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NASA NTRS1975

Wind tunnel investigation of aerodynamic loads on a large-scale externally blown flap model and comparison with theory

Results from a wind-tunnel investigation of a large-scale externally blown flap model are presented. The model was equipped with four turbofan engines, a triple-slotted flap system, and a T-tail. The wing had a quarter-chord sweep of 25 deg, an aspect ratio of 7.28, and a taper ratio of 0.4. Aerodynamic loads and load distributions were determined from a total of 564 static pressure orifices located on the upper and lower surfaces of the slat, wing, and flaps. Loads are presented for variations of angle of attack, engine thrust setting, and flap deflection angle. In addition, the experimental results are compared with analytical results calculated by using a potential flow analysis.

Perry, B., III↗

DOE OSTI2026

Large Load Impacts to Distribution System Hosting Capacity

This work examined the impact of large loads on utility distribution system models using the Sandia-developed open-source software DREAMS. It was shown that hosting capacity varies with location and changes after any asset is added to, or removed from, a system. Despite the tested models having similar rated voltages and other characteristics, their thermal and voltage constrained hosting capacity varied over 2 MW. The addition of a 3-phase balanced constant power large load with power factor of 1.0 exhibited non-linear reductions to all voltage constrained hosting capacities. The reductions to thermal constrained hosting capacity from a load with similar characteristics was more linear, related to the size of the added load, and did not impact all model buses. Co-located capacitors were shown to accommodate demand that was beyond the baseline voltage constrained hosting capacity limits, however, the costs and benefits from this approach were found to not be 1:1 and required additional available thermal capacity.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Modeling Framework for Data Center

This chapter highlights the critical need for advanced modeling of data centers due to their rapidly increasing energy consumption and impact on grid reliability. Driven by the demand for AI applications, data centers are projected to consume a significant portion of US energy by 2028, putting stress on an already challenged power grid. The chapter emphasizes the importance of "fast" time-scale models to understand the dynamic interactions between data centers and the grid, especially given the rapid power fluctuations of AI workloads. It outlines a modeling framework that includes both offline and real-time EMT domain simulations, detailing the necessary representations for various components like utility interfaces, transformers, IT loads, UPS, cooling loads, Battery Energy Storage Systems (BESS), generators, protection systems, and higher-level control systems. While standard simulation tools like PSCAD offer basic models, custom development is often required to accurately capture the unique and fast-changing behaviors of modern data centers. The chapter also discusses key metrics and test cases for validating these models, focusing on transient load responses, protection relay coordination, and demand flexibility. Finally, it addresses the challenges of modeling large-scale data centers, such as computational complexity and the trade-off between model fidelity and practicality, suggesting hybrid modeling approaches as a solution. The overarching goal is to create a robust framework that helps assess data center impacts on grid stability, identify vulnerabilities, and inform the development of standards for reliable integration of these large loads into the bulk power system.

25 ENERGY STORAGE↗

DOE OSTI2020

Data-Driven Linear Parameter-Varying Modeling and Control of Flexible Loads for Grid Services

Flexible loads have great potential to improve the electric grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. This paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environmental impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2020

Data-Driven Linear Parameter-Varying Modeling and Control of Flexible Loads for Grid Services: Preprint

Flexible loads have great potential to improve the power grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. The paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environment impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.

