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DOE OSTI2017

Oxygen migration during resistance switching and failure of hafnium oxide memristors

While the recent establishment of the role of thermophoresis/diffusion-driven oxygen migration during resistance switching in metal oxide memristors provided critical insights required for memristor modeling, extended investigations of the role of oxygen migration during ageing and failure remain to be detailed. Such detailing will enable failure-tolerant design, which can lead to enhanced performance of memristor-based next-generation storage-class memory. Furthermore, we directly observed lateral oxygen migration using in-situ synchrotron x-ray absorption spectromicroscopy of HfO x memristors during initial resistance switching, wear over millions of switching cycles, and eventual failure, through which we determined potential physical causes of failure. Using this information, we reengineered devices to mitigate three failure mechanisms and demonstrated an improvement in endurance of about three orders of magnitude.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Flow-based likelihoods for non-Gaussian inference

We investigate the use of data-driven likelihoods to bypass a key assumption made in many scientific analyses, which is that the true likelihood of the data is Gaussian. In particular, we suggest using the optimization targets of flow-based generative models, a class of models that can capture complex distributions by transforming a simple base distribution through layers of nonlinearities. We call these flow-based likelihoods (FBL). We analyze the accuracy and precision of the reconstructed likelihoods on mock Gaussian data, and show that simply gauging the quality of samples drawn from the trained model is not a sufficient indicator that the true likelihood has been learned. We nevertheless demonstrate that the likelihood can be reconstructed to a precision equal to that of sampling error due to a finite sample size. We then apply FBLs to mock weak lensing convergence power spectra, a cosmological observable that is significantly non-Gaussian (NG). We find that the FBL captures the NG signatures in the data extremely well, while other commonly used data-driven likelihoods, such as Gaussian mixture models and independent component analysis, fail to do so. This suggests that works that have found small posterior shifts in NG data with data-driven likelihoods such as these could be underestimating the impact of non-Gaussianity in parameter constraints. By introducing a suite of tests that can capture different levels of NG in the data, we show that the success or failure of traditional data-driven likelihoods can be tied back to the structure of the NG in the data. Here, unlike other methods, the flexibility of the FBL makes it successful at tackling different types of NG simultaneously. Because of this, and consequently their likely applicability across datasets and domains, we encourage their use for inference when sufficient mock data are available for training.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Few-shot Learning for Post-disaster Structure Damage Assessment

Automating post-disaster damage assessment with remote sensing data is critical for faster surveys of structures impacted by natural disasters. One significant obstacle to training state-of-the-art deep neural networks to support this automation is that large quantities of labelled data are often required. However, obtaining those labels is particularly unrealistic to support post-disaster damage assessment in a timely manner. Few-shot learning methods could help to mitigate this by reducing the amount of labelled data required to successfully train a model while achieving satisfactory results. To this end, we explore a feature reweighting method to the YOLOv3 object detection architecture to achieve few-shot learning of damage assessment models on the xBD dataset. Our results show that the feature reweighting approach yield improved mAP over the baseline with significantly fewer labelled samples. In addition, we use t-SNE to analyze the class-specific reweighting vectors generated by the reweighting module in order to evaluate their inter-class and intra-class similarity. We find that the vectors form clusters based on class, and that these clusters overlap with visually similar classes. Those results show the potential to employ this few-shot learning strategy for rapid damage assessment with post-event remote sensing images.

Bowman, Jordan↗

DOE OSTI2023

DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine. The limited angle coverage in LACT is often a dominant source of severe artifacts in the reconstructed images, making it a challenging inverse problem. We present DOLCE, a new deep model-based framework for LACT that uses a conditional diffusion model as an image prior. Diffusion models are a recent class of deep generative models that are relatively easy to train due to their implementation as image denoisers. DOLCE can form high-quality images from severely under-sampled data by integrating data-consistency updates with the sampling updates of a diffusion model, which is conditioned on the transformed limited-angle data. We show through extensive experimentation on several challenging real LACT datasets that, the same pre-trained DOLCE model achieves the SOTA performance on drastically different types of images. Additionally, we show that, unlike standard LACT reconstruction methods, DOLCE naturally enables the quantification of the reconstruction uncertainty by generating multiple samples consistent with the measured data.

