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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

From minimum-viable-products to full models: a step-wise development of diagnostic forward models in support of design, analysis and modelling on the ST40 tokamak

Like most magnetic confined fusion experiments, the ST40 tokamak started off with a small subset of diagnostics and gradually increased the diagnostic set to include more complex and comprehensive systems. To make the most of each operational phase, forward models of various diagnostics are used and developed to aid design, provide consistency-checks during commissioning, test analysis methods, and build workflows to constrain high-level parameters to inform interpretation, theory and modelling. For new models and new analysis workflows, minimum-viable-products are released early, and their complexity is increased in a step-wise manner, facilitating the support of all programme phases on multiple parallel applications, while enabling learning opportunities and feedback loops. In this contribution we review the philosophy, scope and architecture of the framework under development. We discuss the details of some forward models, with examples on how they are used to aid diagnostic design, to investigate analysis methodologies through synthetic data, and how they are embedded in experimental analysis workflows. We compare previously published experimental results with new, more advanced analysis workflows employing more recent, detailed models and new diagnostic data, providing confirmation of the published material from the 2021–22 experimental campaign.

integrated data analysis↗
DOE OSTI2019

Search for single production of vector-like quarks decaying to a top quark and a $\mathrm {W} $ boson in proton–proton collisions at $\sqrt{s} = 13 \,\text {TeV} $

A search is presented for the single production of vector-like quarks in proton–proton collisions at $\sqrt{s}=13\,\text {TeV} $. The data, corresponding to an integrated luminosity of 35.9 $\,\text {fb}^{-1}$, were recorded with the CMS experiment at the LHC. The analysis focuses on the vector-like quark decay into a top quark and a $\mathrm {W} $ boson, with one muon or electron in the final state. The mass of the vector-like quark candidate is reconstructed from hadronic jets, the lepton, and the missing transverse momentum. Methods for the identification of $\mathrm {b}$ quarks and of highly Lorentz boosted hadronically decaying top quarks and $\mathrm {W} $ bosons are exploited in this search. No significant deviation from the standard model background expectation is observed. Exclusion limits at 95% confidence level are set on the product of the production cross section and branching fraction as a function of the vector-like quark mass, which range from 0.3 to 0.03 $\,\text {pb}$ for vector-like quark masses of 700 to 2000 $\,\text {GeV}$. Mass exclusion limits up to 1660 $\,\text {GeV}$ are obtained, depending on the vector-like quark type, coupling, and decay width. Furthermore, these represent the most stringent exclusion limits for the single production of vector-like quarks in this channel.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Conotoxin Prediction: New Features to Increase Prediction Accuracy

Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy.

collisional cross section↗
DOE OSTI2025

Comparison of wind-farm control strategies under realistic offshore wind conditions: wake quantities of interest

Wind-farm control strategies aim to increase the efficiency, and therefore lower the levelized cost of energy, of wind farms. This is done by using turbine settings such as the yaw angle, blade pitch angles, or generator torque to manipulate the wake that negatively affects downstream turbines in the farm. Two inherently different wind-farm control methods have been identified in the literature: wake steering (WS) and active wake mixing (AWM). As one of two companion papers focused on understanding practical aspects of these two wind-farm control strategies using large-eddy simulation (LES), we below analyze the wake quantities of interest for a single wind turbine performing WS and AWM, while the companion article (Frederik et al., 2025) focuses on turbine quantities of interest including power and structural loads for the same computational setup and also includes two-turbine arrays with full and partial wake overlap. The simulations, which are based in the LES solver AMR-Wind, are tailored to have inflow conditions representative of measurements from a site off the East Coast of the US, including with strong veer and low turbulence. The turbine, which is modeled in OpenFAST and coupled to the LES, is the IEA 15 MW, an open-source offshore design. After presenting an overview of the wake recovery for the different wake-control cases, the analysis probes the fluid-dynamic causes for the different performance of the arrays reported in the companion article by examining control volumes around the wakes and the budget of the mean-flow kinetic energy (MKE) within these volumes. In the high veer environment considered, the MKE recovery is dominated by mean convection, and this is shown to especially benefit the WS strategy when a neighboring turbine is directly downstream: there is ≈65 % more available power for a downstream turbine than in the baseline case, and this power is gained primarily through mean convection on the left-tip and top-tip faces of the control volume. However, the case with imperfect knowledge of the exact wind direction favors the pulse-type AWM strategy, largely because of ≈9 % increased turbulent entrainment from aloft versus the baseline that could be related to an apparent resistance to skewing in the pulsed wake. The general reduced effectiveness of helix-type and other individual-pitch-based AWM strategies for inflow with high veer and low turbulence as reported in the companion paper is due, in part, to low magnitudes of phase-averaged turbulent entrainment. Two main findings of this study are thus that veer has a significant impact on the effectiveness of different wake-control strategies and that pulse-type AWM may be a useful strategy when the objective is power maximization in realistic, offshore flow environments with imperfect knowledge of the exact wake overlap position on the downstream turbine.

