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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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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

KU-Band rendezvous radar performance computer simulation model

The preparation of a real time computer simulation model of the KU band rendezvous radar to be integrated into the shuttle mission simulator (SMS), the shuttle engineering simulator (SES), and the shuttle avionics integration laboratory (SAIL) simulator is described. To meet crew training requirements a radar tracking performance model, and a target modeling method were developed. The parent simulation/radar simulation interface requirements, and the method selected to model target scattering properties, including an application of this method to the SPAS spacecraft are described. The radar search and acquisition mode performance model and the radar track mode signal processor model are examined and analyzed. The angle, angle rate, range, and range rate tracking loops are also discussed.

Griffin, J. W.↗
NASA NTRS2012

Inductive Pulsed Plasma Thruster Model with Time-Evolution of Energy and State Properties

A model for pulsed inductive plasma acceleration is presented that consists of a set of circuit equations coupled to both a one-dimensional equation of motion and an equation governing the partitioning of energy. The latter two equations are obtained for the plasma current sheet by treating it as a single element of finite volume and integrating the governing equations over that volume. The integrated terms are replaced where necessary by physically-equivalent quantities that are calculated through the solution of other parts of the governing equation set. The model improves upon previous one-dimensional performance models by permitting the time-evolution of the energy and state properties of the plasma, the latter allowing for the tailoring of the model to different gases that may be chosen as propellants. The time evolution of the various energy modes in the system and the associated plasma properties, calculated for argon propellant, are presented to demonstrate the efficacy of the model. The model produces a result where efficiency is maximized at a given value of the electrodynamic scaling term known as the dynamic impedance parameter. Qualitatively and quantitatively, the model compares favorably with performance measured for two separate inductive pulsed plasma thrusters, with disagreements attributable to simplifying assumptions employed in the generation of the model solution.

Polzin, Kurt A.↗
NASA NTRS2021

Using SMAP Level-4 Soil Moisture to Constrain MOD16 Evapotranspiration over the Contiguous USA

Evapotranspiration (ET) is a key hydrologic variable linking the Earth’s water, carbon and energy cycles. At large spatial scales, remote sensing-based (RS) models are often used to quantify ET. Despite the large number of RS ET models available, few include soil moisture as a key environmental input, which can degrade model accuracy and utility. Here, we use model assimilation enhanced soil moisture estimates from the NASA SMAP (Soil Moisture Active Passive) mission as a water supply control in the MOD16 ET algorithm framework. SMAP-derived daily surface (0-5cm depth) and root zone (0-1m depth) soil moisture are used with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation observations, and 4km gridded regional surface meteorology (Gridmet) as primary inputs for estimating daily ET and underlying model soil and stomatal conductance terms. We calibrated the model environmental response parameters using tower eddy covariance ET observations representing major North American biomes. The model ET results were validated using a holdout set of tower observations spanning a large regional climate gradient. The updated ET estimates outperform the baseline MOD16 product across all tower validation sites (RMSE = 0.758 vs 1.108 mm day-1; R2 = 0.68 vs0.45, respectively). Smaller relative improvements were obtained using a recalibrated model with 4km Gridmet meteorology, but no soil moisture control (RMSE = 0.813 mm day-1; R2 = 0.66), indicating that these changes are essential for the improved model performance. The soil moisture-constrained model improvements and relative benefits from the SMAP observations are greater in arid climates, consistent with stronger soil moisture control on ET in water-limited regions. The use of SMAP soil moisture as an additional model constraint improves MOD16 regional performance and provides a new framework for investigating both soil and atmosphere controls on ET.

SMAP↗
DOE OSTI2025

Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation

Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the τ-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the τ-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments.

42 ENGINEERING↗
NASA NTRS1991

Cold flow testing of the Space Shuttle Main Engine high pressure fuel turbine model

In order to experimentally determine the performance of the Space Shuttle Main Engine (SSME) High Pressure Fuel Turbopump (HPFTP) turbine, a 'cold' air flow turbine test program was established at NASA's Marshall Space Flight Center. As part of this test program, a baseline test of Rocketdyne's HPFTP turbine has been completed. The turbine performance and turbine diagnostics such as airfoil surface static pressure distributions, static pressure drops through the turbine, and exit swirl angles were investigated at the turbine design point, over its operating range, and at extreme off-design points. The data was compared to pretest predictions with good results. The test data has been used to improve meanline prediction codes and is now being used to validate various three-dimensional codes. The data will also be scaled to engine conditions and used to improve the SSME steady-state performance model.

