Operational Load Monitoring consists of the real-time reading and recording of the number and level of strains and stresses during load cycles withstood by a structure in its normal operating environment, in order to make more reliable predictions about its remaining lifetime in service. This is particularly important in aeronautical and aerospace industries, where it is very relevant to extend the components useful life without compromising flight safety. Sensors, like strain gauges, should be mounted on points of the structure where highest strains or stresses are expected. However, if the structure in its normal operating environment is subjected to variable exciting forces acting in different points over time, the number of places where data will have be acquired largely increases. The main idea presented in this paper is that instead of mounting a high number of sensors, an artificial neural network can be trained on the base of finite element simulations in order to estimate the state of the structure in its most stressed points based on data acquired just by a few sensors. The model should also be validated using experimental data to confirm proper predictions of the artificial neural network. An example with an omega-stiffened composite structural panel (a typical part used in aerospace applications) is provided. Artificial neural network was trained using a high-accuracy finite element model of the structure to process data from six strain gauges and return information about the state of the panel during different load cases. The trained neural network was tested in an experimental stand and the measurements confirmed the usefulness of presented approach.
Abstract. The paper presents a practical implementation of hybrid simulation using Real Time Finite Element Method (RTFEM). Hybrid simulation is a technique for investigating dynamic material and structural properties of mechanical systems by performing numerical analysis and experiment at the same time. It applies to mechanical systems with elements too difficult or impossible to model numerically. These elements are tested experimentally, while the rest of the system is simulated numerically. Data between the experiment and numerical simulation are exchanged in real time. Authors use Finite Element Method to perform the numerical simulation. The following paper presents the general algorithm for hybrid simulation using RTFEM and possible improvements of the algorithm for computation time reduction developed by the authors. The paper focuses on practical implementation of presented methods, which involves testing of a mountain bicycle frame, where the shock absorber is tested experimentally while the rest of the frame is simulated numerically.
Featured Application: Performing effective and efficient hybrid simulations of mechanical systems in real-time by implementing a model order reduction technique to the computational algorithm. The efficiency of the computations is understood as the ability to test systems of a higher number of degrees of freedom while maintaining high accuracy, without increasing the time step. Effective simulation is a simulation that allows acquiring correct results while maintaining the time regime.Abstract: Hybrid simulation is a technique for testing mechanical systems. It applies to structures with elements hard or impossible to model numerically. These elements are tested experimentally by straining them by means of actuators, while the rest of the system is simulated numerically using a finite element method (FEM). Data is interchanged between experiment and simulation. The simulation is performed in real-time in order to accurately recreate the dynamic behavior in the experiment. FEM is very computationally demanding, and for systems with a great number of degrees of freedom (DOFs), real-time simulation with small-time steps (ensuring high accuracy) may require powerful computing hardware or may even be impossible. The author proposed to swap the finite element (FE) model with an artificial neural network (ANN) to significantly lower the computational cost of the real-time algorithm. The presented examples proved that the computational cost could be reduced by at least one number of magnitude while maintaining high accuracy of the simulation; however, obtaining appropriate ANN was not trivial and might require many attempts. subsystem, which are measured in the form of a vector r P i+1 , where subsequent elements of the vector correspond to degrees of freedom (DOFs) of the discretized FE model [4,5]. Appl. Sci. 2019, 9, x FOR PEER REVIEW 2 of 19 are measured in the form of a vector r P i+1, where subsequent elements of the vector correspond to degrees of freedom (DOFs) of the discretized FE model [4,5].
Nitric oxide(NO), nitrogen dioxide (NO2), nitrous oxide (N2O), and their derivatives generally known as nitrogen oxides (NOx) are primary pollutants in the atmosphere originated from natural and anthropogenic sources. The paper presents investigation of electric performance of novel chemiresistor NOx gas sensors. A novel material was utilized for active sensing layer-conductive copolymer and zinc oxide blend. The main advantage of the presented solution is low-cost and environment-friendly production. A series of this type of sensors was manufactured and tested experimentally. During the tests, the gas flow was controlled and signals of sensor responses, temperature, and humidity were computer-acquired using LabVIEW program. Sensor behavior for different thicknesses of the active layer has been investigated and interpreted. The research revealed that the electrical resistance of the sensors has changed in predictable manner depending on the gas concentrations. A recurrent artificial neural network architecture is proposed as a mathematical model to classify sensor responses to gas concentrations variation in a time-dependent regime. In this research, an enhanced method for gas concentration prediction is proposed using non-linear autoregression model with exogenous input (NARX). The performed simulations show good agreement between simulated and experimental data useful for predictions of sensor gas response.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.