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At least 541 records · Page 30

Design, Optimization, and Control of Floating Offshore Wind Farms for Optimal Energy Production (Final report)

The uncertainty and irregularity of ocean waves and the ocean environment is a major factor in the development of commercial scale floating wind turbines as the operation of floating structures in such an environment can lead to irregular and unpredictable loading, fatigue, and ultimately a reduction in the operational life of the turbine system which affects energy production over the lifetime of the turbine. Control solutions that can limit float motions and mitigate stressful events on the structure become essential for extending lifetime and limiting the operational uncertainty of a floating wind turbine. Digital twins are computational replicas of physical systems that operate in parallel with the operation of the physical system. Given advanced knowledge of a systems input, digital twins have the ability to predict the behavior of a system in advance, which can be valuable in the control of that system. In this project, we developed and assessed potential digital twin models developed in house and openly available (OpenFast) for use in the real time control of the six degree of freedom response motions of a floating wind turbine in ocean waves. Coupling these models with near-field real time irregular sea surface (wve) measurement/sensings and prediction models, we used the digital twin to predict how the floating turbine will respond to the incoming waves. Applying this information to a motion control system of the float, one can limit and control float motions to prevent undesirable loading events/large angular motions, thus increasing system life and ultimately contributing to optimizing energy production. Due to the computational intensity of operating a digital twin in real time, we investigated the use of artificial intelligence techniques to speed-up the processes of the digital twin, as well as the wave reconstruction/prediction models. Model tank testing at the University of Rhode Island and University of Maine both validated and demonstrated the developed techniques on simple float geometries and a scale model of the NREL 15 MW reference turbine.

17 WIND ENERGY↗

Model Reduction for Steady Hypersonic Aerodynamics via Conservative Manifold Least-Squares Petrov–Galerkin Projection

High-speed aerospace engineering applications rely heavily on computational fluid dynamics (CFD) models for design and analysis. This reliance on CFD models necessitates performing accurate and reliable uncertainty quantification (UQ) of the CFD models, which can be very expensive for hypersonic flows. Additionally, UQ approaches are many-query problems requiring many runs with a wide range of input parameters. One way to enable computationally expensive models to be used in such many-query problems is to employ projection-based reduced-order models (ROMs) in lieu of the (high-fidelity) full-order model (FOM). In particular, the least-squares Petrov–Galerkin (LSPG) ROM (equipped with hyper-reduction) has demonstrated the ability to significantly reduce simulation costs while retaining high levels of accuracy on a range of problems, including subsonic CFD applications. This allows LSPG ROM simulations to replace the FOM simulations in UQ studies, making UQ tractable even for large-scale CFD models. This work presents the first application of LSPG to a hypersonic CFD application, the Hypersonic International Flight Research Experimentation 1 (HIFiRE-1) in a three-dimensional, turbulent Mach 7.1 flow. This paper shows the ability of the ROM to significantly reduce computational costs while maintaining high levels of accuracy in computed quantities of interest.

42 ENGINEERING↗

SIF-based GPP modeling for evergreen forests considering the seasonal variation in maximum photochemical efficiency

Solar-induced chlorophyll fluorescence (SIF) has shown great potential in estimating gross primary production (GPP). However, their quantitative relationship is not invariant, which undermines the reliability of empirical SIF-based GPP estimation at fine spatiotemporal scales, especially under extreme conditions. In this study, we developed a parsimonious mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) by employing the Mechanistic Light Response framework and Eco-Evolutionary theory to describe the light and dark reactions during photosynthesis, respectively. Specifically, we found that considering the seasonal variation in a key parameter of the MLR framework, the maximum photochemical efficiency of photosystem II (Φ PSIImax ), can avoid the GPP overestimation in winter and early spring due to the relatively low environmental sensitivity of SIF. Compared to the estimates from other benchmark models, our GPP estimates were closer to the 1: 1 line and had higher accuracy (average R 2 = 0.86, RMSE=1.99 μmol m -2 s -1 ) across sites. Furthermore, the changes in the relationship between SIF and J (refers to the electron transport rate) contribute a lot to the dynamic SIF–GPP relationship in this study, while the J–GPP relationship is less variant when the temperature drops. Further, the seasonal variation in the SIF–J relationship, especially the reduction in its slope at low temperatures, is found largely explained by the Φ PSIImax . These results indicate the importance of the uncertainty caused by the variation in the SIF–J relationship for SIF-based GPP estimation, and the consideration of changes in Φ PSIImax under extreme conditions (such as severe winter in this study) is crucial for the improvement of GPP estimation via SIF.

