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This work was conducted under PLN-4560, “GTRI Mechanical Property Characterization Test Plan,” which contains additional requirements and instructions for testing. The following report presents details of the testing system, testing methods, and mechanical properties determined from the test data.
Imaging of tissue elastic properties is a relatively new and powerful approach to one of the oldest and most important diagnostic tools. Imaging of shear wave speed with ultrasound is has been added to most high-end ultrasound systems. Understanding this exciting imaging mode aiding its most effective use in medicine can be a rewarding effort for medical physicists and other medical imaging and treatment professionals. Assuring consistent, quantitative measurements across the many ultrasound systems in a typical imaging department will constitute a major step toward realizing the great potential of this technique and other quantitative imaging. This session will target these two goals with two presentations. A. Basics and Current Implementations of Ultrasound Imaging of Shear Wave Speed and Elasticity - Shigao Chen, Ph.D. Learning objectives-To understand: Introduction: Importance of tissue elasticity measurement Strain vs. shear wave elastography (SWE), beneficial features of SWE The link between shear wave speed and material properties, influence of viscosity Generation of shear waves External vibration (Fibroscan) ultrasound radiation force Point push Supersonic push (Aixplorer) Comb push (GE Logiq E9) Detection of shear waves Motion detection from pulse-echo ultrasound Importance of frame rate for shear wave imaging Plane wave imaging detection How to achieve high effective frame rate using line-by-line scanners Shear wave speed calculation Time to peak Random sample consensus (RANSAC) Cross correlation Sources of bias and variation in SWE Tissue viscosity Transducer compression or internal pressure of organ Reflection of shear waves at boundaries B. Elasticity Imaging System Biomarker Qualification and User Testing of Systems – Brian Garra, M.D. Learning objectives-To understand: Goals Review the need for quantitative medical imaging Provide examples of quantitative imaging biomarkers Acquaint the participant with the purpose of the RSNA Quantitative Imaging Biomarker Alliance and the need for such an organization Review the QIBA process for creating a quantitative biomarker Summarize steps needed to verify adherence of site, operators, and imaging systems to a QIBA profile Underlying Premise and Assumptions Objective, quantifiable results are needed to enhance the value of diagnostic imaging in clinical practice Reasons for quantification Evidence based medicine requires objective, not subjective observer data Computerized decision support tools (eg CAD) generally require quantitative input. Quantitative, reproducible measures are more easily used to develop personalized molecular medical diagnostic and treatment systems What is quantitative imaging? Definition from Imaging Metrology Workshop The Quantitative Imaging Biomarker Alliance Formation 2008 Mission Structure Example Imaging Biomarkers Being Explored Biomarker Selection Groundwork Draft Protocol for imaging and data evaluation QIBA Profile Drafting Equipment and Site Validation Technical Clinical Site and Equipment QA and Compliance Checking Ultrasound Elasticity Estimation Biomarker US Elasticity Estimation Background Current Status and Problems Biomarker Selection-process and outcome US SWS for Liver Fibrosis Biomarker Work Groundwork Literature search and analysis results Phase I phantom testing-Elastic phantoms Phase II phantom testing-Viscoelastic phantoms Digital Simulated Data Protocol and Profile Drafting Protocol: based on UPICT and existing literature and standards bodies protocols Profile-Current claims, Manufacturer specific appendices What comes after the profile Profile Validation Technical validation Clinical validation QA and Compliance Possible approaches Site Operator testing Site protocol re-evaluation Imaging system Manufacturer testing and attestation User acceptance testing and periodic QA Phantom Tests Digital Phantom Based Testing Standard QA Testing Remediation Schemes Profile Evolution Towards additional applications Towards higher accuracy and precision Supported in part by NIH contract HHSN268201300071C from NIBIB. Collaboration with GE Global Research, no personal support.; S. Chen, Some technologies described in this presentation have been licensed. Mayo Clinic and Dr. Chen have financial interests these technologies.
This paper presents a networked hardware-in-the-loop (HIL) simulation system for modeling large-scale power systems. Researchers have developed many HIL test systems for power systems in recent years. Those test systems can model both microsecond-level dynamic responses of power electronic systems and millisecond-level transients of transmission and distribution grids. By integrating individual HIL test systems into a network of HIL test systems, we can create large-scale power grid digital twins with flexible structures at required modeling resolution that fits for a wide range of system operating conditions. This will not only significantly reduce the need for field tests when developing new technologies but also greatly shorten the model development cycle. In this paper, we present a networked OPAL-RT based HIL test system for developing transmission-distribution coordinative Volt-VAr regulation technologies as an example to illustrate system setups, communication requirements among different HIL simulation systems, and system connection mechanisms. Impacts of communication delays, information exchange cycles, and computing delays are illustrated. Simulation results show that the performance of a networked HIL test system is satisfactory.
