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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Subcontract B635525 (Final Report)

During my visits to LLNL July 8–12 and July 22–26, 2019, I worked on linear system solvers. The two level hierarchical solver that initiated our study was developed to solve linear systems arising from $\textit{hp}$ adaptive finite element calculations, and is implemented in the $\textit{PLTMG}$ software package, version 13. This preconditioner typically requires 3–20% of the space used by the stiffness matrix for higher order elements. It has multigrid like convergence rates for a wide variety of PDEs (self-adjoint positive definite elliptic equations, convection dominated convection-diffusion equations, and highly indefinite Helmholtz equations, among others). The convergence rate is not independent of the polynomial degree $\textit{p}$ as $\textit{p} → ∞$, but remains strong for $\textit{p}$ ≤ 9, which is the highest polynomial degree allowed in $\textit{PLTMG}$, due to limitations of the numerical quadrature rules implemented in the soft- ware package. A more complete description of the method and some numerical experiments illustrating its effectiveness appear in. Like traditional geometric multilevel methods, this scheme relies on knowledge of the underlying finite element space in order to construct the smoother and the coarse grid correction.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1999

Solving Upwind-Biased Discretizations: Defect-Correction Iterations

This paper considers defect-correction solvers for a second order upwind-biased discretization of the 2D convection equation. The following important features are reported: (1) The asymptotic convergence rate is about 0.5 per defect-correction iteration. (2) If the operators involved in defect-correction iterations have different approximation order, then the initial convergence rates may be very slow. The number of iterations required to get into the asymptotic convergence regime might grow on fine grids as a negative power of h. In the case of a second order target operator and a first order driver operator, this number of iterations is roughly proportional to h-1/3. (3) If both the operators have the second approximation order, the defect-correction solver demonstrates the asymptotic convergence rate after three iterations at most. The same three iterations are required to converge algebraic error below the truncation error level. A novel comprehensive half-space Fourier mode analysis (which, by the way, can take into account the influence of discretized outflow boundary conditions as well) for the defect-correction method is developed. This analysis explains many phenomena observed in solving non-elliptic equations and provides a close prediction of the actual solution behavior. It predicts the convergence rate for each iteration and the asymptotic convergence rate. As a result of this analysis, a new very efficient adaptive multigrid algorithm solving the discrete problem to within a given accuracy is proposed. Numerical simulations confirm the accuracy of the analysis and the efficiency of the proposed algorithm. The results of the numerical tests are reported.

Diskin, Boris↗
DOE OSTI2023

A Simulation Framework for Analyzing the Impact of Stochastic Occupant Behaviors on Demand Flexibility in Typical Commercial Buildings

As one of the primary users of the electric grid, buildings and building equipment, including heating, ventilation, and air conditioning (HVAC) systems, can be leveraged to provide the flexible demand needed to balance the grid. Typical strategies to achieve demand flexibility are to reduce electricity use during peak or critical periods by shutting down equipment or relaxing system setpoints, which will inevitably impact the occupants’ comfort. When occupants feel uncomfortable, they may take actions to regain their comfort, and some of those actions (such as turning on a personal fan) may have a negative impact on meeting the demand response goal. Therefore, it is important to incorporate occupant behaviors into the assessment ofthe building demand flexibility potential. In this study, a simulation framework that includes simulation of zone thermal loads, an HVAC system, and occupant behaviors, was developed to investigate the impact of occupant behaviors on demand flexibility. A case study was conducted using a small office model from the U.S. Department of Energy (DOE) Commercial Prototype Building Models to simulate the building envelope and zone loads. An agent-based occupant thermal behavior model was adapted to forecast occupants’ thermal comfort and their resulting thermal behaviors. An artificial neural network (ANN) based airflow model trained from a computational fluid dynamics (CFD) model of the zone was adopted to better predict the ambient environment of each occupant. An air-source heat pump simulation model that was calibrated from a real two-stage air-source heat pump system was used as the HVAC system. A typical load shedding event during peak hours was studied. Repeated simulations were conducted to capture the stochastic effects of occupant behaviors. The interplay between the demand flexibility, occupant comfort and behavior were analyzed by evaluating key performance indicators, including the energy use, occupant discomfort duration, and occupant behavior duration during the peak period. The results suggest that this framework can be used to analyze typical commercial buildings and their HVAC systems in terms ofdemand flexibility potential under the impact of occupant behaviors.