59 BASIC BIOLOGICAL SCIENCES↗

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗

Distances to Prompt Effects for a Nuclear Device

In 2010, nuclear weapon effects experts at Sandia National Laboratories (SNL) were asked to provide a quick reference document containing estimated prompt nuclear effects. This report is an update to the 2010 document that includes updated model assumptions. This report addresses only the prompt effects associated with a nuclear detonation (e.g., blast, thermal fluence, and prompt ionizing radiation). The potential medium- and longer-term health effects associated with nuclear fallout are not considered in this report because, in part, of the impracticality of making generic estimates given the high dependency of fallout predictions on the local meteorological conditions at the time of the event. The results included in this report also do not consider the urban environment (e.g., shielding by or collapse of structures) which may affect the extent of prompt effects. It is important to note that any operational recommendations made using the estimates in this report are limited by the generic assumptions considered in the analysis and should not replace analyses made for a specific scenario/device. Furthermore, nuclear effects experts (John Hogan, SNL, and Byron Ristvet, Defense Threat Reduction Agency (DTRA)) have indicated that the accuracy of effects predictions below 0.5 kilotons (kT) or 500 tons nuclear yield have greater uncertainty because of the limited data available for the prompt effects in this regime. The Specialized Hazard Assessment Response Capability (SHARC) effects prediction tool was used for these analyses. Specifically, the NUKE model within SHARC 2021 Version 10.2 was used. NUKE models only the prompt effects following a nuclear detonation. The algorithms for predicting range-to-output data contained within the NUKE model are primarily based on nuclear test effects data. Probits have been derived from nuclear test data and the U.S. Environmental Protection Agency (EPA) protective action guides. Probits relate the probability of a hazard (e.g., fatality or injury) caused by a given insult (e.g., overpressure, thermal fluence, dose level). Several probits have been built into SHARC to determine the fatality and injury associated with a given level of insult. Some of these probits differ with varying yield. Such probits were used to develop the tables and plots in this report.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Stability and control flight test results of the space transportation system's orbiter

Flight testing of the Space Shuttle Orbiter is in progress and current results of the post-flight aerodynamic analyses are discussed. The purpose of these analyses is to reduce the pre-flight aerodynamic uncertainties, thereby leading to operational certification of the Orbiter flight envelope relative to the integrated airframe and flight control system. Primary data reduction is accomplished with a well documented maximum likelihood system identification techniques.

Culp, M. A.↗

A digital video model deformation system

The use of solid-state array cameras and a PC-controlled image acquisition system to measure model deformation in a wind tunnel is discussed. This digital system improves an earlier video model deformation system that used high-resolution tube cameras and required the manual measurement of targets on video hardcopy images. The new system eliminates both the vibration-induced distortion associated with tube cameras and the manual readup of video images necessary in the earlier version. Camera calibration and data reduction procedures necessary to convert pixel image plane data from two cameras into wing deflections are presented. Laboratory tests to establish the uncertainty of the system with the geometry to be used are described.

Burner, A. W.↗

Conceptual design environment

Opportunities in conceptual design reside in the fact that the greatest chance for cost reduction occurs during the early stages of product development. The conceptual design problem encompasses managing complex engineering information, multiple interacting groups, and uncertainty. Presented in viewgraph form are the opportunities in conceptual design, the design scenario, and technical issues and approaches.

Buckley, Michael J.↗

A Digital Video Model Deformation System

The use of slid-state array cameras and a PC controlled image acquisition system to measure model deformation in a wind tunnel is discussed. This digital system is an improvement to an earlier video model deformation system used at the National Transonic Facility (NTF) which employed high-resolution tube cameras and required the manual measurement of targets on video hardcopy images. The new system eliminates both the vibration-induced distortion associated with tube cameras and the manual readup of video images necessary in the earlier version. Camera calibration and data reduction procedures necessary to convert pixel image plane data from two cameras into wing deflections are presented. Laboratory tests to establish the uncertainty of the new system with the geometry to be used at the NTF are described.

