Hybrid Simulation Framework Research Articles

A hybrid simulation approach to predicting noise in restaurants

High levels of noise are a well-known source of occupant discomfort in dining environments, and noise ranks highly among the most common customer complaints in restaurant reviews. Prior work has focused on measuring existing restaurant soundscapes and attempting to predict acoustic performance; however, current techniques are still limited in accuracy for restaurants with time-varying occupancy and complex layouts. This presentation demonstrates a novel approach to predicting ambient noise in restaurants via a hybrid simulation framework. Acoustical computations for a given restaurant space are performed in a room acoustics model, while a discrete event simulation (DES) model is used to simulate the arrival patterns of patrons and their conversational behaviors. Predictions from the room acoustics model inform the DES model, allowing for noise level predictions at each seat position. Results from the prediction model are validated against measurements in a case study restaurant. Further development of this framework may support the practical assessment of design interventions in any restaurant while also accounting for time-varying patterns of occupancy.

Topology reduction through machine learning to accelerate dynamic simulation of district heating

District heating networks (DHNs) provide an efficient heat distribution solution in urban areas, accomplished through interconnected and insulated pipes linking local heat sources to local consumers. This efficiency is further enhanced by the capacity of these networks to integrate renewable heat sources and thermal storage systems. However, integration of these systems adds complexity to the physical dynamics of the network, necessitating complex dynamic simulation models. These dynamic physical simulations are computationally expensive, limiting their adoption, particularly in large-scale networks. To address this challenge, we propose a methodology utilizing Artificial Neural Networks (ANNs) to reduce the computational time associated with the DHNs dynamic simulations. Our approach consists in replacing predefined clusters of substations within the DHNs with trained surrogate ANNs models, effectively transforming these clusters into single nodes. This creates a hybrid simulation framework combining the predictions of the ANNs models with the accurate physical simulations of remaining substation nodes and pipes. We evaluate different architectures of Artificial Neural Network on diverse clusters from four synthetic DHNs with realistic heating demands. Results demonstrate that ANNs effectively learn cluster dynamics irrespective of topology or heating demand levels. Through our experiments, we achieved a 27% reduction in simulation time by replacing 39% of consumer nodes while maintaining acceptable accuracy in preserving the generated heat powers by sources.

A real‐time hybrid simulation framework for reliability‐based design optimization of structures subjected to pulse‐like ground motions

A real‐time hybrid simulation framework for reliability‐based design optimization of structures subjected to pulse‐like ground motions

Towards resilient cities: A hybrid simulation framework for risk mitigation through data-driven decision making

Providing a comprehensive view of the city operation and offering useful metrics for decision-making is a well-known challenge for urban risk-analysis systems. Existing systems are, in many cases, generalizations of previous domain specific tools/methodologies that may not cover all urban interdependencies and makes it difficult to have homogeneous indicators. In order to overcome this limitation while seeking for effective support to decision makers, this article introduces a novel hybrid simulation framework for risk mitigation. The framework is built on a proposed city concept that considers the urban space as a Complex Adaptive System composed by interconnected Critical Infrastructures. In this concept, a Social System, which models daily patterns and social interactions of the citizens in the Urban Landscape, drives the CIs demand to configure the full city picture. The framework's hybrid design integrates agent-based and network-based modeling by breaking down city agents into system-dependent subagents, to enable both inter and intra-system interaction simulation, respectively. A layered structure of indicators at different aggregation levels is also developed, to ensure that decisions are not only data-driven but also explainable. Therefore, the proposed simulation framework can serve as a DSS tool that allows the quantitative analysis of the impact of threats at different levels. First, system-level metrics can be used to get a broad view on the city resilience. Then, agent-level metrics back those figures and provide better explainability. On implementation, the proposed framework enables component reusability (for eased coding), simulation federation (enabling the integration of existing system-oriented simulators), discrete simulation in accelerated time (for rapid scenario simulation) and decision-oriented visualization (for informed outputs). The system built under the proposed approach facilitates to simulate various risk mitigation strategies for a scenario under analysis, allowing decision-makers to foresee potential outcomes. A case study has been deployed on a framework prototype to demonstrate how the DSS can be used in real-world situations, specifically combining cyber hazards over health and traffic infrastructures. The proposal aims at pushing the boundaries of urban city simulation towards more real, intelligent, and automated frameworks.