Chen, Zhelun↗
DOE OSTI2023

Projected changes in extreme rainfall and temperature events and possible implications for Cameroon's socio-economic sectors

Extreme events like flooding, droughts and heatwave are among the factors causing huge socio-economic losses to Cameroonians. Investigating the potential response of rainfall and temperature extremes to global warming is therefore critically needed for tailoring and adjusting the country's policies. Recent datasets have been developed for this purpose within the Coordinated Output for Regional Evaluations (CORDEX-CORE) initiative, at ~25 km grid spacing. These regional climate models were used to dynamically downscaled four global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5), under the optimistic and pessimistic representative concentration pathways (RCPs) 2.6 and 8.5, respectively. These models were employed in this study for characterizing the response of Cameroon's extreme precipitation and temperature events to global warming, using seven indices defined by the Expert Team on Cli mate Change Detection and Indices. Under global warming, the maximum number of consecutive dry (wet) days' is expected to increase (decrease). However, the annual total rainfall amount is expected to increase, mainly due to the intensification of very wet days and daily rainfall intensity. Furthermore, the temperature-based indices reveal an increase (decrease) in the total annual hot (cold) days, and overall, changes intensify with increased radiative forcing. The high-mitigated low-emission pathway RCP2.6 features attenuated changes, and even sometimes adapts to reverse the sign of changes. Designing reliable policies to limit the risks associated with the above changes is required, as their socio-economic consequences are likely to include food insecurity, heat-related illness, population impoverishment, price rises and market instability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Recursive Blind Forecasting of Photovoltaic Generation and Consumer Load for Microgrids

Existing forecasting frameworks that predict time-series photovoltaic (PV) generation and consumer load for micro-grids' operation and control assume near-continuous availability of real-time predictors from the field. The incoming data are used to periodically re-train the models and update forecast snapshots over a moving horizon window. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. This paper bridges the shortcoming by leveraging a previously proposed forecasting framework that is resilient to abrupt changes in data quality caused by communication losses. Assuming no availability of real-time field system data, which is typical in extreme weather events such as hurricanes, the framework uses lightweight recursive time-series models to independently forecast solar irradiance, ambient temperature, PV power, and consumer load for three horizon windows: 24 hours, 12 hours, and 1 hour. Four types of ensemble-based regression trees-simple gradient boosted trees (GBR), GBR with an adaptive component (A-GBR), random forests (RF), and extra trees (ExTR)-are leveraged and their performances are compared against a simple historical weekly mean. Numerical results show that A-GBR performs better on average by 32% for 24-hour horizon and 39% for 12-hour horizon, whereas ExTR outdoes the other models on average by 10% for 1-hour horizon.

