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DOE OSTI2021

A Review of Nuclear Forensics: 2016-2020

The study of nuclear forensics harkens back to the Manhattan Project-era, when scientists first started to analyze the debris from the 1945 Trinity test. Political turmoil stemming from the Cold War and the rehabilitation of Germany following WWII has led to new challenges in international security involving nuclear proliferation. Nuclear materials have, on occasion, been lost, misplaced, or stolen from former Soviet countries, and illicit materials have been interdicted all over the world. The National Technical Nuclear Forensics Center (NTNFC) was established in 2006, and has been at the forefront of drive to advance nuclear forensic capabilities in the United States. The ultimate goal of nuclear forensics is to examine nuclear and other radioactive materials using analytical techniques to determine origin and history of the material, particularly in the context of law enforcement investigations. Nuclear forensics can be divided into two parts: predetonation and post-detonation. Pre-detonation forensics, as the name implies, is the investigation of a nuclear material or weapon that has not been detonated or involved in an explosion, whereas postdetonation forensics is the study of activation or fission products in debris or the environment following the use of a nuclear or radiological dispersal device (RDD). Both parts require a number of analytical chemical and radiochemical techniques to determine identification of the material. Many advancements in analytical techniques, including rapidity, sample size, and forensic signatures have been made in recent years. The analytical methods that can be used in a nuclear forensic investigation, such as mass spectrometry and gamma spectroscopy, have been described in detail in previous reviews, including Straub et.al, and will not be explained here. This review will discuss recent publications (from 2016 to present) describing advancements of techniques such as radiochronometry, morphology, development of novel reference materials, and inter-laboratory collaborations for both pre- and post-detonation nuclear forensics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Big Data For Operation and Maintenance Cost Reduction

The purpose of this research is to develop a first-of-a-kind framework for integrating Big Data capability into the daily activities of our current fleet of nuclear power plants. Big Data is traditionally defined as data sets with high volume, velocity, and heterogeneity, and the existing Big Data analytics capabilities are now widely popular in fields such as finance, weather, e-commerce, healthcare and sports. In the nuclear industry, while the volume and velocity of data may present computational challenges for existing analytics capabilities, data heterogeneity are seen to present the major challenge. This research project mainly focuses on incorporating the wide range of data heterogeneities in nuclear power plants into an integrated Big Data Analytics capability. The primary end-product of this project is a Big Data framework that is capable of dealing with the large volume and heterogeneity of the data found in nuclear power plants to extract timely and valuable information on equipment performance. The framework can generate system insights that are actionable relations between measurable impacts and the corresponding maintenance action plans and enable optimization of plant operation and maintenance based on the extracted information. The developed framework is capable of handling heterogeneous data including both image data and time-series sensor data. Specifically, this developed framework includes the following components. The first component is an overarching maintenance ontology which includes system insights required by maintenance optimization. The maintenance ontology interacts with other components in the developed framework. The second component handles Piping & Instrumentation Diagram (P&ID) data. It can be used to extract system components and their relations automatically from the P&IDs. This extracted information is stored in the first component, i.e., maintenance ontology, and is also used as input to the third component, i.e., a tool for generating the fault tree for the corresponding system. The generated fault tree in turn is stored in the ontology for assessing risk that is used as a criterion in maintenance policy optimization. The fourth component is a tool for inferring the parameters in the Markov degradation model for a nuclear system. It uses basic information from the ontology. The fifth component is a tool for assessing the degradation level using sensor measurement data, for example, pressure, flowrate. This tool can be used for determining corrective maintenance actions. The results obtained from components four and five are returned to the ontology. The sixth component of the framework is a tool for optimizing the maintenance policy for a nuclear system of interest. It takes certain basic information from the ontology, e.g., costs of maintenance actions and system failures, as input, and returns the optimal maintenance policy to the ontology. This tool can be used for determining predictive maintenance actions. A set of experiments have also been conducted to verify the algorithms developed in this project for nuclear system degradation monitoring. The experiments are based on four solenoid valves, similar to the ones used in nuclear power plants. The analyses based on the experimental data using two algorithms, i.e., the Randomized Window Decomposition (RWD) algorithm and the particle filtering algorithm, and the results are introduced in the report. The Big Data framework developed in this project can be used as a support tool in daily activities of plant operation and maintenance and will reduce current costs while maintaining or improving safety levels. Overall, the project will not only benefit existing reactors, however it will open new frontiers to realize the long overdue value of Big Data Analytics in the nuclear sphere.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗

