Nikolaos Tsokanas

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Tsokanas

First Name

Nikolaos

ORCID

0000-0001-9388-0593

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Publications 1 - 10 of 14

A stochastic real-time hybrid simulation of the seismic response of a magnetorheological damper
Tsokanas, Nikolaos; Stojadinovic, Bozidar (2021)
WCEE Online Proceedings ~ Proceedings of the Seventeenth World Conference on Earthquake Engineering Japan 2021
Real-time hybrid simulation is an effective method to obtain the response of an emulated system subjected to dynamic excitation by combining loading-rate-sensitive numerical and physical substructures. Albeit, the parameters that character ize the hybrid model are often deterministic. The values of these parameters are regularly determined through deliberate simplifications, ignoring the associated uncertainties. However, the effect of uncertainties may be significant. Stochastic hybrid simulation is an extension of the state-of-the-art hybrid simulation to address the dynamic response of uncertain structural systems under uncertain operating conditions. Under this concept, the parameters of the emulated sys tem are treated as random variables with known probability distributions. The results of stochastic hybrid simulations are probability distributions of the structural response quantities of interest. The arising question is one of sensitivity, namely, to what extent each of the random variables affects the outcomes of stochastic hybrid simulations. In this study, the seismic response of a structure with a magnetorheological (MR) damper is examined. The strongly non linear behavior of MR dampers amplifies the effects of the uncertainties of the model parameters on the response. A virtual hybrid model is used, where the structure and the MR damper are represented using numerical substructures. In a virtual stochastic hybrid simulation, parameters of the hybrid model and the excitation are treated as random variables in repeated real-time hybrid response simulations to the same random excitation. Based on this simulation data, surrogate models are developed. Multiple additional runs of the surrogate model give a deeper insight into the performance of the examined system under uncertainties. Global Sensitivity Analysis is performed to identify the effect of model and excitation parameters on the response of the system.
Multi-axial hybrid fire testing based on dynamic relaxation
Tsokanas, Nikolaos; Abbiati, Giuseppe; Kanellopoulos, Constantinos; et al. (2021)
Fire Safety Journal
This technical note presents the experimental validation of a hybrid fire testing coordination algorithm recently developed by some of the authors. For the first time, the algorithm is applied to solve the static response of a multiple-degrees-of-freedom hybrid model.
Model order reduction for real-time hybrid simulation: Comparing polynomial chaos expansion and neural network methods
Tsokanas, Nikolaos; Simpson, Thomas; Pastorino, Roland; et al. (2022)
Mechanism and Machine Theory
Hybrid simulation is used to investigate the dynamic response of a system by combining numerical and physical substructures. To ensure high fidelity results, it is necessary to conduct hybrid simulation in real-time. One challenge in real-time hybrid simulation originates from high-dimensional nonlinear numerical substructures and, in particular, from the computational cost linked to the accurate computation of their dynamic responses. When the computation takes longer than the actual simulation time, time delays are introduced distorting the simulation timescale. In such cases, the only viable solution for performing hybrid simulation in real-time is to reduce the order of such complex numerical substructures. In this study, a model order reduction framework is proposed for real-time hybrid simulation, based on polynomial chaos expansion and feedforward neural networks. A parametric case study is used to validate the framework. Selected numerical substructures are substituted with their respective reduced-order models. To determine the framework’s robustness, parameter sets are defined covering the design space of interest. Comparisons between the full- and reduced-order hybrid model response are delivered. The attained results demonstrate the performance of the proposed framework.
Adaptive model predictive control for actuation dynamics compensation in real-time hybrid simulation
Tsokanas, Nikolaos; Pastorino, Roland; Stojadinovic, Bozidar (2022)
Mechanism and Machine Theory
Hybrid simulation is used to obtain the dynamic response of a system whose components consist of physical and numerical substructures. The coupling of these substructures is achieved by actuation systems, which are commanded in closed-loop control setting. To ensure high fidelity of such hybrid simulations, performing them in real-time is necessary. However, real-time hybrid simulation poses challenges since the inherent dynamics of the actuation system introduce time delays, thus modifying the dynamic response of the investigated system. Therefore, a tracking controller is required to adequately compensate for such time delays. In this study, a novel tracking controller is proposed for dynamics compensation in real-time hybrid simulations. It is based on adaptive model predictive control, a linear time-varying Kalman filter, and a real-time model identification algorithm. Within the latter, auto-regressive exogenous polynomial models are identified in real-time to estimate the changing plant dynamics. A parametric virtual case study, encompassing a virtual motorcycle, is used to validate the performance and robustness of the proposed controller. Results demonstrate the effectiveness of the proposed controller for real-time hybrid simulations.
A Comparison of Surrogate Modeling Techniques for Global Sensitivity Analysis in Hybrid Simulation
Tsokanas, Nikolaos; Pastorino, Roland; Stojadinovic, Bozidar (2021)
Machine Learning & Knowledge Extraction