Sundararajan, Aditya↗
DOE OSTI2014

SU-E-T-278: Realization of Dose Verification Tool for IMRT Plan Based On DPM

Purpose: To build a Monte Carlo dose verification tool for IMRT Plan by implementing a irradiation source model into DPM code. Extend the ability of DPM to calculate any incident angles and irregular-inhomogeneous fields. Methods: With the virtual source and the energy spectrum which unfolded from the accelerator measurement data,combined with optimized intensity maps to calculate the dose distribution of the irradiation irregular-inhomogeneous field. The irradiation source model of accelerator was substituted by a grid-based surface source. The contour and the intensity distribution of the surface source were optimized by ARTS (Accurate/Advanced Radiotherapy System) optimization module based on the tumor configuration. The weight of the emitter was decided by the grid intensity. The direction of the emitter was decided by the combination of the virtual source and the emitter emitting position. The photon energy spectrum unfolded from the accelerator measurement data was adjusted by compensating the contaminated electron source. For verification, measured data and realistic clinical IMRT plan were compared with DPM dose calculation. Results: The regular field was verified by comparing with the measured data. It was illustrated that the differences were acceptable (<2% inside the field, 2–3mm in the penumbra). The dose calculation of irregular field by DPM simulation was also compared with that of FSPB (Finite Size Pencil Beam) and the passing rate of gamma analysis was 95.1% for peripheral lung cancer. The regular field and the irregular rotational field were all within the range of permitting error. The computing time of regular fields were less than 2h, and the test of peripheral lung cancer was 160min. Through parallel processing, the adapted DPM could complete the calculation of IMRT plan within half an hour. Conclusion: The adapted parallelized DPM code with irradiation source model is faster than classic Monte Carlo codes. Its computational accuracy and speed satisfy the clinical requirement, and it is expectable to be a Monte Carlo dose verification tool for IMRT Plan. Strategic Priority Research Program of the China Academy of Science(XDA03040000); National Natural Science Foundation of China (81101132)

60 APPLIED LIFE SCIENCES↗
NASA NTRS2007

NASA Tech Briefs, December 2007

Topics include: Ka-Band TWT High-Efficiency Power Combiner for High-Rate Data Transmission; Reusable, Extensible High-Level Data-Distribution Concept; Processing Satellite Imagery To Detect Waste Tire Piles; Monitoring by Use of Clusters of Sensor-Data Vectors; Circuit and Method for Communication Over DC Power Line; Switched Band-Pass Filters for Adaptive Transceivers; Noncoherent DTTLs for Symbol Synchronization; High-Voltage Power Supply With Fast Rise and Fall Times; Waveguide Calibrator for Multi-Element Probe Calibration; Four-Way Ka-Band Power Combiner; Loss-of-Control-Inhibitor Systems for Aircraft; Improved Underwater Excitation-Emission Matrix Fluorometer; Metrology Camera System Using Two-Color Interferometry; Design and Fabrication of High-Efficiency CMOS/CCD Imagers; Foam Core Shielding for Spacecraft CHEM-Based Self-Deploying Planetary Storage Tanks Sequestration of Single-Walled Carbon Nanotubes in a Polymer PPC750 Performance Monitor Application-Program-Installer Builder Using Visual Odometry to Estimate Position and Attitude Design and Data Management System Simple, Script-Based Science Processing Archive Automated Rocket Propulsion Test Management Online Remote Sensing Interface Fusing Image Data for Calculating Position of an Object Implementation of a Point Algorithm for Real-Time Convex Optimization Handling Input and Output for COAMPS Modeling and Grid Generation of Iced Airfoils Automated Identification of Nucleotide Sequences Balloon Design Software Rocket Science 101 Interactive Educational Program Creep Forming of Carbon-Reinforced Ceramic-Matrix Composites Dog-Bone Horns for Piezoelectric Ultrasonic/Sonic Actuators Benchtop Detection of Proteins Recombinant Collagenlike Proteins Remote Sensing of Parasitic Nematodes in Plants Direct Coupling From WGM Resonator Disks to Photodetectors Using Digital Radiography To Image Liquid Nitrogen in Voids Multiple-Parameter, Low-False-Alarm Fire-Detection Systems Mosaic-Detector-Based Fluorescence Spectral Imager Plasmoid Thruster for High Specific-Impulse Propulsion Analysis Method for Quantifying Vehicle Design Goals Improved Tracking of Targets by Cameras on a Mars Rover Sample Caching Subsystem Multistage Passive Cooler for Spaceborne Instruments GVIPS Models and Software Stowable Energy-Absorbing Rocker-Bogie Suspensions

