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At least 307 records · Page 17

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DOE OSTI2014

Poster — Thur Eve — 15: Improvements in the stability of the tomotherapy imaging beam

Use of helical TomoTherapy based MVCT imaging for adaptive planning requires the image values (HU) to remain stable over the course of treatment. In the past, the image value stability was suboptimal, which required frequent change to the image value to density calibration curve to avoid dose errors on the order of 2–4%. The stability of the image values at our center was recently improved by stabilizing the dose rate of the machine (dose control servo) and performing daily MVCT calibration corrections. In this work, we quantify the stability of the image values over treatment time by comparing patient treatment image density derived using MVCT and KVCT. The analysis includes 1) MVCT - KVCT density difference histogram, 2) MVCT vs KVCT density spectrum, 3) multiple average profile density comparison and 4) density difference in homogeneous locations. Over two months, the imaging beam stability was compromised several times due to a combination of target wobbling, spectral calibration, target change and magnetron issues. The stability of the image values were analyzed over the same period. Results show that the impact on the patient dose calculation is 0.7% +− 0.6%.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-J-12: An Image-Guided Soft Robotic Patient Positioning System for Maskless Head-And-Neck Cancer Radiotherapy: A Proof-Of-Concept Study

Purpose: We propose a surface-image-guided soft robotic patient positioning system for maskless head-and-neck radiotherapy. The ultimate goal of this project is to utilize a soft robot to realize non-rigid patient positioning and real-time motion compensation. In this proof-of-concept study, we design a position-based visual servoing control system for an air-bladder-based soft robot and investigate its performance in controlling the flexion/extension cranial motion on a mannequin head phantom. Methods: The current system consists of Microsoft Kinect depth camera, an inflatable air bladder (IAB), pressured air source, pneumatic valve actuators, custom-built current regulators, and a National Instruments myRIO microcontroller. The performance of the designed system was evaluated on a mannequin head, with a ball joint fixed below its neck to simulate torso-induced head motion along flexion/extension direction. The IAB is placed beneath the mannequin head. The Kinect camera captures images of the mannequin head, extracts the face, and measures the position of the head relative to the camera. This distance is sent to the myRIO, which runs control algorithms and sends actuation commands to the valves, inflating and deflating the IAB to induce head motion. Results: For a step input, i.e. regulation of the head to a constant displacement, the maximum error was a 6% overshoot, which the system then reduces to 0% steady-state error. In this initial investigation, the settling time to reach the regulated position was approximately 8 seconds, with 2 seconds of delay between the command start of motion due to capacitance of the pneumatics, for a total of 10 seconds to regulate the error. Conclusion: The surface image-guided soft robotic patient positioning system can achieve accurate mannequin head flexion/extension motion. Given this promising initial Result, the extension of the current one-dimensional soft robot control to multiple IABs for non-rigid positioning control will be pursued.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-T-645: Qualification of a 2D Ionization Chamber Array for Beam Steering and Profile Measurement

Purpose: Establish a procedure for beam steering and profile measurement using a 2D ionization chamber array and show equivalence to a water scanning system. Methods: Multiple photon beams (30×30cm{sup 2} field) and electron beams (25×25cm{sup 2} cone) were steered in the radial and transverse directions using Sun Nuclear’s IC PROFILER (ICP). Solid water was added during steering to ensure measurements were beyond the buildup region. With steering complete, servos were zeroed and enabled. Photon profiles were collected in a 30×30cm{sup 2} field at dmax and 2.9 cm depth for flattened and FFF beams respectively. Electron profiles were collected with a 25×25cm{sup 2} cone and effective depth (solid water + 0.9 cm intrinsic buildup) as follows: 0.9 cm (6e), 1.9 cm (9e), 2.9 cm (12e, 16e, 20e). Profiles of the same energy, field size and depth were measured in water with Sun Nuclear’s 3D SCANNER (3DS). Profiles were re-measured using the ICP after the in-water scans. Profiles measured using the ICP and 3DS were compared by (a) examining the differences in Varian’s “Point Difference Symmetry” metric, (b) visual inspection of the overlaid profile shapes and (c) calculation of point-by-point differences. Results: Comparing ICP measurements before and after water scanning showed very good agreement indicating good stability of the linac and measurement system. Comparing ICP Measurements to water phantom measurements using Varian’s symmetry metric showed agreement within 0.5% for all beams. The average magnitude of the agreement was within 0.2%. Comparing ICP Measurements to water phantom measurements using point-by-point difference showed agreement within 0.5% inside of 80% area of the field width. Conclusion: Profile agreement to within 0.5% was observed between ICP and 3DS after steering multiple energies with the ICP. This indicates that the ICP may be used for steering electron beams, and both flattened and FFF photon beams. Song Gao: Sun Nuclear’s invitation of speak and financial support for attending the 8th QA & Dosimetry Symposium in Orlando, Florida 2015.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2018