Hybrid simulation framework with mixed displacement and force control for fully compatible displacements

AbstractA novel framework for hybrid simulation of the seismic response of structures is presented that employs a mixed displacement and force control strategy. A key feature of this framework is a controller‐based displacement‐to‐force transformation that enables compatible displacements between the numerical and experimental model for force‐controlled degrees of freedom. The framework can be applied within conventional displacement‐based time‐integration algorithms allowing for implementation across a variety of software and hardware architectures; it is demonstrated here using OpenSees software with detailed nonlinear numerical models. This study includes numerical verification of the proposed force control strategy and actual implementation in a hybrid simulation of special moment frames with full‐scale beam‐column subassemblies. In the hybrid tests, the experimental column axial load is applied in force control due to the large stiffness and when combined with lateral seismic loads, results in column local buckling with significant axial shortening. The hybrid simulation results verify that the proposed mixed displacement and force control strategy effectively enforces displacement compatibility and force equilibrium between the numerical and experimental structures at the boundary degrees of freedom.

A hybrid simulation framework for predicting virus impacts on Chinese cabbage yields

AbstractSustainable production of Chinese cabbage (Brassica rapa L.) is essential for the economy and food security in South Korea. However, climate change poses significant threats to its growth and quality. Changes in precipitation patterns, such as drought and heavy rain, can affect the development of crop, leading to stunted growth or disease. This can lead to significant losses in crop yield. In this study, a hybrid modeling system was developed to predict the incidence of viral disease and evaluate impacts of combination of virus infection and environmental variabilities (including climate and location) on marketable cabbage yields. The crop growth model was successfully developed using a limited number (n = 7) of previous studies (root mean square error 0.25–0.33 Mg ha−1, R2 = 0.87–0.99). The developed hybrid modeling system was composed of a virus incident model and a crop growth model. According to simulated results, all study locations had around 20% incidence rates. However, based on the simulation results, the rates of viral disease incidence varied depending on the climate of each year. Among several climatic factors, precipitation had the greatest effect on virus outbreaks. Jeju Island, which had relatively high rainfall, had a higher disease incidence rate than other provinces. According to shared socioeconomic pathways (SSPs), in SSP245 (an intermediate development pathway), the yield of 1.58 Mg ha−1 was reduced to 1.26 Mg ha−1 due to a 25% viral disease outbreak. In SSP585 (a high development pathway), with an incidence of 23%, the yield was reduced by 0.2 Mg ha−1. These results will be useful for efficiently cultivating crops under various climatic conditions and seeking management methods to minimize damage from viral disease.

Simulation of the Thermoacoustic Response of an Aero-Engine Gas Turbine Fuel Injector Using a Hybrid CFD-CAA Method

Abstract Given the stringent emission regulations of aircraft engines, the trend in the aero industry is toward developing leaner combustion systems, which are prone to produce combustion instabilities. Hybrid methods of simultaneous acoustics and fluid dynamics simulations offer an elegant solution for the numerical prediction of these instabilities, taking advantage of the appropriate discretization of relevant scales. The presented work employed a hybrid simulation framework to identify the thermoacoustic response of a practically relevant configuration. The fluid dynamics were described using a computational fluid dynamics solver employing the low Mach formulation of the Navier–Stokes equations, while the acoustics were simulated using a computational aeroacoustics solver employing the acoustic perturbation equations. The coupling was implemented to exchange information between both solvers during runtime. The considered real-life configuration was designed to investigate the thermoacoustic behavior of realistic gas turbine injectors. It is acoustically excited to characterize the given injector via the flame transfer function approach under controlled operating conditions. The computational fluid dynamics simulation results were postprocessed to obtain the acoustic behavior of the combustor. The reacting scattering matrix was constructed and then compared to the experimental reference, both obtained using the multimicrophone method. Finally, two different postprocessing approaches were used to calculate the flame transfer function and discuss the applied hybrid computation method. This work demonstrated that the hybrid method can capture general features of the flame response of a complex three-dimensional combustion system.

Circuit-Level Memory Cell Simulation of Magnetic Bloch Line Racetrack Memory

The Bloch line racetrack memory (BL RTM) has recently been proposed as a novel device to overcome the problems of the conventional domain wall (DW) RTM such as stochastic shift and high shift threshold current due to process-induced roughness. In this work, we assess the performance of BL RTM memory cell by conducting circuit-level simulations. A micromagnetics-SPICE hybrid simulation framework is proposed and implemented as an optimal solution to guarantee both the computational efficiency and the micromagnetics-level accuracy. The feasibility of multi-bit BL memory operations is demonstrated as the stochastic Landau-Lifshitz-Gilbert (s-LLG) and Monte-Carlo simulations are carried out at room temperature to take into account the temperature effect and process-induced device mismatch. Furthermore, some crucial considerations in designing and optimizing the BL memory are addressed.