Kim, Hyojin↗

DOE OSTI2016

Searching for Physics Beyond the Standard Model: Strongly-Coupled Field Theories at the Intensity and Energy Frontiers

This proposal is to develop the software and algorithmic infrastructure needed for the numerical study of quantum chromodynamics (QCD), and of theories that have been proposed to describe physics beyond the Standard Model (BSM) of high energy physics, on current and future computers. This infrastructure will enable users (1) to improve the accuracy of QCD calculations to the point where they no longer limit what can be learned from high-precision experiments that seek to test the Standard Model, and (2) to determine the predictions of BSM theories in order to understand which of them are consistent with the data that will soon be available from the LHC. Work will include the extension and optimizations of community codes for the next generation of leadership class computers, the IBM Blue Gene/Q and the Cray XE/XK, and for the dedicated hardware funded for our field by the Department of Energy. Members of our collaboration at Brookhaven National Laboratory and Columbia University worked on the design of the Blue Gene/Q, and have begun to develop software for it. Under this grant we will build upon their experience to produce high-efficiency production codes for this machine. Cray XE/XK computers with many thousands of GPU accelerators will soon be available, and the dedicated commodity clusters we obtain with DOE funding include growing numbers of GPUs. We will work with our partners in NVIDIA's Emerging Technology group to scale our existing software to thousands of GPUs, and to produce highly efficient production codes for these machines. Work under this grant will also include the development of new algorithms for the effective use of heterogeneous computers, and their integration into our codes. It will include improvements of Krylov solvers and the development of new multigrid methods in collaboration with members of the FASTMath SciDAC Institute, using their HYPRE framework, as well as work on improved symplectic integrators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

A Non-hydrostatic Variable Resolution Atmospheric Model in ACME

The Accelerated Climate Modeling for Energy (ACME) Project is designed to perform coupled climate simulations at high resolution on current and next generation DOE leadership class supercomputers. Prior to this project, the ACME atmospheric model employed the primitive equations of motion, which constrained its maximum resolution and limited the physical phenomena that it could resolve. Our team removed this constraint by implementing a non-hydrostatic (NH) atmospheric model in the ACME Framework. Collaboration with SciDAC FASTMath institutes facilitated improvements to key technologies needed for the non-hydrostatic model. This model was tested in a variable resolution framework, which enables high resolution simulation regions at reasonable computational costs. The correct performance of the NH model was verified with standard dynamical-core tests, and by demonstrating that it reproduced expected climate statistics in the hydrostatic regime. The completed model was applied to evaluate the limitations of existing turbulent mountain stress and gravity wave drag parameterizations, in order to demonstrate the power of the new nonhydrostatic model to model new phenomena and enhance the quality of present day 1/4 degree simulations.