17 WIND ENERGY↗
DOE OSTI2016

Parameterizing the Variability and Uncertainty of Wind and Solar in CEMs

We present current and improved methods for estimating the capacity value and curtailment impacts from variable generation (VG) in capacity expansion models (CEMs). The ideal calculation of these variability metrics is through an explicit co-optimized investment-dispatch model using multiple years of VG and load data. Because of data and computational limitations, existing CEMs typically approximate these metrics using a subset of all hours from a single year and/or using statistical methods, which often do not capture the tail-event impacts or the broader set of interactions between VG, storage, and conventional generators. In our proposed new methods, we use hourly generation and load values across all hours of the year to characterize the (1) contribution of VG to system capacity during high load hours, (2) the curtailment level of VG, and (3) the reduction in VG curtailment due to storage and shutdown of select thermal generators. Using CEM model outputs from a preceding model solve period, we apply these methods to exogenously calculate capacity value and curtailment metrics for the subsequent model solve period. Preliminary results suggest that these hourly methods offer improved capacity value and curtailment representations of VG in the CEM from existing approximation methods without additional computational burdens.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

R2 relaxometry based MR imaging for estimation of liver iron content: A comparison between two methods

Purpose: To compare the reproducibility and accuracy of R2-relaxometry MRI for estimation of liver iron concentration (LIC) between in-house analysis and FDA-approved commercially available third party results. Methods: All MR studies were performed on a 1.5T scanner. Multi-echo spin-echo scans with a fixed TR and increasing TE values of 6 ms, 9 ms, 12 ms, 15 ms, and 18 ms (spaced at 3 ms intervals) were used. Post-processing of the images to calculate mean relaxivity, R2, included drawing of regions of interest to include the whole liver on mid-slice. The relationship between liver R2 values and estimated LIC calculated with in-house analysis and values reported by an external company (FerriScan{sup ®}, Resonance Health, Australia) were assessed with correlation coefficients and Bland–Altman difference plots. Continuous variables are presented as mean ± standard deviation. Significance was set at p value < 0.05. Results: 474 studies from 175 patients were included in the study (mean age 10.4 ± 4.2 years (range 1–18 years); 254 studies from girls, 220 studies from boys). LIC ranged from 0.6 to 43 mg/g dry tissue, covering a broad range from normal levels to extremely high iron levels. Linearity between proprietary and in-house methods was excellent across the observed range for R2 (31.5 to 334.8 s{sup −1}); showing a correlation coefficient of r = 0.87, p < 0.001. Bland–Altman R2 difference plot between the two methods shows a mean bias of + 21.5 s{sup −1} (range − 47.0 to + 90.0 s{sup −1} between two standard deviations). LIC reported by FerriScan{sup ®} compared with LIC estimated in-house with R2 as reported by FerriScan{sup ®} agreed strongly, (r = 1.0, p < 0.001). Conclusion: R2 relaxometry MR imaging for liver iron concentration estimation is reproducible between proprietary FDA-approved commercial software and in-house analysis methods.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Exploiting a Shortcoming of Coupled-Cluster Theory: The Extent of Non-Hermiticity as a Diagnostic Indicator of Computational Accuracy