Hybrid RFID-IoT simulation modeling approach for analyzing scrubs’ distribution solutions in operating rooms

PurposeAssess the realistic impacts of implementing an Radio Frequency Identification (RFID)/Internet of Things (IoT) uniforms’ distribution system for managing medical personnel’s scrubs in operating rooms. The authors use a hybrid simulation framework to address the following objectives and challenges: a) reduce and control operating rooms’ level of inventory; b) stabilize scrubs’ demand and c) improve infection control and prevention of cross-contamination (through scrubs over manipulation and hoarding).Design/methodology/approachThe authors adopt a Design Science approach. This methodological approach is used to design, develop, create and evaluate information technology “artifacts” (e.g. constructs, models, methods and instantiations) intended to solve organizational problems and make research contributions (Peffers et al., 2007). More specifically, the authors follow the Design Science Research Methodology process model which includes six steps: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication.FindingsTo assess the realistic impacts of implementing an RFID-IoT uniforms’ distribution system for managing medical personnel’s scrubs in operating rooms, the authors adopted a design science approach and initiated the research by documenting the business case and reviewed the existing literature to build a comparative analysis of existing uniforms’ distribution systems. The authors used a hybrid simulation model to assess the impact of three business cases: present mode of operation, implementing smart shelves or the smart distributors. The authors show that smart dispensers allow a greater control on scrubs’ utilization while eliminating the cross-contamination of the medical personnel.Practical implicationsThrough this research study, the authors provide hospitals’ managers a scientific support for uniforms’ (scrubs) distribution process improvement. The authors use a hybrid simulation model to compare innovative solutions for uniforms’ distribution systems in the form of “smart cabinets” supported by Radio Frequency Identification (RFID)/Internet of Things (IoT) technologies and choose the most appropriate design for the hospital to meet two main challenges: a) inefficiency of uniform replenishment-distribution system and b) noncompliancy with infection control regulations caused by the distribution system.Originality/valueFrom a methodological perspective, this paper addresses concerns from researchers calling quantitative research methods and using case-based research strategy to address IoT issues and assess the system in practice. From a broader point of view, this work confirms the predominant interest of RFID-IoT research work in the arena of supply chain management and logistics as the technology is used for tracking purpose and for monitoring applications. It is also one response to the research community suggesting that “hospitals should evaluate the medical effectiveness of the new technologies as well as the cost before adoption”.

Pseudo‐dynamic hybrid simulations of steel eccentrically braced frames equipped with cast steel replaceable modular yielding links

AbstractPrevious studies have underlined the importance of establishing a database of high‐fidelity benchmark experimental test results under random loading histories such as earthquake excitations for both existing structural systems as well as newly proposed ones. Such experimental results are useful for understanding the limitations of previously developed numerical models, proposing new numerical models for more recently developed structural systems, better assessing the ultra‐low cycle fatigue (ULCF) life of energy dissipative components, and improving the loading protocols used for element assessments or developments of numerical model. This paper presents experimental results from pseudo‐dynamic hybrid simulations on steel eccentrically braced frames (EBF) equipped with novel cast steel replaceable modular yielding links. The first set of experiments are carried out on a four‐story EBF, with the first‐floor yielding link physically tested in the laboratory. The second set of experiments are performed on a two‐story EBF where the second‐floor link is physically tested in the laboratory, while considering axial loads due to the imbalanced distribution of the seismic weight in the building. Each building structure is subjected to three Maximum Credible Event (MCE) level earthquakes. A framework for performing hybrid simulations on EBFs is proposed where the response of the yielding link is physically captured in a single degree of freedom control system, with or without axial loads. A substructuring strategy is proposed in conjunction with a modeling approach in the integration module. The results confirm the effectiveness of the hybrid simulation framework, which can be adopted in similar studies. Two numerical models are proposed for capturing the response of cast steel links using a simplified and a more advanced approach. The models are first calibrated using incremental reversed‐cyclic experimental test results, and then refined using the hybrid simulation test results. The effects of this calibration improvement are quantified through critically comparing the response prediction for different response parameters before and after improving the calibration with the experimental results. The numerical model which captures the physical mechanism of the cast steel links provides a more accurate response prediction compared to the simplified numerical model using a phenomenological truss with calibrated force‐deformation. The remaining ULCF life of the tested yielding links is experimentally investigated after having sustained three MCE level earthquakes, which indicates a large reserve ductility capacity which is more than twice the required plastic rotation capacity for cast steel links after seismic events.

