Qu-antitative analysis of diffraction by liquids using a pink-spectrum X-ray source
Explore the source record for details and available documents.
Singh, Saransh↗
SEARCH · Engineering Papers
Results for “distributed simulation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Home Energy Management System for Price-Responsive Operation of Consumer Technologies Under an Export Rate
Not Available
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System
Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the powerflow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.
Dahale, Shweta↗
Thermal Analysis of a 50 kW Three-Phase Wireless Charging System
In this paper, the thermal analysis of a 50 kW three-phase wireless charging system (WCS) is presented. Addressing the thermal challenge is essential for designing a compact charging pad for 50 kW and higher-power WCS pads. Low thermal conductivity of the Litz wire and ferrite, and uneven distribution of the coil, and core loss cause the high temperature in the charging pads. In this paper, the loss distribution of a 50 kW three-phase WCS is investigated, and the temperature distribution is simulated using finite element analysis (FEA) considering the magnetic and non-magnetic materials of a charging pad. The thermal characteristics of an extremely high-power density 50 kW WCS prototype are tested experimentally for 10 minutes of operation. The simulation and experimental results show that the coil temperature increases to 65°C, the core temperature varies between 50°C to 150°C, and the packaging temperature increases to 65°C after 10 minutes of operation at rated 50 kW output power.
Mohammad, Mostak↗
EV charging site day-ahead load prediction in a synthetic environment for RL based grid-informed charging
Ensuring grid health in the face of increasing demand for power is an emerging challenge especially due to transportation electrification. A free market approach to influencing electric vehicle (EV) load through grid-informed hourly dynamic pricing is introduced in this work. The setting of charging price is done by a reinforcement learning (RL) agent that learns the complicated dynamics by interacting with a synthetic environment. This synthetic environment is a combination of distribution feeder simulation, EV charger user behavior dynamics, and EV charging simulation. A key module in this synthetic environment involves obtaining the day-ahead charging profile of EV charging stations based on real-world past data. The day-ahead prediction is also useful in other traditional optimizations related to EV charge scheduling. The proposed approach involves using EV charging data from two different past time horizons – one to determine the shape of the daily profile and the other to determine a scaling value to capture actual energy consumption. Real-world charging data over many years from the ACN charging network has been used to demonstrate the ability to predict the day-ahead profile with only charge session data. Both Python and MATLAB have been used for data cleaning, processing, analysis, and prediction.
Suryanarayana, Harish↗
ALTERNATIVE CONFIGURATION AND TIMING CONTROL FOR BEAM CHOPPING SYSTEM AT THE SNS LINAC
The Spallation Neutron Source (SNS) beam chopping system uses a segmented electrostatic lens in the Low Energy Beam Transport (LEBT) line to deflect the beam out of the Radio Frequency Quadrupole (RFQ) input aperture to create gaps in the 1 ms beam macro-pulse for extraction from the Ring, or fully displace the beam. The lens is split azimuthally into four quadrants which are pulsed independently by four bipolar high voltage pulse generators. The chopper timing control system creates trigger pulses to the pulse generators which deflect the beam sequentially to four positions on the chopper target. In the present chopper configuration, all four segments are powered simultaneously with a 1 MHz burst repetition rate within the macro-pulse. To improve chopping performance, faster switches and higher voltages are required. An alternative chopping system configuration which can meet this request has been proposed, where only two opposite segments are used at a time. This will facilitate pulse generator performance by reducing switching frequency and power dissipation in the high voltage switches while operating at increased voltages, and make beam deflection more effective, stable and reliable. The new chopping configuration requires changes in the LEBT timing control patterns, upgrading the pulse generator, and changing the azimuthal position of the lens segments in the LEBT structure.This paper will review the timing control patterns for present and suggested configurations, compare the pulse generator performance for both cases, and show the advantages of the new chopping modes. The results of the simulated beam distribution at the RFQ input for different deflecting voltages will also be presented.
Peplov, Vladimir↗
SU-E-T-520: Four-Dimensional Dose Calculation Algorithm Considering Variations in Dose Distribution Induced by Sinusoidal One-Dimensional Motion Patterns
Purpose: To investigate quantitatively the variations in dose-distributions induced by motion by measurements and modeling. A four-dimensional (4D) motion model of dose distributions that accounts for different motion parameters was developed. Methods: Variations in dose distributions induced by sinusoidal phantom motion were measured using a multiple-diode-array-detector (MapCheck2). MapCheck2 was mounted on a mobile platform that moves with adjustable calibrated motion patterns in the superior-inferior direction. Various plans including open and intensity-modulated fields were used to irradiate MapCheck2. A motion model was developed to predict spatial and temporal variations in the dose-distributions and dependence on the motion parameters using pencil-beam spread-out superposition function. This model used the superposition of pencil-beams weighted with a probability function extracted from the motion trajectory. The model was verified with measured dose-distributions obtained from MapCheck2. Results: Dose-distribution varied considerably with motion where in the regions between isocenter and 50% isodose-line, dose decreased with increase of the motion amplitude. Dose levels increased with increase in the motion amplitude in the region beyond 50% isodose-line. When the range of motion (ROM=twice amplitude) was smaller than the field length both central axis dose and the 50% isodose-line did not change with variation of motion amplitude and remained equal to the dose of stationary phantom. As ROM became larger than the field length, the dose level decreased at central axis dose and 50% isodose-line. Motion frequency and phase did not affect the dose distributions which were delivered over an extended time longer than few motion cycles, however, they played an important role for doses delivered with high-dose-rates within one motion cycle . Conclusion: A 4D-dose motion model was developed to predict and correct variations in dose distributions induced by one-dimensional sinusoidal motion. This model was verified with measured dose-distributions and can be expanded to consider complicated patient motion patterns.