The fundamental non-Hermitian nature of the forms of the coupled-cluster (CC) theory widely used in quantum chemistry has usually been viewed as a negative, but the present paper shows how this can be used to an advantage. Specifically, the non-symmetric nature of the reduced one-particle density matrix (in the molecular orbital basis) is advocated as a diagnostic indicator of computational quality. In the limit of the full coupled-cluster theory [which is equivalent to full configuration interaction (FCI)], the electronic wave function and correlation energy are exact within a given one-particle basis set, and the symmetric character of the exact density matrix is recovered. The extent of the density matrix asymmetry is shown to provide a measure of “how difficult the problem is” (like the well-known T 1 diagnostic), but its variation with the level of theory also gives information about “how well this particular method works”, irrespective of the difficulty of the problem at hand. The proposed diagnostic is described and applied to a select group of small molecules, and an example of its overall utility for the practicing quantum chemist is illustrated through its application to the beryllium dimer (Be 2 ). Future application of this idea to excited states, open-shell systems, and symmetry-breaking problems and an extension of the method to the two-particle density are then proposed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Composite vibrational spectroscopy of the group 12 difluorides: ZnF{sub 2}, CdF{sub 2}, and HgF{sub 2}

The vibrational spectra of group 12 difluorides, MF{sub 2} (M = Zn, Cd, Hg), were investigated via coupled cluster singles, doubles, and perturbative triples, CCSD(T), including core correlation, with a series of correlation consistent basis sets ranging in size from triple-zeta through quintuple-zeta quality, which were then extrapolated to the complete basis set (CBS) limit using a variety of extrapolation procedures. The explicitly correlated coupled cluster method, CCSD(T)-F12b, was employed as well. Although exhibiting quite different convergence behavior, the F12b method yielded the CBS limit estimates closely matching more computationally expensive conventional CBS extrapolations. The convergence with respect to basis set size was examined for the contributions entering into composite vibrational spectroscopy, including those from higher-order correlation accounted for through the CCSDT(Q) level of theory, second-order spin-orbit coupling effects assessed within four-component and two-component relativistic formalisms, and vibrational anharmonicity evaluated via a perturbative treatment. Overall, the composite results are in excellent agreement with available experimental values, except for the CdF{sub 2} bond-stretching frequencies compared to spectral assignments proposed in a matrix isolation infrared and Raman study of cadmium difluoride vapor species [Loewenschuss et al., J. Chem. Phys. 50, 2502 (1969); Givan and Loewenschuss, J. Chem. Phys. 72, 3809 (1980)]. These assignments are called into question in the light of the composite results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Search for neutron decay into an antineutrino and a neutral kaon in 0.401 megaton-years exposure of Super-Kamiokande

We searched for bound neutron decay via 𝑛 → $\bar{𝜈}$ +𝐾 0 predicted by the grand unified theories in 0.401 Mton·years exposure of all pure water phases in the Super-Kamiokande detector. About 4.4 times more data than in the previous search have been analyzed by a new method including a spectrum fit to kaon invariant mass distributions. No significant data excess has been observed in the signal regions. As a result of this analysis, we set a lower limit of 7.8 × 10 32 years on the neutron lifetime at a 90% confidence level.

Cherenkov detectors↗
DOE OSTI2017

A Data-Driven Methodology for Probabilistic Wind Power Ramp Forecasting

With increasing wind penetration, wind power ramps (WPRs) are currently drawing great attention to balancing authorities, since these wind ramps largely affect power system operations. To help better manage and dispatch the wind power, this paper develops a data-driven probabilistic WPR forecasting (p-WPRF) method based on a large number of simulated scenarios. A machine learning technique is first adopted to forecast the basic wind power forecasting scenario and produce the historical forecasting errors. To accurately model the distribution of wind power forecasting errors, a generalized Gaussian mixture model is developed and the cumulative distribution function (CDF) is also analytically deduced. The inverse transform method based on the CDF is used to generate a large number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The p-WPRF is generated based on all generated scenarios under different weather and time conditions. Numerical simulations on publicly available wind power data show that the developed p-WPRF method can predict WPRs with a high level of reliability and accuracy.