DOE OSTI2022

Analysis of Cutter Blade Wear in Rotary Shear Mills

Following development of an analytical abrasive wear model to predict wear of components in a rotary shear mill, the team used a finite element analysis (FEA) approach to calculate forces and loads acting on particles responsible for abrasive wear. The analytical model related critical rotary shear process parameters (shear geometry and rotational speed) to critical material attributes of inorganic mineral species in feedstock (density, size, and aspect ratio) and substrate (hardness and elastic modulus) that enabled us to model the wear of shear cutters in a rotary shear milling system developed by Forest Concepts. With proper knowledge of the forces acting between abrasive particles and cutter components built into it, the model can accurately predict wear of the cutters and provides a quality by design (QbD) approach to predict component reliability based on scientific engineering principles in lieu of trial-and-error approaches. During this reporting period, researchers at Oak Ridge National Laboratory applied an FEA package to simulate the local stresses and forces between an abrasive silica particle and two rotary shear cutters as the distance between the cutters decreases. The output of the FEA was used to provide more accurate projections of the loads applied to the particles in the analytical wear model. Comparison of the FEA force calculations are in good agreement with the loads assumed in the analytical predictions.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Thin Film Electrodes for Anodic Stripping Voltammetry: A Mini-Review

Anodic stripping voltammetry (ASV) is a powerful electrochemical analytical technique that allows for the detection and quantification of a variety of metal ion species at very low concentrations in aqueous media. While early, traditional ASV measurements relied on macroscopic electrodes like Hg drop electrodes to provide surfaces suitable for plating/stripping, more recent work on the technique has replaced these electrodes with thin film metal electrodes generated in situ . Such electrodes are plated alongside the analyte species onto the surface of a primary electrode, producing a composite metal electrode from which the analyte(s) can then be stripped, identified, and quantified. In this minireview, we will explore the development and use of these unique electrodes in a variety of different applications. A number of metals (e.g., Hg, Bi, Sn, etc.) have shown promise as thin film ASV electrodes in both acidic and alkaline media, and frequently multiple metals in addition to the analyte of interest are deposited together to optimize the plating/stripping behavior, improving sensitivity. Due to the relatively simple nature of the measurement and its suitability for a wide range of pH, it has been used broadly: To measure toxic metals in the environment, characterize battery materials, and enable biological assays, among other applications. We will discuss these applications in greater detail, as well as provide perspective on future development and uses of these thin film electrodes in ASV measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Application of a headspace GC–MS method to evaluate the product quality of alcohol‐based hand wipe sanitizers

Abstract The investigation of marketed hand wipe sanitizers presented an analytical challenge owing to the need for extraction from the solid matrix of the products. The present work describes the development of a new sample preparation method for the extraction of analytes from the hand wipe sanitizer matrix into dimethyl sulfoxide for analysis using headspace GCMS. Alcohol‐based hand sanitizer (ABHS) wipe products labeled to contain ethanol or isopropanol as active ingredients were tested, varying in the size and weight of the wipes. The spike recovery assay was confirmed using spiking solutions containing impurities at concentrations equivalent to 50, 100 and 200% of the interim concentration limits. All of the tested analytes showed recovery within the allowable limits (80–120%). Six marketed ABHS wipe products were tested and no impurities above the FDA interim limits were observed. One product contained ethanol below the 60% v/v limit and another product was mislabeled for isopropanol and was found to contain ethanol instead. Four of the six ABHS products did not meet the label claim, which may affect the product quality. The analytical method and sample preparation procedures will provide the FDA and ABHS manufacturers with the capability to conduct quality assurance testing of hand wipe sanitizers for active ingredient content and impurities.

Biochemistry & Molecular Biology↗

DOE OSTI2022

What is the iε for the S-matrix?

Can the S-matrix be complexified in a way consistent with causality? Since the 1960's, the affirmative answer to this question has been well-understood for 2→2 scattering of the lightest particle in theories with a mass gap at low momentum transfer, where the S-matrix is analytic everywhere except at normal-threshold branch cuts. We ask whether an analogous picture extends to realistic theories, such as the Standard Model, that include massless fields, UV/IR divergences, and unstable particles. Especially in the presence of light states running in the loops, the traditional iε prescription for approaching physical regions might break down, because causality requirements for the individual Feynman diagrams can be mutually incompatible. We demonstrate that such analyticity problems are not in contradiction with unitarity. Instead, they should be thought of as finite-width effects that disappear in the idealized 2→2 scattering amplitudes with no unstable particles, but might persist at higher multiplicity. To fix these issues, we propose an iε -like prescription for deforming branch cuts in the space of Mandelstam invariants without modifying the analytic properties. This procedure results in a complex strip around the real part of the kinematic space, where the S-matrix remains causal. In addition to giving a pedagogical introduction to the analytic properties of the perturbative S-matrix from a modern point of view, we illustrate all the points on explicit examples, both symbolically and numerically. Furthermore, to help with the investigation of related questions, we introduce a number of tools, including holomorphic cutting rules, new approaches to dispersion relations, as well as formulae for local behavior of Feynman integrals near branch points.

