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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development and application of two-step uncertainty propagation and sensitivity analysis methodology for fast reactor safety analysis

Uncertainty quantification (UQ) in nuclear reactors for transients is directly linked with safety assessment through the cross-sections uncertainties, provided as a covariance matrix, which are propagated through the reactor system to output of interest pertaining to reactor safety, such as peak temperatures in fuel/clad/coolant. Using a two-step approach, uncertainties are first quantified and propagated from basic input variables (such as reaction cross-sections) to intermediate quantities (such as reactivity feedback coefficients) through lattice level calculations. Uncertainties of intermediate quantities (from the first step) are then propagated through the system transient calculations, in the second step, to obtain uncertainties on reactor safety output parameters of interest. The scope of this work consists of Uncertainty Quantification & Propagation of nuclear data uncertainties that are highly correlated through unprotected transient overpower and unprotected loss of flow to assess their impact on core safety parameters. This two-step approach in the presence of covariance renders the sensitivity analysis very challenging. In fact, usually the sensitivity analysis is restricted to each step, which limits its application since the sensitivities between the system output quantities and the basic input variables are difficult to obtain. Here, in this work, we address this issue by proposing a simple, general methodology to combine the sensitivity indices obtained in each step by assuming the model behavior being linear. For the first step Generalized Perturbation theory based indices are used while in the second step the recently studied Johnson indices. The uncertainty quantification and sensitivity methodologies discussed here are demonstrated on a generic LFR design which is based on the 500 MWth demonstration Lead-cooled fast reactor (DLFR) using oxide fuel, developed by Westinghouse Electric Company (WEC).

42 - ENGINEERING↗
DOE OSTI2021

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Furthermore, fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Quantum optimality of photon counting for temperature measurement of thermal astronomical sources

Using the quantum Cramér–Rao bound from quantum estimation theory, we derive a fundamental quantum limit on the sensitivity of a temperature measurement of a thermal astronomical source. This limit is expressed in terms of the source temperature T{sub s}, input spectral bandwidth Δν, and measurement duration T, subject to a long measurement time assumption TΔν≫1. It is valid for any measurement procedure that yields an unbiased estimate of the source temperature. The limit agrees with the sensitivity of direct detection or photon counting, and also with that of the ideal radiometer in the regime kT{sub s}/hν{sub 0}≫1 for which the Rayleigh–Jeans approximation is valid, where ν{sub 0} is the center frequency at which the radiometer operates. While valid across the electromagnetic spectrum, the limit is especially relevant for radio astronomy in this regime, since it implies that no ingenious design or technological improvement can beat an ideal radiometer for temperature measurement. In this connection, our result refutes the recent claim of a radio astronomy technique with much-improved sensitivity over the radiometer.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2019

Air Pollution Inputs to the Mojave Desert By Fusing Surface Mobile and Airborne in Situ and Airborne and Satellite Remote Sensing: A Case Study of Interbasin Transport With Numerical Model Validation

Deserts are fragile and highly sensitive ecosystems that increasingly are affected by upwind urban areas and industrial activities. The Los Angeles Basin (LAB) contributes to poor air quality in downwind deserts including the Mojave Desert. Few studies have investigated potential air pollution inputs to the Mojave, whose fragile ecosystem includes endangered plant and animal species. Data were collected on 19 August 2015 by a mobile air quality laboratory, AMOG (AutoMObile trace Gas) Surveyor, that observed inputs can arise from the LAB as well as the San Joaquin Valley (SJV), California. The campaign used a strong methane (CH4) plume as a tracer for the downwind fate of emissions from Bakersfield area petroleum production and also measured ozone (O3). Additional in situ concurrent airborne GHG and O3 data were collected by AJAX - Alpha Jet Atmospheric eXperiment. Both AMOG and AJAX measure winds. Mojave Desert air quality was very poor (visibility ~4 km). Based on the winds, an additional source was inferred beyond the LAB and SJV Basins. Numerical transport modeling and analysis of aerosol lidar data collected the same day by the Cloud Profiling LiDAR onboard the Earth Research-2 stratospheric airplane demonstrated that fires in Northern California were responsible, with prevailing winds transporting air southwards along the eastern Sierra Nevada Range (Bishop Valley) to the Mojave. Whereas the southern and eastern Mojave are impacted by SJV and LAB outflow, the north Mojave generally avoids these inputs. This study shows it can be affected by even distant wildfires, which likely will increase in occurrence and intensity from climate change. Thus, regulatory efforts to reduce air quality impacts on the endangered Mojave ecosystem must include wildfires and also account for the significant differences between different regions of the Mojave. Currently, there is a paucity of studies, highlighting the critical need for field research.

