Mimicry of whole-body blood circulation through genetic algorithm in reduced-order hemodynamic model

The parametric reduced-order model (ROM) in a state-space form is necessary for real-time aeroelastic simulation and control synthesis with varying flight conditions. However, the existing ROM methods inherently suffer from a longstanding issue of state inconsistency, which makes ROMs non-interpolatable and these methods difficult to accommodate the full parameter space of interest. This paper presents a new method that combines the state consistence enforcement (SCE) and the genetic algorithm (GA) for rapid development of parametric aeroelastic ROMs. For SCE, a regularization term is introduced to the system identification approach – AutoRegressive model with eXogenous inputs (ARX) to specifically penalize state inconsistency. GA is used to find the optimal hyperparameters for configuring SCE-ARX that balances between state consistence and ROM accuracy. The GA-guided SCE-ARX is applied to construct aerodynamic ROMs (A-ROMs) at various grid flight conditions. A-ROM is then coupled with physics-based structural ROMs (S-ROMs) to form aeroelastic ROMs (AE-ROMs) at the grid conditions. Because of their salient state consistence, the aeroelatic ROMs can be interpolated to create ROMs at any flight conditions (where ROMs are not initially available), rapidly establishing the full coverage of the entire parameter space. The GA-guided SCE-ARX is compared with parametric ROMs generated by ARX, SCE, and GA-guided ARX in terms of prediction accuracy, and individual effects of SCE and GA on state consistency are analyzed by virtue of pole migration and model coefficient change wth varying Mach numbers. It is found that the proposed GA-guided SCE-ARX method retains excellent state consistence among local ROMs, and the interpolated AE-ROMs achieve the highest prediction accuracy with an overall relative error of only 1.81% in the broad Mach regime.

Various semi-active control methods have been proposed for vibration control of civil structures. In contrast to active vibration control systems, all semi-active control systems are essentially asymptotically stable because of the stability of the structural systems (with structural damping) themselves and the energy dissipating nature of the semi-active control law. In this study, by utilizing the above property on the stability of semi-active control systems, a reduced-order structural model and a semi-active control law are simultaneously obtained so that the performance of the resulting semi-active control system becomes good. Based on the above fact any semi-active control laws derived from some models stabilize all real-existing structural systems that have structural damping. It means that the difference of dynamic behaviors between the real structural system and the reduced-order mathematical model in the sense of the open-loop response is no longer an important issue. In other words, we do not have to consider the closed-loop stability, which is one of the most important constraints in active control, in the process of the reduced-order structural modeling and the semi-active control design. We can only focus on the control performance of the closed-loop system with the real structure with the (model-based) semi-active control law in obtaining the reduced-order model. The semi-active control law in the present study is based on the one step ahead prediction of the structural response. The Genetic Algorithm (GA) is adopted to obtain the reduced-order model and the semi-active control law based on the reduced order model.

Various semi-active control methods have been proposed for vibration control of civil structures. In contrast to active vibration control systems, all semi-active control systems are essentially asymptotically stable because of the stability of the structural systems themselves (with structural damping) and the energy dissipating nature of the semi-active control law. In this study, by utilizing the above property on the stability of semi-active control systems, a reduced-order structural model and a semi-active control law are simultaneously obtained so that the performance of the resulting semi-active control system becomes good. Based on the above fact any semi-active control laws derived from some models stabilize all real-existing structural systems that have structural damping. It means that the difference of dynamic behaviors between the real structural system and the reduced-order mathematical model in the sense of the open-loop response is no longer an important issue. In other words, we do not have to consider the closed-loop stability, which is one of the most important constraints in active control, in the process of the reduced-order structural modeling and the semi-active control design. We can only focus on the control performance of the closed-loop system with the real structure with the (model-based) semi-active control law in obtaining the reduced-order model. The semi-active control law in the present study is based on the one step ahead prediction of the structural response. The Genetic Algorithm (GA) is adopted to obtain the reduced-order model and the semi-active control law based on the reduced order model.

Data-driven parametric reduced-order models (ROMs) in state-space form are valuable tools for rapid aeroelastic (AE) analysis and aerostructure control synthesis. However, the issue of state inconsistence (significant variations in model parameters over tradespaces) makes ROMs noninterpolatable, and therefore unable to accommodate use over broad flight parameter space. This paper presents a holistic framework that combines a system identification technique with state-consistence enforcement (SCE) and a genetic algorithm (GA) for the automated development of interpolatable AE ROMs across broad flight regimes. The SCE technique introduces a regularization term to the AutoRegressive model with eXogenous inputs (ARX) to specifically penalize model parameter variation between flight conditions. The GA autonomously guides the ROM development process toward optimal SCE-ARX hyperparameter selection that balances between model parameter variations and ROM accuracy. The GA-guided SCE-ARX approach is applied to build a parametric AE-ROM database at selected flight conditions, which, because of its state consistence, can be interpolated to create ROMs at any interstitial conditions, where training data or ROMs are not initially available, hence rapidly establishing the full coverage of the entire parameter space. The ROMs generated by the proposed method are compared with those by ARX, SCE, and GA-guided ARX in prediction accuracy. The individual and combined effects of SCE and GA on model parameter variation and ROM interpolatability are thoroughly investigated. The present method demonstrates the most accurate and robust performance for parametric ROM construction across the broad flight envelope.

