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DOE OSTI2020

Wind Turbine Rotor Design Optimization Using Importance Sampling: Preprint

Probabilistic methods are commonly adopted to estimate both ultimate and fatigue loads in wind turbines, and multiple papers have presented a variety of approaches. In contrast, system-level, integrated design optimization methods for wind turbines have so far relied on deterministic methods to estimate loads and deflections. This work aims at addressing this gap by adopting importance sampling to estimate ultimate blade deflection for use within a rotor design optimization. A comparison between the results of this approach against traditional approaches using deterministic load evaluations show that the probabilistic approach is more conservative and that the solutions found with deterministic approaches violate the probabilistic constraint on tip deflection between 13% and 17%. This highlights the importance of accurate load and deflection estimates and demonstrates that probabilistic approaches can be successfully integrated within a wind turbine design process despite the higher computational costs.

17 WIND ENERGY↗

DOE OSTI2017

Characterization and photocatalytic activity of Bi{sub 3}TaO{sub 7} prepared by hydrothermal method

In this paper, Bi{sub 3}TaO{sub 7} nanoparticles with visible light response have been prepared via a facile hydrothermal route and solid-state reaction. The photocatalytic performance of as-prepared samples was evaluated by the degradation of Tetracycline hydrochloride (TC). It was observed that the sample obtained by hydrothermal method showed an enhanced photocatalytic activity in contrast to the sample prepared by solid-state method. This could be ascribed to the efficient separation of photogenerated electrons and holes in the sample prepared via hydrothermal process, which is certified by the photoluminescence spectroscopy (PL) and transient photocurrent analysis. Moreover, the result of nitrogen adsorption-desorption isotherm indicated the existence of mesoporous structure in as-prepared sample with hydrothermal process, and specific surface area is significantly higher than the sample obtained by solid-state method. The optimal conditions involved in the photocatalytic reaction, such as TC concentration, catalyst concentration and pH value were investigated. - Graphical abstract: The preparation process of Bi{sub 3}TaO{sub 7} photocatalyst by hydrothermal method. Display Omitted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Absorber Clamp for Microcalorimeter Decay Energy Spectrometry

Microcalorimeter Decay Energy Spectrometry (DES) is of interest to nuclear safeguards due to its ability to provide high precision isotopic compositions of nanogram-to microgram-scale samples of Pu and U and related daughter products. The DES method is able to record decay energy of each alpha-decay event in a sample that is embedded in a metal matrix (absorber) and thermally linked to a microcalorimeter detector. This work optimizes the DES technique used to thermally link the absorber and microcalorimeter detector element to allow for more rapid assembly and to increase detector performance and operating life. Optimized attachment methods are crucial for enhancing the viability of DES in high-sample-throughput facilities, such as those that support international nuclear safeguards measurements. Here, in this study, we designed and implemented a pressure-based absorber clamp and evaluated the performance of this new attachment method relative to pressed indium bond attachment. Results using the absorber clamp demonstrate a streamlined detector assembly procedure that minimizes accidental damage to detectors, as well as increasing detector pulse speeds by 57%. Spectral comparison shows the clamp preserves detector performance relative to indium attachment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Design and Analysis of Multifidelity Finite Element Simulations

Abstract The numerical accuracy of finite element analysis (FEA) depends on the number of finite elements used in the discretization of the space, which can be varied using the mesh size. The larger the number of elements, the more accurate the results are. However, the computational cost increases with the number of elements. In current practice, the experimenter chooses a mesh size that is expected to produce a reasonably accurate result, and for which the computer simulation can be completed in a reasonable amount of time. Improvements to this approach have been proposed using multifidelity modeling by choosing two or three mesh sizes. However, mesh size is a continuous parameter, and therefore, multifidelity simulations can be performed easily by choosing a different value for the mesh size for each of the simulations. In this article, we develop a method to optimally find the mesh sizes for each simulation and satisfy the same time constraints as a single or a double mesh size experiment. A range of different mesh sizes used in the proposed method allows one to fit multifidelity models more reliably and predict the outcome when meshes approach infinitesimally small, which is impossible to achieve in actual simulations. We illustrate our approach using an analytical function and a cantilever beam finite element analysis experiment.