Source record↗
DOE OSTI2019

Concepts for Autonomous Operation of Microreactors

Microreactor concepts have seen increasing interest in recent years given their potential use for meeting energy needs in remote and grid-isolated communities. Given the small power outputs of microreactors and the potential for remote siting, there is a need to reduce operational staffing levels to improve their economic viability. A key enabling technology for this purpose is autonomous control that enables autonomous operation of microreactors. While several options for autonomous control exist, the level of human operator involvement in operational decision making needs to be defined as part of the system design process. Once this separation of function is achieved, several options exist for autonomous control ranging from simple automation of some procedures to fully autonomous operational mode decision making and execution.A critical aspect of autonomous decision making is the ability to have as complete an awareness of the system state as possible. In addition to data on temperature, pressure, flow, and neutron flux, measurements of the condition of important components will be necessary. This type of information will allow the autonomous control system to adapt its decision making to any changing conditions within the reactor, thereby not compromising safety while continuing to operate for as long as possible. Several technical advances are needed before widespread use of autonomous control in microreactors. These include sensor and instrumentation technologies that are capable of long term, unattended operation in harsh environments, technologies for inferring the state of the microreactor system or subsystems in an automated fashion, algorithms for predictive decision making that account for the assessed condition of the microreactor subsystems as well as the potential impact on those components of any operational decision, and actuators and control system hardware that are also long-lived in a harsh environment. In addition, given the need to remotely monitor the operations, cybersecurity requirements will likely need to be imposed to ensure secure operations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

TU-AB-BRA-01: Abdominal Synthetic CT Generation in Support of Liver SBRT Dose Calculation

Purpose: To demonstrate and validate a technique for generating MRI-derived synthetic CT volumes (MRCTs) in support of adaptive liver SBRT. Methods: Under IRB approval, ten hepatocellular carcinoma patients were scanned using a single MR sequence (T1 Dixon-VIBE), yielding inherently-registered water, fat, and T1-weighted images. Air-containing voxels were identified by intensity thresholding. The envelope of the anterior vertebral bodies was segmented from the fat image by fitting a shape model to vertebral body candidate voxels, then using level sets to expand the contour outward. Fuzzy-C-Means (FCM) was then used to classify each non-air voxel in the image as fat, water, bone, or marrow. Bone and marrow only were classified within the vertebral body envelope. The MRCT was created by integrating the product of the FCM class probability with the assigned class density for each voxel. The resulting MRCTs were deformably aligned with planning CTs and 2-ARC SBRT VMAT plans were optimized on the MRCT density maps. Fluence was copied onto the CT density grids and dose recalculated. Results: The MRCTs faithfully reproduced most of the features visible in the corresponding CT image volumes, with exceptions of ribs and posterior spinous processes. The liver, vertebral bodies, kidneys, spleen and cord all had median HU differences of less than 75 between MRCT and CT images. PTV D99% values had an average 0.2% difference (standard deviation: 0.46%) between calculations on MRCT and CT density grids. The maximum difference in dose to 0.1cc of the PTV was 0.25% (std:0.49%). OAR dose differences were similarly small (mean:0.03Gy, std:0.26Gy). The largest normal tissue complication percentage (NTCP) difference was 1.48% (mean:0.06%, std:0.54%). Conclusions: MRCTs from a single abdominal imaging sequence are promising for use in SBRT dose calculation. Future work will focus on extending models to better define bones in the upper abdomen. Supported by NIHR01EB016079 and NIH1L30CA199594-01.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Multipole moments in the effective fragment potential method