Hall, David Matthew↗

DOE OSTI2014

Draft Electric Rule 21 Test Protocols for Advanced Inverter Functions

Disclaimer: The following document includes draft certification protocols and should not be viewed as a consensus-based performance standard. Distributed energy resources (DERs), such as photovoltaic (PV) systems, when deployed in a large scale, are capable of significantly influencing the operation of bulk and local power systems. Looking to the future, European and American stakeholders are working on standards to make it possible to manage the potentially complex interactions between DER and the power system. One of the jurisdictions considering modifications to the DER interconnection requirements is California. To determine what changes could improve electric grid reliability and allow greater penetrations of renewable energy, the California Public Utilities Commission (CPUC) and the California Energy Commission (CEC), in conjunction with consultant Frances Cleveland, convened the Smart Inverter Working Group (SIWG) in January 2013. The SIWG--composed of state agencies, utility engineers, national laboratories, manufacturers, trade associations, and advocacy groups--provided the CPUC a set of recommendations in early 2014 1 . The recommendations included specific advanced DER functions and interoperability requirements, along with a proposed timeline, for California to add new capabilities to grid-interconnected DER. On August 18, 2014 the three California Investor-Owned Utilities (IOUs)--Pacific Gas and Electric Company (PG&E), Southern California Edison Company (SCE) and San Diego Gas & Electric Company (SDG&E)--drafted a Advice Letter filing to the CPUC setting forth revisions to Electric Tariff Rule 21 to conform to the seven recommendations made by the Working Group 2 . After a comment period, the CPUC issued an update to the IOU recommendations 3 . At the time of this publication, there was no final legal ruling on the CPUC Rulemaking (R.) 11-09-011 ("Order Instituting Rulemaking on the Commission's Own Motion to improve distribution level interconnection rules and regulations for certain classes of electric generators and electric storage resources"). In the U.S., Nationally Recognized Test Laboratories (NRTLs) independently verify products to safety and functional standards. PV inverters are certified to Underwriters Laboratories (UL) Standard 1741 4 . However, new advanced inverter functions described in the SIWG and IOU proposals are not included in this standard, so there is a critical need to develop test protocols for these functions in preparation of a positive ruling by the CPUC. Through a California Solar Initiative Grant, Sandia National Laboratories (Sandia or SNL), Underwriters Laboratories (UL), Electric Power Research Institute, Inc. (EPRI), Xanthus Consulting, SunSpec Alliance, Loggerware, and utility and PV inverter manufacturers have collaborated to draft this certification protocol for the Rule 21 SIWG Phase 1 advanced inverter functions. This report also includes test procedures for Phase 2 and Phase 3 functions that will be demonstrated later in the CSI4 project. This collaborative effort was performed in close junction with the UL 1741 Standards Technical Panel (STP) working group. This document represents a snapshot of the draft testing protocols at the time of publication and not a consensus certification protocol for advanced inverters. 1 California Public Utilities Commission, "Recommendations for Updating the Technical Requirements for Inverters in Distributed Energy Resources, Smart Inverter Working Group Recommendations," Filed 7 Feb 2014. 2 J.J. Newlander, R.G. Litteneker, S.W. Walter, M. Dwyer, Joint motion of Pacific Gas and Electric Company (U 39 E), Southern California Edison Company (U 338 E) and San Diego Gas & Electric Company (U 902 E) regarding implementation of smart inverter functionalities, Rulemaking 11-09-011 Advice Letter, 18 July, 2014. 3 J.T. Sullivan, CPUC Rulemaking 11-09-011 Agenda ID #13460, 13 Nov, 2014. 4 Underwriters Laboratories Std. 1741 Ed. 2, "Inverters, Converters, Controllers and Interconnection System Equipment for use with Distributed Energy Resources," 2010.

42 ENGINEERING↗

DOE OSTI2011

Thermophotovoltaic power conversion systems: Current performance and future potential

Thermophotovoltaic (TPV) systems offer a unique, solid-state approach to converting heat into electricity based on thermal radiation. TPV is particularly suitable for certain classes of power generation applications that are not well served by standard engines, such as long, remote missions where repairs are difficult, and portable generation where space and weight are at a premium. While standard thermophotovoltaics are limited in their conversion efficiency, photonic crystals can improve performance by an order of magnitude for a number of systems. While there are many potential applications, two exemplary systems are discussed: TPV μreactors for portable power generation in a mm-scale form factor, and solar TPV for long-term off-grid power generation from sunlight. In both cases, photonic crystals can enable potential performance exceeding that of many other well-known technologies, such as single-junction photovoltaics.