Anomalous thresholds↗

DOE OSTI2020

Large-d phase transitions in holographic mutual information

In the AdS/CFT correspondence, the entanglement entropy of subregions in the boundary CFT is conjectured to be dual to the area of a bulk extremal surface at leading order in G N in the holographic limit. Under this dictionary, distantly separated regions in the CFT vacuum state have zero mutual information at leading order, and only attain nonzero mutual information at this order when they lie close enough to develop significant classical and quantum correlations. Previously, the separation at which this phase transition occurs for equal-size ball-shaped regions centered at antipodal points on the boundary was known analytically only in 3 spacetime dimensions. Inspired by recent explorations of general relativity at large- d , we compute the separation at which the phase transition occurs analytically in the limit of infinitely many spacetime dimensions, and find that distant regions cannot develop large correlations without collectively occupying the entire volume of the boundary theory. We interpret this result as illustrating the spatial decoupling of holographic correlations in the large- d limit, and provide intuition for this phenomenon using results from quantum information theory. We also compute the phase transition separation numerically for a range of bulk spacetime dimensions from 4 to 21, where analytic results are intractable but numerical results provide insight into the dimension-dependence of holographic correlations. For bulk dimensions above 5, our exact numerical results are well approximated analytically by working to next-to-leading order in the large- d expansion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Crossing beyond scattering amplitudes

We find that different asymptotic measurements in quantum field theory can be related to one another through new versions of crossing symmetry. Assuming analyticity, we conjecture generalized crossing relations for multi-particle processes and the corresponding paths of analytic continuation. We prove them to all multiplicity at tree-level in quantum field theory and string theory. We illustrate how to practically perform analytic continuations on loop-level examples using different methods, including unitarity cuts and differential equations. We study the extent to which anomalous thresholds away from the usual physical region can cause an analytic obstruction to crossing when massless particles are involved. In an appendix, we review and streamline historical proofs of four-particle crossing symmetry in gapped theories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Holographic probes of inner horizons

We study the inner horizons of rotating and charged black holes in anti-de Sitter space. These black holes have a classical analytic extension through the inner horizon to additional asymptotic regions. If this extension survives in the quantum theory, it requires particular analytic properties in a dual CFT, which give a prescription for calculating correlation functions for operators placed on any asymptotic boundary of the maximally extended spacetime. We show that for charged black holes in three or greater dimensions, and rotating black holes in four or greater dimensions, these analytic properties are inconsistent in the dual CFT, implying the absence of an analytic extension for quantum fields past the inner horizon. Thus, we find that strong cosmic censorship holds for all AdS black holes except rotating BTZ. To further study the latter case, we insert classical perturbations near the boundary at late times, producing shockwaves traveling along the inner horizon. We holographically compute CFT correlators in this background that probe a high energy scattering process near the inner horizon and argue that the shockwave does not destabilize the inner horizon violently enough to prevent signaling between different asymptotic regions of the Penrose diagram. This provides evidence that the rotating BTZ black hole does violate the strong cosmic censorship conjecture.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

First saturation correction in high energy proton-nucleus collisions. Part I. Time evolution of classical Yang-Mills fields beyond leading order

In high energy proton-nucleus collisions, the single- and double-inclusive soft gluon productions at the leading order have been calculated and phenomenologically studied in various approaches for many years. These studies do not take into account the saturation and multiple rescatterings in the field of the proton. The first saturation correction to these leading order results (the terms that are enhanced by the combination $ {\alpha}_s^2{\mu}^2 $ , where μ 2 is the proton’s color charge squared per unit transverse area) has not been completely derived despite recent attempts using a diagrammatic approach. This paper is the first in a series of papers towards analytically completing the first saturation correction to physical observables in high energy proton-nucleus collisions. Our approach is to analytically solve the classical Yang-Mills equations in the dilute-dense regime using the Color Glass Condensate effective theory and compute physical observables constructed from classical gluon fields. In the current paper, the Yang-Mills equations are solved perturbatively in the field of the dilute object (the proton). Next-to-leading order and next-to-next-to-leading order analytic solutions are explicitly constructed. A systematic way to obtain all higher order analytic solutions is outlined.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Two-loop master integrals for leading-color $$ pp\to t\overline{t}H $$ amplitudes with a light-quark loop