In the effective fragment potential (EFP) method the Coulomb potential is represented using a set of multipole moments generated by the distributed multipole analysis (DMA) method. Misquitta, Stone, and Fazeli recently developed a basis space-iterated stockholder atom (BS-ISA) method to generate multipole moments. This study assesses the accuracy of the EFP interaction energies using sets of multipole moments generated from the BS-ISA method, and from several versions of the DMA method (such as analytic and numeric grid-based), with varying basis sets. Both methods lead to reasonable results, although using certain implementations of the DMA method can result in large errors. With respect to the CCSD(T)/CBS interaction energies, the mean unsigned error (MUE) of the EFP method for the S22 data set using BS-ISA–generated multipole moments and DMA-generated multipole moments (using a small basis set and the analytic DMA procedure) is 0.78 and 0.72 kcal/mol, respectively. Here, the MUE accuracy is on the same order as MP2 and SCS-MP2. The MUEs are lower than in a previous study benchmarking the EFP method without the EFP charge transfer term, demonstrating that the charge transfer term increases the accuracy of the EFP method. Regardless of the multipole moment method used, it is likely that much of the error is due to an insufficient short-range electrostatic term (i.e., charge penetration term), as shown by comparisons with symmetry-adapted perturbation theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Anticipating how rain-on-snow events will change through the 21st century: lessons from the 1997 new year’s flood event

The California-Nevada 1997 New Year’s flood was an atmospheric river (AR)-driven rain-on-snow (RoS) event and remains the costliest in their history. The joint occurrence of saturated soils, rainfall, and snowmelt generated inundation throughout northern California-Nevada. Although AR RoS events are projected to occur more frequently with climate change, the warming sensitivity of their flood drivers across scales remains understudied. We leverage the regionally refined mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) to recreate the 1997 New Year’s flood with horizontal grid spacings of 3.5 km across California, with forecast lead times of up to 4 days, and across six warming levels ranging from pre-industrial conditions to +3.5° C. We describe the sensitivity of the flood drivers to warming including AR duration and intensity, precipitation phase, intensity and efficiency, snowpack mass and energy changes, and runoff efficiency. Our findings indicate current levels of climate change negligibly influence the flood drivers. At warming levels ≥ 1.7° C, AR hazard potential increases, snowpack nonlinearly decreases, antecedent soil moisture decreases (except where the snowline retreats), and runoff decreases (except in the southern Sierra Nevada where antecedent snowpack persists). Storm total precipitation increases, but at rates below warming-induced increases in saturation-specific humidity. Warming intensifies short-duration, high-intensity rainfall, particularly where snowfall-to-rainfall transitions occur. This study highlights the nonlinear tradeoffs in 21st-century RoS flood hazards with warming and provides water management and infrastructure investment adaptation considerations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Data-Driven PMU Noise Emulation Framework using Gradient-Penalty-Based Wasserstein GAN

Availability of phasor measurement unit (PMUs) data has led to research on data-driven algorithms for event monitoring, control and ensuring stability of the grid. Unavailability of infrequent critical event field PMU data with component failures is driving the need to generate realistic synthetic PMU data for research. The synthetic data from power system simulation software often neglect noise profiles of received phasors, thus creating some discrepancies between real PMU data and synthetic ones. To address this issue, this work presents an initial study on the noise characteristics of PMUs, as well as presenting models for recreating their unique noise signatures. The proposed method, utilizing the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) architecture, provides an excellent benchmark for matching the noise distribution. One can use a well-learned GAN model to draw noise signatures from a distribution that seemingly mirrors the real PMU noise distribution, while also being able to be detached from the PMU data once the training is done. Based on the observed results and employed data-driven methodology, it is expected that the proposed methods can be adapted to replicate the behavior of other sensors, providing research and other applications with a tool for data synthesis and sensor characterization.

PMU↗
DOE OSTI2025

Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone

Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have been developed to understand the mechanisms leading to complex morphologies. However, the LMD governing equations in these models often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, numerical stiffness in the PDEs requires an extremely refined time step size (on the order of 10 -12 s or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose a U-shaped adaptive Fourier neural operator (U-AFNO), a machine learning (ML) based model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics of mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g., the deformation of the liquid-metal interface, lost metal, etc.) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in spite of the chaotic nature of LMD. Most notably, our model reproduces the key microstructure statistics and QoIs with a level of accuracy on par with the high-fidelity numerical solver, while achieving a significant 11, 200 × speed-up on a high-resolution grid when comparing the computational expense per time step. Finally, we also investigate the opportunity of using hybrid simulations, in which we alternate forward leaps in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Ensemble Forecasting/Assimilation/Emissions Estimation with WRF-Chem/DART: Accomplishments, Lessons Learned, and Future Plans