control↗

DOE OSTI2024

DRDMannTurb: A Python package for scalable, data-driven synthetic turbulence

Synthetic turbulence models (STMs) are used in wind engineering to generate realistic flow fields and are employed as inputs to industrial wind simulations. Examples include prescribing inlet conditions in large eddy simulations that model loads on wind turbines and tall buildings. We are interested in STMs capable of generating fluctuations based on prescribed second-moment statistics since such models can simulate environmental conditions that closely resemble on-site observations. To this end, the widely used Mann model (see Mann, 1994, 1998) is the inspiration for DRDMannTurb. The Mann model is described by three physical parameters: a magnitude parameter influencing the global variance of the wind field and corresponding to the Kolmogorov constant multiplied by the rate of viscous dissipation of the turbulent kinetic energy to the two-thirds, αϵ 2/3 , a turbulence length scale parameter L, and a nondimensional parameter Γ related to the lifetime of the eddies. A number of studies, as well as international standards (e.g., those by the International Electrotechnical Commission (IEC)), include recommended values for these three parameters with the goal of standardizing wind simulations according to observed energy spectra. Yet, having only three parameters, the Mann model faces limitations in accurately representing the diversity of observable spectra. This Python package enables users to extend the Mann model and more accurately fit field measurements through flexible neural network models of the eddy lifetime function. Following Keith et al. (2021), we refer to this class of models as Deep Rapid Distortion (DRD) models. DRDMannTurb also includes a general module implementing an efficient method for synthetic turbulence generation based on a domain decomposition technique. This technique is also described in Keith et al. (2021).

17 WIND ENERGY↗

DOE OSTI2025

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗

DOE OSTI2023

Comparison of three DWM-based wake models at above-rated wind speeds

In this study we investigate three mid-fidelity wind turbine wake models based on the dynamic wake meandering (DWM) model principle, and compare their performance with a reference dataset, produced with large-eddy simulations using the actuator line model. The models are compared with respect to flow field, power, and loads on a row of four 5MW reference turbines experiencing above-rated wind conditions. In general, the DWM models show fairly good agreement with large-eddy simulation for the time-averaged flow fields, blade forces and power, with increasing differences along the turbine row. Also when comparing fatigue loads of blade root moments, the differences between the models increase further into the row, with deviations up to 25 % of the reference case. However, while the development in blade root moment fatigue along the turbine row is predominantly driven by the energy content at the frequency corresponding to the turbine's rotational period (1 P ) for the DWM models, the large-eddy simulation results suggest that the key drivers for the blade root and tower loads are the increase in meandering and energy at higher frequencies (> 1 P ) deeper into the turbine row. For the tower loads, the DWM models highly underestimate the fatigue for the waked turbines. From these results, we suggest priorities for future model developments so that robust model implementations can be used in wind farm design and operation.

17 WIND ENERGY↗

NASA NTRS1985

Large deflection behavior of quasi-isotropic laminates under low-velocity impact type point loading

Eight-ply quasi-isotropic circular composite plates of Thornel 300 graphite in Narmco 5208 epoxy resin (T300/5208) were analyzed to obtain the large deformation behavior under low-velocity impact type point loads. A simple plate-membrane coupling model was developed. The impact type point loads were replaced by equivalent quasi-static point loads. The plate-membrane coupling model was used to obtain the large deformation shapes for the thin circular composite laminates. The analyses indicated that the large deformation shapes of the composite plates under point loads vary with the center point displacements, and hence are different for different load levels. Quasi-isotropic plates were analyzed by replacing anisotropic bending stiffness components with the equivalent flexural stiffness for the isotropic plates. The plate-membrane coupling model was verified by conducting a series of tests on clamped circular quasi-isotropic laminates. Deflected shapes for the thin composite plates were experimentally obtained. These shapes agreed well with the analytically predicted shapes.