Ira Leifer↗
NASA NTRS1993

A Numerical Simulation Study to Develop an Acceptable Wake Encounter Boundary for a B737-100 Airplane

The National Aeronautics and Space Administration (NASA) is conducting research with the goal of enabling safe improvements in the capacity of the nation's air transportation system. The wake-vortex upset hazard is an important factor in establishing the minimum safe spacing between aircraft during landing and take-off operations, thus impacting airport capacity. A batch simulation study was conducted to assess the sensitivity of various safe landing criteria in the development of an acceptable wake encounter boundary. A baseline six-degree-of-freedom simulation of a B737-100 airplane was modified to include a wake model and the vortex-induced forces and moments. The guidance and control input for the airplane was provided by an auto-land system. The wake strength and encounter geometry were varied. A sensitivity study was also conducted to assess the effects of encounter modeling methods and accuracy.

Vicroy, Dan D.↗
DOE OSTI2014

SPE-4-prime Calculations on Sympathetic detonation and Preliminary Confinement [Slides]

Peak pressure is very low compared with input pressures that lead to detonation in most explosives (> 100 MPa). As an illustration, Gibbs and Popolato data for PETN at density 1.4 g/cc shows a 500 MPa shock transits to detonation only after a run distance of > 10 mm. The above data are consistent with the RP-83 design - the PETN in the detonator gets a real whack from the EBW to achieve prompt initiation. 500 MPa is 5 kilobar, which is a low pressure in the shock initiation arena. The 4 MPa input pressure is 0.04 kilobar, which is very low. After traveling through the intervening geologic media, the pressure will arrive as a ramp rather than a shock, which is much less effective at initiating explosives. In fact, a compression wave may desensitize the explosive. The most sensitive HE, the PETN in the detonator, is shielded by the mass of ANFO and the pentolite boosters, and so the pressure input to the detonator will be significantly less than that experienced by the ANFO. An additional factor is that the abandoned charge was thermally compromised, and the effect of the somewhat elevated temperature on the PETN and the SHANFO is most likely to desensitize them. This has been observed at LLNL with PETN, and the manufacturer of the SHANFO is the source of the information on its response to temperature. The only possibility posited by one of us is failure of the geologic media resulting in a puncture / perforation of the SHANFO charge. If sufficient shear energy were put into this charge, it could conceivably initiate. This is difficult to quantify and seems like a remote possibility, and at any rate is outside the expected behavior of the geologic media as I understand the problem. Information on the effect of temperature on SHANFO is in the enclosed report that analyzes the failure of SPE 4. I think there are additional possibilities that were not evaluated in the report, but it does provide some useful data on SHANFO sensitivity at elevated temperatures. I don't know if you have seen it, so I enclose a copy. PETN (Jon L. Maienschein, PhD Director, LLNL Energetic Materials Center)

58 GEOSCIENCES↗
NASA NTRS1968

Thermal short improves sensitivity of cryogenically cooled maser

In-line, quarter-wave thermal short cools the center conductor of the signal-input coaxial transmission line to a cryogenically cooled traveling wave maser. It reduces both the thermal noise contribution of the coaxial line and the heat leak through the center conductor to the maser at 4.4 degrees K.