Hudson, Susan T.↗
DOE OSTI2024

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

58 GEOSCIENCES↗
DOE OSTI2024

FEW questions, many answers: using machine learning to assess how students connect food–energy–water (FEW) concepts

There is growing support and interest in postsecondary interdisciplinary environmental education which integrate concepts and disciplines in addition to providing varied perspectives. There is a need to assess student learning in these programs as well as rigorous evaluation of educational practices, especially of complex synthesis concepts. This work tests a text classification machine learning model as a tool to assess student systems thinking capabilities using two questions anchored by the Food-Energy-Water (FEW) Nexus phenomena by answering two questions (1) Can machine learning models be used to identify instructor-determined important concepts in student responses? (2) What do college students know about the interconnections between food, energy and water, and how have students assimilated systems thinking into their constructed responses about FEW? Reported here are a broad range of model performances across 26 text classification models associated with two different assessment items, with model accuracy ranging from 0.755 to 0.992. Expert-like responses were infrequent in our dataset compared to responses providing simpler, incomplete explanations of the systems presented in the question. For those students moving from describing individual effects to multiple effects, their reasoning about the mechanism behind the system indicates advanced systems thinking ability. Specifically, students exhibit higher expertise for explaining changing water usage than discussing tradeoffs for such changing usage. This research represents one of the first attempts to assess the links between foundational, discipline-specific concepts and systems thinking ability. These text classification approaches to scoring student FEW Nexus Constructed Responses (CR) indicate how these approaches can be used, in addition to several future research priorities for interdisciplinary, practice-based education research. Development of further complex question items using machine learning would allow evaluation of the relationship between foundational concept understanding and integration of those concepts as well as more nuanced understanding of student comprehension of complex interdisciplinary concepts.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1989

Turbulent hydrocarbon combustions kinetics - Stochastic modeling and verification

Idealized reactors, that are designed to ensure perfect mixing and are used to generate the combustion kinetics for complex hydrocarbon fuels, may depart from the ideal and influence the kinetics model performance. A complex hydrocarbon kinetics model that was established by modeling a jet-stirred combustor (JSC) as a perfectly stirred reactor (PSR), is reevaluated with a simple stochastic process in order to introduce the unmixedness effect quantitatively into the reactor system. It is shown that the comparisons of the predictions and experimental data have improved dramatically with the inclusion of the unmixedness effect in the rich combustion region. The complex hydrocarbon kinetics is therefore verified to be mixing effect free and be applicable to general reacting flow calculations.

Wang, T. S.↗
NASA NTRS2003

Hydrostatic Stress Effect on the Yield Behavior of Inconel 100

Classical metal plasticity theory assumes that hydrostatic stress has negligible effect on the yield and postyield behavior of metals. Recent reexaminations of classical theory have revealed a significant effect of hydrostatic stress on the yield behavior of various geometries. Fatigue tests and nonlinear finite element analyses (FEA) of Inconel 100 (IN100) equal-arm bend specimens and new monotonic tests and nonlinear finite element analyses of IN100 smooth tension, smooth compression, and double-edge notch tension (DENT) test specimens have revealed the effect of internal hydrostatic tensile stresses on yielding. Nonlinear FEA using the von Mises (yielding is independent of hydrostatic stress) and the Drucker-Prager (yielding is linearly dependent on hydrostatic stress) yield functions were performed. A new FEA constitutive model was developed that incorporates a pressure-dependent yield function with combined multilinear kinematic and multilinear isotropic hardening using the ABAQUS user subroutine (UMAT) utility. In all monotonic tensile test cases, the von Mises constitutive model, overestimated the load for a given displacement or strain. Considering the failure displacements or strains for the DENT specimen, the Drucker-Prager FEM s predicted loads that were approximately 3% lower than the von Mises values. For the failure loads, the Drucker Prager FEM s predicted strains that were up to 35% greater than the von Mises values. Both the Drucker-Prager model and the von Mises model performed equally-well in simulating the equal-arm bend fatigue test.