Burner, A. W.↗

Quantitative Gas Property Measurements By Filtered Rayleigh Scattering: A Review

Filtered Rayleigh scattering (FRS) is a laser-based diagnostic technique used to nonintrusively quantify various thermodynamic properties of a light-scattering gas. The backbone of FRS is the molecular filtering of Rayleigh scattered light. This concept was initially introduced by the atmospheric LIDAR community before being adopted within the aerospace research field in the early 1990s. Since then, FRS has matured into a versatile quantitative diagnostic tool and has found use in a variety of flow regimes ranging from sub- to supersonic speeds in both reacting and nonreacting environments. This adoption can be attributed to the wealth of information that can be obtained via FRS, including the gas density, pressure, temperature, velocity, species composition, or, in some cases, several of these properties at once. This article reviews the current state of FRS methodology in recovering such gas properties. As knowledge of the fundamentals of Rayleigh scattering and spectral light filtering is crucial to the design of an FRS experiment, we begin by briefly reviewing these areas. Subsequently, we conduct a survey of experimental design strategies, assumptions, and data reduction methods used to measure different gas properties using FRS. We conclude the review with a short discussion on quantification of experimental uncertainty and future trends in FRS.

filtered Rayleigh scattering↗

Model reduction for hypersonic aerodynamics via conservative LSPG projection and hyper-reduction

High-speed aerospace engineering applications rely heavily on computational fluid dynamics (CFD) models for design and analysis due to the expense and difficulty of flight tests and experiments. This reliance on CFD models necessitates performing accurate and reliable uncertainty quantification (UQ) of the CFD models. However, it is very computationally expensive to run CFD for hypersonic flows due to the fine grid resolution required to capture the strong shocks and large gradients that are typically present. Furthermore, UQ approaches are “many-query” problems requiring many runs with a wide range of input parameters. One way to enable computationally expensive models to be used in such many-query problems is to employ projection-based reduced-order models (ROMs) in lieu of the (high-fidelity) full-order model. In particular, the least-squares Petrov–Galerkin (LSPG) ROM (equipped with hyper-reduction) has demonstrated the ability to significantly reduce simulation costs while retaining high levels of accuracy on a range of problems including subsonic CFD applications. This allows computationally inexpensive LSPG ROM simulations to replace the full-order model simulations in UQ studies, which makes this many-query task tractable, even for large-scale CFD models. This work presents the first application of LSPG to a hypersonic CFD application. In particular, we present results for LSPG ROMs of the HIFiRE-1 in a three-dimensional, turbulent Mach 7.1 flow, showcasing the ability of the ROM to significantly reduce computational costs while maintaining high levels of accuracy in computed quantities of interest.

Fluid Dynamics↗

Low‐dimensional manifold learning for uncertainty quantification in complex multi‐scale stochastic systems

Broadly speaking, the goals of the project are to develop techniques to use manifold learning to develop reduced‐order and surrogate models for "hyper‐reduction" of very high‐dimensional complex multi‐scale systems. This is being achieved by employing a newly proposed form of manifold projection and learning that leverages recent advancements in computational geometry and data‐driven modeling. In particular, we are applying a manifold projection technique to project the solutions of very high‐dimensional systems onto the so‐called Grassmannmanifold, a Reimannian manifold comprised of orthonormal matrices. We then apply data‐driven machine learning techniques to classify the solutions on the manifold (e.g. clustering techniques) according to their proximity on the manifold and leverage a further nonlinear dimension reduction to organize the structured data on the manifold. Finally, we are developing novel techniques that enable us to directly interpolate the hyper‐reduced data such that we can predict the solution of the complex, high‐ dimensional system without need to call the full expensive computational model. Given their adherence to the underlying structure of the solution of the physical system, it is expected that these approximate solutions will be sufficiently constrained so as to (approximately) adhere to physical principles.

97 MATHEMATICS AND COMPUTING↗

The Influence of Ozone Precursor Emissions from Four World Regions on Tropospheric Composition and Radiative Climate Forcing