Hybrid simulation is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario. The system under consideration is divided into multiple individual substructures, out of which one or more are tested physically, whereas the remaining are simulated numerically. The coupling of all substructures forms the so-called hybrid model. Although hybrid simulation is extensively used across various engineering disciplines, it is often the case that the hybrid model and related excitation are conceived as being deterministic. However, associated uncertainties are present, whilst simulation deviation, due to their presence, could be significant. In this regard, global sensitivity analysis based on Sobol’ indices can be used to determine the sensitivity of the hybrid model response due to the presence of the associated uncertainties. Nonetheless, estimation of the Sobol’ sensitivity indices requires an unaffordable amount of hybrid simulation evaluations. Therefore, surrogate modeling techniques using machine learning data-driven regression are utilized to alleviate this burden. This study extends the current global sensitivity analysis practices in hybrid simulation by employing various different surrogate modeling methodologies as well as providing comparative results. In particular, polynomial chaos expansion, Kriging and polynomial chaos Kriging are used. A case study encompassing a virtual hybrid model is employed, and hybrid model response quantities of interest are selected. Their respective surrogates are developed, using all three aforementioned techniques. The Sobol’ indices obtained utilizing each examined surrogate are compared with each other, and the results highlight potential deviations when different surrogates are used.
Design of a Controller for Physical Substructures in Stochastic Real-Time Hybrid Simulations
Tsokanas, Nikolaos; Stojadinovic, Bozidar (2019)
Book of Abstracts: Engineering Mechanics Institute 2019 Conference
Design of an Actuation Controller for Physical Substructures in Stochastic Real-Time Hybrid Simulations
Tsokanas, Nikolaos; Stojadinovic, Bozidar (2020)
Conference Proceedings of the Society for Experimental Mechanics Series ~ Model Validation and Uncertainty Quantification, Volume 3
Real-time hybrid simulation is a method to obtain the response of a system subjected to dynamic excitation by combining loading-rate-sensitive numerical and physical substructures. The interfaces between physical and numerical substructures are usually implemented using closed-loop-controlled actuation systems. In current practice, the parameters that characterize the hybrid model are deterministic. However, the effect of uncertainties may be significant. Stochastic hybrid simulation is an extension of the deterministic hybrid simulation where the parameters of the system are treated as random variables with known probability distributions. The results are probability distributions of the structural response quantities of interest. The arising question is to what extent does the actuation control system at the interface between physical and numerical substructures affect the outcomes of stochastic hybrid simulations. This question is most acute for real-time hybrid simulations. The response of a benchmark stochastic prototype to random excitation will be computed. Then, a part of the prototype will be replaced by a hybrid model whose substructure interfaces are actuated in closed-loop control. A controller that guarantees robustness and stability of the interfaces will be designed. The parameters of this hybrid model will be treated as random variables in repeated real-time hybrid response simulations to the same random excitation. The difference between the prototype and hybrid model responses will be used to determine if the controller design has an effect on the simulation outcomes, to predict such effects, and to propose guidelines for real-time controller design such that it has a predictable effect on the hybrid simulation. Additional criteria based on peak and root mean square tracking errors, as well as energy errors, are addressed in order to assess the overall system performance. Based on simulation data, surrogate models will be developed. Multiple additional runs of the surrogate models will give insight into the robustness and performance of the control system under uncertainties. Global sensitivity analysis of the overall system response will also be performed, identifying the most sensitive stochastic input variables. Cross-check validation of the results will take place using different meta-modeling techniques.
A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures
Tsokanas, Nikolaos; Zhu, Xujia; Abbiati, Giuseppe; et al. (2021)
Frontiers in Built Environment
Hybrid simulation is an experimental method used to investigate the dynamic response of a reference prototype structure by decomposing it to physically-tested and numerically simulated substructures. The latter substructures interact with each other in a real-time feedback loop and their coupling forms the hybrid model. In this study, we extend our previous work on metamodel-based sensitivity analysis of deterministic hybrid models to the practically more relevant case of stochastic hybrid models. The aim is to cover a more realistic situation where the physical substructure response is not deterministic, as nominally identical specimens are, in practice, never actually identical. A generalized lambda surrogate model recently developed by some of the authors is proposed to surrogate the hybrid model response, and Sobol’ sensitivity indices are computed for substructure quantity of interest response quantiles. Normally, several repetitions of every single sample of the inputs parameters would be required to replicate the response of a stochastic hybrid model. In this regard, a great advantage of the proposed framework is that the generalized lambda surrogate model does not require repeated evaluations of the same sample. The effectiveness of the proposed hybrid simulation global sensitivity analysis framework is demonstrated using an experiment.
Three-level verification and validation approach for motorcycle subsystem testing using physical/virtual co-simulation
Tsokanas, Nikolaos; Thielemans, Laurane; Sputh, Bernhard; et al. (2021)
Real-Time and Stochastic Hybrid Simulation
Tsokanas, Nikolaos (2021)

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Nikolaos Tsokanas

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