scholarly journals DEVELOPMENTS IN STORM SURGE ESTIMATION USING SURROGATE MODELING TECHNIQUES

2020 ◽  
pp. 37
Author(s):  
Alexandros Taflanidis ◽  
Jize Zhang ◽  
Aikaterini Kyprioti ◽  
Andrew Kennedy ◽  
Tracy Kijewksi-Correa
Keyword(s):  
Storm Surge ◽  
Numerical Models ◽  
Surrogate Modeling ◽  
Simulation Models ◽  
Breaking Waves ◽  
High Fidelity ◽  
Adaptive Selection ◽  
Modeling Techniques ◽  
Near Shore ◽  
Hydrodynamic Processes

Numerical advances in storm surge prediction over the past couple of decades have produced high-fidelity simulation models that permit a detailed representation of hydrodynamic processes and therefore support high accuracy forecasting. Unfortunately, the computational burden of such numerical models is large, requiring thousands of CPU hours for each simulation, something that limits their applicability for hurricane risk assessment. Use of Kriging-based surrogate modeling techniques has been examined to address the aforementioned challenge Jia et al. [2016], Zhang et al. [2018]. This approach can provide fast predictions using a database of high-fidelity, synthetic storms, with the goal of maintaining the accuracy of the numerical model utilized to produce this database, while offering computational efficiency. This contribution overviews initially recent research developments for the application of Kriging for storm surge predictions. Topics discussed include: enhancement of the initial database for nodes (i.e., geographical locations) that have remained dry in some of the database storms; adaptive selection of storms forming the initial database; use of different surrogate modeling tuning techniques and their impact on the metamodel predictive capabilities for storm surge estimation; implementation for estimation of impact due to near-shore processes (breaking waves), something that requires coupling of different numerical models.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/vL38Kv3kLDM

scholarly journals The Accelerations of a Wave Measurement Buoy Impacted by Breaking Waves in the Surf Zone

2021 ◽  
Vol 9 (2) ◽  
pp. 214
Author(s):  
Adam C. Brown ◽  
Robert K. Paasch
Keyword(s):  
Inertial Measurement Unit ◽  
Surf Zone ◽  
Spherical Wave ◽  
Breaking Waves ◽  
Measurement Unit ◽  
High Fidelity ◽  
Wave Measurement ◽  
Near Shore ◽  
Shoaling Waves ◽  
Multiple Deployments

A spherical wave measurement buoy capable of detecting breaking waves has been designed and built. The buoy is 16 inches in diameter and houses a 9 degree of freedom inertial measurement unit (IMU). The orientation and acceleration of the buoy is continuously logged at frequencies up to 200 Hz providing a high fidelity description of the motion of the buoy as it is impacted by breaking waves. The buoy was deployed several times throughout the winter of 2013–2014. Both moored and free-drifting data were acquired in near-shore shoaling waves off the coast of Newport, OR. Almost 200 breaking waves of varying type and intensity were measured over the course of multiple deployments. The characteristic signature of spilling and plunging breakers was identified in the IMU data.

Strategies for computationally feasible multi-objective simulation-driven design of compact RF/microwave components

2016 ◽  
Vol 33 (1) ◽  
pp. 184-201 ◽  
Author(s):  
Slawomir Koziel ◽  
Adrian Bekasiewicz
Keyword(s):  
Surrogate Modeling ◽  
Simulation Models ◽  
Time Frame ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Trade Offs ◽  
Microwave Components ◽  
Modeling Techniques ◽  
Em Simulation

Purpose – Strategies for accelerated multi-objective optimization of compact microwave and RF structures are investigated, including the possibility of exploiting surrogate modeling techniques for electromagnetic (EM)-driven design speedup for such components. The paper aims to discuss these issues. Design/methodology/approach – Two algorithmic frameworks are described that are based on fast response surface approximation models, structure decomposition, and Pareto front refinement. Numerical case studies are provided demonstrating feasibility of solving real-world problems involving multi-objective optimization of miniaturized microwave passives and expensive EM-simulation models of such structures. Findings – It is possible, through appropriate combination of the surrogate modeling techniques and response correction methods, to identify the set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity EM simulations of the respective structures. Research limitations/implications – The present study sets a direction for further studied on expedited optimization of computationally expensive simulation models for miniaturized microwave components. Originality/value – The proposed algorithmic framework proved useful for fast design of microwave structures, which is extremely challenging when using conventional methods. To the authors’ knowledge, this is one of the first attempts to surrogate-assisted multi-objective optimization of compact components at the EM-simulation level.