Allen, Phillip A.↗
NASA NTRS2002

Leveraging Improvements in Precipitation Measuring from GPM Mission to Achieve Prediction Improvements in Climate, Weather and Hydrometeorology

The main scientific goal of the GPM mission, currently planned for start in the 2007 time frame, is to investigate important scientific problems arising within the context of global and regional water cycles. These problems cut across a hierarchy of scales and include climate-water cycle interactions, techniques for improving weather and climate predictions, and better methods for combining observed precipitation with hydrometeorological prediction models for applications to hazardous flood-producing storms, seasonal flood/draught conditions, and fresh water resource assessments. The GPM mission will expand the scope of precipitation measurement through the use of a constellation of some 9 satellites, one of which will be an advanced TRMM-like "core" satellite carrying a dual-frequency Ku-Ka band precipitation radar and an advanced, multifrequency passive microwave radiometer with vertical-horizontal polarization discrimination. The other constellation members will include new dedicated satellites and co-existing Operational/research satellites carrying similar (but not identical) passive microwave radiometers. The goal of the constellation is to achieve approximately 3-hour sampling at any spot on the globe. The constellation's orbit architecture will consist of a mix of sun-synchronous and non-sun-synchronous satellites with the core satellite providing measurements of cloud-precipitation microphysical processes plus calibration-quality rainrate retrievals to be used with the other retrieval information to ensure bias-free constellation coverage. GPM is organized internationally, currently involving a partnership between NASA in the US and the National Space Development Agency in Japan. Additionally, the program is actively pursuing agreements with other international partners and domestic scientific agencies and institutions, as well as participation by individual scientists from academia, government, and the private sector to fulfill mission goals and to pave the way for what ultimately is expected to become an internationally-organized operational global precipitation observing system. Notably, the broad societal applications of GPM are reflected in the United Nation s identification of this mission as a foremost candidate for its Peaceful Uses of Space Program. In this presentation, an overview of the GPM mission design will be presented, followed by an explanation of its scientific agenda as an outgrowth of making improvements in rain retrieval accuracy, microphysics dexterity, sampling frequency, and global coverage. All of these improvements offer new means to observe variability in precipitation and water cycle fluxes and to achieve improved predictability of weather, climate, and hydrometeorology. Specifically, the scientific agenda of GPM has been designed to leverage the measurement improvements to improve prognostic model performance, particularly quantitative precipitation forecasting and its linked phenomena at short, intermediate, and extended time scales. The talk will address how GPM measurements will enable better detection of accelerations and decelerations in regional and global water cycle processes and their relationship to climate variability, better impacts of precipitation data assimilation on numerical weather prediction and global climate reanalysis, and better performance from basin scale hydrometeorological models for short and long term flood-drought forecasting and seasonal fresh water resource assessment. Improved hydrometeorological forecasting will be possible by using continuous global precipitation observations to obtain better closure in water budgets and to generate more realistic forcing of the models themselves to achieve more accurate estimates of interception, infiltration, evaporation/transpiration fluxes, storage, and runoff.

Smith, Eric A.↗
NASA NTRS2003

Anticipated Improvements in Precipitation Physics and Understanding of Water Cycle from GPM Mission

The GPM mission is currently planned for start in the late-2007 to early-2008 time frame. Its main scientific goal is to help answer pressing scientific problems arising within the context of global and regional water cycles. These problems cut across a hierarchy of scales and include climate-water cycle interactions, techniques for improving weather and climate predictions, and better methods for combining observed precipitation with hydrometeorological prediction models for applications to hazardous flood-producing storms, seasonal flood/draught conditions, and fresh water resource assessments. The GPM mission will expand the scope of precipitation measurement through the use of a constellation of some 9 satellites, one of which will be an advanced TRMM-like core satellite carrying a dual-frequency Ku-Ka band precipitation radar and an advanced, multifrequency passive microwave radiometer with vertical-horizontal polarization discrimination. The other constellation members will include new dedicated satellites and co-existing operational/research satellites carrying similar (but not identical) passive microwave radiometers. The goal of the constellation is to achieve approximately 3-hour sampling at any spot on the globe -- continuously. The constellation s orbit architecture will consist of a mix of sun-synchronous and non-sun-synchronous satellites with the core satellite providing measurements of cloud-precipitation microphysical processes plus calibration-quality rainrate retrievals to be used with the other retrieval information to ensure bias-free constellation coverage. GPM is organized internationally, involving existing, pending, projected, and under-study partnerships which will link NASA and NOAA in the US, NASDA in Japan, ESA in Europe, ISRO in India, CNES in France, and possibly AS1 in Italy, KARI in South Korea, CSA in Canada, and AEB in Brazil. Additionally, the program is actively pursuing agreements with other international collaborators and domestic scientific agencies and institutions, as well as participation by individual scientists from academia, government, and the private sector to fulfill mission goals and to pave the way for what is expected to become an internationally-organized operational global precipitation observing system. Notably, the broad societal applications of GPM are reflected in the United Nation s identification of GPM as a foremost candidate for its Peaceful Uses of Space Program. An overview of the GPM mission design is given, followed by an explanation of its scientific agenda as an outgrowth of making improvements in rain retrieval accuracy, microphysics dexterity, sampling frequency, and global coverage. All of these improvements offer new means to observe variability in precipitation and water cycle fluxes and to achieve improved predictability of weather, climate, and hydrometeorology. Specifically, the scientific agenda of GPM has been designed to leverage the measurement improvements to improve prognostic model performance, particularly quantitative precipitation forecasting and its linked phenomena at short, intermediate, and extended time scales. The talk addresses how GPM measurements will enable better detection of accelerations and decelerations in regional and global water cycle processes and their relationship to climate variability, better impacts of precipitation data assimilation on numerical weather prediction and global climate reanalysis, and better performance from basin scale hydrometeorological models for short and long term flood-drought forecasting and seasonal fresh water resource assessment. These improvements become possible by using more accurate, more microphysically-centric, more frequent, and fully global precipitation observations to achieve better water budget closure and to provide more realistic forcing and assessment of prediction models.