Full-wave multiphysics model for simulation and investigation of terahertz photoconductive antenna using LUMERICAL and CST softwares

In this work, the physical mechanism of the full-wave multiphysics model is described underlying the simulation of terahertz photoconductive antenna (PCA) using hybrid softwares incorporating LUMERICAL and CST. The feature of this simulation is that the multiphysical phenomena that occur in the PCA, such as light-matter interaction, photoexcited carriers, and full-wave propagation, are considered and embodied in the simulation. The accuracy of the presented simulation framework is verified by comparing its results with some commercial softwares such as COMSOL, SILVACO, and the numerical of three-dimensional finite difference time domain (3-D FDTD) method.Themethod. The maximum discrepancy of the simulation results between this hybrid software framework and others is less than 2%, which indicates the accuracy of these simulation results. In addition, this hybrid simulation framework can be used to characterize the parameter-dependent performance of a PCA. The obtained results are used as a guideline to present modified PCAs where a nanostructure is added inside the antenna gap to enhance the PCA performance. The two modified PCAs are defined as nano-line and nano-crossline PCAs, where nano-lines are connected to one or both contacts. The conversion efficiencies of the nano-line and nano-crossline PCAs are enhanced by 28.5 and 64 times, respectively, with respect to conventional PCA.

A digital twin-based framework for multi-element seismic hybrid simulation of structures

This paper proposes a digital twin-based multi-element hybrid simulation (DMHS) framework to predict the nonlinear cyclic response of structural components (digital twin), e.g., seismic fuses, that are not physically tested due to laboratory limitations by leveraging the experimental test data collected from the physical test specimen (physical twin) during hybrid simulation. This data-based simulation approach can address biased results of hybrid simulation of structures that contain multiple critical components while improving the efficiency of the seismic hybrid simulation. The digital twin is trained in two phases: (1) passive (initial) training phase using past experimental test data before the hybrid simulation starts, and (2) recursive model updating phase using the active (real-time) data produced by the physical specimen during hybrid simulation. The passive training is achieved using the Prandtl–Ishlinskii (PI) hysteresis model combined with the sparse identification technique, while the recursive least-squares algorithm is used in the second phase as the model updating scheme. In particular, the sparse identification technique facilitates the selection of the optimal number of hysteretic model parameters in the passive training phase, which are then tuned in the model updating phase. The architecture of the proposed DMHS framework is first presented, followed by digital twin training steps. The application of the proposed DMHS is then demonstrated, and its simulation accuracy is assessed through virtual hybrid simulation of a two-storey steel buckling-restrained braced frame, which consists of a digital twin (second-storey brace) and a virtual experimental specimen (first-storey brace) integrated into the numerical model of the structure that is subjected to a set of earthquake ground motion accelerations. The results obtained from the verification study serve to validate the proposed architecture of the DMHS framework and evaluate the accuracy and efficiency of this technique in simulating the nonlinear seismic response of structural systems.

A real-time hybrid simulation framework for floating offshore wind turbines

Offshore wind farms are experiencing rapid growth globally where floating offshore wind turbines (FOWTs) have been attracting increasing research and industry investment. To achieve secure application, it is essential to develop numerical models and conduct laboratory testing to develop an in-depth understanding of the complex structural behavior of FOWTs under combined wind-wave loading. However, model testing of FOWTs is complicated by the Reynolds-Froude scaling incompatibilities. This research proposes a real-time hybrid simulation (RTHS) framework to study the structural performance of FOWTs under wind-wave loading. In the framework, the blades (nacelle) and tower are numerically modeled, and the floating platform is tested in real-time via an actuation system in a laboratory. The numerical and physical substructures communicate at the tower-floater interface. The National Renewable Energy Lab 5 MW spar-type FOWT on a scale of 1:50 is simulated to evaluate the feasibility of the proposed framework. Errors caused by delays and noises are quantified through sensitivity analyses. Results show that the delays in the sensors and actuators influence the performance of the RTHS framework more significantly than the noises. Overall, the response relative errors in the RTHS framework are small and tolerable under delays, noises, and representative wind-wave conditions. The proposed framework and sensitivity analyses presented in this study provide important information for future implementation and further development of the RTHS technology for similar marine structures.