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This paper presents a network hardware-in-the-loop (HIL) simulation system for modeling large-scale power systems. Researchers have developed many HIL test systems for power systems in recent years. Those test systems can model both microsecond-level dynamic responses of power electronic systems and millisecond-level transients of transmission and distribution grids. By integrating individual HIL test systems into a network of HIL test systems, we can create large-scale power grid digital twins with flexible structures at required modeling resolution that fits for a wide range of system operating conditions. This will not only significantly reduce the need for field tests when developing new technologies but also greatly shorten the model development cycle. In this paper, we present a networked OPAL-RT based HIL test system for developing transmission-distribution coordinative Volt-VAR regulation technologies as an example to illustrate system setups, communication requirements among different HIL test systems, and connection mechanisms. Impacts of communication delays, information exchange cycles, and computing delays are illustrated. Simulation results show that the performance of a networked HIL test system is satisfactory.
A testing system for testing a missile component having a sense axis includes a centrifuge, a support arm, an orientation assembly, and a controller. The centrifuge rotates the orientation assembly about a vertical axis in a substantially horizontal plane. The orientation assembly includes a first motor, a first gimbal, and a gimballed support. The first motor has a first rotatable shaft defining a first gimbal axis. The first gimbal is coupled with the first rotatable shaft to rotate about the first gimbal axis while the centrifuge rotates the orientation assembly about the vertical axis such that missile component is simultaneously rotated about both the vertical axis and the first gimbal axis to simulate a missile launch of the missile component. The gimballed support is coupled with the first gimbal for supporting the missile component such that the sense axis of the missile component is not parallel to the substantially horizontal plane. The orientation assembly may also include a second gimbal that is rotated about a second gimbals axis by a second motor.
A testing system for testing a missile component having a sense axis includes a centrifuge, a support arm, an orientation assembly, and a controller. The centrifuge rotates the orientation assembly about a vertical axis in a substantially horizontal plane. The orientation assembly includes a first motor, a first gimbal, and a gimballed support. The first motor has a first rotatable shaft defining a first gimbal axis. The first gimbal is coupled with the first rotatable shaft to rotate about the first gimbal axis while the centrifuge rotates the orientation assembly about the vertical axis such that missile component is simultaneously rotated about both the vertical axis and the first gimbal axis to simulate a missile launch of the missile component. The gimballed support is coupled with the first gimbal for supporting the missile component such that the sense axis of the missile component is not parallel to the substantially horizontal plane. The orientation assembly may also include a second gimbal that is rotated about a second gimbals axis by a second motor.
Disclosed herein are systems, devices and methods for creep testing selected materials within a high-temperature molten salt environment. Exemplary creep testing systems include a load train for holding a test specimen under a load within a heated inert gas vessel. An extensometry system can be included to measure elongation of the test specimen while under load. The extensometry system can include fixed members and axially translating member that move along with the elongation of the test specimen, and the system can include a sensor to measure the relative axial motion between such components to measure elongation of the test specimen over time. The test specimen can include a cylindrical gage portion having an internal void filled with a molten salt during creep testing to simulate the corrosive effect of the molten salt on the specimen material during testing.
A two-terminator RF adapter for background noise measurement in a test environment comprises a system test port comprising a system test port termination and a system test port connector to connect to a system under test; and a data acquisition port, comprising a data acquisition port termination and a data acquisition port connector to connect to a data acquisition system.
The explosion of new ideas for distributed energy resources and advanced distribution grid algorithms faces a critical challenge: how can one effectively develop and evaluate these breakthrough technologies without an adequate set of power systems network, load, and resource data to test against? Today's open test systems, particularly for distribution systems, are tiny and incomplete; and though with enough effort, obtaining utility data under NDA can overcome scale challenges, the results can't be openly shared, preventing fair comparison. The Smart-DS project-led by NREL in partnership with MIT, Universidad Pontificia Comillas, CYME, and EDD-is working to overcome this gap by generating multiple large-scale, open, synthetic, distribution systems that are realistic but not real. These datasets go beyond a single medium voltage feeder (~1000 customers) to cover entire metropolitan areas and their surrounds with up to hundreds of feeders and millions of customers, complete with low voltage customer connections and high voltage sub-transmission. This scale allows adequate testing and analysis of not just local controls and advanced DERs, but also multiple substation interactions, optimized switching, full-scale distribution-OPF, and more. To accompany these datasets, the project is also building a rich set of scenario generation tools that can be used for distribution and transmission systems alike. This includes automated, spatially aware access to world-class, high-resolution solar, wind, and weather data: forward looking generation mixes: and highly configurable DER, load, climate, outage, control-scheme, and other scenarios. We will also highlight DiTTo, an exciting open-source, multi-way distribution dataset transformation tool for many-to-many format translation with scenario, merge/split, and many other manipulation capabilities.