KiteFAST

KiteFAST is a simulator for airborne wind energy systems based on NREL's OpenFAST whole turbine simulator. KiteFAST is a multi-physics engineering tool for modeling the coupled aero-servo-elastic dynamics of airborne wind energy systems i.e. a wind-energy kite. The engineering model is like NREL's OpenFAST tool for land-based or offshore horizontal-axis wind turbines atop towers, in that KiteFAST models the important physical phenomena and system couplings, including the wind/aerodynamic excitation and full-system dynamics response (fuselage, wings, tail, stabilizers, pylons, nacelles, rotors, tether, ground station, control surfaces, and controller) under both normal (for fatigue) and extreme (for ultimate) loading conditions. The configuration of the wind-energy kite is assumed to have one fuselage; two wings (starboard, port) attached to the fuselage; one vertical stabilizer attached the fuselage; two horizontal stabilizers (starboard, port) attached to the vertical stabilizer; one or more pylons per wing, each with two rotors (top, bottom); one ground-station-fixed tether with a bridal connection to the wings, and nominal circular motion of the energy kite in the port (left) direction. The general configuration of the control surfaces assumes one or more flaps per wing, two rudders on the vertical stabilizer, two elevators per horizontal stabilizer, and rotors with variable speed and rotor-collective blade pitch. KiteFAST was developed from (1) the existing MBDyn open-source multi-body dynamics code; (2) the existing/modified OpenFAST modules NWTC Subroutine Library, AirfoilInfo, InflowWind, MoorDyn, and TurbSim; and (3) new source code for a) generating MBDyn models of the energy kite (KiteMBDyn Preprocessor), b) computing quasi-steady energy-kite aerodynamics (KiteAeroDyn, KiteAeroDyn Driver, KiteVSM, ActuatorDisk), c) interfacing an energy-kite controller, and d) coupling the various KiteFAST modules together (KiteFASTMBD).

Jonkman, Jason↗

DOE OSTI2022

RAFT (Response Amplitudes of Floating Turbines) [SWR-22-40]

RAFT is a Python code for frequency-domain analysis of floating wind turbines. RAFT works in the frequency domain to provide efficient computations of a floating wind system's response spectra accounting for platform hydrodynamics, quasi-static mooring reactions, rotor aerodynamics, and linearized control. It can be used to calculate response amplitude operators (RAO's), power spectral densities, mean/static properties, and response metrics based on mean and root-mean-square values of each output. RAFT serves as the Level 1 model in the WEIS (Wind Energy with Integrated Servo-control) toolkit, and features an OpenMDAO wrapper.

Hall, Matthew↗

DOE OSTI2017

FAST.Farm [SWR-17-56]

FAST.Farm is a multi-physics engineering tool for predicting the performance and loads of wind turbines within a wind farm. FAST.Farm uses FAST to solve the aero-hydro-servo-elastic dynamics of an individual turbine, but adds additional physics for wind farm-wide ambient wind in the atmospheric boundary layer; a wind-farm super controller; and wake deficits, advection, deflection, meandering, and merging. FAST.Farm is based on the principles of the Dynamic Wake Meandering (DWM) model—including passive tracer modeling of wake meandering—but addresses many of the limitations of previous DWM limitations. FAST.Farm maintains a low computational cost to the often highly iterative and probabilistic design process. Applications of FAST.Farm include reducing wind farm underperformance and loads uncertainty, developing wind farm controls to enhance operation, optimizing wind farm siting and topology, and innovating the design of wind turbines for the wind-farm environment.

Jonkman, Jason↗

DOE OSTI2024

Offshore Wind ENergy Simulation Toolkit (OWENS)

SAND2021-2751 O The Offshore Wind ENergy Simulation Toolkit (OWENS) is a collection of aerodynamic, structural, hydrodynamic, drivetrain, controls, composite structure and mesh preprocessing, and data postprocessing. OWENS is primarily an ontology, or glue code, pulling together many open-source and Sandia-developed libraries to model the aero-servo-hydro-elastic physics of wind and marine energy turbines. The toolkit’s intended use is for arbitrary aeroelastic rotor configurations analysis including vertical-axis wind turbines, horizontal-axis wind turbines, and analogous marine energy applications for fixed-bottom and floating configurations. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525