A generic learning simulation framework to assess security strategies in cyber-physical production systems

Connected systems through computerized networks are at the heart of the Industry of the future. As they merge physical entities with cyber spaces, they fall under the paradigm of cyber-physical production systems. Cybersecurity is a key challenge for such systems, as they are subject to daily attempts of intruders to gain unauthorized access to their internal resources or to compromise their integrity. The fast increase of new attack strategies requires the rapid design and assessment of new defense strategies. It entails a complex, error-prone and time-consuming process, including the clear specification of the attack and defense strategies involved, and the design and implementation of the simulation model allowing to evaluate the performances of the defense strategy. This work intends to make such a process transparent to cybersecurity managers by limiting their workload to the sole specification of the characteristics of the system and the logic of the attack and the defense. It provides a generic hybrid simulation framework for flexible evaluation of cybersecurity policies, which is demonstrated on a SYN flooding application. Therefore, the contribution is twofold: (1) The proposed framework offers a high-level environment allowing various experts to collaborate by graphically modeling a given attack strategy and the envisioned defense strategy, without engaging in heavy implementation efforts. Then the framework's executable infrastructure, which combines simulation with machine learning to understanding the interactions between the attackers & the defender, will allow them assessing the performances of these strategies. The proposed framework differs from state-of-the-art cybersecurity simulation environments in its uniqueness to combining the expressive power of a universal simulation modeling formalism with the user-friendliness of a visual simulation tool. Therefore, it offers at one side, a very high modeling flexibility for easy exploration of various cybersecurity strategies, and at the other side, integrated learning capabilities for allowing self-adaptive user-based cybersecurity strategy design. (2) The application demonstrating the framework focuses on the most encountered and still uncontrolled threats in cybersecurity, i.e. the SYN-Flooding based Denial of Service (DoS) attack. The application targeted is not meant to propose yet another SYN flood detection algorithm or to improve the state-of-the-art in that domain, but to prove the framework operationality. The experimental results obtained showcase the ability of the framework to support learning simulation-based SYN flood defense algorithm design and validation.

Bridge modal property identification based on asynchronous mobile sensing data

There is a growing attention in real-time bridge condition assessment using data from drive-by vehicles as a potentially scalable approach. Most system identification methods are based on synchronized vibration data collection for this purpose. This study presents an approach for bridge modal identification that estimates high-resolution absolute value of the operational mode shapes using asynchronous mobile data. With each trip of a vehicular sensor, the spatio-temporal response of the bridge is sampled, along with various sources of noise, e.g. vehicle dynamics, environmental effects, road profile, etc. The crowdsourced modal identification using continuous wavelet (CMICW) method is proposed that gradually magnifies the bridge dynamical signatures and mitigates noise over the spatio-temporal map. The performance of the CMICW method is validated in an experimental setting. The method successfully identifies natural frequencies and absolute value of the operational mode shapes of a bridge with high resolution and accuracy. Notably, by including data collected from various bridge lanes, the method can reconstruct 3D representation of the mode shapes. The influence of the speed of the mobile sensors on the accuracy of the estimated modal properties is investigated as well. Using a hybrid simulation framework, the effect of vehicle dynamic is included in the mobile sensing data. The study shows that the CMICW method is successful in discounting the effect of vehicle dynamics, thereby strengthening the bridge modal information. Finally, a blind source separation technique is implemented to separate the effects of road irregularities, which further improves the accuracy of modal property estimates. This study contributes to the growing body of knowledge on mobile crowdsensing for physical properties of transportation infrastructure.

Moving Safety Evaluation of High-speed Train on Post-earthquake Bridge Utilizing Real-time Hybrid Simulation

ABSTRACT This study aimed to provide an experimental evaluation method for train’s moving safety on a post-earthquake bridge. First, an improved real-time hybrid simulation (RTHS) framework was constructed, based on utilizing the moving load superposition algorithm to solve the train-track-bridge interaction (TTBI) problem. An RTHS test was then conducted for the TTBI problem. The train model was tested using a shake table and was dynamically linked to the numerical substructure. A post-earthquake seven-span high-speed railway simply supported bridge was studied, in which earthquake-induced damage, such as stiffness reductions, residual displacements of piers, girder gap expansions, and pier settlements were all tested.

