expert feedback, we demonstrate that the two approaches complement each other, and together they could facilitate an effective comparative analysis process and provide a more confident outlook on an AR's shape, area, and onshore impact.

54 ENVIRONMENTAL SCIENCES↗