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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Fast automated analysis of strong gravitational lenses with convolutional neural networks

Quantifying image distortions caused by strong gravitational lensing—the formation of multiple images of distant sources due to the deflection of their light by the gravity of intervening structures—and estimating the corresponding matter distribution of these structures (the ‘gravitational lens’) has primarily been performed using maximum likelihood modelling of observations. Our procedure is typically time- and resource-consuming, requiring sophisticated lensing codes, several data preparation steps, and finding the maximum likelihood model parameters in a computationally expensive process with downhill optimizers. Accurate analysis of a single gravitational lens can take up to a few weeks and requires expert knowledge of the physical processes and methods involved. Tens of thousands of new lenses are expected to be discovered with the upcoming generation of ground and space surveys. We report the use of deep convolutional neural networks to estimate lensing parameters in an extremely fast and automated way, circumventing the difficulties that are faced by maximum likelihood methods. We also show that the removal of lens light can be made fast and automated using independent component analysis of multi-filter imaging data. Our networks can recover the parameters of the ‘singular isothermal ellipsoid’ density profile, which is commonly used to model strong lensing systems, with an accuracy comparable to the uncertainties of sophisticated models but about ten million times faster: 100 systems in approximately one second on a single graphics processing unit. These networks can provide a way for non-experts to obtain estimates of lensing parameters for large samples of data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

User Guidelines and Best Practices for CASL VUQ Analysis Using Dakota

Sandia’s Dakota software (available at http://dakota.sandia.gov) supports science and engineering transformation through advanced exploration of simulations. Specifically it manages and analyzes ensembles of simulations to provide broader and deeper perspective for analysts and decision makers. This enables them to enhance understanding of risk, improve products, and assess simulation credibility. In its simplest mode, Dakota can automate typical parameter variation studies through a generic interface to a physics-based computational model. This can lend efficiency and rigor to manual parameter perturbation studies already being conducted by analysts. However, Dakota also delivers advanced parametric analysis techniques enabling design exploration, optimization, model calibration, risk analysis, and quantification of margins and uncertainty with such models. It directly supports verification and validation activities. Dakota algorithms enrich complex science and engineering models, enabling an analyst to answer crucial questions of - Sensitivity: Which are the most important input factors or parameters entering the simulation, and how do they influence key outputs?; Uncertainty: What is the uncertainty or variability in simulation output, given uncertainties in input parameters? How safe, reliable, robust, or variable is my system? (Quantification of margins and uncertainty, QMU); Optimization: What parameter values yield the best performing design or operating condition, given constraints? Calibration: What models and/or parameters best match experimental data? In general, Dakota is the Consortium for Advanced Simulation of Light Water Reactors (CASL) delivery vehicle for verification, validation, and uncertainty quantification (VUQ) algorithms. It permits ready application of the VUQ methods described above to simulation codes by CASL researchers, code developers, and application engineers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Task-based strategy for optimized contrast enhanced breast imaging: Analysis of six imaging techniques for mammography and tomosynthesis

Purpose: The use of contrast agents in breast imaging has the capability of enhancing nodule detectability and providing physiological information. Accordingly, there has been a growing trend toward using iodine as a contrast medium in digital mammography (DM) and digital breast tomosynthesis (DBT). Widespread use raises concerns about the best way to use iodine in DM and DBT, and thus a comparison is necessary to evaluate typical iodine-enhanced imaging methods. This study used a task-based observer model to determine the optimal imaging approach by analyzing six imaging paradigms in terms of their ability to resolve iodine at a given dose: unsubtracted mammography and tomosynthesis, temporal subtraction mammography and tomosynthesis, and dual energy subtraction mammography and tomosynthesis. Methods: Imaging performance was characterized using a detectability index d{sup ′}, derived from the system task transfer function (TTF), an imaging task, iodine signal difference, and the noise power spectrum (NPS). The task modeled a 10 mm diameter lesion containing iodine concentrations between 2.1 mg/cc and 8.6 mg/cc. TTF was obtained using an edge phantom, and the NPS was measured over several exposure levels, energies, and target-filter combinations. Using a structured CIRS phantom, d{sup ′} was generated as a function of dose and iodine concentration. Results: For all iodine concentrations and dose, temporal subtraction techniques for mammography and tomosynthesis yielded the highest d{sup ′}, while dual energy techniques for both modalities demonstrated the next best performance. Unsubtracted imaging resulted in the lowest d{sup ′} values for both modalities, with unsubtracted mammography performing the worst out of all six paradigms. Conclusions: At any dose, temporal subtraction imaging provides the greatest detectability, with temporally subtracted DBT performing the highest. The authors attribute the successful performance to excellent cancellation of inplane structures and improved signal difference in the lesion.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Multiobjective optimization to a TB-HIV/AIDS coinfection optimal control problem