17 WIND ENERGY↗
DOE OSTI2015

SU-E-T-780: Use Robustness Optimization (RO) Method to Improve the Planning Efficiency for Pencil Beam Scanning Cranial Spinal Irradiation

Purpose: We evaluate the feasibility of using robustness optimization (RO) function to improve the planning efficiency of pencil beam scanning (PBS) craniospinal irradiation (CSI) with gradient matching technique. Methods: A CSI patient was planned with 2 lateral brain fields and 4 posterior fields to cover the entire spine to maximal field of 24 cm × 20 cm on a compact PBS gantry, ProteusONE. CSI plans were generated using traditional volumetric gradient dose optimization (VGDO) and robustness optimization (RO) method respectively. In traditional VGDO, besides the sectioned spine target volumes, gradient volume (GV) were generated as 4 equally spaced structures e.g. 80%, 60%, 40%, and 20% of prescription dose. In RO method, only sectioned spine target volumes with an overlap of 4cm were created. In the robustness optimization settings, 5mm uncertainty in superior and inferior direction was defined for auto gradient optimization. Dosimetric metrics of conformity number (CN), homogeneity index (HI), and maximal cord doses were compared in Raystation version 4.6.100.6. Results: In VGDO method, total 16 GV structures and five 100% dose level target structures were contoured compared to total 5 target structures in RO method which saves 30 min in contour. With the same PTV coverage (95% volume receive 30.6Gy prescription dose), maximum cord dose is 32.64Gy in VGDO and 31.94Gy in RO. HI is 1.03 and 1.04 for VGDO and RO respectively. CN is 0.93 and 0.94 for VGDO and RO respectively. Conclusions: The dosimetric comparison demonstrated both methods are equivalent in terms of plan quality. With robust optimization for CSI gradient matching, it efficiently reduces the amount of planning target contour structure by factor of 4 and thus improves the planning efficiency especially for 4 or more gradient junction area.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

BDEM (Discrete-element-simulator for high-solids granular flows) [SWR-22-72]

BDEM is a discrete element method based simulation tool developed specifically for modeling high-solids granular flows that include polydispersity, heat-transfer, moving boundaries and chemistry. Our solver provides facilities for simulating spherical/non-spherical particles with modified contact and friction models in complex dynamic geometries defined using level-sets or triangulated files. The solver is developed on top of NREL's open-source performance portable library, AMReX, providing parallel execution capabilities on current and upcoming high-performance-computing (HPC) architectures. Simulations at the scale of several millions to billion particles have been performed using this software on large scale computing resources. This software can be applied to non-reacting solids dominant flows in silos, hoppers and screw conveyors as well as in high temperature reacting systems such as screw kilns and auger reactors.

Sitaraman, Hariswaran↗
DOE OSTI2023

Robust Machine Learning

UQ4ML is a code repository for a set of tools for the development of robust machine learning methods, uncertainty quantification and explainability of machine learning methods. The goal of these tools is to develop more robust and statistically rigorous machine learning methods for scientific applications. These tools are developed in Python, a high-level programming language that takes advantage of the Python ecosystem of high-quality open-source packages for machine learning.

Oyen, Diane↗
DOE OSTI2019

Preoperative Radiomic Approach to Evaluate Tumor-Infiltrating CD8{sup +} T Cells in Hepatocellular Carcinoma Patients Using Contrast-Enhanced Computed Tomography