Clauss, R. C.↗
NASA NTRSDate not supplied

Quantifying the Sensitivity of Condition Incidence Parameters in the Evidence Library

One approach to quantifying spaceflight risk at NASA makes use event driven probabilistic techniques. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is such a tool that estimates medical risk metrics via simulation and enables optimization of medical resources subject to mission constraints [1]. Previous analyses have informed medical set composition, exercise countermeasures, and water intake, where each analysis quantifies the risk associated with proposed variations in system design. As future mission profiles extend beyond Low-Earth Orbit (LEO) and lengthen in duration, understanding these risks and contributing factors is critical. MEDPRAT employs Monte Carlo sampling techniques to simulate missions and track the occurrence of medical events. These events follow fault-tree-like progressions through levels of severity and mitigation via medical treatment to many possible outcomes and these are reported throughout the mission. Making this possible, are the medical databases that contain evidence gathered by the Human Research Program (HRP). Quantifying the impact of uncertainty or variability in the input data is an important step in evaluating the credibility of modeling and simulation results. In this work, we investigate the sensitivity of medical risk metrics with respect to the condition incidence parameters within the Evidence Library (EL) [2] as the medical database input for MEDPRAT. The medical conditions, contained in the EL, are equipped with incidence rates that describe the likelihood that the condition will occur. These incidence rates reflect historical spaceflight data or when appropriate, terrestrial data. In this presentation, we will explore how uncertainty in these rates propagate to the medical risk described by MEDPRAT. These results identify the conditions and parameters with the largest contribution to medical risks.

Ian Lim↗
DOE OSTI2016

Climate Change Feedbacks from Interactions Between New and Old Carbon

Priming effects, or responses of SOM decomposition rates to inputs of new, labile carbon (C), have the potential to dramatically alter projections of ecosystem C storage. Priming effects occur in most ecosystems, are significant in magnitude, and are highly sensitive to global changes. Nevertheless, our mechanistic understanding of priming effects remains poor, and this has prevented the inclusion of these dynamics into current Earth system models (ESMs). We conducted two manipulative experiments in the field to quantify how priming effects influence SOM dynamics. Specifically, we asked: To what extent do inputs of “new” root-derived carbon (C) influence “older” C in SOM, and are the magnitude and direction of these effects sensitive to climate? We addressed these questions within the Boston-Area Climate Experiment - an old-field ecosystem that has been subjected to three precipitation treatments (ambient, -50%, and +50% of each precipitation event during the growing season) and four warming treatments (from ambient to +4°C) since 2008. In the first experiment, we installed root and fungal ingrowth cores into the plots. Each core was filled with SOM that had an isotopic signature (of its C compounds) that differed from the vegetation in the plots such that inputs of “new” C from roots/fungi could be quantified using the change in isotopic signatures of C in the cores. Further, we used cores with different mesh sizes to isolate root vs. mycorrhizal fungal inputs. We found that belowground C fluxes were dominated by root inputs (as opposed to mycorrhizal inputs), and that root-derived inputs were greatest in the plots subjected to experimental warming. Given that that the warming-induced increase in belowground C flux did not result in a net increase in soil C, we conclude that the warming treatment likely enhanced priming effects in these soils. In the second experiment, we experimentally dripped dissolved organic C compounds into soils in the BACE plots to simulate root-derived C fluxes. Specifically, we constructed artificial roots attached to an automated peristaltic pump to deliver the compounds to soil semi-continuously during the peak of the growing season. We found that changes in exudate quality had small but significant effects on microbial activities, often interacting with N availability and temperature-induced changes. These results further underscore the importance of priming effects, especially under warming conditions. Collectively, our results provide some of the first field-based estimates of how soil moisture and temperature can directly and indirectly alter root-induced changes in SOM dynamics. This exploratory project lays the groundwork for further research on priming that incorporates effects of plant species and microbial communities to global changes. Such information should enable the development of more mechanistic and better predictive models of SOM decomposition under increased greenhouse gas levels, with the ultimate goal of reducing the level of uncertainty in projections of future climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Uncertainty analysis in geospatial merit matrix–based hydropower resource assessment