Owens, Brian↗

DOE OSTI2021

Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a non-overlapping window from the original acoustic data. The first column is the time of the window. The second and third columns are the mean and the variance of the acoustic data in this window, respectively. The 4th-11th column is the the power spectrum density ranging from low to high frequency. And the last column is the corresponding label (shear stress level). The name of the file means which driving velocity the sequence is generated from. Data were generated from laboratory friction experiments conducted with a biaxial shear apparatus. Experiments were conducted in the double direct shear configuration in which two fault zones are sheared between three rigid forcing blocks. Our samples consisted of two 5-mm-thick layers of simulated fault gouge with a nominal contact area of 10 by 10 cm^2. Gouge material consisted of soda-lime glass beads with initial particle size between 105 and 149 micrometers. Prior to shearing, we impose a constant fault normal stress of 2 MPa using a servo-controlled load-feedback mechanism and allow the sample to compact. Once the sample has reached a constant layer thickness, the central block is driven down at constant rate of 10 micrometers per second. In tandem, we collect an AE signal continuously at 4 MHz from a piezoceramic sensor embedded in a steel forcing block about 22 mm from the gouge layer The data from this experiment can be used with the deep learning algorithm to train it for future fault property prediction.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Utah FORGE: Fault Shear Reactivation Experimental Data for Fluid Injection-Rate Controls on Seismic Moment

Included are experimental data recorded from shear experiments that specifically explore the link between fluid-injection rate and seismic moment resulting from shear reactivation of laboratory faults. Raw mechanical data from three experiments are included alongside corresponding MATLAB scripts that import and plot the data, as well as use it to calculate shear and normal stress. Experiments are performed on 2.5-3 inch long granitoid cores from the Utah FORGE EGS demonstration site, containing a single inclined fracture with small-scale roughness added to the fracture surface. The raw data included here were recorded from an aluminum triaxial pressure vessel (TEMCO) configured with three independent servo-controlled pumps, with distilled water used as the working fluid. The pumps control confining pressure, upstream pore pressure, and axial pressure, with each pump connected to a LabView interface to record applied pressures, cumulative injected water volumes, and pump flow rates. The downstream outlet from the fracture is closed to allow pressurization, which is measured by an external pressure transducer. A linear variable differential transformer (LVDT) attached to the axial piston measures axial displacement, from which we calculate shear displacement along the fracture. Additionally, P-wave transducers are used to record acoustic signatures, where acoustic emission events and maximum amplitudes are compared against seismic moment and shear slip velocity. Fluid injection rates range between 0.05 mL/min, 0.25 mL/min, and 0.75 mL/min for each experiment. Along-fault pressure distributions are progressively less uniform as injection rates increase, representing a switch from steady-state to transient conditions. Triggered shear displacement is used as a proxy for seismic moment and is indexed against cumulative injection volume and rate. Each experiment is performed under constant shear stress conditions, and the sample is fully saturated with DI water. Axial and confining stresses are applied to 3 MPa through pressure-stepping in 500 kPa increments. The pore pressure is held constant at 200 kPa prior to initiating the experiment, and initial axial displacement is recorded. The axial stress is then increased to initiate shear mobilization during the loading phase (run-in) until a peak steady state is achieved. The initial shear stress is reduced to approximately 80% of the peak shear stress by decreasing the axial stress, then held constant for the duration of each experiment.

15 GEOTHERMAL ENERGY↗

DOE OSTI2014

Definition of the Semisubmersible Floating System for Phase II of OC4

Phase II of the Offshore Code Comparison Collaboration Continuation (OC4) project involved modeling of a semisubmersible floating offshore wind system as shown below. This report documents the specifications of the floating system, which were needed by the OC4 participants for building aero-hydro-servo-elastic models.

17 WIND ENERGY↗

DOE OSTI2015

SubDyn User's Guide and Theory Manual

SubDyn is a time-domain structural-dynamics module for multimember fixed-bottom substructures created by the National Renewable Energy Laboratory (NREL) through U.S. Department of Energy Wind and Water Power Program support. The module has been coupled into the FAST aero-hydro-servo-elastic computer-aided engineering (CAE) tool. Substructure types supported by SubDyn include monopiles, tripods, jackets, and other lattice-type substructures common for offshore wind installations in shallow and transitional water depths. SubDyn can also be used to model lattice support structures for land-based wind turbines. This document is organized as follows. Section 1 details how to obtain the SubDyn and FAST software archives and run both the stand-alone SubDyn or SubDyn coupled to FAST. Section 2 describes the SubDyn input files. Section 3 discusses the output files generated by SubDyn; these include echo files, a summary file, and the results file. Section 4 provides modeling guidance when using SubDyn. The SubDyn theory is covered in Section 5. Section 6 outlines future work, and Section 7 contains a list of references. Example input files are shown in Appendixes A and B. A summary of available output channels are found in Appendix C. Instructions for compiling the stand-alone SubDyn program are detailed in Appendix D. Appendix E tracks the major changes we have made to SubDyn for each public release.