Kelkar, A.↗

DOE OSTI2026

Numerical Modeling of a Two-Stage Ocean Current Turbine

The Equinox Ocean Turbines (EQOT) current energy converter has a unique design with power generation in two small-diameter turbines attached to the tips of a large-diameter passive rotor. This configuration offers some key advantages for capturing ocean currents. With no centrally placed generator, almost no reaction torque is required at the nacelle of the main large-diameter rotor, and the small-diameter tip turbine generators operate at a higher speed and lower torque. The physics that determine the performance and loads on the turbine are also unique. The interactions of the flow field between the two stages and the general architecture of the system cannot be captured with traditional mid-fidelity modeling tools. For design iterations and large sets of load cases, it is important to have mid-fidelity models that can capture the important phenomenon with enough accuracy to identify global trends. This work uses a limited set of high-fidelity computational fluid dynamics (CFD) simulations to help inform the selection of and construction of a custom mid-fidelity model. Mid-fidelity modeling approaches were verified by comparing key turbine performance quantities to those found with the CFD model. Hydrodynamic interactions of the two-stage rotor were identified through high-fidelity CFD modeling. This highlighted the impact of the main rotor tip vortex and wake on the secondary rotor apparent inflow. This results in a relative flow rotation and sharp deficit, that change the optimal secondary rotor rotation speed and adds unsteadiness to the blade loading respectively. Multiple mid-fidelity approaches were evaluated for their ability to capture these effects. A simple approximation of the combined-stage performance based on single-stage BEM provides a reasonable rough prediction, especially near the peak TSR values, with some larger discrepancy at higher TSRs. Predicting the combined-stage performance based on single-stage CFD data improves this prediction across the TSR range. Although the combined-stage modeling in OLAF was not successful in this stage of the project, it showed promise as a mid-fidelity method, assuming the parameters can be tuned to account for the significant differences in time and length scales between the main and secondary rotors. This may be addressed through code changes in future work. A significant finding from the OLAF work was the agreement between the vortex core radius values found independently via a parameter space search and via CFD. The technique of using single-stage secondary rotor BEM, with a custom inflow taken from single-stage main rotor CFD or OLAF, provides an efficient method to capture one-way coupled flow interactions. This method provided generally good predictions of the impact of the flow rotation on the secondary rotor but struggled to accurately predict the peaks of the unsteady load progression. Future work could include some superposition of a tuned main rotor trailing edge viscous wake into the custom inflow to better predict this interaction.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

An Overview of Electric Vehicle Load Modeling Strategies for Grid Integration Studies

The adoption of electric vehicles (EVs) has emerged as a solution to reduce greenhouse gas emissions in the transportation sector, which has motivated the implementation of public policies to promote their use in several countries. However, the high adoption of EVs poses challenges for the electricity sector, as it would imply an increase in energy demand and possible impacts on the power quality (PQ) of the power grid. Therefore, it is important to conduct EV integration studies in the power grid to determine the amount that can be incorporated without causing problems and identify the areas of the power sector that will require reinforcements. Accurate EV load patterns are required for this type of study that, through mathematical modeling, reflect both the dynamic behavior and the factors that influence the decision to recharge EVs. This article aims to present an overview of EVs, examine the different factors considered in the literature for modeling EV load patterns, and review modeling methods. EV load modeling methods are classified into deterministic, statistical, and machine learning. The article shows that each modeling method has its advantages, disadvantages, and data requirements, ranging from simple load modeling to more accurate models requiring large datasets.

Computer Science↗

DOE OSTI2023

Coupled wave-current modeling for hydrodynamic load analysis of macroalgae cultivation farms

Large-scale cultivation of macroalgae is one of the most promising biofuel sources that could reduce our consumption of fossil fuels and would be particularly economically competitive when grown for the co-production of additional goods such as food and textiles. Current production of biofuel from algal biomass is limited by labor costs and lack of data on potential impacts of cultivation and harvesting on marine and coastal environments as well as a comprehensive characterization of the impact of environmental conditions on macroalgal feedstock and their bioproduct and biofuel yield and quality. Awarded by the United States (U.S.) Department of Energy Advanced Research Projects Agency-Energy (ARPA-E) Macroalgae Research Inspiring Novel Energy Resources (MARINER) program, which seeks to enable the U.S. as a global leader in the production of marine biomass, the Marine Biological Laboratory (MBL) has been developing a test system for tropical seaweed cultivation in the Gulf of Mexico and the Caribbean. An integral step in designing macroalgae cultivation farms is to adequately characterize the hydrodynamic climate at potential growth sites. In regions like the Gulf of Mexico and the Caribbean, which are highly exposed to tropical cyclones, it is critical to not only consider the day-to-day hydrodynamic conditions but to also assess the risk of extreme sea states to ensure the survivability of future macroalgae farms. Under these considerations, the Pacific Northwest National Laboratory (PNNL) is providing modeling support for MBL to inform the design and siting of the farm systems that have been proposed for their ARPA-E MARINER project. This report describes the development of a high-resolution coupled storm surge and wave model to simulate the hydrodynamics and wave climate at proposed macroalgae cultivation sites selected by MBL in Florida and Puerto Rico. Model results, including model validation, water level, current distributions, and sea states, are discussed for both selected sites in Florida and Puerto Rico coast. These simulations provide an accurate insight into the hydrodynamic conditions that a macroalgae farm is likely to experience during its operational lifetime, including current information to support fine-scale hydrodynamic load modeling, risk analysis and system design.