Hydraulic head and mean annual streamflow, two main input parameters in hydropower resource assessment, are not measured at every point along the stream. Translation and interpolation are used to derive these parameters, resulting in uncertainties. This study estimates the uncertainties and their effects on model output parameters: the total potential power and the number of potential locations (stream-reach). These parameters are quantified through Monte Carlo Simulation (MCS) linking with a geospatial merit matrix based hydropower resource assessment (GMM-HRA) Model. The methodology is applied to flat, mild, and steep terrains. Results show that the uncertainty associated with the hydraulic head is within 20% for mild and steep terrains, and the uncertainty associated with streamflow is around 16% for all three terrains. Output uncertainty increases as input uncertainty increases. However, output uncertainty is around 10% to 20% of the input uncertainty, demonstrating the robustness of the GMM-HRA model. Hydraulic head is more sensitive to output parameters in steep terrain than in flat and mild terrains. Furthermore, mean annual streamflow is more sensitive to output parameters in flat terrain.

13 HYDRO ENERGY↗
DOE OSTI2024

Modeling electron temperature profiles in the pedestal with simple formulas for ETG transport

Abstract This paper reports on the refinement (building on (Hatch D.R. et al 2022 Phys. Plasmas 29 062501)) and application of simple formulas for electron heat transport from electron temperature gradient (ETG) driven turbulence in the pedestal. The formulas are improved by (1) improving the parameterization for certain key parameters and (2) carefully accounting for the impact of geometry and shaping in the underlying gyrokinetic simulation database. Comparisons with nonlinear gyrokinetic simulations of ETG transport in the MAST pedestal demonstrate the model’s applicability to spherical tokamaks in addition to standard aspect ratio tokamaks. We identify bounds for model applicability: the model is accurate in the steep gradient region, where the ETG turbulence is largely slab-like, but accuracy decreases as the temperature gradient becomes weaker in the pedestal top. We use the formula to model the electron temperature profile in the pedestal for four experimental scenarios while extensively varying input parameters to represent uncertainties. In all cases, the predicted electron temperature profile exhibits extreme sensitivity to separatrix temperature and density, which has implications for core-edge integration. The model reproduces the electron temperature profile for high η e = L n e / L T e scenarios but not for low η e scenarios in which microtearing modes (MTMs) have been identified. We develop a proof-of-concept model for MTM transport and explore the relative roles of ETG and MTM in setting the electron temperature profile.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Artificial intelligence-based predictive modeling for imaging neutral particle analyzers on the DIII-D tokamak

The Imaging Neutral Particle Analyzer (INPA) at DIII-D is a diagnostic system used to accurately resolve the energy and spatial distributions of fast ions in fusion plasmas. A novel artificial intelligence (AI) technique named INPA-net is based on Reservoir Computing Networks and developed here to predict active and passive signals produced by charge-exchange reactions from injected and edge-cold neutrals, respectively, in magnetically confined fusion plasmas. This model is trained using a set of 21 time domain signals between 0 s to 3.35 s that includes injected beam and thermal plasma information, and 6444 real 2D experimental images of the INPA in 12 plasma discharges at DIII-D. The trained neural network is able to forecast experimental images in real-time. The model achieves an R-squared value of 0.91, which is higher than the 0.83 value achieved by a simple linear regression model. This improvement highlights the model's enhanced predictive accuracy for measured images from the validation set. This AI approach is valuable due to its rapid response times and potential for integration into real-time plasma control systems. A version of this model capable of generating syntehic images would be useful for the real-time monitoring of fast-ion transport. A comprehensive sensitivity study reveals that INPA-net maintains high performance even with variations in the input parameters, indicating the model's robustness and reliability. While developed for the INPA, the underlying architecture is adaptable and may be applied to various 2D imaging diagnostics in fusion research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Production of the entire range of r -process nuclides by black hole accretion disc outflows from neutron star mergers