17 WIND ENERGY↗

DOE OSTI2016

Final Report on Developing Microstructure-Property Correlation in Reactor Materials using in situ High-Energy X-rays

This report provides a summary of research activities on understanding microstructure – property correlation in reactor materials using in situ high-energy X-rays. The report is a Level 2 deliverable in FY16 (M2CA-13-IL-AN_-0403-0111), under the Work Package CA-13-IL-AN_- 0403-01, “Microstructure-Property Correlation in Reactor Materials using in situ High Energy Xrays”, as part of the DOE-NE NEET Program. The objective of this project is to demonstrate the application of in situ high energy X-ray measurements of nuclear reactor materials under thermal-mechanical loading, to understand their microstructure-property relationships. The gained knowledge is expected to enable accurate predictions of mechanical performance of these materials subjected to extreme environments, and to further facilitate development of advanced reactor materials. The report provides detailed description of the in situ X-ray Radiated Materials (iRadMat) apparatus designed to interface with a servo-hydraulic load frame at beamline 1-ID at the Advanced Photon Source. This new capability allows in situ studies of radioactive specimens subject to thermal-mechanical loading using a suite of high-energy X-ray scattering and imaging techniques. We conducted several case studies using the iRadMat to obtain a better understanding of deformation and fracture mechanisms of irradiated materials. In situ X-ray measurements on neutron-irradiated pure metal and model alloy and several representative reactor materials, e.g. pure Fe, Fe-9Cr model alloy, 316 SS, HT-UPS, and duplex cast austenitic stainless steels (CASS) CF-8 were performed under tensile loading at temperatures of 20-400°C in vacuum. A combination of wide-angle X-ray scattering (WAXS), small-angle X-ray scattering (SAXS), and imaging techniques were utilized to interrogate microstructure at different length scales in real time while the specimen was subject to thermal-mechanical loading. In addition, in situ X-ray studies were complemented and benchmarked by ex situ characterization using advanced electron microscopy, atom probe tomography (APT) and micro/nano-indentation. The report presented in situ tensile test results on neutron-irradiated pure Fe, Fe-9Cr model alloy, 316 SS and CASS CF-8. These in situ experiments demonstrate the broad applications of the new capability in understanding several outstanding issues related to irradiated materials.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

1:50 Scale Testing of Three Floating Wind Turbines at MARIN and Numerical Model Validation Against Test Data

The primary goal of the basin model test program discussed herein is to properly scale and accurately capture physical data of the rigid body motions, accelerations and loads for different floating wind turbine platform technologies. The intended use for this data is for performing comparisons with predictions from various aero-hydro-servo-elastic floating wind turbine simulators for calibration and validation. Of particular interest is validating the floating offshore wind turbine simulation capabilities of NREL’s FAST open-source simulation tool. Once the validation process is complete, coupled simulators such as FAST can be used with a much greater degree of confidence in design processes for commercial development of floating offshore wind turbines. The test program subsequently described in this report was performed at MARIN (Maritime Research Institute Netherlands) in Wageningen, the Netherlands. The models considered consisted of the horizontal axis, NREL 5 MW Reference Wind Turbine (Jonkman et al., 2009) with a flexible tower affixed atop three distinct platforms: a tension leg platform (TLP), a spar-buoy modeled after the OC3 Hywind (Jonkman, 2010) and a semi-submersible. The three generic platform designs were intended to cover the spectrum of currently investigated concepts, each based on proven floating offshore structure technology. The models were tested under Froude scale wind and wave loads. The high-quality wind environments, unique to these tests, were realized in the offshore basin via a novel wind machine which exhibits negligible swirl and low turbulence intensity in the flow field. Recorded data from the floating wind turbine models included rotor torque and position, tower top and base forces and moments, mooring line tensions, six-axis platform motions and accelerations at key locations on the nacelle, tower, and platform. A large number of tests were performed ranging from simple free-decay tests to complex operating conditions with irregular sea states and dynamic winds.