We consider a recent coinfection model for Tuberculosis (TB), Human Immunodeficiency Virus (HIV) infection, and Acquired Immunodeficiency Syndrome (AIDS) proposed in Silva and Torres (Discr Contin Dyn Syst 35(9):4639–4663, 2015). We introduce and analyze a multiobjective formulation of an optimal control problem, where the two conflicting objectives are minimization of the number of HIV-infected individuals with AIDS clinical symptoms and coinfected with AIDS and active TB; and costs related to prevention and treatment of HIV and/or TB measures. The proposed approach eliminates some limitations of previous works. The results of the numerical study provide comprehensive insights about the optimal treatment policies and the population dynamics resulting from their implementation. Some nonintuitive conclusions are drawn. Overall, the simulation results demonstrate the usefulness and validity of the proposed approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Overmoded subterahertz surface wave oscillator with pure TM{sub 01} mode output

Overmoded O-type Cerenkov generators using annular electron beams are facing the problem of multi-modes output due to the inevitable structural discontinuities. A simple but effective method to achieve the pure TM{sub 01} mode output is applied on the 0.14 THz overmoded surface wave oscillator (SWO) in this paper. In spite of still using an overmoded slow wave structure to ensure the easy fabrication, the followed smooth circular waveguide is shrinkingly tapered to the output waveguide with appropriate radius that it cuts off other higher modes except TM{sub 01} mode. Moreover, the modified device here has the same power capacity as the previous one according to the numerical analysis. By optimized lengths of the transition waveguide and tapered waveguide, particle-in-cell simulation results indicate that the subterahertz wave with output power increased 14.2% at the same frequency is obtained from the proposed SWO under the previous input conditions, and importantly, the output power is all carried by TM{sub 01} mode as expected. Further simulation results in the pulse regime confirm the feasibility of the optimized structure in the actual experiments. This simple and viable design is also applicable to overmoded devices in the lower frequency band of subterahertz wave.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Global Life Cycle and Techno-Economic Assessment of Algal-Based Biofuels

Techno-economic analyses (TEAs) and life cycle assessments (LCAs) of algal biofuels often focus on locations in suboptimal latitudes for algal cultivation, which can under-represent the sustainability potential of the technology. This study identifies the optimal global productivity potential, environmental impacts, and economic viability of algal biofuels by using validated biophysical and sustainability modeling. The biophysical model simulates growth rates of Scenedesmus obliquusbased on temperature, photoinhibition, and respiration effects at 6685 global locations. Region-specific labor costs, construction factors, and tax rates allow for spatially resolved TEA, while the LCA includes regional impacts of electricity, hydrogen, and nutrient markets across ten environmental categories. The analysis identifies optimal locations for algal biofuel production in terms of environmental impacts and economic viability which are shown to follow biomass yields. Modeling results highlight the global variability of productivity with maximum yields ranging between 24.8 and 27.5 g m -2 d -1 in equatorial regions. Environmental impact results show favorable locations tracked with low-carbon electricity grids, with the well-to-wheels global warming potential (GWP) ranging from 31 to 45 g CO 2eq MJ -1 in South America and Central Africa. When including direct land use change impacts, the GWP ranged between 44 and 55 g CO 2eq MJ -1 in these high-productivity regions. Low-carbon electricity also favors air quality and eutrophication impacts. The TEA shows that minimum algal fuel prices of $\$1.89$-$\$2.15$ per liter of gasoline-equivalent are possible in southeast Asia and Venezuela. Furthermore, this discussion focuses on the challenges and opportunities to reduce fuel prices and the environmental impacts of algal biofuels in various global regions.