09 BIOMASS FUELS↗

DOE OSTI2027

Nodal capacity expansion planning with flexible large-scale load siting

We propose explicitly incorporating large-scale load siting into a stochastic nodal power system capacity expansion planning model that concurrently co-optimizes generation, transmission, and storage expansion. The potential operational flexibility of some of these large loads is also taken into account by considering them as consisting of a set of tranches with different reliability requirements, which are modeled as a constraint on expected served energy across operational scenarios. We implement our model as a two-stage stochastic mixed-integer optimization problem with cross-scenario expectation constraints. To overcome the challenge of scalability, we build upon existing work to implement this model on a high performance computing platform and exploit scenario parallelization using an augmented Progressive Hedging Algorithm. The algorithm is implemented using the bounding features of mpisppy, which have shown to provide satisfactory provable optimality gaps despite the absence of theoretical guarantees of convergence. We test our approach and assess the value of this proactive planning framework on total system cost and reliability metrics using realistic testcases geographically assigned to San Diego and South Carolina, with datacenter and direct air capture facilities as large loads.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Benchmarking of three DWM-based wake models at below-rated wind speeds

Wind turbine wake models are essential tools for predicting power losses and structural loads in wind farms. Among these, the dynamic wake meandering (DWM) model, included as a recommended approach in the International Electrotechnical Commission design standard, is a widely used engineering-fidelity method that balances accuracy and computational cost. This study compares the performance of three DWM-based wake model implementations (from the Technical University of Denmark, the National Renewable Energy Laboratory, and the Institute for Energy Technology) under below-rated wind speed conditions. Model predictions of wake flow, power output, and structural loads for a four-turbine row are evaluated across different ambient turbulence levels and wind-direction misalignments and compared against high-fidelity large-eddy simulation results. All three models captured the overall wake evolution and mean turbine performance with reasonable accuracy; their predicted time-averaged thrust and power were typically within 5 %–10 % of the large-eddy simulation benchmark. However, notable differences emerged in wake structure and unsteady load predictions, with discrepancies increasing for turbines further downstream. These differences highlight the importance of modelling choices such as wake summation and turbulence treatment, which strongly influence power-deficit and fatigue-load predictions. Comparison with large-eddy simulations reveals each approach's strengths and weaknesses, indicating where improvements are needed. Overall, the findings point to specific refinements for DWM models to improve their fidelity, ultimately enabling more robust wake predictions for wind farm design and operation.

17 WIND ENERGY↗

DOE OSTI2020

A Comparison of PV Resource Modeling for Sizing Microgrid Components

Microgrid systems are being deployed with increased frequency to meet critical building loads during unplanned power outages. Determining the appropriate type and size of components that make up a microgrid can greatly affect its ability to meet this need. This paper presents a new tool to appropriately size PV, battery, and generator capacities to meet all site loads, given a resilience goal. It uses a statistical model to simulate microgrid behavior under a large range of solar resource conditions, providing resilience planners with confidence that critical loads will be met even under extreme weather conditions. We compare this model with one that uses typical weather data for sizing microgrids, and quantify the resilience effects of using these different methods. Our method produces more conservative generator capacities and fuel requirements in an outage, predicting up to 30% more fuel required in the most extreme case. Microgrid systems that are designed using more conservative estimates are more likely to continue to serve load during emergency outages under a large range of conditions.