14 SOLAR ENERGY↗

DOE OSTI2016

SciDAC-Data, A Project to Enabling Data Driven Modeling of Exascale Computing

The SciDAC-Data project is a DOE funded initiative to analyze and exploit two decades of information and analytics that have been collected by the Fermilab Data Center on the organization, movement, and consumption of High Energy Physics data. The project will analyze the analysis patterns and data organization that have been used by the NOvA, MicroBooNE, MINERvA and other experiments, to develop realistic models of HEP analysis workflows and data processing. The SciDAC-Data project aims to provide both realistic input vectors and corresponding output data that can be used to optimize and validate simulations of HEP analysis. These simulations are designed to address questions of data handling, cache optimization and workflow structures that are the prerequisites for modern HEP analysis chains to be mapped and optimized to run on the next generation of leadership class exascale computing facilities. We will address the use of the SciDAC-Data distributions acquired from Fermilab Data Center’s analysis workflows and corresponding to around 71,000 HEP jobs, as the input to detailed queuing simulations that model the expected data consumption and caching behaviors of the work running in HPC environments. In particular we describe in detail how the Sequential Access via Metadata (SAM) data handling system in combination with the dCache/Enstore based data archive facilities have been analyzed to develop the radically different models of the analysis of HEP data. We present how the simulation may be used to analyze the impact of design choices in archive facilities.

Mubarak, M.↗

DOE OSTI2021

ExaWind: Predictive Wind Energy Simulations

This presentation describes the ExaWind project and the team's progress in creating a suite of performance-portable codes designed for predictive simulations of wind farms on next-generation exascale-class supercomputers. Such simulations will require the resolution of scales spanning many orders of magnitude, from blade boundary layers to wind farm flow structures. In the U.S., the first exascale systems will be GPU accelerated, and different GPU manufacturers have been chosen for the different systems. At the heart of the ExaWind software is a hybrid-solver approach based on the codes Nalu-Wind and AMR-Wind, which are computational fluid dynamics solvers for the incompressible Navier-Stokes equations. Nalu-Wind is an unstructured-grid code used to resolve wind turbine geometry and blade boundary layers, whereas AMR-Wind is a structured-grid background solver for atmospheric turbulent flow and turbine wake propagation. The models are coupled with overset meshes and global linear systems are approximated through a loose-coupling algorithm. Results will include validation-quality high-fidelity simulations and strong/weak scaling results from the Summit supercomputer.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2017

Evaluation of The Thermophysical Properties and Microstructure of UPu{sub 24}Zr{sub 15} Metallic Fuel

Featuring high burn-up, high fissile and fertile isotopic density, inherent safety characteristics, and favorable thermal response, actinide transmutation metallic fuels are considered to be among the most promising fuel classes for next generation fast reactors. During reactor operation, it is anticipated that fluctuations in chemical potential along the temperature gradient will lead to migration and redistribution of constituents between radial zones of the cylindrical fuel pin, resulting in compositional inhomogeneity and hence unpredictable fuel performance. Limited experimental fuel performance data is available for fuel selection and certification, therefore different compositions of options uranium-plutonium-zirconium (UPu- Zr) alloys have been investigated. Several complementary techniques, including scanning and transmission electron microscopy (SEM and TEM, respectively), differential scanning calorimetry (DSC), and laser flash diffusivity (LFD) techniques were used to characterize the phase distribution, microstructure, and thermophysical properties of unirradiated U-Pu-Zr fuels in both the as-cast and annealed condition. This information is necessary to understand the irradiated microstructure, properties, and performance of U-Pu-Zr fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

AICCA: AI-Driven Cloud Classification Atlas

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra instruments—198 million patches, each roughly 100 km × 100 km (128 × 128 pixels)—into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories—for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

FLORIS Wake Map [SWR-25-71]

The FLORIS Wake Map repository is a set of tools for creating maps of cluster wake impacts based on NREL's FLORIS software and modeling tool (https://github.com/nrel/floris). The floriswakemap package comprises two main modules: the wake_map module, dedicated to the WakeMap class, and the area_selector module, dedicated to the AreaSelector class. The WakeMap class is used to generate maps of wake impacts on both new and existing clusters, while the AreaSelector class provides selections of candidate areas for new developments based on an instantiated WakeMap object.