Smith, Eric A.↗
NASA NTRS1998

Forebody/Inlet of the Joint Strike Fighter Tested at Low Speeds

As part of a national cooperative effort to develop a multinational fighter aircraft, a model of a Joint Strike Fighter concept was tested in several NASA Lewis Research Center wind tunnels at low speeds over a range of headwind velocities and model attitudes. This Joint Strike Fighter concept, which is scheduled to go into production in 2005, will greatly improve the range, capability, maneuverability, and survivability of fighter aircraft, and the production program could ultimately be worth $100 billion. The test program was a team effort between Lewis and Lockheed Martin Tactical Aircraft Systems. Testing was completed in September 1997, several weeks ahead of schedule, allowing Lockheed additional time to review the results and analysis data before the next test and resulting in significant cost savings for Lockheed. Several major milestones related to dynamic and steady-state data acquisition and overall model performance were reached during this model test. Results from this program will contribute to both the concept demonstration phase and the production aircraft.

Johns, Albert L.↗
NASA NTRS2011

Advanced UVOIR Mirror Technology Development for Very Large Space Telescopes

Objective of this work is to define and initiate a long-term program to mature six inter-linked critical technologies for future UVOIR space telescope mirrors to TRL6 by 2018 so that a viable flight mission can be proposed to the 2020 Decadal Review. (1) Large-Aperture, Low Areal Density, High Stiffness Mirrors: 4 to 8 m monolithic & 8 to 16 m segmented primary mirrors require larger, thicker, stiffer substrates. (2) Support System:Large-aperture mirrors require large support systems to ensure that they survive launch and deploy on orbit in a stress-free and undistorted shape. (3) Mid/High Spatial Frequency Figure Error:A very smooth mirror is critical for producing a high-quality point spread function (PSF) for high-contrast imaging. (4) Segment Edges:Edges impact PSF for high-contrast imaging applications, contributes to stray light noise, and affects the total collecting aperture. (5) Segment-to-Segment Gap Phasing:Segment phasing is critical for producing a high-quality temporally stable PSF. (6) Integrated Model Validation:On-orbit performance is determined by mechanical and thermal stability. Future systems require validated performance models. We are pursuing multiple design paths give the science community the option to enable either a future monolithic or segmented space telescope.

Stahl, H. Philip↗
NASA NTRS2012

Community Coordinated Modeling Center: Addressing Needs of Operational Space Weather Forecasting

Models are key elements of space weather forecasting. The Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) hosts a broad range of state-of-the-art space weather models and enables access to complex models through an unmatched automated web-based runs-on-request system. Model output comparisons with observational data carried out by a large number of CCMC users open an unprecedented mechanism for extensive model testing and broad community feedback on model performance. The CCMC also evaluates model's prediction ability as an unbiased broker and supports operational model selections. The CCMC is organizing and leading a series of community-wide projects aiming to evaluate the current state of space weather modeling, to address challenges of model-data comparisons, and to define metrics for various user s needs and requirements. Many of CCMC models are continuously running in real-time. Over the years the CCMC acquired the unique experience in developing and maintaining real-time systems. CCMC staff expertise and trusted relations with model owners enable to keep up to date with rapid advances in model development. The information gleaned from the real-time calculations is tailored to specific mission needs. Model forecasts combined with data streams from NASA and other missions are integrated into an innovative configurable data analysis and dissemination system (http://iswa.gsfc.nasa.gov) that is accessible world-wide. The talk will review the latest progress and discuss opportunities for addressing operational space weather needs in innovative and collaborative ways.