scholarly journals Rapid prediction of peak storm surge from tropical cyclone track time series using machine learning

2021 ◽  
Author(s):  
Jun-Whan Lee ◽  
Jennifer Irish ◽  
Michelle Bensi ◽  
Doug Marcy
Keyword(s):  
Time Series ◽  
Tropical Cyclone ◽  
Storm Surge ◽  
Coastal Region ◽  
Surrogate Modeling ◽  
Storm Surges ◽  
High Fidelity ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Modeling Approaches

Rapid and accurate prediction of peak storm surges across an extensive coastal region is necessary to inform assessments used to design the systems that protect coastal communities’ life and property. Significant advances in high-fidelity, physics-based numerical models have been made in recent years, but use of these models for probabilistic forecasting and probabilistic hazard assessment is computationally intensive. Several surrogate modeling approaches based on existing databases of high-fidelity synthetic storm surge simulations have been recently suggested to reduce computational burden without substantial loss of accuracy. In these previous studies, however, the surrogate modeling approaches relied on a tropical cyclone condition at one moment (usually at or near landfall), which is not always most correlated with the peak storm surge. In this study, a new one-dimensional convolutional neural network model combined with principal component analysis and a k-means clustering (C1PKNet) is presented that can rapidly predict peak storm surge across an extensive coastal region from time-series of tropical cyclone conditions, namely the storm track. The C1PKNet model was trained and cross-validated for the Chesapeake Bay area of the United States using existing database of 1031 high-fidelity storm surge simulations, including both landfalling and bypassing storms. Moreover, the performance of the C1PKNet model was evaluated based on observations from three historical hurricanes (Hurricane Isabel in 2003, Hurricane Irene in 2011, and Hurricane Sandy in 2012). The results indicate that the C1PKNet model is computationally e cient and can predict peak storm surges from realistic tropical cyclone track time-series. We believe that this new surrogate model can enhance coastal resilience by providing rapid storm surge predictions.

Improvements in storm surge surrogate modeling for synthetic storm parameterization, node condition classification and implementation to small size databases

2021 ◽  
Author(s):  
Aikaterini P. Kyprioti ◽  
Alexandros A. Taflanidis ◽  
Matthew Plumlee ◽  
Taylor G. Asher ◽  
Elaine Spiller ◽  
...  
Keyword(s):  
Storm Surge ◽  
Surrogate Modeling

Adaptive response prediction for aerodynamic shape optimization

2017 ◽  
Vol 34 (5) ◽  
pp. 1485-1500
Author(s):  
Leifur Leifsson ◽  
Slawomir Koziel
Keyword(s):  
Shape Optimization ◽  
Adaptive Response ◽  
Simulation Models ◽  
Response Prediction ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Aerodynamic Shape Optimization ◽  
Content Type ◽  
Aerodynamic Shape ◽  
Fidelity Model

Purpose The purpose of this paper is to reduce the overall computational time of aerodynamic shape optimization that involves accurate high-fidelity simulation models. Design/methodology/approach The proposed approach is based on the surrogate-based optimization paradigm. In particular, multi-fidelity surrogate models are used in the optimization process in place of the computationally expensive high-fidelity model. The multi-fidelity surrogate is constructed using physics-based low-fidelity models and a proper correction. This work introduces a novel correction methodology – referred to as the adaptive response prediction (ARP). The ARP technique corrects the low-fidelity model response, represented by the airfoil pressure distribution, through suitable horizontal and vertical adjustments. Findings Numerical investigations show the feasibility of solving real-world problems involving optimization of transonic airfoil shapes and accurate computational fluid dynamics simulation models of such surfaces. The results show that the proposed approach outperforms traditional surrogate-based approaches. Originality/value The proposed aerodynamic design optimization algorithm is novel and holistic. In particular, the ARP correction technique is original. The algorithm is useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces, which is challenging using conventional methods because of excessive computational costs.

scholarly journals Assessing storm surge hazard and impact of sea level rise in the Lesser Antilles case study of Martinique