Engineering↗

DOE OSTI2025

Preliminary study of auto-differentiation algorithm in beam dynamics with stochastic process

Modern particle accelerator optimization requires sophisticated computational methods to address the inherently stochastic nature of beam dynamics. This research develops a framework applying AD to SDEs that specifically addresses beam dynamics challenges in particle accelerators, focusing on accurately modeling and optimizing beam behavior in regimes dominated by stochastic processes. By incorporating key physical phenomena such as synchrotron radiation, wakefield effects, and quantum excitation, the framework aims to provide auto differentiation on the figure of merit of the phase space evolution and beam dynamics. The methodology will enable effective optimization method in a dynamic system with stochastic process.

Accelerator Physics↗

DOE OSTI2016

A promising limited angular computed tomography reconstruction via segmentation based regional enhancement and total variation minimization

X-ray computed tomography (CT) is a powerful and common inspection technique used for the industrial non-destructive testing. However, large-sized and heavily absorbing objects cause the formation of artifacts because of either the lack of specimen penetration in specific directions or the acquisition of data from only a limited angular range of views. Although the sparse optimization-based methods, such as the total variation (TV) minimization method, can suppress artifacts to some extent, reconstructing the images such that they converge to accurate values remains difficult because of the deficiency in continuous angular data and inconsistency in the projections. To address this problem, we use the idea of regional enhancement of the true values and suppression of the illusory artifacts outside the region to develop an efficient iterative algorithm. This algorithm is based on the combination of regional enhancement of the true values and TV minimization for the limited angular reconstruction. In this algorithm, the segmentation approach is introduced to distinguish the regions of different image knowledge and generate the support mask of the image. A new regularization term, which contains the support knowledge to enhance the true values of the image, is incorporated into the objective function. Then, the proposed optimization model is solved by variable splitting and the alternating direction method efficiently. A compensation approach is also designed to extract useful information from the initial projections and thus reduce false segmentation result and correct the segmentation support and the segmented image. The results obtained from comparing both simulation studies and real CT data set reconstructions indicate that the proposed algorithm generates a more accurate image than do the other reconstruction methods. The experimental results show that this algorithm can produce high-quality reconstructed images for the limited angular reconstruction and suppress the illusory artifacts caused by the deficiency in valid data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Building Battery Energy Storage System Performance Data into an Economic Assessment

Economic assessments of battery energy storage systems (BESSs) rely on optimal dispatch methods to capture temporal interdependency of BESS operations and coupling among different grid applications. Many existing BESS economic assessment studies are based on a simplified first-order scalar model using BESS nameplate numbers. Such a method cannot accurately capture varying capabilities and nonlinear dynamics of a BESS, leading to inaccurate assessment results. This paper presents a practical implementation of performance data into the economic assessment of the BESSs recently developed within the Snohomish Public Utility District system in Washington State. Extensive testing conducted over multiple seasons is used to assess the technical performance and develop high-fidelity models of the BESSs. A dynamic programming algorithm is proposed to optimally dispatch the BESSs and assess the potential benefits from stacked value streams. The proposed method can be adapted for the economic assessment of other BESSs with different applications.