Sinner, Michael (Misha) [National Renewable Energy↗

DOE OSTI2022

Realistic galaxy image simulation via score-based generative models

ABSTRACT We show that a denoising diffusion probabilistic model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies. Our method is tested with Dark Energy Spectroscopic Instrument (DESI) grz imaging of galaxies from the Photometry and Rotation curve OBservations from Extragalactic Surveys (PROBES) sample and galaxies selected from the Sloan Digital Sky Survey. Subjectively, the generated galaxies are highly realistic when compared with samples from the real data set. We quantify the similarity by borrowing from the deep generative learning literature, using the ‘Fréchet inception distance’ to test for subjective and morphological similarity. We also introduce the ‘synthetic galaxy distance’ metric to compare the emergent physical properties (such as total magnitude, colour, and half-light radius) of a ground truth parent and synthesized child data set. We argue that the DDPM approach produces sharper and more realistic images than other generative methods such as adversarial networks (with the downside of more costly inference), and could be used to produce large samples of synthetic observations tailored to a specific imaging survey. We demonstrate two potential uses of the DDPM: (1) accurate inpainting of occluded data, such as satellite trails, and (2) domain transfer, where new input images can be processed to mimic the properties of the DDPM training set. Here we ‘DESI-fy’ cartoon images as a proof of concept for domain transfer. Finally, we suggest potential applications for score-based approaches that could motivate further research on this topic within the astronomical community.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve forward, inverse, and mixed stochastic problems in a unified manner based on a limited number of scattered measurements. Unlike standard GANs relying solely on data for training, here we encoded into the architecture of GANs the governing physical laws in the form of stochastic differential equations (SDEs) using automatic differentiation. In particular, we applied Wasserstein GANs with gradient penalty (WGAN-GP) for its enhanced stability compared to vanilla GANs. We first tested WGAN-GP in approximating Gaussian processes of different correlation lengths based on data realizations collected from simultaneous reads at sparsely placed sensors. We obtained good approximation of the generated stochastic processes to the target ones even if there is a mismatch between the input noise dimensionality and the effective dimensionality of the target stochastic processes. We also studied the overfitting issue for both the discriminator and the generator, and we found that overfitting occurs also in the generator in addition to the discriminator as previously reported. Subsequently, we considered the solution of elliptic SDEs requiring approximations of three stochastic processes, namely the solution, the forcing, and the diffusion coefficient. Here again, we assumed data collected from simultaneous reads at a limited number of sensors for the multiple stochastic processes. Furthermore, three generators were used for the PI-GANs: two of them were feed forward deep neural networks (DNNs), while the other one was the neural network induced by the SDE. For the case where we have one group of data, we employed one feed forward DNN as the discriminator, while for the case of multiple groups of data we employed multiple discriminators in PI-GANs. We solved forward, inverse, and mixed problems without changing the framework of PI-GANs, obtaining both the means and the standard deviations of the stochastic solution and the diffusion coefficient in good agreement with benchmarks. In this work, we have demonstrated the effectiveness of PI-GANs in solving SDEs for about 120 dimensions. In principle, PI-GANs could tackle very high dimensional problems given more sensor data with low-polynomial growth in computational cost.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