09 BIOMASS FUELS↗
DOE OSTI2025

From Structure to Function: Zn/Mn-Modified Maghemite as an Advanced Nanoplatform for Magnetic Hyperthermia and Radionuclide Therapy

The development of nanoplatforms capable of efficient heat generation and stable radionuclide delivery is essential for effective bimodal cancer therapy. Here, in this study, binary (Fe–M) and ternary (Fe–M–M′) metal oxide nanoparticles were synthesized via a polyol method optimized to produce flower-like γ-Fe 2 O 3 (maghemite) structures, with M and M′ representing Zn and/or Mn. Comprehensive structural and magnetic characterization was conducted to explain the relationship between composition, defect structure, and hyperthermic performance. The analyses revealed that cation substitution induced an Fe-site vacancy, primarily at octahedral positions, leading to local structural distortions, as confirmed by powder X-ray diffraction and pair distribution function analysis. The optimized composition, with Zn/Mn/Fe = 0.040:0.182:1, exhibited the highest concentration of vacancies and structural disorder. These vacancies altered the bonding environment, enhancing magnetic interactions at tetrahedral sites while weakening those at the octahedral positions. The resulting multicore nanoflowers (20–63 nm; core size 13–18 nm) displayed strong heating performance, with intrinsic loss power ranging from 0.34 to 5.77 nHm 2 kg –1 . The optimized sample achieved a temperature increase of 30 °C within 2 min and a specific absorption rate of 369 W g –1 . This composition was further coated with citrate (CA) and successfully radiolabeled with 177 Lu, achieving a radiolabeling yield of 92.7% and excellent stability, thus forming a robust nanoplatform for combined magnetic hyperthermia and radionuclide therapy. Biological evaluation of the optimized S5 composition revealed selective cytotoxicity toward HeLa and LS174 cells, while toxicity was significantly lower to A549, A375, and normal MRC-5 cells. Citrate coating of S5 nanoparticles (S5@CA) drastically reduced their cytotoxicity across all tested cell lines (IC 50 > 200 μg mL –1 ), confirming their enhanced biocompatibility for therapeutic applications. In HeLa cells subjected to magnetic hyperthermia, the viability decreased to approximately 84% after 30 min and 61% after 60 min of treatment, demonstrating the sustained hyperthermic effect at a controlled working temperature of 48 °C. These results underscore the effectiveness of cation substitution and vacancy engineering in tailoring the functional properties of maghemite-based nanomaterials for advanced multimodal cancer therapies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High order interpolation of magnetic fields with vector potential reconstruction for particle simulations

We propose a method for interpolating divergence-free continuous magnetic fields via vector potential reconstruction using Hermite interpolation, which ensures high-order continuity for applications requiring adaptive, high-order ordinary differential equation (ODE) integrators, such as the Dormand-Prince method. The method provides C(m) continuity and achieves high-order accuracy, making it particularly suited for particle trajectory integration and Poincaré section analysis under optimal integration order and timestep adjustments. Through numerical experiments, we demonstrate that the Hermite interpolation method preserves volume and continuity, which are critical for conserving toroidal canonical momentum and magnetic moment in guiding center simulations, especially over long-term trajectory integration. Furthermore, we analyze the impact of insufficient derivative continuity on Runge-Kutta schemes and show how it degrades accuracy at low error tolerances, introducing discontinuity-induced truncation errors. Lastly, we demonstrate performant Poincaré section analysis in two relevant settings of field data collocated from finite element meshes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Numerical design of SiC bulk crystal growth for electronic applications

Presented study concerns numerical simulation of Physical Vapor Transport (PVT) growth of bulk Silicon Carbide (SiC) crystals. Silicon Carbide is a wide band gap semiconductor, with numerous applications due to its unique properties. Wider application of SiC is limited by high price and insufficient quality of the product. Those problems can be overcame by optimizing SiC production methods. Experimental optimization of SiC production is expensive because it is time consuming and requires large amounts of energy. Numerical modeling allows to learn more about conditions inside the reactor and helps to optimize the process at much lower cost. In this study several simulations of processes with different reactor geometries were presented along with discussion of reactor geometry influence on obtained monocrystal shape and size.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Experimental design applications for modeling and assessing carbon dioxide sequestration in saline aquifers