Wu, Di↗

DOE OSTI2019

Technique for the Formation of Antireflection Coatings Based on ITO Films

A technique for forming transparent conducting indium-tin-oxide films for applications, which require high current densities, are considered. These films have to combine a good surface conductivity with an increased transmittance of radiation within a wide wavelength range. A two-stage process is designed to fabricate such coatings: at the first stage, a dense indium-tin-oxide film with high electrical conductivity is formed; then, an additional antireflection layer of the same material is deposited. Possible methods for optimizing these additional indium-tin-oxide films obtained by forming nanostructured self-assembled coatings on the surface of dense films are investigated. It is shown that the best light extraction over a wide wavelength range is provided by the composite film-deposition technique, which consists in the deposition of self-assembled nanostructured coatings by electron-beam evaporation on a hot substrate and the further filling of voids by magnetron sputtering at room temperature. Since the optical properties of the obtained antireflection coatings depend on the mass ratio of materials deposited at each stage, the technique makes it possible to optimize the film properties on the basis of problems specified by the area of their application. Computer simulation of the behavior of the material distribution in the structure of the studied coatings confirms the ability of the method of combined film deposition to form the smoothest character of the change in the effective refractive index of the coating structure, which leads to the most efficient extraction of optical-range light.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Natural evolutionary strategies for variational quantum computation

Abstract Natural evolutionary strategies (NES) are a family of gradient-free black-box optimization algorithms. This study illustrates their use for the optimization of randomly initialized parameterized quantum circuits (PQCs) in the region of vanishing gradients. We show that using the NES gradient estimator the exponential decrease in variance can be alleviated. We implement two specific approaches, the exponential and separable NES, for parameter optimization of PQCs and compare them against standard gradient descent. We apply them to two different problems of ground state energy estimation using variational quantum eigensolver and state preparation with circuits of varying depth and length. We also introduce batch optimization for circuits with larger depth to extend the use of ES to a larger number of parameters. We achieve accuracy comparable to state-of-the-art optimization techniques in all the above cases with a lower number of circuit evaluations. Our empirical results indicate that one can use NES as a hybrid tool in tandem with other gradient-based methods for optimization of deep quantum circuits in regions with vanishing gradients.

Anand, Abhinav (ORCID:0000000280812310)↗

DOE OSTI2016

Hierarchical models and iterative optimization of hybrid systems

A class of hybrid control systems on the base of two-level discrete-continuous model is considered. The concept of this model was proposed and developed in preceding works as a concretization of the general multi-step system with related optimality conditions. A new iterative optimization procedure for such systems is developed on the base of localization of the global optimality conditions via contraction the control set.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Transient Storage Model Parameter Optimization Using the Simulated Annealing Method

Hyporheic exchange in streams is critical to ecosystem functions such as nutrient cycling along river corridors, especially for slowly moving or small stream systems. The transient storage model (TSM) has been widely used for modeling of hyporheic exchange. TSM calibration, for hyporheic exchange, is typically used to estimate four parameters, including the mass exchange rate coefficient, the dispersion coefficient, stream cross-sectional area, and hyporheic zone cross-sectional area. Prior studies have raised concerns regarding the non-uniqueness of the inverse problem for the TSM, that is, the occurrence of different parameter vectors resulting in TSM solution that reproduces the observed in-stream tracer break through curve (BTC) with the same error. This leads to practical non-identifiability in determining the unknown parameter vector values even when global-optimal values exist, and the parameter optimization becomes practically non-unique. To address this problem, we applied the simulated annealing method to calibrate the TSM to BTCs, because it is less susceptible to local minima-induced non-identifiability. A hypothetical (or synthetic) tracer test data set with known parameters was developed to demonstrate the capability of the simulated annealing method to find the global minimum parameter vector, and it identified the “hypothetically-true” global minimum parameter vector even with input data that were modified with up to 10% noise without increasing the number of iterations required for convergence. The simulated annealing TSM was then calibrated using two in-stream tracer tests conducted in East Fork Poplar Creek, Tennessee. In conclusion, simulated annealing was determined to be appropriate for quantifying the TSM parameter vector because of its search capability for the global minimum parameter vector.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Operator learning for energy-efficient building ventilation control with computational fluid dynamics simulation of a real-world classroom