Newman, Sarah F.↗

DOE OSTI2022

Generalizability analysis of tool condition monitoring ensemble machine learning models

Tool condition monitoring (TCM) is an essential research area for the optimization and automation of metal machining processes, and could help manufacturers reduce costs, production time, machine downtime, energy use, and part scrappage. However, TCM systems developed in prior studies have struggled to reach the high level of generalizability which is necessary for industrial applications. This study addresses TCM system generalizability to new machining conditions, how variations in machining and environmental conditions may be used to improve model generalizability, and ensemble machine learning techniques for TCM. Further, milling tool life experiments were conducted using various machining conditions, and the processes' sound, spindle power, and axial load signals were collected. Different machine learning models were evaluated for the prediction of tool wear levels, including four individual models and five ensemble models. Changes in cutting speed were found to display a large effect on model performance, while the chip load showed some effect, and the feed rate had little effect. A simulated noise data augmentation technique for model improvement is applied within TCM for the first time, and resulted in increased model generalizability and reduced overfitting. Across several performance metrics the extremely randomized trees ensemble machine learning model generally performed the best for this application, achieving a leave-one-group-out cross validation accuracy score of 92.4 %, a 10-fold cross validation score of 98.9 %, and an averaged accuracy across 11 generalizability tests of 87.3 %.

42 ENGINEERING↗

NASA NTRS2020

Progressive Failure Analysis Correlation with Notched Composite Laminate Test Data

Testing of small and intermediate laminate panels with notches was performed and Progressive Failure Analyses (PFA) models were developed for aid in test planning and for correlation with the test data. Two progressive damage failure models were included in the study: the commercially available Abaqus built-in damage model and COmplete STress Reduction (COSTR) damage model developed at NASA Langley Research Center (LaRC). The finite elements models used for the analysis were developed using shell elements. The pre-test PFA results obtained from the two damage models were compared to test data. Then a post-test PFA with updated material properties based on additional available material property data was executed with the COSTR damage model and compared to the test data. The panels tested in compression all exhibited less scatter in the failure load with self-similar failure behavior. The panels tested in tension exhibited more scatter in the failure load with a failure mode that included delamination of plies. However, a self-similar crack path was also observed as the overall failure mode for all panels tested. When comparing test data to the pre-test analysis results, both damage models with shell element models were considered adequate for predicting the behavior and failure load of both the small and intermediate panels when tested in compression. However, for the tension load cases, larger differences were observed between the test and analysis results. When comparing test data to the post-test analysis performed using the COSTR damage model, better correlation was observed. However, large discrepancies were still observed for the tension load cases. Consequently, a higher fidelity finite element model including solid elements for sub-laminates and the modeling of cohesive layers between sub-laminate layers is recommended for tension loading of notched composite laminates.

Damage Model,Progressive Failure Analysis,Open Hol↗

NASA NTRS2003

Load Diffusion in Composite and Smart Structures

The research carried out here builds on our previous NASA supported research on the general topic of edge effects and load diffusion in composite structures. Further fundamental solid mechanics studies were carried out to provide a basis for assessing the complicated modeling necessary for the multi-functional large scale structures used by NASA. An understanding of the fundamental mechanisms of load diffusion in composite subcomponents is essential in developing primary composite structures. Some specific problems recently considered were those of end effects in smart materials and structures, study of the stress response of pressurized linear piezoelectric cylinders for both static and steady rotating configurations, an analysis of the effect of pre-stressing and pre-polarization on the decay of end effects in piezoelectric solids and investigation of constitutive models for hardening rubber-like materials. Our goal in the study of load diffusion is the development of readily applicable results for the decay lengths in terms of non-dimensional material and geometric parameters. Analytical models of load diffusion behavior are extremely valuable in building an intuitive base for developing refined modeling strategies and assessing results from finite element analyses. The decay behavior of stresses and other field quantities provides a significant aid towards this process. The analysis is also amenable to parameter study with a large parameter space and should be useful in structural tailoring studies. Special purpose analytical models of load diffusion behavior are extremely valuable in building an intuitive base for developing refined modeling strategies and in assessing results from general purpose finite element analyses. For example, a rational basis is needed in choosing where to use three-dimensional to two-dimensional transition finite elements in analyzing stiffened plates and shells. The decay behavior of stresses and other field quantities furnished by this research provides a significant aid towards this element transition issue. A priori knowledge of the extent of boundary-layers induced by edge effects is also useful in determination of the instrumentation location in structural verification tests or in material characterization tests.

Horgan, C. O.↗