Parametric analysis of core-noise from a realistic gas-turbine combustor for cruise and take-off conditions

Combustion noise from a realistic gas-turbine combustor geometry is investigated parametrically using a hybrid simulation framework that combines large-eddy simulation and a linearized Euler formulation. The effect of operating conditions on the relative noise-source contributions arising from direct and indirect noise is examined by considering take-off and cruise conditions. To quantify the predictions of the combustion flow field, comparisons with available experimental data for velocity and spray droplet diameter are performed. Analysis of combustion dynamics shows that tonal combustion noise from a thermoacoustic instability is present at both operating conditions, which is consistent with experimental data. The unsteady combustion and dilution are the main sources for the fluctuations of pressure, temperature and mixture composition at the combustor exit. While the thermo-chemical properties do not significantly change between the two operating conditions, the simulation results show that the level of unsteadiness in the flow field is significantly higher for the take-off condition. This leads to an overall increase in the noise emissions by up to 20 dB at take-off compared to the cruise condition. Indirect noise arising from compositional inhomogeneities is found to exceed the entropy noise for both operating conditions. Phase cancellation between composition noise and temperature-induced noise is shown to result in an overall reduction of the indirect combustion-noise emissions.

Hybrid simulation framework for the production management of an ethanol biorefinery

To make ethanol cost-competitive with gasoline, it is critical to devise a cost-effective production plan for ethanol refineries. However, unlike with petroleum refineries, the managers of the corn-based ethanol refineries are faced with multiple challenges. Particularly, the refineries should have a consistent production rate despite the uncertain yield of feedstock production that results from a dynamic environment, including climate change. In this study, a hybrid simulation framework was developed for operating biofuel refineries. The framework consisted of two major simulation platforms: (1) Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) for yield estimation of feedstock considering dynamic environmental factors and (2) simulation-based optimization with agent-based simulation (ABS) to identify the optimal production plan in terms of operational cost based on yield and relevant parameters given by ALMANAC. In ABS, eight major operations (e.g., milling, cooking, liquefaction, and fermentation) of a refinery were modeled via AnyLogic® ABS software to accurately compute operational costs. We used a geographic information system (GIS) map to compute transportation costs between feedstock farms, refineries, and consumers’ facilities. The proposed framework was demonstrated using real data of feedstock and ethanol production in Tazewell County, Illinois, U.S. The experiments identified that the ethanol production planning without considering feedstock loss could cause overproduction at a refinery. The daily overproduction costs with 28% dry matter and 45% dry matter are 23.56% and 14.07% of their total operational costs, respectively. The framework will provide a reliable and practical ethanol production plan considering feedstock supply under dynamic environmental factors.

Development of a Hybrid Simulation Framework for the Production Planning Process in the Atlantic Salmon Supply Chain

The farmed salmon supply chain has a highly complex and integrated structure, where activities occur both in the sea and on land. Due to this complexity, the supply chain needs appropriate decision-support tools to aid the production planning process, which capture the material flows, information flows and behaviours of the decision makers in the chain. This paper proposes a hybrid simulation framework for production planning using the case of the Norwegian Atlantic salmon supply chain. This hybrid simulation comprises agent-based modelling (ABM) to capture the autonomous and interacting decision making behaviour of the supply chain actors, while discrete-event simulation (DES) is employed to model the various production processes within the chain. The simulation is implemented using AnyLogic™ version 8.0 simulation software, using a case study from the Norwegian farmed salmon sector. The proposed modelling framework provides a deeper understanding of the activities in the salmon supply chain, thereby enabling improved decision making.

Real-time hybrid simulation framework for the investigation of soil-structure interaction effects on the vibration control performance of shape memory alloys

Shape memory alloys (SMAs) are two-phase polycrystal smart materials with hysteretic energy dissipation capacity. Under a mechanically applied strain, SMAs can transform their phases and recover large deformations in a superelastic fashion. Both the hysteretic damping and the deformation recovery are key features for new type of maintenance-free vibration control devices. Various factors influence the energy dissipation process of SMAs. Particularly rates and amplitudes of the applied deformation pattern are crucial for the damping. The studies have focused so far on the interaction between structures and SMAs. This paper goes one step further and seeks to consider the soil-structure interaction (SSI) in the treatment of the SMA control performance as well. A real-time hybrid simulation based cyber-physical methodology is developed for this purpose. Using finite-element modeling technique, a semi-infinite boundary definition is realized for the soil and the soil-foundation interaction is replicated for SMA-controlled structures. The proposed approach is applied to a shear frame structure equipped with SMA wires. The responses of the structure to historic earthquake records are assessed from a macroscopic perspective of the SMA behavior. The study shows that the material and radiation damping of the soil-foundation interaction have a significant influence on the vibration control performance of SMAs.