Energy-efficient ventilation control plays an important role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD) simulations provide detailed and physically accurate representations of indoor airflow, their high computational cost limits their use in real-time building control. In this work, we present a neural operator learning framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable building ventilation control with the high-fidelity fluid dynamics models. Our method jointly optimizes the airflow supply rates and vent angles to reduce energy use and adhere to air quality constraints. We train an ensemble of neural operator transformer models to learn the mapping from building control actions to airflow fields using high-resolution CFD data. This learned neural operator is then embedded in an optimization-based control framework for building ventilation control. Experimental results show that our approach achieves significant energy savings compared to maximum airflow rate control, rule-based control, as well as data-driven control methods using spatially averaged CO 2 prediction and deep learning–based reduced-order models, while consistently maintaining safe indoor air quality. These results highlight the practicality and scalability of our method in maintaining energy efficiency and indoor air quality in real-world buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Finding the Pareto front for high-entropy-alloy catalysts

Finding catalysts that have both high activity and high stability presents a long-standing challenge. Since optimizing activity and stability are conflicting objectives, the best one can do is find the Pareto front that yields optimal tradeoffs between these features. On the Pareto front, there is a trade-off where a portion of catalytic activity must be sacrificed to gain further stability and vice versa . Here, we provide a method to optimize the front by designing a multi-objective genetic algorithm that combines machine learning, graph neural network calculations, and density functional calculations. The application considered is the oxygen evolution reaction catalyzed by high-entropy alloys. We find that the Pareto front generally contains alloys with diverse elements, but that enhancing stability inevitably inflicts a toll on activity. We compare the general conclusions of our work to a survey of 545 experiments.

Zhang, Chengyi [Univ. of Auckland (New Zealand)]↗

DOE OSTI2016

Lidar-Enhanced Wind Turbine Control: Past, Present, and Future

The main challenges in harvesting energy from the wind arise from the unknown incoming turbulent wind field. Balancing the competing interests of reduction in structural loads and increasing energy production is the goal of a wind turbine controller to reduce the cost of producing wind energy. Conventional wind turbines use feedback methods to optimize these goals, reacting to wind disturbances after they have already impacted the wind turbine. Lidar sensors offer a means to provide additional inputs to a wind turbine controller, enabling new techniques to improve control methods, allowing a controller to actuate a wind turbine in anticipation of an incoming wind disturbance. This paper will look at the development of lidar-enhanced controls and how they have been used for various turbine load reductions with pitch actuation, as well as increased energy production with improved yaw control. Ongoing work will also be discussed to show that combining pitch and torque control using feedforward nonlinear model predictive control can lead to both reduced loads and increased energy production. Future work is also proposed on extending individual wind turbine controls to the wind plant level and determining how lidars can be used for control methods to further lower the cost of wind energy by minimizing wake impacts in a wind farm.

17 WIND ENERGY↗

DOE OSTI2016

Using Machine Learning in Adversarial Environments.

Intrusion/anomaly detection systems are among the first lines of cyber defense. Commonly, they either use signatures or machine learning (ML) to identify threats, but fail to account for sophisticated attackers trying to circumvent them. We propose to embed machine learning within a game theoretic framework that performs adversarial modeling, develops methods for optimizing operational response based on ML, and integrates the resulting optimization codebase into the existing ML infrastructure developed by the Hybrid LDRD. Our approach addresses three key shortcomings of ML in adversarial settings: 1) resulting classifiers are typically deterministic and, therefore, easy to reverse engineer; 2) ML approaches only address the prediction problem, but do not prescribe how one should operationalize predictions, nor account for operational costs and constraints; and 3) ML approaches do not model attackers’ response and can be circumvented by sophisticated adversaries. The principal novelty of our approach is to construct an optimization framework that blends ML, operational considerations, and a model predicting attackers reaction, with the goal of computing optimal moving target defense. One important challenge is to construct a realistic model of an adversary that is tractable, yet realistic. We aim to advance the science of attacker modeling by considering game-theoretic methods, and by engaging experimental subjects with red teaming experience in trying to actively circumvent an intrusion detection system, and learning a predictive model of such circumvention activities. In addition, we will generate metrics to test that a particular model of an adversary is consistent with available data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Wind Energy Modeling and Simulation, Volume 2: Turbine and System