This project was a computer modeling effort to couple reservoir simulation and ED/RSM using Sensitivity Analysis, Uncertainty Analysis, and Optimization Methods, to assess geologic, geochemical, geomechanical, and rock-fluid effects and factors on CO 2 injectivity, capacity, and plume migration. The project objective was to develop proxy models to simplify the highly complex coupled geochemical and geomechanical models in the utilization and storage of CO 2 in the subsurface. The goals were to investigate and prove the feasibility of the ED/RSM processes and engineering development, and bridge the gaps regarding the uncertainty and unknowns of the many geochemical and geomechanical interacting parameters in the development and operation of anthropogenic CO 2 sequestration and storage sites. The bottleneck in this workflow is the high computational effort of reactive transport simulation models and large number of input variables to optimize with ED/RSM techniques. The project was not to develop the reactive transport, geomechanical, or ED/RSM software, but was to use what was commercially and/or publically available as a proof of concept to generate proxy or surrogate models. A detailed geologic and petrographic mineral assemblage and geologic structure of the doubly plunging anticline was defined using the USDOE RMOTC formations of interest data (e.g., Lower Sundance, Crow Mountain, Alcova Limestone, and Red Peak). The assemblage of 23 minerals was primarily developed from literature data and petrophysical (well log) analysis. The assemblage and structure was input into a commercial reactive transport simulator to predict the effects of CO 2 injection and complex reactions with the reservoir rock. Significant impediments were encountered during the execution phase of the project. The only known commercial reactive transport simulator was incapable of simulating complex geochemistry modeled in this project. Significant effort and project funding was expended to determine the limitations of both the commercial simulator and the Lawrence Berkeley National Laboratory (LBNL) R&D simulator, TOUGHREACT available to the project. A simplified layer cake model approximating the volume of the RMOTC targeted reservoirs was defined with 1-3 minerals eventually modeled with limited success. Modeling reactive transport in porous media requires significant computational power. In this project, up to 24 processors were used to model a limited mineral set of 1-3 minerals. In addition, geomechanical aspects of injecting CO 2 into closed, semi-open, and open systems in various well completion methods was simulated. Enhanced Oil Recovery (EOR) as a storage method was not modeled. A robust and stable simulation dataset or base case was developed and used to create a master dataset with embedded instructions for input to the ED/RSM software. Little success was achieved toward the objective of the project using the commercial simulator or the LBNL simulator versions available during the time of this project. Several hundred realizations were run with the commercial simulator and ED/RSM software, most having convergence problems and terminating prematurely. A proxy model for full field CO 2 injection sequestration utilization and storage was not capable of being developed with software available for this project. Though the chemistry is reasonably known and understood, based on the amount of effort and huge computational time required, predicting CO 2 sequestration storage capacity in geologic formations to within the program goals of ±30% proved unsuccessful.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

GPU code optimization using abstract kernel emulation and sensitivity analysis

In this paper, we develop a novel approach to GPU kernel optimization by focusing on identification of bottleneck resources and determining optimization parameters that can alleviate the bottleneck. Performance modeling for GPUs is done by abstract kernel emulation along with latency/gap modeling of resources. Sensitivity analysis with respect to resource latency/gap parameters is used to predict the bottleneck resource for a given kernel's execution. Here, the utility of the bottleneck analysis is demonstrated in two contexts: 1) Enhancing the OpenTuner auto-tuner with the new bottleneck-driven optimization strategy. Experimental results on all kernels from the Rodinia suite and GPU tensor contraction kernels from the NWChem computational chemistry suite demonstrate effectiveness. 2) Manual code optimization: two case studies illustrate the use of the bottleneck analysis to iteratively improve performance of code from state-of-the-art DSL code generators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Geometric and dynamic distortions in anisotropic galaxy clustering