17 WIND ENERGY↗

DOE OSTI2018

Uncertainty and Risk Assessment in the Design Process for Wind

This report summarizes the concepts and opinions that emerged from an initial study on the subject of uncertainty in wind design that included expert elicitation during a workshop held at the National Wind Technology Center at the National Renewable Energy Laboratory July 12-13, 2016. In this paper, five major categories of uncertainties are identified. The first category is associated with direct impacts on turbine loads, (i.e., the inflow including extreme events, aero-hydro-servo-elastic response, soil-structure inter- action, and load extrapolation). The second category encompasses material behavior and strength. Site suitability and due-diligence aspects pertain to the third category. Calibration of partial safety factors and optimal reliability levels make up the fourth one. And last but not least, is the category associated with uncertainties in computational modeling. The main sections of this paper follow this organization.

17 WIND ENERGY↗

DOE OSTI2018

Verification of Pilger Mill Performance

36 MATERIALS SCIENCE↗

DOE OSTI2018

Development of Integrated Biomimetic Framework with Intelligent Monitoring, Cognition and Decision Capabilities for Control of Advanced Energy Plants

The objective of the project entitled “Development of Integrated Biomimetic Framework with Intelligent Monitoring, Cognition, and Decision Capabilities for Control of Advanced Energy Plants” is to develop algorithms and methodologies for designing a biomimetic control system for optimal control of advanced energy plants. Traditionally, control systems have been designed based on assumed a priori knowledge of the process system. A number of variables are manipulated to accomplish disturbance rejection and/or servo control performance. While it is possible to adapt the process model based on available data, the control structure and the controllers rarely change. Thus, the knowledge learnt during process operation is lost or remains underutilized. In addition, the business models of today, especially for power plants, are rapidly changing. The power plant operators are being pushed to the edge due to the penetration of renewable energy derived power to the grid, the demand for high efficiency, ever-tightening environmental regulations, and requirements for increased plant availability. Thus, power plant operations need to be agile and should adapt quickly to the dynamic requirements. A number of characteristics distinguish biological systems from the traditional process control systems. Self-organization, distributed intelligence, adaptability, intelligent monitoring, cognition, and decision capabilities are some of the powerful characteristics of the biological world that can be effectively utilized in process control. At the top, the central nervous system (CNS) integrates the information from and coordinates the activities of all parts of the bodies (for bilaterian animals). Inspired by these distinct characteristics, this project seeks to develop methodologies and algorithms to accomplish: (i) self-organization of the control structure for maximizing the plant’s operating profit by mimicking the function of the cortical areas in the human brain, (ii) distributed and adaptive controllers that mimic the rule of pursuit present in ants, (iii) intelligent monitoring of the controllers powered with cognition and decision capabilities that mimic the artificial immune systems, and (iv) seamless coordination and integration in the control system that mimics the CNS.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2019

Processing and Characterization of Pilgered 14YWT Tubing

The Nuclear Technology Research & Development program of the Office of Nuclear Energy has implemented a program to develop an advanced reactor cladding fabrication capability by using the extrusion and pilgering processes. Oxide-dispersion strengthened (ODS) steels are a promising class of advanced materials, but processes need to be developed to produce tubing for these advanced alloys. The Pacific Northwest National Laboratory (PNNL) has converted its rolling mill into a lab-scale pilger mill, which required designing pilger tooling and rolls that contained multiple pilgering grooves and designing a servo-driven feed mechanism. Upon assembling and checking out the system, pilgering runs on stainless steel, MA956, and 14YWT iron (Fe)-based alloys were completed. Once the feed rates and rotation rates were established, the reduction schedules were determined. This report will focus on the processing and characterization of pilgered 14YWT thin-wall tubing that was processed from an extruded thick-wall tube using the PNNL laboratory pilger rolling mill. Characterization included optical metallography of the pilgered tube and microhardness of the processed material. The 14YWT characterization is being done in cooperation with researchers at Los Alamos National Laboratory (LANL), with LANL performing mechanical property testing of pilgered 14YWT and 14YWT tubes produced by hydrostatic extrusion at Case Western Reserve University (CWRU).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Reinforcement Learning for Small Autonomous Ground Vehicle

Microcontroller: • ESP-WROOM 32 control board • Wi-Fi 802.11 b/g/n Actuators: • 2 Futaba S3003 Servos • Commanded angles of 0 to 180 • Effectors • Piano wire “legs” and “feet” • Current, Voltage, Power Sensor: • Adafruit INA260 • Range Finder (1D LiDAR) Sensor: • SparkFun VL53L1X • Power Supply: • Battery powered, Adafruit UN3480 Li Ion Polymer 1200 mAh • Adafruit JST 2 PH power breakout board • Body: • Additively manufactured frame

Wells, William Willie↗