2017 ◽  
Vol 17 (9) ◽  
pp. 1559-1571 ◽  
Author(s):  
Yann Krien ◽  
Bernard Dudon ◽  
Jean Roger ◽  
Gael Arnaud ◽  
Narcisse Zahibo
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Storm Surge ◽  
Numerical Models ◽  
Lesser Antilles ◽  
Adaptation Measures ◽  
Worst Case ◽  
Mitigation And Adaptation ◽  
The Impact

Abstract. In the Lesser Antilles, coastal inundations from hurricane-induced storm surges pose a great threat to lives, properties and ecosystems. Assessing current and future storm surge hazards with sufficient spatial resolution is of primary interest to help coastal planners and decision makers develop mitigation and adaptation measures. Here, we use wave–current numerical models and statistical methods to investigate worst case scenarios and 100-year surge levels for the case study of Martinique under present climate or considering a potential sea level rise. Results confirm that the wave setup plays a major role in the Lesser Antilles, where the narrow island shelf impedes the piling-up of large amounts of wind-driven water on the shoreline during extreme events. The radiation stress gradients thus contribute significantly to the total surge – up to 100 % in some cases. The nonlinear interactions of sea level rise (SLR) with bathymetry and topography are generally found to be relatively small in Martinique but can reach several tens of centimeters in low-lying areas where the inundation extent is strongly enhanced compared to present conditions. These findings further emphasize the importance of waves for developing operational storm surge warning systems in the Lesser Antilles and encourage caution when using static methods to assess the impact of sea level rise on storm surge hazard.

scholarly journals Characterization of the unsteady aerodynamic response of a floating offshore wind turbine

2020 ◽  
Author(s):  
Simone Mancini ◽  
Koen Boorsma ◽  
Marco Caboni ◽  
Marion Cormier ◽  
Thorsten Lutz ◽  
...  
Keyword(s):  
Numerical Models ◽  
Aerodynamic Performance ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
High Fidelity ◽  
Aerodynamic Damping ◽  
Unsteady Aerodynamic ◽  
Future Work ◽  
Good Agreement

Abstract. The disruptive potential of floating wind turbines has attracted the interest of both industry and scientific community. Lacking a rigid foundation, such machines are subject to large displacements whose impact on the aerodynamic performance is not yet fully acknowledged. In this work, the unsteady aerodynamic response to an harmonic surge motion of a scaled version of the DTU10MW turbine is investigated in detail. The imposed displacements have been chosen representative of typical platform motions. The results of different numerical models are validated against high fidelity wind tunnel tests specifically focused on the aerodynamics. Also a linear analytical model, relying on the quasi-steady assumption, is presented as a theoretical reference. The unsteady responses are shown to be dominated by the first surge harmonic and a frequency domain characterization, mostly focused on the thrust oscillation, is conducted involving aerodynamic damping and mass parameters. A very good agreement among codes, experiments and quasi-steady theory has been found clarifying some literature doubts. A convenient way to describe the unsteady results in non-dimensional form is proposed, hopefully serving as reference for future work.

Model-Based Simulation to Examine Command and Control Issues with Remotely Operated Vehicles

2008 ◽  
pp. 199-218 ◽  
Author(s):  
Sasanka Prabhala ◽  
Subhashini Ganapathy ◽  
S. Narayanan ◽  
Jennie J. Gallimore ◽  
Raymond R. Hill
Keyword(s):  
Cognitive Model ◽  
Command And Control ◽  
Simulation Models ◽  
High Fidelity ◽  
Remotely Operated Vehicles ◽  
Design Concepts ◽  
Model Based ◽  
Critical Issues ◽  
Commercial Applications ◽  
And Control

With increased interest in the overall employment of pilotless vehicles functioning in the ground, air, and marine domains for both defense and commercial applications, the need for high-fidelity simulation models for testing and validating the operational concepts associated with these systems is very high. This chapter presents a model-based approach that we adopted for investigating the critical issues in the command and control of remotely operated vehicles (ROVs) through an interactive model-based architecture. The domain of ROVs is highly dynamic and complex in nature. Hence, a proper understanding of the simulation tools, underlying system algorithms, and user needs is critical to realize advanced simulation system concepts. Our resulting simulation architecture integrates proven design concepts such as the model-view-controller paradigm, distributed computing, Web-based simulations, cognitive model-based high-fidelity interfaces and object-based modeling methods.

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