We consider r -process nucleosynthesis in outflows from black hole accretion discs formed in double neutron star and neutron star–black hole mergers. These outflows, powered by angular momentum transport processes and nuclear recombination, represent an important – and in some cases dominant – contribution to the total mass ejected by the merger. Here we calculate the nucleosynthesis yields from disc outflows using thermodynamic trajectories from hydrodynamic simulations, coupled to a nuclear reaction network. We find that outflows produce a robust abundance pattern around the second r -process peak (mass number A ~ 130), independent of model parameters, with significant production of A < 130 nuclei. This implies that dynamical ejecta with high electron fraction may not be required to explain the observed abundances of r -process elements in metal poor stars. Disc outflows reach the third peak (A ~ 195) in most of our simulations, although the amounts produced depend sensitively on the disc viscosity, initial mass or entropy of the torus, and nuclear physics inputs. Some of our models produce an abundance spike at A = 132 that is absent in the Solar system r -process distribution. The spike arises from convection in the disc and depends on the treatment of nuclear heating in the simulations. We conclude that disc outflows provide an important – and perhaps dominant – contribution to the r -process yields of compact binary mergers, and hence must be included when assessing the contribution of these systems to the inventory of r -process elements in the Galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Techno-Economic Impact of Diesel Fuel, Labor, and Interest Rates on Bulk-Format and Round Bale Logistics Expenses for Switchgrass

Highlights System-wide sensitivity of bulk-format and round bale logistics responded to different degrees of expense inputs. Biomass size-reduction expenses for bulk-format were consistently less than round-bale chopping. Stack/reclaim operations with high-density-capacity equipment for bulk-format were the highest expenses. Clean-sheet design of stacker/reclaimer and self-compacting bulk trailer may significantly reduce expenses. Abstract . Deterministic cost analyses were conducted for a harvest, storage, preprocessing, and supply system providing 371,870 dry Mg (dMg) year-1 of milled switchgrass (SG) to a biorefinery from 21,929 ha of production fields within 72 km of the biorefinery. Varied input costs of diesel fuel, labor, and interest rate were input into a highly detailed cost analysis for bulk-format and baseline round bale logistics. These operational input factors, potentially reflecting an evolving economic climate or paradigm shift, were examined to provide insight into the cost to deliver preprocessed biomass feedstock for centralized commercial-scale conversions. Total annual costs per dry unit of SG were determined for all combinations of diesel fuel prices of $0.53 L-1, $0.92 L-1, and $1.32 L-1; total labor costs of $20.00 h-1, $28.85 h-1, and $40.00 h-1 singly and uniformly applied across all labor inputs; and three representative interest rates of 6%, 8%, and 12%. Increases in total cost most depended on increases in diesel fuel cost, followed by labor rate, and finally interest rate. Increases in fuel costs from $0.53 L-1 to $1.32 L-1 yielded a total cost increase of about $11.56 dMg-1 and $13.36 dMg-1 for bulk-format and round bale logistics, respectively, for all combinations of labor and interest rates. The overall increase in labor rate ($20.00 h-1 to $40.85 h-1) resulted in increased total costs of about $10.32 dMg-1 for bulk-format logistics and $9.94 dMg-1 for round bale logistics for all fuel and interest rates. The overall increase in interest rate (6% to 12%) resulted in increased total costs of about $6.72 dMg-1 for bulk-format logistics and $4.21 dMg-1 for round bale logistics for all fuel and labor rates. Increases in total costs for combined increases in fuel cost, labor rate, and interest rate were $28.60 dMg-1 and $27.48 dMg-1, yielding maximum total costs of $87.87 dMg-1 and $86.53 dMg-1 for bulk-format and round bale logistics, respectively. Costs were determined for existing equipment systems, including novel use of waste compaction and ejector semi-trailers. The highest potential for cost reductions may require clean-sheet equipment designs specifically tailored for SG, such as forage harvesters that direct-cut standing SG, mobilized stacker-reclaimer technology to not inhibit stack-area footprint, self-compacting ejector trailer technology, and bale grinding technology incorporating controlled mass feeding and shear failure elements. Keywords: Bales, Bioenergy, Bulk-format, Densification, Field chop, Logistics, Transportation

Agriculture↗
DOE OSTI2019

Recommended Strategy for Implementing PORFLOW Subsidence Infiltration Boundary Conditions