Abstract We compute the two-loop master integrals for leading-color QCD scattering amplitudes including a closed light-quark loop in$$ t\overline{t}H $$ t t ¯ H production at hadron colliders. Exploiting numerical evaluations in modular arithmetic, we construct a basis of master integrals satisfying a system of differential equations inϵ-factorized form. We present the analytic form of the differential equations in terms of a minimal set of differential one-forms. We explore properties of the function space of analytic solutions to the differential equations in terms of iterative integrals which can be exploited for studying the analytic form of related scattering amplitudes. Finally, we solve the differential equations using generalized series expansions to numerically evaluate the master integrals in physical phase space. As the first computation of a set of two-loop seven-scale master integrals, our results provide valuable input for analytic studies of scattering amplitudes in processes involving massive particles and a large number of kinematic scales.

Physics↗

DOE OSTI2024

Gravitational production of heavy particles during and after inflation

Abstract We investigate the gravitational production of a scalar fieldχwith a mass exceeding the Hubble scale during inflationm χ ≳H I , employing both analytical and numerical approaches. We demonstrate that the steepest descent method effectively captures the epochs and yields of gravitational production in a compact and simple analytical framework. These analytical results align with the numerical solutions of the field equation. Our study covers three spacetime backgrounds: de Sitter, power-law inflation, and the Starobinsky inflation model. Within these models, we identify two distinct phases of particle production: during and after inflation. During inflation, we derive an accurate analytic expression for the particle production rate, accounting for a varying Hubble rate. After inflation, the additional burst of particle production depends on the inflaton mass around its minimum. When this mass is smaller than the Hubble scale during inflation,H I , there is no significant extra production. However, if the inflaton mass is larger, post-inflation production becomes the dominant contribution. Furthermore, we explore the implications of gravitationally produced heavy fields for dark matter abundance, assuming their cosmological stability.

Physics↗

DOE OSTI2020

A mass-transfer particle-tracking method for simulating transport with discontinuous diffusion coefficients

The problem of a spatially discontinuous diffusion coefficient (D(x)) is one that may be encountered in hydrogeologic systems due to natural geological features or as a consequence of numerical discretization of flow properties. To date, mass-transfer particle-tracking (MTPT) methods, a family of Lagrangian methods in which diffusion is jointly simulated by random walk and diffusive mass transfers, have been unable to solve this problem. This manuscript presents a new mass-transfer (MT) algorithm that enables MTPT methods to accurately solve the problem of discontinuous D(x). To achieve this, we derive a semi-analytical solution to the discontinuous D(x) problem by employing a predictor-corrector approach, and we use this semi-analytical solution as the weighting function in a reformulated MT algorithm. Furthermore, this semi-analytical solution is generalized for cases with multiple 1D interfaces as well as for 2D cases, including a 2 × 2 tiling of 4 subdomains that corresponds to a numerically-generated diffusion field. The solutions generated by this new mass-transfer algorithm closely agree with an analytical 1D solution or, in more complicated cases, trusted numerical results, demonstrating the success of our proposed approach.

42 ENGINEERING↗

DOE OSTI2021

Lie group analysis of the point-reactor neutron kinetics equations for various reactivity models

This work applies Lie Group Theory to the nonlinear point-reactor neutron kinetics equations for several reactivity feedback and reactivity insertion models. The goal of this work is to connect known analytical solutions to their corresponding Lie groups as well as to obtain new analytical solutions and the circumstances from which they arise. The reactivity models we consider are the Nordheim-Fuchs Model, an arbitrary-order polynomial reactivity insertion model, and the Fuchs Ramp-Insertion Model. In all three models, we generalize the analysis when possible and obtain analytical solutions in every case. For the first two models, we succeed in finding the Lie groups corresponding to classical solutions and find additional solutions that were otherwise unknown. Finally, for the final model, we obtain a reduced-order nonlinear kinetics equation which is not analytically solvable, but by applying a second-order truncation, we find a novel closed-form solution and demonstrate its validity with a numerical example.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Estimation of constituent properties of concrete materials with an artificial neural network based method