We examine the signature of dynamic (redshift-space) distortions and geometric distortions (including the Alcock-Paczynski effect) in the context of the galaxy power spectrum measured in upcoming galaxy redshift surveys. Information comes from both the baryon acoustic oscillation (BAO) feature and the broadband power spectrum shape. Accurate modeling is required to extract this information without systematically biasing the result. We consider an analytic model for the power spectrum of dark matter halos in redshift space, based on the distribution function expansion, and compare with halo clustering measured in N-body simulations. We forecast that the distribution function model is sufficiently accurate to allow the inclusion of broadband information on scales down to k ∼ 0.2h Mpc{sup −1}, with somewhat better accuracy for higher bias halos. Compared with a BAO-only analysis with reconstruction, including broadband shape information can improve unbiased constraints on distance measures H(z) and D{sub A}(z) by ∼ 30% and 20%, respectively, for a galaxy sample similar to the DESI luminous red galaxies. The gains in precision are larger in the absence of BAO reconstruction. Furthermore, including broadband shape information allows the measurement of structure growth, through redshift-space distortions. For the same galaxy sample, the distribution function model is able to constrain fσ{sub 8} to ∼ 2%, when simultaneously fitting for H(z) and D{sub A}(z). We discuss techniques to optimize the analysis of the power spectrum, including removing modes near the line-of-sight that are particularly challenging to model, and whether these approaches can improve parameter constraints. We find that such techniques are unlikely to significantly improve constraints on geometry, although they may allow higher precision measurements of redshift-space distortions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Numerical Simulation of Ion Transport in a Nano-Electrospray Ion Source at Atmospheric Pressure

Understanding ion transport properties from the ion source to the mass spectrometer (MS) is essential for optimizing device performance. Numerical simulation helps in understanding of ion transport properties and, furthermore, facilitates instrument design. In contrast to previously reported numerical studies, ion transport simulations in a continuous injection mode whilst considering realistic space-charge effects have been carried out. The flow field was solved using Reynolds-averaged Navier-Stokes (RANS) equations, and a particle-in-cell (PIC) method was applied to solve a time-dependent electric field with local charge density. A series of ion transport simulations were carried out at different cone gas flow rates, ion source currents, and capillary voltages. A force evaluation analysis reveals that the electric force, the drag force, and the Brownian force are the three dominant forces acting on the ions. Both the experimental and simulation results indicate that cone gas flow rates of ≤250 slph (standard liter per hour) are important for high ion transmission efficiency, as higher cone gas flow rates reduce the ion signal significantly. The simulation results also show that the ion transmission efficiency reduces exponentially with an increased ion source current. Additionally, the ion loss due to space-charge effects has been found to be predominant at a higher ion source current, a lower capillary voltage, and a stronger cone gas counterflow. The interaction of the ion driving force, ion opposing force, and ion dispersion is discussed to illustrate ion transport mechanism in the ion source at atmospheric pressure. .

42 ENGINEERING↗
DOE OSTI2013

Optimization of beam configuration in laser fusion based on the laser beam pattern

A simple method based on the laser beam pattern is proposed and numerically demonstrated to optimize a beam configuration for direct drive laser fusion. In this method, both the geometrical factor G{sub l} and the single beam factor B{sub l} are considered. By diminishing the product of B{sub l}·G{sub l}, the irradiation nonuniformity can be decreased to the order of 10{sup −5}. This optimization method can be applied on the design of irradiation systems for an arbitrary number of beams and any axially symmetric beam patterns.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Crystallographic Insight of Reduced Lattice Volume Expansion in Mesoporous Cu 2+ -Doped TiNb 2 O 7 Microspheres during Li + Insertion

TiNb 2 O 7 represents a promising anode material for lithium-ion batteries (LIBs), but its practical applications are currently hampered by the non-negligible volumetric expansion and contraction during the charge/discharge process and the sluggish ion/electron kinetics. Here a combination technique is reported by systematically optimizing the porous and spherical morphology, crystal structure, and surface decoration of mesoporous Cu 2+ -doped TiNb 2 O 7 microspheres to enhance the electrochemical Li + storage performance and stability simultaneously. The Cu 2+ dopants preferentially replace Ti 4+ in crystal lattices, which decreases the Li + diffusion barrier and increases the electronic conductivity, as confirmed by density functional theory (DFT) calculation and demonstrated by diverse electrochemical characterizations. The successful Cu 2+ doping significantly reduces the lattice expansion coefficient from 7.26% to 4.61% after Li + insertion along the b-axis of TiNb 2 O 7 , as visualized from in situ and ex situ XRD analysis. The optimal 5% Cu 2+ -doped TiNb 2 O 7 with surface coating of N-doped carbon exhibits significantly enhanced specific capacity and rate and cyclic performances in both half- and full-cell configurations, demonstrating an excellent electrochemical behavior for fast-charging LIB applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Designing Monte Carlo Simulation and an Optimal Machine Learning to Optimize and Model Space Missions