Ozone (O3) precursor emissions influence regional and global climate and air quality through changes in tropospheric O3 and oxidants, which also influence methane (CH4) and sulfate aerosols (SO4 (sup 2-)). We examine changes in the tropospheric composition of O3, CH4, SO4 (sup 2-) and global net radiative forcing (RF) for 20% reductions in global CH4 burden and in anthropogenic O3 precursor emissions (NOx, NMVOC, and CO) from four regions (East Asia, Europe and Northern Africa, North America, and South Asia) using the Task Force on Hemispheric Transport of Air Pollution Source-Receptor global chemical transport model (CTM) simulations, assessing uncertainty (mean plus or minus 1 standard deviation) across multiple CTMs. We evaluate steady state O3 responses, including long-term feedbacks via CH4. With a radiative transfer model that includes greenhouse gases and the aerosol direct effect, we find that regional NOx reductions produce global, annually averaged positive net RFs (0.2 plus or minus 0.6 to 1.7 2 mWm(sup -2)/Tg N yr(sup -1), with some variation among models. Negative net RFs result from reductions in global CH4 (-162.6 plus or minus 2 mWm(sup -2) for a change from 1760 to 1408 ppbv CH4) and regional NMVOC (-0.4 plus or minus 0.2 to 0.7 plus or minus 0.2 mWm(sup -2)/Tg C yr(sup -1) and CO emissions (-0.13 plus or minus 0.02 to -0.15 plus or minus 0.02 mWm(sup-2)/Tg CO yr(sup-1). Including the effect of O3 on CO2 uptake by vegetation likely makes these net RFs more negative by -1.9 to- 5.2 mWm(sup -2)/Tg N yr(sup -1), -0.2 to -0.7 mWm(sup -2)/Tg C yr(sup -1), and -0.02 to -0.05 mWm(sup -2)/ Tg CO yr(sup -1). Net RF impacts reflect the distribution of concentration changes, where RF is affected locally by changes in SO4 (sup -2), regionally to hemispherically by O3, and globally by CH4. Global annual average SO4 2 responses to oxidant changes range from 0.4 plus or minus 2.6 to -1.9 plus or minus 1.3 Gg for NOx reductions, 0.1 plus or minus 1.2 to -0.9 plus or minus 0.8 Gg for NMVOC reductions, and -0.09 plus or minus 0.5 to -0.9 plus or minus 0.8 Gg for CO reductions, suggesting additional research is needed. The 100-year global warming potentials (GWP(sub 100)) are calculated for the global CH4 reduction (20.9 plus or minus 3.7 without stratospheric O3 or water vapor, 24.2 plus or minus 4.2 including those components), and for the regional NOx, NMVOC, and CO reductions (18.7 plus or minus 25.9 to 1.9 plus or minus 8.7 for NOx, 4.8 plus or minus 1.7 to 8.3 plus or minus 1.9 for NMVOC, and 1.5 plus or minus 0.4 to 1.7 plus or minus 0.5 for CO). Variation in GWP(sub 100) for NOx, NMVOC, and CO suggests that regionally specific GWPs may be necessary and could support the inclusion of O3 precursors in future policies that address air quality and climate change simultaneously. Both global net RF and GWP100 are more sensitive to NOx and NMVOC reductions from South Asia than the other three regions.

ozone↗

A methodology for decay heat characterization in molten salt reactors

Accurate decay heat prediction in molten salt reactors (MSRs) faces dual challenges: complex operational uncertainties and the need for interpretable models compatible with engineering workflows. This work presents a hybrid machine learning and segmented polynomial methodology that addresses both requirements through three key innovations. First, a modular data architecture encodes MSR-specific operational parameters (power density: 1-100 W cm -3 , humidity: 0-0.1 wt %, air ingress: 0-0.1 mol %) with uncertainty-aware temporal discretization spanning 15 orders of magnitude. Second, region-optimized machine learning models achieve 92.3 % root mean square error (RMSE) reduction over conventional polynomials while maintaining physical interpretability through automated piecewise equation generation. Third, dual front-end interfaces accelerate safety analyses — a Jupyter environment enables researchers to explore 10,000+ parameter combinations via interactive widgets, while a Streamlit web application reduces design iteration cycles through production-grade visualization tools. Operational deployment demonstrates prediction times of only a couple hundred milliseconds for 10 4 years decay profiles, enabling real-time optimization of spent fuel container designs.

42 - ENGINEERING↗

Microgrid Frequency Stability: A Proactive Scheme Based on Dynamic Predictions

The dynamic nature of microgrids introduces challenges in the context of frequency stability. This work presents a framework where the future state of microgrid frequency is predicted and corrective actions are optimized. Predictions are generated through Bayesian filters leveraging synchronized data acquired via PMUs. Taking a proactive approach makes it possible to optimize corrective actions considering dynamic system conditions. Testing is conducted via Matlab simulations. The performance of the solution presented in this work is compared to traditional load-shedding schemes and to predictive solutions found in literature. The results indicate that the proposed framework outperforms both. Some of the advantages of this framework include a reduction in amount of load dropped during compensation, the use of adaptive parameters which eliminates the need to simulate contingency conditions, and dynamic uncertainty quantification provided the particle filter.