Background: To help identify potential hepatocellular carcinoma (HCC) candidates for immunotherapies, we aimed to develop and validate a radiomics-based biomarker (Rad score) to predict the infiltration of tumor-infiltrating CD8{sup +} T cells in HCC patients, and to evaluate the correlation of Rad score with tumor immune characteristics. Methods: Overall, 142 HCC patients (n = 100 and n = 42 in the training and validation sets, respectively) were subjected to radiomic feature extraction. Imaging features and immunochemistry data of patients in the training set were subjected to elastic-net regularized regression analysis to predict the level of CD8{sup +} T cell infiltration. Results: A Rad score for CD8{sup +} T-cell infiltration, which contained seven variables, was developed and was validated in the validation set (area under the curve [AUC]: training set 0.751, 95% confidence interval [CI] 0.656–0.846; validation set 0.705, 95% CI 0.547–0.863). The decision curve indicated the clinical usefulness of the Rad score. A higher Rad score correlated with superior overall and disease-free survival outcomes (p = 0.012 and 0.0088, respectively). Using the pathological slides, we found that the Rad score positively correlated with the percentage of tumor-infiltrating lymphocytes (TILs; Spearman rho = 0.51, p < 0.0001). Moreover, the Rad score could also discriminate inflamed tumors from immune-desert and immune-excluded tumors (Kruskal–Wallis, p < 0.0001), and higher Rad scores could be found in patients with positive programmed cell death ligand 1 expression in tumor/immune cells, as well as those with positive programmed cell death protein 1 expression. Conclusion: The newly developed Rad score was a powerful predictor of CD8{sup +} T-cell infiltration, which could be useful in identifying potential HCC patients who can benefit from immunotherapies when validated in large-scale prospective cohorts.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Aqueous Organic Matter from Kougarok Fire Complex, Alaska, 2023

Chemical analyses of aqueous organic matter extracted by filtration from a small set of organic layer samples collected from burned and unburned tussock tundra sites in the Kougarok Fire Complex, near Nome, Alaska. There are five files in *.csv format with one data file and four data description files including data dictionary, methods, terminology, and file-level metadata.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Numerical Codes for the DESC-LSST Analysis Pipeline: Core Cosmology Library Standard Modules and Beyond wCDM Modules (Final Technical Report)

The overall objective of the project is to investigate and develop specific software modules and analysis components for the software pipeline of LSST Dark Energy Science Collaboration (DESC). Following the key projects of DESC Science Roadmap (SRM), we will write and test computer codes for the Core Cosmology Library (CCL) in order to complete its modules, functionalities, and interface to work with the analysis pipelines from the five science probes of DESC (parts of SRM deliverables CX4.2TJP, CX6.2CS). We will also code modules for CCL to test models beyond w-Cold-Dark-Matter (wCDM) and modification to gravity (MG). Interfaces for MG models will also be developed for the TJPCOSMO software which is the main pipeline of the Theory and Joint Probe (TJP) working group (deliverable TJP2.3). In order to use the full power of LSST data to constrain MG models, we will also work on constraints from nonlinear regime by running and analyzing MG N-Body simulations using a Parameterized-Post-Friedmann framework into the Gadget-2 simulation package (deliverable TJP2.2). Preliminary results for the simulations were obtained in the past. In collaboration with other DESC groups, we plan to make these simulations feedable to cosmic emulators that are practical for likelihood analyzes (deliverable TJP2.2, parts of CX6.2CS). We will also modify and integrate our current codes for consistency tests between data sets and probes into the pipeline (parts of deliverables CX8.2TJP, TJP2.3). We understand that other groups will contribute to some of these objectives but our team will focus and collaborate with others on the particular part of testing MG and models beyond wCDM and refine the DESC pipeline for this purpose. PI has been coordinating his work with the TJP and CS working groups and the DESC management team. PI is a full member of DESC since June 2013. He and his students have been contributing to LSST-DESC activities and work including TJP telecons, collaboration meetings, hack-weeks, and workshops. PI chaired or co-chaired sessions at collaboration meetings and hack-weeks about testing gravity and models beyond wCDM using LSST. He is coordinating the TJP2 projects for testing models beyond wCDM including the writing of DESC-research-note, development of code for pipeline, and N-Body simulations for MG and beyond wCDM models testable with LSST analyses. As stressed in the DESC white paper, SRM, and P5 report, one of the important questions in understanding cosmic acceleration and dark energy is to be able to distinguish whether the acceleration is due to a dark energy component in the universe or a modification to gravity. Answering these questions will have a significant impact on the question of cosmic acceleration and dark energy. The methods that we will use include analytical work, numerical code, and N-Body simulations. A first approach that that we will use consists of using growth rate parameters that enter the perturbed dynamics equations. These parameters take distinctive values for distinct gravity theories and have potential to distinguish between Dark Energy and Modified Gravity. The second method is to look for inconsistencies in Dark Energy parameter spaces using specific combinations of cosmological data sets. Our investigation addresses the Dark Energy problem that is relevant to the mission of the HEP program to understand how our universe works at its most fundamental level. It will allow us to make progress on the HEP mission to explore the nature of Dark Energy and the basic nature of space and time using future surveys such as LSST. The investigation supports the DOE HEP program Cosmic Frontier as it will contribute to the study and understanding of dark energy and fundamental properties of the universe. The investigation contributes directly to LSST-DESC key projects and their deliverables as described in the Science Road-map document to build analysis pipeline and to test dark energy and beyond wCDM models using LSST.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Product Defect Detection System: SYSM- 5620 Final Project