07 ISOTOPE AND RADIATION SOURCES↗
MO-G-304-02: Knowledge Based DVH Prediction Using a Geometric Dose Transform
Purpose: To demonstrate a novel method for predicting patient dose-volume histograms (DVHs) using a prior database of optimized radiotherapy treatment plans. Such predicted DVHs could be useful for automating treatment planning. Methods: Our initial demonstration utilized a database of 100 prostate intensity-modulated radiotherapy (IMRT) data-sets. Each data-set contained a CT image with contours of the planning target volume (PTV), rectum, and bladder, the parameters of a clinically approved IMRT plan, and a corresponding simulated dose distribution. We applied a novel geometric transformation to remove the influence of the PTV size, shape, and location on the dose distribution. We termed the transformed distribution the geometrically normalized dose distribution (GNDD). This normalization transform was applied to 80 data-sets randomly selected from the database, and a population GNDD was computed as the average. Next, the population GNDD was mapped onto each of the remaining 20 patient datasets using the reverse of the geometric normalization transform, and predicted DVHs were calculated from the reverse transformed dose distributions (GNDD-DVHs). In addition, a state of the art machine learning based method from the literature was tested for comparison. Results: DVH prediction accuracy was quantified by calculating the relative root mean squared error (rRMSE) on predicted DVHs for the 20 test patients using their known DVHs. For bladder, rectum, and PTV average rRMSEs for the GNDD method were 9.7 ± 4.2%, 13.9 ± 6.0%, and 2.3 ± 0.5% respectively. Prediction results using GNDD were roughly equivalent to that from the machine learning method. Conclusion: We developed a new method for predicting DVH curves from a database of prior patient plans. We demonstrated that our simple approach achieves accuracy comparable to a method using a complicated machine learning based approach.
60 APPLIED LIFE SCIENCES↗
SU-F-T-580: New Tumor Modeling Using 3D Gel Dosimeter for Brain Stereoctactic Radiotherpy (SRT)
Purpose: The purpose of this study is to develop new tumor model using 3D printing with 3D dosimeter for brain stereoctactic radiotherpy (SRT). Methods: BANG{sup 3} polymer gel was prepared and the gel-filled glass vials were irradiated with a 6 MV photon beam to acquire the calibration curve that present the change of R2 (1/T{sub 2}) value with dose. Graded doses from 0 to 12 Gy with an interval of 2 Gy were delivered. A kit for calibration of gel dosimeter and an 2 tumor model phantoms with a spherical shape were produced using a 3D printer with a polylactic acid after its 3D images were created using Autodesk software. 3D printed tumor phantoms and EBT3 films were irradiated to compare with treatment plan. After irradiation, vials for calibration and tumor model phantoms were scanned at 9.4T MRI. The spin-spin relaxation times (T{sub 2}) according to the each dose were calculated to evaluate the dose response. Acquired images were analyzed using an ImageJ. Scanned MRI images of tumor models were transferred treatment planning system and these were registered to the CT images. In all treatment plans, two arc plans (CW and CCW) were designed to deliver 50 Gy for 10 fractions. For first PTV, treatment plan was accurately designed that 95% of dose to cover 100% of PTV. But 2nd PTV was not intentionally cover 100% of PTV to evaluate the intensity of delivered tumor phantom with polymer gel. We compared the 3D dose distributions obtained from measurements with the 3D printed phantom and calculated with the TPS. Results: 3D printed phantom with a polymer gel was successfully produced. The dose distributions showed qualitatively good agreement among gel, film, and RTP data. Conclusion: A hybrid phantom represents a useful to validate the 3D dose distributions for patient-specific QA.