Multi-scale models are developed for heterogeneous concrete materials to estimate their macroscopic mechanical properties in terms of micro-structural data. One crucial challenge of those models is the identification of local properties of constituent phases. In this paper, we present an efficient method based on Artificial Neural Networks (ANN). Typical concrete materials are taken as example. A macroscopic analytical strength criterion is established from three steps of nonlinear homogenization procedure. The macroscopic strength of materials is determined as a function of the frictional coefficient and cohesion of solid cement particles at nanometer scale, intra-particle pores, inter-particle pores and aggregates (inclusions). The objective is to identify the nanoscopic frictional coefficient and cohesion of cement particle from measured macroscopic values of uniaxial compression and tensile strengths. For this purpose, a numerical method based on the ANN is developed. With the analytical macroscopic strength criterion, sensitivity studies are first realized to identify the most important micro-structural parameters influencing the macroscopic strength of concrete. A simplified analytical macroscopic strength criterion is then proposed. A large dataset is further constructed through the inversion of the analytical strength criterion by using the aggregates volume fraction, porosity, macroscopic uniaxial tensile and compressive strengths as input variables and the frictional coefficient and cohesion of cement particles as output unknowns. An ANN model containing four hidden layers and 100 neurons in each layer is constructed and trained by using this dataset. Various types of validation of the ANN model are performed. It is found that the proposed ANN based model can effectively predict the frictional coefficient and cohesion of porous cement paste at the microscopic scale with a very good accuracy.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Determination of transition metal ions in fossil fuel associated wastewaters using chelation ion chromatography

Here, this study outlines the development and subsequent validation of a method using chelation ion chromatography (CIC) pretreatment followed by traditional ion chromatography (IC) and post column UV/vis detection to measure transition metals in fossil fuel wastewaters, such as oil & gas (O&G) brines and coal mine drainage (CMD) waters. Measurement of transition metals is often an important characterization step in the research of environmental and energy systems. IC represents one way to measure these metals with the advantages of being versatile, simple and relatively low cost compared to other analytical methods. However, high concentrations of alkali and alkaline earth metals present in fossil fuel wastewaters will decrease IC detectability of transition metals in these waters. In this study, a CIC method was developed for the analysis of transition metal ions (Fe 3+ , Cu 2+ , Ni 2+ , Zn 2+ , Co 2+ , Mn 2+ , and Fe 2+ ) in fossil fuel associated wastewaters such as Appalachian CMD and O&G wastewaters from the Permian and Bakken shale basins in the United States. CIC system incorporated an on-line chelator column (e.g., the MetPac CC-1) with high selectivity for transition metals over alkali and alkaline earth metals for salt matrix removal prior to transition metal separation and detection. Additional method developments also included acidifying all samples to 2% v/v HCl and using gradient elution rather than isocratic. The recoverability of transition metals in simple salt solutions commonly found in CMD and brine samples (e.g. NaCl, Na 2 SO 4 , CaCl 2 ) using CIC was evaluated and compared to that using traditional IC. Our results found that the CIC system significantly improved transition metal recoveries for samples in 10,000 mg/L CaCl 2 matrix, reaching 87%-108% recovery for all analytes, as opposed to 2-323% recovery in traditional IC. The limits of detection in this study achieved 10.09 – 161.2 μg/L, comparable to reported values in similar IC studies. The developed method was also verified with certified water samples, resulting in 89% - 111% recoveries in samples with higher analyte concentrations (i.e. >4x the LoDs). The developed method achieved 87% -112% recoveries for most analytes in CMD samples and 72%-138% recoveries for Bakken shale samples, relative to ICP-MS values. Overall, the current IC method can be a very good screening tool for fast and cheap analysis for transition metals at mg/L level, to facilitate selection of samples for more detailed ICP-MS analysis.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Coupled aero-hydro-geotech real-time hybrid simulation of offshore wind turbine monopile structures

Real-time hybrid simulation (RTHS) divides a structural system into an analytical and experimental substructure. The former is based on a well-established analytical model while the latter consists of a physical model in the laboratory, for which there is not a well-established analytical model. This paper extends real-time hybrid simulation to monopile-type Offshore Wind Turbines (OWTs) to enable the investigation of their behavior considering the response of pile foundations under operational and more severe conditions. The embedded foundation and surrounding soil of the OWT are modeled physically in a soil box in the laboratory while the remaining parts of the system and loading are modeled analytically. The program OpenFAST, developed by the National Renewable Energy Laboratory (NREL), is linked to the RTHS coordinator to determine the hydrodynamic and aerodynamic loads acting on the OWT, along with modeling the dynamics of the electric power generation equipment and associated controller for the OWT. The RTHS framework along with its initial implementation and validation are described in this paper. RTHSs of a 5 MW OWT subjected to operational and more severe conditions are performed to experimentally validate the framework. The framework offers a realistic approach to investigate the behavior of OWT structures supported on monopiles. Furthermore, this approach accounts for the coupled response of the OWT structure with its foundation, while experimentally capturing the nonlinearities of the soil-foundation interaction in real-time.

17 WIND ENERGY↗