This technical memorandum summarizes a limited-in-scope sensitivity analysis that addresses uncertainty in the conservatism of blending infiltration rates for use as input boundary conditions in PORFLOW vadose zone simulations as opposed to blending flux-to-the-water-table outputs for different subsidence infiltration scenarios, as has been done historically.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Using AI to build a hydrobiogeochemical soil model

Soil water content is a function of inputs from precipitation and outputs via evaporation, transpiration, lateral flow, and vertical percolation, and is sensitive to biogeochemical processes. As such, soils serve as an ideal integrator of atmospheric, hydrological, and biogeochemical processes affecting the water cycle. In addition, soil water retention capacity, infiltration rates, and hydraulic conductivity can buffer or exacerbate the effects of extreme precipitation events (e.g., flooding, runoff, subsurface transport, erosion, greenhouse gas emissions) and mitigate the impact of droughts and heat waves on land systems (e.g., fire, crop failure). However, integrating water cycle measurements spanning different land atmosphere compartments across scales is a fundamental barrier for numerical model predictability. A significant challenge is that each domain (soil, hydrology, biology, and atmosphere) typically collects different sets of data at different temporal and spatial frequencies/scales, and even different dimensionalities (2D vs 3D). To implement soil as an integrator of the water cycle in land models, we suggest that novel machine learning (ML) tools can be developed to effectively simulate complex landscapes across various domains and scales, extended to regions with sparse or no data. The ultimate goals are to improve predictive understanding of land-atmosphere interactions and to extend the predictability of current Earth System Models (ESMs) through better integration of hydrological and biogeochemical data. We envision a framework in which: (1) ML-aided data reconstructions enable the merger of data sources into a unified geospatial product; (2) automated detection techniques are used to improve the knowledge of complex soil processes and interactions; and (3) this knowledge is leveraged and incorporated into models through AI-based emulators to distinctly connect the land and atmospheric compartments of the water cycle in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Parametric Sensitivity Comparison of Simulation Models for Flyer Plate Impact Experiments

This paper compares the sensitivities of different modeling approaches for fundamental flyer plate impact experiments to variations of several input parameters. Two modeling approaches are used to model a copper flyer plate experiment: the CTH hydrodynamic code and the LS-DYNA code using the smoothed particle hydrodynamic (SPH) approach. Simulations using these two methods are compared to experimental data from a gas gun experiment in which a copper flyer plate impacted a copper target plate. The experiment was designed to cause spall in the target. Numerical simulations are conducted using typical input physics models including the Mie-Grüneisen equation of state, the Johnson-Cook compressive strength model, and a pressure cutoff (Pmin) model for tensile damage (spall). Effects of artificial viscosity, spall strength, and computational cell size are studied and discussed with the objective of improving the accuracy of these simulations.

CTH, smoothed particle hydrodynamics, SPH, Johnson↗
DOE OSTI2023

Insensitivity to propagation timing in a preview-enabled wind turbine control experiment

Lidar scanners are capable of taking measurements of a wind field upstream of a wind turbine. The wind turbine controller can use these measurements as a “preview” of future disturbances impacting the turbine. Such preview-enabled (or feedforward) controllers show superior performance to standard wind turbine control configurations based purely on a feedback architecture. To capitalize on the performance improvements that preview wind measurements can provide, feedforward control actions should be timed to coincide with the arrival of the wind field at the wind turbine location. However, the time of propagation of the wind field between the lidar measurement location and the wind turbine is not perfectly known. Moreover, the best time to take feedforward control action may not perfectly coincide with the true arrival time of the wind disturbance. This contribution presents results from an experiment where preview-enabled model predictive control was deployed on a fully-actuated, scaled model wind turbine operating in a wind tunnel testbed. In the study, we investigate the sensitivity of the controller performance to the assumed propagation delay using a range of wind input sequences. We find that the preview-enabled controller outperforms the feedback only case across a wide range of assumed propagation delays, demonstrating a level of robustness to the time alignment of the incoming disturbances.

17 WIND ENERGY↗