60 APPLIED LIFE SCIENCES↗
SU-F-T-659: Nanoparticle-Aided Eye Plaque Radiotherapy
Purpose: Eye plaque brachytherapy is one of the approaches for radiotherapy treatment for ocular cancers: retinoblastoma and choroidal melanoma. This study, investigates the potential benefits of using gold nanoparticles to enhance therapeutic efficacy during eye plaque brachytherapy. Methods: The EYE PHYSICS Inc. Plaque Simulator program distributed by IsoAid, LLC, Port Richey, Florida was used. It is based on the superposition of dose contributions from individual seeds following the TG–43 formalism. Dose enhancement factor (DEF) values for feasible nanoparticle concentrations from previous studies was used to investigate the benefit of using nanoparticles to enhance dose to tumour or reduce dose to healthy tissue. The dose enhancement factor (DEF) represents the ratio of the dose deposited in tumour with nanoparticles divided by dose deposited in the tumour without nanoparticles. The investigation was done for I–125 and Pd–103 typical sources employed for eye plaque brachytherapy. The prescription dose used is 85 Gy. Results: Lower dose enhancement values were obtained for Pd–103. With DEF of 2 due to gold nanoparticles, critical structure doses reduce by a factor of 2. Optic disc dose is 6.69 Gy and 4.571 Gy, opposite retina dose is 4.064 and 2.484 Gy, lens dose is 12.66 Gy and 9.870 Gy, and fovea dose is 9.85 Gy and 7.275 Gy. With DEF of 3 due to gold nanoparticles, critical structure doses reduce by a factor of 3. Optic disc dose is 4.352 Gy and 2.975 Gy, opposite retina dose is 2.644 Gy and 1.618 Gy, lens dose is 8.322 Gy and 6.427 Gy, and fovea dose is 4.815 Gy and 4.737 Gy. Conclusion: The results of this research predict that using gold nanoparticles will lead to major sparing of dose to critical structures. The finding provides more impetus for the development of nanoparticle–aided brachytherapy.
60 APPLIED LIFE SCIENCES↗
OpenDSSDirect.py [SWR-17-42]
A Python direct-mode interface to OpenDSS, a distribution system simulator for electric power systems.
Krishnamurthy, Dheepak↗
OTM-MPI (Open Traffic Models - MPI)
This software is an extension to OTM (Open Traffic Models), which is an open-source traffic simulator maintained at the Institute of Transportation Studies at U.C. Berkeley. OTM-MPI provides functionality for running OTM simulations in distributed computing environments. Its functionality includes min-cut division of traffic networks (using METIS), deployment of sub-networks on multiple compute nodes, and inter-node MPI communications.
Ugirumurera, Juliette↗
OpenDSSDirect.jl
OpenDSSDirect.jl is a cross-platform Julia package implements a "direct" library interface to OpenDSS using dss_capi. OpenDSS is an open-source distribution system simulator. See OpenDSSDirect.py for a similar package in Julia. This package is now available for Windows, Mac, and Linux.
Krishnamurthy, Dheepak↗
Regression of 3D Sky Map to Cosmological Parameters (CosmoFlow) v1.0
The neural network constructed of 3D convolutional and fully connected layers was trained on the simulated mass distribution of the Universe with the purpose to recover the values of cosmological parameters used to generate the Universe.
Balewski, Jan↗
CyMEpy [SWR-21-20]
CYMEpy is a high-level Python package that provides users HELICS bindings for CYMEDIST, CYME’s distribution system simulator.
Latif, Aadil↗
Adaptive Computing (AC) [SWR-24-106]
The Adaptive Computing (AC) software stack supports goal-based computing, for which a simulation workload is created on the fly adapting to the results of calculations. Application-specific code defines an objective, which may be to solve an optimization problem or to train a surrogate model with minimal uncertainty. Then, the AC driver decides where in the design parameter space to run simulations to best achieve that objective. This process is iterative and online; as new data is returned from simulations, the AC driver chooses new simulations to run. The AC driver can strategically run simulations on distributed hardware resources (including high performance computing machines, cloud resources, and edge devices) to maximize throughput and obey resource constraints.
Griffin, Kevin [National Renewable Energy Laborato↗
DISTRI: Distributed Multi-Facility HPC Simulator (DISTRI) v2.1
DISTRI is an advanced network simulator designed for multi-facility computational infrastructures with agentic behavior. It simulates HPC facilities where computational resources act as autonomous agents, making intelligent decisions about job scheduling, load balancing, and resource allocation. The simulator focuses on developing and testing decentralized algorithms that promote resilience and efficiency in multi-facility environments. Key Features: - Agentic Resource Behavior: Processors and DTNs act as autonomous agents with decision-making capabilities - Pheromone-Based Load Balancing: Decentralized load balancing inspired by ant colony optimization - Dual Topology Support: Mesh (normal operations) and Dumbell (network testing) topologies - Comprehensive TCP Simulation: Realistic TCP implementations with multiple congestion control algorithms - Failure Resilience Testing: Processor failure simulation with automatic job reassignment - Extensive Visualization: Detailed performance analysis and metrics collection - Research-Ready: Designed for algorithm development and benchmarking
Bez, Jean Luca [Lawrence Berkeley National Laborat↗
Adaptive Computing (AC) (Open Source) [SWR-24-106]
The Adaptive Computing (AC) software stack supports goal-based computing, for which a simulation workload is created on the fly, adapting to the results of calculations. Application-specific code defines an objective, which may be to solve an optimization problem or to train a surrogate model with minimal uncertainty. Then, the AC driver decides where in the design parameter space to run simulations to best achieve that objective. This process is iterative and online; as new data is returned from simulations, the AC driver chooses new simulations to run. The AC driver can strategically run simulations on distributed hardware resources (including high performance computing machines, cloud resources, and edge devices) to maximize throughput and obey resource constraints.
Griffin, Kevin [National Laboratory of the Rockies↗