Retail sales is a growing market estimated to up to seven percent year over year. With this growing market there is also a trend in growing rate of retail returns, estimated just last year at $\$$850 billion. Retail stores must ensure that products available for purchase remain safe, undamaged, and acceptable to customers throughout their time in the store. This job exists regardless of the specific solution used because stores are always responsible for preventing damaged or defective products from reaching customers and when they fail to this is categorized under operation inefficiencies which accounts for an estimated $\$$12 billion in returns. When defective items remain on the sales floor, stores may experience increased returns, reduced customer satisfaction, loss of customer trust, and potential safety concerns depending on the product type. As a result, the core job to be done is to identify defective products quickly, remove them from the sales floor before they are purchased, and preserve useful information about the defect so that the store can improve its handling, stocking, and supplier coordination over time. The need for a more reliable process is especially important in high volume retail environments where employees manage large numbers of products across many aisles, shelves, and storage areas. In these settings, manual inspection alone can be inconsistent and difficult to sustain at the individual item level. At the same time, broader retail trends continue to emphasize operational efficiency, product visibility, and improved customer experience, creating an opportunity for more automated and data driven defect detection methods.

42 ENGINEERING↗
DOE OSTI2025

Comparison of wind farm control strategies under realistic offshore wind conditions: turbine quantities of interest

Abstract. Wind farm flow control is a strategy to increase the efficiency and therefore lower the levelized cost of energy of a wind farm. This is done using turbine settings such as the yaw angle, blade pitch angles, or generator torque to manipulate the flow behind the turbine, affecting downstream turbines in the farm. Two inherently different wind farm flow control methods have been identified in the literature: wake steering and wake mixing. This paper focuses on comparing the turbine quantities of interest between these methods for a simple two-turbine wind farm setup, while a companion article (Brown et al., 2025) focuses on the wake quantities of interest for a single wind turbine setup. Both papers use the same set of wind farm simulations based on high-fidelity large-eddy simulations (LESs) coupled with OpenFAST turbine models. First, precursor simulations are executed in order to match wind conditions measured with lidars in an offshore wind farm off the east coast of the USA. These measurements show general wind conditions that exhibit substantially higher vertical wind shear and veer than any of the LES studies performed with wind farm flow control strategies currently available in the literature. The precursors are used to evaluate the effectiveness of the control methods. In the LES, the wind veer leads to highly skewed wakes, which have considerable influence on the power uplift of wind farm flow control strategies. In addition to a baseline controller, four different control strategies, each of which uses either pitch or yaw control, are performed on the upstream turbine of a simple two-turbine wind farm. Assuming that the wind direction is known and constant over time, the simulations show that wake steering is generally the superior wind farm flow control strategy, considering both wind farm power production and turbine damage equivalent loads when substantial wind veer is present. This result is consistent over different wind speeds and wind directions. On the other hand, for similar wind conditions with lower veer, wake mixing was found to yield the highest power production, although at the expense of generally higher loads. This leads us to conclude that the effect of wind veer, which has so far not usually been considered, can not be neglected when determining the optimal wind farm flow control strategy.

17 WIND ENERGY↗