This paper investigates applying artificial intelligence (AI) algorithms to attitude control system of satellites to optimally tune the controller using high performance computing. This methodology is applied to the Virtual Telescope for X-ray Observation mission, which is a precise formation of two separate spacecraft observing multiple objects in the space in the X-ray domain. The mission is divided into phases based on the instrumentation and the mission goal. To reach an stable precise formation robust to stochastic slew and slew rate (i.e., Euler angles and angular velocities) in a minimal constrained time T , consumed energy of the attitude control system, denoted as E, and root-mean-square state error of attitude control system, denoted as e, are minimized. Monte-Carlo simulation is used for the sensitivity analysis of optimization and designing a controller. Deep neural networks (DNN), Gaussian processes (GP), and support vector regression (SVR) learn this optimization as a surrogate model, while their hyperparameters are optimized in a novel approach. THETA supercomputer at Argonne Leadership Computing Facility (ALCF) is used for optimizing the hyperparameters of DNN. The surrogate model meets the requirements of the mission, and it shows a better performance over the optimization and Monte-Carlo. The optimal DNN can satisfy the mission requirements e and T while reducing E for 90% compared to the other given methods.

42 ENGINEERING↗
DOE OSTI2019

Phosphate glass matrix composites incorporating trisilanol phenyl polyhedral oligomeric silsesquioxane prepared by viscous flow sintering method with enhanced benefits

The effect of mixing and sintering processes to prepare tin fluorophosphate glass (Pglass) matrix composites incorporating trisilanol phenyl polyhedral oligomeric silsesquioxane (TSP-POSS) was investigated by comparing manual and suspension mixing, one-step and stepwise sintering processes to explore the structure dynamics and physical properties in the composites as a function of the different process conditions used. Energy Dispersive X-ray analysis confirmed optimal homogeneous dispersion of the TSP-POSS molecules in the composites prepared by the suspension method. The observed increase of glass transition temperature and the reduction of non-bridging bonds in the composites are believed to be the reason for the effective dispersion of TSP-POSS molecules in the composites. The chemical reaction between the TSP-POSS and Pglass was strongly influenced by the mixing/dispersion and sintering processes investigated. 13 C cross polarized magic angle spinning (CP MAS) solid state nuclear magnetic resonance (NMR) spectroscopy confirmed the chemical stability of the TSP-POSS during the sintering process at elevated temperatures. In addition, a chemical reaction between the TSP-POSS and Pglass was evidenced by 29 Si CP MAS NMR analysis. Here, this study will provide a better fundamental understanding of the effective dispersion mechanism of the TSP-POSS molecules in the Pglass matrix that will facilitate tailoring the physicochemical properties of the composites with addition of various small concentrations of TSP-POSS for a number of applications where the pure Pglass is not applicable due to the intrinsic properties of the Pglass.

36 MATERIALS SCIENCE↗
DOE OSTI2022

System Modeling Frameworks for Wind Turbines and Plants: Review and Requirements Specifications

System modeling frameworks for wind turbines and plants are used by research groups and industry to design wind energy systems that take into account key trade-offs across performance, cost, and reliability at both the turbine and plant level. The frameworks are exercised using a variety of multi-disciplinary design, analysis and optimization (MDAO) methods. To improve inter-operability and foster collaboration, this report proposes a classification system for the frameworks along dimensions of model fidelity and scope. The classification system is first motivated with reviews the state-of-the-art in the development of software frameworks for integrated wind turbine and plant simulation. Within each major wind turbine and power plant subsystem, a matrix is developed for the disciplines used and the fidelity levels with which each discipline can be modeled. The existing frameworks are then classified according to the matrix. Next, an ontology is proposed that will allow for standardizing how data is transferred between the most common discipline-fidelity combinations used in the frameworks. A common representation of data creates the ability to 1) share system descriptions and analysis results, supporting more transparent benchmarks and comparison, and 2) integrate models together into workflows within and across organizations for improving the efficiency and performance of wind turbine and power plant design processes. Ultimately, this integration leads to better overall wind energy system designs with high performance and low costs.

17 WIND ENERGY↗