Over the past ten years, we have been conducting research on regional ensemble atmospheric composition forecasting/data assimilation/emissions estimation with WRF-Chem/DART. WRF- Chem/DART integrates the Weather Research and Forecasting model (WRF) with online chemistry (WRF-Chem) into the Data Assimilation Research Testbed (DART). DART is an ensemble data assimilation system based on the ensemble adjustment Kalman filter (EAKF) with adaptive inflation, localization (physical and state space), and an optional non-Gaussian formulation of the EAKF. DART includes assimilation of meteorological and limited chemical observations. WRF-Chem/DART extends DART to include assimilation of: MOPITT CO; IASI CO and O3; MODIS AOD; OMI O3, NO2, and SO2; TROPOMI CO, O3, NO2, and SO2, TES CO, CO2 (research mode), O3, NH3, and CH4 (research mode); CrIS CO, O3, NH3, CH4 (research mode), and PAN; SCIAMACHY NO2; GOME2a NO2; MLS O3 and HNO3; and proxy TEMPO O3, and NO2 satellite retrievals as raw retrievals or as ‘compact phase space retrievals’ (CPSRs) for profile retrievals. WRF-Chem/DART also assimilates in situ atmospheric composition measurements and uses the ‘state augmentation method’ for emissions estimation. In our presentation, we will provide an overview of WRF-Chem/DART: • applications and results; • lessons learned from: (i) independent versus joint assimilation; (ii) total/partial column versus profile retrieval assimilation; (iii) joint in situ and retrieval assimilation; (iv) assimilation at grid resolutions ranging from 100 km to 4 km; (v) dynamic emissions estimation; and • future work related to wildfire emissions estimation and intercomparison of CMAQ (online)/JEDI and CMAQ (online and offline)/DART.

WRF-Chem/DART↗
NASA NTRS2016

The Vulnerability, Impacts, Adaptation and Climate Services Advisory Board (VIACS AB V1.0) Contribution to CMIP6

This paper describes the motivation for the creation of the Vulnerability, Impacts, Adaptation and Climate Services (VIACS) Advisory Board for the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), its initial activities, and its plans to serve as a bridge between climate change applications experts and climate modelers. The climate change application community comprises researchers and other specialists who use climate information (alongside socioeconomic and other environmental information) to analyze vulnerability, impacts, and adaptation of natural systems and society in relation to past, ongoing, and projected future climate change. Much of this activity is directed toward the co-development of information needed by decisionmakers for managing projected risks. CMIP6 provides a unique opportunity to facilitate a two-way dialog between climate modelers and VIACS experts who are looking to apply CMIP6 results for a wide array of research and climate services objectives. The VIACS Advisory Board convenes leaders of major impact sectors, international programs, and climate services to solicit community feedback that increases the applications relevance of the CMIP6-Endorsed Model Intercomparison Projects (MIPs). As an illustration of its potential, the VIACS community provided CMIP6 leadership with a list of prioritized climate model variables and MIP experiments of the greatest interest to the climate model applications community, indicating the applicability and societal relevance of climate model simulation outputs. The VIACS Advisory Board also recommended an impacts version of Obs4MIPs (observational datasets) and indicated user needs for the gridding and processing of model output.

Vulnerability↗
DOE OSTI2016

A framework to analyze the stochastic harmonics and resonance of wind energy grid interconnection