Kuznetsova, M.↗
NASA NTRS2022

Global Gridded Crop Models Underestimate Yield Responses to Droughts and Heatwaves

Extreme events can lead to crop yield declines, resulting in financial losses and threats to food security, and the frequency and intensity of such events is projected to increase. As global gridded crop models (GGCMs) are commonly used to assess climate change impacts on agricultural yields, there is a need to understand whether these models are able to reproduce the observed yield declines. We evaluated 13 GGCMs from the Inter-Sectoral Impact Model Intercomparison Project and compared observed and simulated impact of past droughts and heatwaves on yields for four crops (maize, rice, soy, wheat). We found that most models detect but underestimate the impact of droughts and heatwaves on yield. Specifically, the drought signal was detected by 12 of 13 models for maize and all models for wheat, while the heat signal was detected by eleven models for maize and six models for wheat. To investigate whether the difference between simulated and observed yield declines is due to a misrepresentation of simulated exposure to heat or water scarcity (i.e. misrepresentation of growing season), we analysed the relationship between average discrepancies between observed and simulated yield losses, and average simulated exposure to extreme weather conditions across all crop models. We found a positive correlation between simulated exposure to heat and model performance for heatwaves, but found no correlation for droughts. This suggests that there is a systematic underestimation of yield responses to heat and drought and not only a misrepresentation of exposure. Assuming that performance for the past indicates models' capacity to project future yield impacts, models likely underestimate future yield decline from climate change. High-quality temporally and spatially resolved observational data on growing seasons will be highly valuable to further improve crop models' capacity to adequately respond to extreme weather events.

extreme events↗
NASA NTRS2024

Modeling Atmospheric Brown Carbon in the GISS ModelE Earth System Model

Brown carbon (BrC) is an absorbing organic aerosol (OA), primarily emitted through biomass burning (BB), which exhibits light absorption unique to both black carbon (BC) and other organic aerosols. Despite many field and laboratory studies seeking to constrain BrC properties, the radiative forcing (RF) of BrC is still highly uncertain. To better understand its climate impact, we introduced BrC to the One-Moment Aerosol (OMA) module of the GISS ModelE Earth system model (ESM). We assessed ModelE sensitivity to primary BrC processed through a novel chemical aging scheme and to secondary BrC formed from biogenic volatile organic compounds (BVOCs). Initial results show that BrC typically contributes a top-of-the-atmosphere (TOA) radiative effect of 0.04 W m −2 . Sensitivity tests indicate that explicitly simulating BrC (separating it from other OAs), including secondary BrC, and simulating chemical bleaching of BrC contribute distinguishable radiative effects and should be accounted for in BrC schemes. This addition of prognostic BrC to ModelE allows greater physical and chemical complexity in OA representation with no apparent trade-off in model performance, as the evaluation of ModelE aerosol optical depth against Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) retrieval data, with and without the BrC scheme, reveals similar skill in both cases. Thus, BrC should be explicitly simulated to allow more physically based chemical composition, which is crucial for more detailed OA studies like comparisons to in situ measurement campaigns. We include a summary of best practices for BrC representation within ModelE at the end of this paper.

brown carbon aerosols↗
DOE OSTI2026

Teleseismic Network Association with GENIE

In this report we investigate adapting the Graph Neural Interpretation Engine (GENIE), an associator developed for three-component dense monitoring networks, to regional to teleseismic association using a sparse network of array stations. We expand GENIE’s input features to include first-P detection time, azimuth, and slowness estimates. Additionally, we include a probability of detection (PDET) term which measures a station’s likelihood of detecting an event. To assess each feature’s relative importance, we train four models, each using an increasing set of node features and find that the PDET models perform the best. We define two measures of event complexity which demonstrate that all GENIE model versions perform better than the standard backprojection stack.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Subhourly Clipping Correction Model Comparison

This work will compare the Allen method and Walker method of accounting for subhourly inverter clipping power losses in hourly PV performance models. The Allen method uses a matrix lookup based on DNI clearness and clipping potential to assign a clipping correction loss at each simulation timestep. The Walker method models the PV DC power input to the inverter as a distribution over the hourly timestep and uses integration over the timestep to determine the amount of clipping that occurs within the timestep. Both these models have been recently implemented in the System Advisor Model's (SAM) open-source code, and will be applied to hourly SURFRAD datasets to analyze the subhourly clipping loss predicted by each model for different system designs and inverter loading conditions. Both models will be compared to "true" 1-minute SURFRAD data simulations to see their accuracy against more accurate 1-minute clipping correction loss predictions. This model comparisons will be investigated in more detail at the PVSC conference in Seattle, Washington June 2024.

ENERGY PLANNING, POLICY, AND ECONOMY,MATHEMATICS A↗