In order to optimise the yield of wind power from existing and future wind plants, the entire breadth of the system of a plant, from the wind field to the turbine components, needs to be modelled in the design process. The modelling and simulation approaches used in each subsystem as well as the system-wide solution methods to optimize across subsystem boundaries are described in this reference. Chapters are written by technical experts in each field, describing the current state of the art in modelling and simulation for wind plant design. This comprehensive, two-volume research reference will provide long-lasting insight into the methods that will need to be developed for the technology to advance into its next generation. Volume 2 covers turbine level aerodynamics, aeroelasticity, rotors drivetrains and electrical systems, wind turbine control, offshore foundations, system optimization, and grid modelling.

17 WIND ENERGY↗

DOE OSTI2021

Ensuring reliable connectivity to cellular-connected UAVs with up-tilted antennas and interference coordination

To integrate unmanned aerial vehicles (UAVs) in future large-scale deployments, a new wireless communication paradigm, namely, the cellular-connected UAV has recently attracted interest. However, the line-of-sight dominant air-to-ground channels along with the antenna pattern of the cellular ground base stations (GBSs) introduce critical interference issues in cellular-connected UAV communications. In particular, the complex antenna pattern and the ground reflection (GR) from the down-tilted antennas create both coverage holes and patchy coverage for the UAVs in the sky, which leads to unreliable connectivity from the underlying cellular network. To overcome these challenges, in this paper, we propose a new cellular architecture that employs an extra set of co-channel antennas oriented towards the sky to support UAVs on top of the existing down-tilted antennas for ground user equipment (GUE). To model the GR stemming from the down-tilted antennas, we propose a path-loss model, which takes both antenna radiation pattern and configuration into account. Next, we formulate an optimization problem to maximize the minimum signal-to-interference ratio (SIR) of the UAVs by tuning the up-tilt (UT) angles of the up-tilted antennas. Since this is an NP-hard problem, we propose a genetic algorithm (GA) based heuristic method to optimize the UT angles of these antennas. After obtaining the optimal UT angles, we integrate the 3GPP Release-10 specified enhanced inter-cell interference coordination (eICIC) to reduce the interference stemming from the down-tilted antennas. Our simulation results based on the hexagonal cell layout show that the proposed interference mitigation method can ensure higher minimum SIRs for the UAVs over baseline methods while creating minimal impact on the SIR of GUEs.

3GPP↗

DOE OSTI2015

Diverging polygon-based modeling (DPBM) of concentrated solar flux distributions

This paper presents an efficient and robust methodology for modeling concentrated solar flux distributions. Compared to ray tracing methods, which provide high accuracy but can be computationally intensive, this approach makes a number of simplifying assumptions in order to reduce complexity by modeling incident and reflected flux as a series of simple geometric diverging polygons, then applying shading and blocking effects. A reduction in processing time (as compared to ray tracing) allows for evaluating and visualizing numerous combinations of engineering and operational variables (easily exceeding 106 unique iterations) to ascertain instantaneous, transient, and annual system performance. The method is demonstrated on a linear Fresnel reflector array and a number of variable iteration examples presented. While some precision is sacrificed for computational speed, flux distributions were compared to ray tracing (SolTrace) and average concentration ratio generally found to agree within ~3%. Furthermore, this method presents a quick and very flexible coarse adjust method for concentrated solar power (CSP) field design, and can be used to both rapidly gain an understanding of system performance as well as to narrow variable constraint windows for follow-on high accuracy system optimization.

14 SOLAR ENERGY↗