NREL's advanced distribution management system (ADMS) test bed is a vendor-neutral resource for evaluating existing and future ADMS functionalities in a realistic laboratory setting. The test bed uses Opal-RT's ePHASORsim and eMEGAsim products interfaced with an OpenDSS power system simulation through the HELICS cosimulation platform developed by multiple DOE laboratories; and with power and controller hardware. NREL will present an overview of the test bed and the technologies included within it, and also some examples of how the test bed has been used to address industry challenges.
Comparing effects of inhaled particles across rodent test systems and between rodent test systems and humans is a key obstacle to the interpretation of common toxicological test systems for human risk assessment. These comparisons, correlation with effects and prediction of effects, are best conducted using measures of tissue dose in the respiratory tract. Differences in lung geometry, physiology and the characteristics of ventilation can give rise to differences in the regional deposition of particles in the lung in these species. Differences in regional lung tissue doses cannot currently be measured experimentally. Regional lung tissue dosimetry can however be predicted using models developed for rats, monkeys, and humans. A computational model of particle respiratory tract deposition and clearance was developed for BALB/c and B6C3F1 mice, creating a cross-species suite of available models for particle dosimetry in the lung. Airflow and particle transport equations were solved throughout the respiratory tract of these mice strains to obtain temporal and spatial concentration of inhaled particles from which deposition fractions were determined. Particle inhalability (Inhalable fraction, IF) and upper respiratory tract (URT) deposition were directly related to particle diffusive and inertial properties. Measurements of the retained mass at several post-exposure times following exposure to iron oxide nanoparticles, micro- and nanoscale C60 fullerene, and nanoscale silver particles were used to calibrate and verify model predictions of total lung dose. Interstrain (mice) and interspecies (mouse, rat and human) differences in particle inhalability, fractional deposition and tissue dosimetry are described for ultrafine, fine and coarse particles.
This presentation provides a Smart-DS project overview and status update for the ARPA-e GRID DATA program meeting 2017, including distribution systems, models, and scenarios, as well as opportunities for GRID DATA collaborations.
A true stress testing system broadly comprising a force input machine, an imaging system, and a computer. The imaging system includes a light source for projecting a light beam at the specimen in a first direction and a camera positioned on an opposite side of the specimen for receiving portions of the light beam not blocked by the specimen such that a shadow image of the specimen is created via the camera. The computer may determine a minimum dimension of the specimen perpendicular to the first direction at a point in time over a plurality of points along the force axis via the shadow image of the specimen such that the processor accounts for changes in position of the minimum dimension along the specimen. A true stress of the specimen may then be determined according to the minimum dimension of the specimen perpendicular to the first direction at the point in time.
This presentation provides an overview of the National Renewable Energy Laboratory's advanced distribution management system test bed overview.
The proposed system is a test system that verifies the cables functionality in the expected environments defined in the ES. Verification methods include test, inspect, demonstrate, and analyze. Since we are defining the architecture for a test system we will focus on the customer expectations and requirements that will be satisfied or verified via testing
LLNL produced two lots of lyophilized LAMP assay containing cartridges, LNL022818 and LNL071818, for testing on Gen3 BioID systems. Thirty-nine cartridges were tested for lot LNL022818. All thirty-nine cartridges performed as expected regarding correct positive control (IPC) and negative control (NTC) reactions. Five different templates were tested on these 39 cartridges. The BioID system detected 79.1% true positives and 9.3% false positives along with 98.9% true negatives and 2.9% false negatives for cartridge lot LNL022818. Thirty cartridges were tested for lot LNL071818. It was noted early that due to a manufacturing issue with the cartridges related to incomplete sonic welding of the cartridge, a large fraction of lot LNL071818 cartridges leaked. Insufficient funds were available to replace this lot of cartridges. Twenty three of 30 cartridges performed as expected regarding correct positive control (IPC) and negative control (NTC) reactions. Four different templates were tested on these 30 cartridges. The BioID system detected 77% true positives and 23% false positives along with 96.9% true negatives and 3.1% false negatives for cartridge lot LNL071818. For all tested cartridges using DNA template, DNA was added at a final concentration of 25 pg/ul. All reactions contained positive control DNA at 2.5 pg/ul.
Due to the newly issued standards on distributed energy resources (DERs), the droop-based operation modes (Q-V and P-V droops) become a requirement for all the interconnected DER units at the distribution level. Voltage references of the DER units can be assigned to improve the operational behavior of the distribution system in the smart grid context. This paper presents a novel formulation for optimally setting voltage reference of the droop-based DER units in distribution systems with the objective of minimizing the system ohmic loss. This problem is formulated as a mixed-integer quadratic convex optimization problem in MATLAB and solved by the MATLAB and CPLEX solvers. The IEEE 123-bus system and the EPRI test system Ckt5 are used to validate the proposed formulation. The result obtained from the proposed formulation is compared with the result of the original problem solved by the heuristic method. Based on the numerical study results, system loss can be reduced after the proposed voltage reference assignment. The computational expense of the new formulation is also much lower than that of the heuristic approach.