Paramo, Gian↗

A comparison of 8.415-, 32.0- and 565646-GHz deep space telemetry links

An economic and performance comparison is made of spacecraft telecommunication links at 8.415, 32.0, and 565646 GHz (0.53-micron wavelength) for the return of 3.43 x 10 to the 11th power bits from a Saturn Orbiter/Titan Probe mission in year 2000. Technical performance and costs for both ends of the links are included. Spacecraft antenna or telescope efficiencies, pointing losses, ground-based or Earth-orbiting relay terminals efficiencies, noise temperatures, recurring and nonrecurring engineering, and maintenance and operations costs are modeled. Weather effects, dc-to-RF or laser power conversion efficiencies, gravity and other environment distortions gain reductions, and the cost of pointing and tracking are analyzed. The effort is focused primarily on the microwave frequency links. There are large uncertainties in the cost results, but conclusions indicate that for a mid-1990's launch, the Ka-band system is as cost effective as X-band. The Ka-band system has a data rate advantage as compared to the X-band system for the same dc power input to the spacecraft. The magnitude of the advantage is a complex function of the weather at the DSN stations and the elevation angle of the ground antenna. A simple numerical comparison of the advantage is difficult and curves are provided. The optical frequency link is more costly based on the launch-to-orbit costs for the orbiting terminal. A more detailed study of the optical system is recommended to quantify astrometric tracking benefits and improve the accuracy of the cost estimate.

Dickinson, R. M.↗

Inspection planning development: An evolutionary approach using reliability engineering as a tool

This paper proposes an evolutionary approach for inspection planning which introduces various reliability engineering tools into the process and assess system trade-offs among reliability, engineering requirement, manufacturing capability and inspection cost to establish an optimal inspection plan. The examples presented in the paper illustrate some advantages and benefits of the new approach. Through the analysis, reliability and engineering impacts due to manufacturing process capability and inspection uncertainty are clearly understood; the most cost effective and efficient inspection plan can be established and associated risks are well controlled; some inspection reductions and relaxations are well justified; and design feedbacks and changes may be initiated from the analysis conclusion to further enhance reliability and reduce cost. The approach is particularly promising as global competitions and customer quality improvement expectations are rapidly increasing.

Graf, David A.↗

Airframe Noise Results from the QTD II Flight Test Program

With continued growth in air travel, sensitivity to community noise intensifies and materializes in the form of increased monitoring, regulations, and restrictions. Accordingly, realization of quieter aircraft is imperative, albeit only achievable with reduction of both engine and airframe components of total aircraft noise. Model-scale airframe noise testing has aided in this pursuit; however, the results are somewhat limited due to lack of fidelity of model hardware, particularly in simulating full-scale landing gear. Moreover, simulation of true in-flight conditions is non-trivial if not infeasible. This paper reports on an investigation of full-scale landing gear noise measured as part of the 2005 Quiet Technology Demonstrator 2 (QTD2) flight test program. Conventional Boeing 777-300ER main landing gear were tested, along with two noise reduction concepts, namely a toboggan fairing and gear alignment with the local flow, both of which were down-selected from various other noise reduction devices evaluated in model-scale testing at Virginia Tech. The full-scale toboggan fairings were designed by Goodrich Aerostructures as add-on devices allowing for complete retraction of the main gear. The baseline-conventional gear, faired gear, and aligned gear were all evaluated with the high-lift system in the retracted position and deployed at various flap settings, all at engine idle power setting. Measurements were taken with flyover community noise microphones and a large aperture acoustic phased array, yielding far-field spectra, and localized sources (beamform maps). The results were utilized to evaluate qualitatively and quantitatively the merit of each noise reduction concept. Complete similarity between model-scale and full-scale noise reduction levels was not found and requires further investigation. Far-field spectra exhibited no noise reduction for both concepts across all angles and frequencies. Phased array beamform maps show inconclusive evidence of noise reduction at selective frequencies (1500 to 3000 Hz) but are otherwise in general agreement with the far-field spectra results (within measurement uncertainty).

Elkoby, Ronen↗