FcTp( R ) ( R = i Pr or t Bu): third-generation ferrocenyl scorpionates

Scorpionate (or trispyrazolylborate) ligands have seen much structural variation due to the relative ease of modifying their electronic and steric effects. Second-generation scorpionates were created by increasing the bulk in the 3-position of the pyrazole (pz) ring. A new class of third-generation scorpionates was obtained by modifying the remaining boron substituent. A series of thallium(I) and cobalt(II) complexes of the ferrocenyltris(3- R -pyrazolyl)borate ligand [FcTp R ; R = isopropyl ( i Pr) or tert -butyl ( t Bu)] have been synthesized in order to expand the range of redox-active third-generation scorpionates. These are [ferrocenyltris(3- tert -butylpyrazol-1-yl-κ N 2 )borato]thallium(I), [FeTl(C 5 H 5 )(C 26 H 37 BN 6 )], [ferrocenyltris(3-isopropylpyrazol-1-yl-κ N 2 )borato]thallium(I), [FeTl(C 5 H 5 )(C 23 H 31 BN 6 )], chlorido[ferrocenyltris(3- tert -butylpyrazol-1-yl-κ N 2 )borato]cobalt(II), [CoFe(C 5 H 5 )(C 26 H 37 BN 6 )Cl], [ferrocenyltris(3- tert -butylpyrazol-1-yl-κ N 2 )borato]iodidocobalt(II) benzene disolvate, [CoFe(C 5 H 5 )(C 26 H 37 BN 6 )I]·2C 6 H 6 , and [ferrocenyltris(3-isopropylpyrazol-1-yl-κ N 2 )borato]iodidocobalt(II), [CoFe(C 5 H 5 )(C 23 H 31 BN 6 )I]. The structures demonstrate that the metal coordination site can easily be modified by using bulkier substituents at the pz 3-position.

Chemistry↗

DOE OSTI2022

ThunderSecure: deploying real-time intrusion detection for 100G research networks by leveraging stream-based features and one-class classification network

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks (40/100G) across distributed sites for processing, storage, and analysis. Like general purpose networks, research networks experience intrusions. However, monitoring anomalies in such high-speed network traffics is challenging given current cyber-infrastructure. Moreover, traditional network intrusion detection systems (NIDS) are signature based. However, anomaly patterns are difficult to define and that rulesets are often not updated frequently enough to reflect the changes of attack behaviors. We present ThunderSecure, a high-throughput, unsupervised learning-based intrusions detection system for 100G research networks. ThunderSecure implements an efficient packet processing and detection pipeline using multi-cores and GPUs. It extracts statistical and temporal features from real-time network data streams and feeds them to a one-class anomaly detection network. A baseline of normal distribution will be created based on the training observation. Testing traffic deviated from the learned profile will be marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets mirrored from two 100G network connections at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. Results show that ThunderSecure can recognize science data traffic captured long after the training and made nearly certain detection on the segment of the streams where anomalous flows were injected.

100G research network↗

DOE OSTI2021

Bias Characterization, Vertical Interpolation, and Horizontal Interpolation for Distributed Wind Siting Using Mesoscale Wind Resource Estimates

Much like their counterparts in utility-scale wind energy, developers of industrial, small-scale and distributed wind turbine deployments need to understand and accurately characterize the wind resource to properly assess the power generation and financial ramifications during siting and planning. National Renewable Energy Laboratory’s WIND (Wind Integration National Dataset) Toolkit (WTK) provides a best-in-class wind resource dataset generated using the Weather Research and Forecasting (WRF) model. This dataset includes parameters such as the wind speed, wind direction, and temperature at various heights, plus atmospheric stability near the surface. This data is available at 2-km spatial resolution and five-minute temporal resolution across 7 years, from 2007 to 2013 through a publicly accessible API interface. The Tools Assessing Performance (TAP) project seeks to extend this dataset to allow long term resource estimates and leverage it to better equip distributed wind equipment manufacturers, owner-operators, and installation professionals with better tools for practical siting applications. In this report, we present the results from our investigation within the TAP project focused on characterization of bias in WTK-based wind speed estimates and evaluation of vertical and horizontal interpolation techniques. We discuss the tradeoffs between different techniques and their combinations, as well as describe the lower bounds we determine for the studied validation errors. While the specific estimates we present are specific to WTK and the validation dataset we have chosen for this investigation (NREL's Wind Resource Meteorological Database), the overall analysis and the studied techniques are general enough to be applied to a broader set of wind datasets, both simulation-based and observational.

17 WIND ENERGY↗