This study addresses a modeling and analysis methodology for investigating the stochastic harmonics and resonance concerns of wind power plants (WPPs). Wideband harmonics from modern wind turbines are observed to be stochastic, associated with real power production, and they may adversely interact with the grid impedance and cause unexpected harmonic resonance if not comprehensively addressed in the planning and commissioning of the WPPs. These issues should become more critical as wind penetration levels increase. We thus propose a planning study framework comprising the following functional steps: First, the best-fitted probability density functions (PDFs) of the harmonic components of interest in the frequency domain are determined. In operations planning, maximum likelihood estimations followed by a chi-square test are used once field measurements or manufacturers' data are available. Second, harmonic currents from the WPP are represented by randomly-generating harmonic components based on their PDFs (frequency spectrum) and then synthesized for time-domain simulations via inverse Fourier transform. Finally, we conduct a comprehensive assessment by including the impacts of feeder configurations, harmonic filters, and the variability of parameters. We demonstrate the efficacy of the proposed study approach for a 100-MW offshore WPP consisting of 20 units of 5-MW full-converter turbines, a realistic benchmark system adapted from a WPP under development in Korea, and discuss lessons learned through this research.

17 WIND ENERGY↗
DOE OSTI2021

Spatial–Temporal Patterns of Historical, Near-Term, and Projected Drought in the Conterminous United States

Major droughts in the United States have heavily impacted the hydrologic system, negatively effecting energy and food production. Improved understanding of historical drought is critical for accurate forecasts. Data from global climate models (GCMs), commonly used to assess drought, cannot effectively evaluate local patterns because of their low spatial scale. This research leverages downscaled (~4 km grid spacing) temperature and precipitation estimates from nine GCMs’ data under the business-as-usual scenario (Representative Concentration Pathway 8.5) to examine drought patterns. Drought severity is estimated using the Palmer Drought Severity Index (PDSI) with the Thornthwaite evapotranspiration method. The specific objectives were (1) To reproduce historical (1966–2005) drought and calculate near-term to future (2011–2050) drought patterns over the conterminous USA. (2) To uncover the local variability of spatial drought patterns in California between 2012 and 2018 using a network-based approach. Our estimates of land proportions affected by drought agree with the known historical drought events of the mid-1960s, late 1970s to early 1980s, early 2000s, and between 2012 and 2015. Network analysis showed heterogeneity in spatial drought patterns in California, indicating local variability of drought occurrence. The high spatial scale at which the analysis was performed allowed us to uncover significant local differences in drought patterns. This is critical for highlighting possible weak systems that could inform adaptation strategies such as in the energy and agricultural sectors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SU-E-J-195: Quantification of Rotations and Deformations in Head and Heck Radiotherapy

Purpose: To quantify the magnitude of patient rotations and deformations over the course of head and neck radiotherapy. Methods: 45 CBCT images acquired weekly were selected retrospectively from 5 head and neck cancer patients. CBCT images were registered to the corresponding planning CT images using translation, rigid-body, and cubic B-spline deformable transformations. Elastix ( elastix.isi.uu.nl ), an open-source public domain registration algorithm, was used for all registrations with mutual information as similarity metric and gradient descent as optimization. Four-level multiresolution approach was employed with 2 cm B-spline grid spacing at the finest resolution. Registration qualities were visually verified. Rotational occurrences were quantified from the rigid-body registrations. The magnitude of deformations in planning target volumes (PTV) and organs were quantified from the deformation vector fields with rigid registrations as baseline. Results: The measured rotations (mean±std) were 1.0±1.0 deg, - 0.3±1.0 deg, and 0.1±0.8 deg in the sagittal, coronal, and axial plains respectively. The estimated magnitudes of deformations (mean±std) were 3.4±2.1 mm for PTVs, 2.9±1.2 mm for parotid glands, 2.8±1.2 mm for mandible, and 2.2±1.1 mm for spinal cord. The overall deformation in the body contour was 4.3±4.3 mm. The organ spatial deviation due to rotation was small and approximately 8.4% of that from deformations. Results varied among patients. Conclusion: Rotations occurred about 1.0 deg (1SD) in the H'N area. Daily geometric deviation of target volumes was considerable and mainly from tissue deformation than rotations. Proper safety margin is necessary for adequate target coverage. Online plan adaptation may mitigate the daily differences between the plan and actual patient poses. Partially supported by a grant from Varian Medical Systems, Palo Alto, CA.

60 APPLIED LIFE SCIENCES↗