Model Order Reduction (MOR) of complex and large systems in Electrical engineering, continuous to be an attractive field for Engineers and Scientists over the last few decades, this complexity of models makes the control designs and simulation using Computer Aided Design (CAD) more and more difficult and consuming a lot of time. There for, accurate, robust and fast algorithms for simulation are needed. The goal of MOR is to replace the original system by an appropriate reduced system which preserves the main properties of the original one such that stability and passivity. Several analytical MOR techniques have been proposed in the literature over the past few decades, to approximate high order linear dynamic systems like Krylov sub-space techniques and SVD (Singular Value Decomposition) techniques. However, most of these techniques lead to computationally demanding, time consuming, iterative procedures that usually result in non-robustly stable models with poor frequency response resemblance to the original high order model in some frequency ranges. Recently a set of new techniques based on Artificial Intelligence (AI) were proposed in [1] for MOR. This article considers the problem of model order reduction of Linear Time In varying (LTI) systems. It is described by first and second order ordinary differential equations model. A tow steps method for model order reduction of LTI systems is proposed here, which combined features of an analytic technique (Krylov approach) and an AI technique (Genetic Algorithm). In the first step, the size of the original model is reduced to an intermediate order, using an analytical technique based on Krylov sub-spaces. In the final step of the reduction process, an AI approach based on Genetic Algorithm (GA) is applied to obtain an optimized nominal model.

A frequency-based model order reduction (MOR) via genetic algorithm (GA) approach is presented in this paper. An exogenous autoregressive model with a smaller dimensionality, which can mimic the full order model, maybe obtained using the GA MOR approach. For a general MOR, the GA predicts the elements of the system state matrix [A] defined in a state space representation along with the elements of the [B] and [C] matrices of the reduced order model. As a frequency-based MOR technique, the GA predicts only the elements of the [B] and [C] matrices of the reduced order model while [A] is set in the modal form. The proposed GA model order reduction approach is compared to recently published work for method evaluation.

Model order reduction has been one of the most challenging topics in the past years. Conventional mathematical methods have been used to obtain a reduced order model of high order complex models. In this paper, genetic algorithm (GA) which is one of the artificial intelligence algorithms is used to approximate high-order transfer functions (TFs) as lower-order TFs. Genetic algorithm is considered as one of the evolutionary techniques which are used in optimization problems. In this approach, genetic algorithm is applied to model order reduction putting in consideration improving accuracy and preserving the properties of the original model which are two important issues for improving the performance of simulation and computation and maintaining the behavior of the original complex models being reduced. The proposed technique could be used in EDA tools.

Analysis of a higher order physical system is often too difficult to carry out which is why it is necessary to replace the higher order system with a lower order system with reduced complexity while retaining the important characteristics of the original system. This is termed as Reduced Order Modelling (ROM). ROM is usually carried out to simplify the original system thereby reducing the number of control parameters which makes the controller design for the system simpler. This paper discusses ROM using one of the evolutionary techniques. i.e., Genetic Algorithm (GA). The simulation results obtained by ROM using genetic algorithm is then compared with those obtained by other classical methods of ROM such as Routh approximation, Pade´ approximation and pole clustering. This is followed by design of a controller for the reduced model with can also be implemented on the original higher order system. Here, a fuzzy logic-based control scheme is employed to obtain the gain values of a PID controller. Fuzzy rules are formulated based on human knowhow of the system and desired response. The application of fuzzy logic to calculate the controller parameters of a conventional PID controller generates an enhanced system response in an efficient way. The simulation results shown comparing the responses of conventional PID controller with that of fuzzy-PID controller illustrate the same.

A novel genetic algorithm (GA) approach with frequency selectivity advantage for model order reduction (MOR) of multi-input–multi-output (MIMO) systems is presented in this article. Motivated by singular perturbation and other reduction techniques, the new MOR method is formulated using GAs, which can be applied to single-input–single-output (SISO)- or MIMO-type systems. The GA procedure is based on maximizing the fitness function corresponding to the response deviation between the full-order model and the reduced-order model with the option of substructure preservation. The proposed GA-MOR method is compared to the well-known reduction techniques, such as the Schur decomposition balanced truncation, proper orthogonal decomposition (POD) and state elimination through balancing-related frequency-weighted realization in addition to other recent methods. Simulation results validate the superiority and robustness of the new MOR technique as it can search the solution space for almost optimal solutions.

Parametric reduced-order models (ROMs) across various flight regimes are crucial for rapid aeroelastic and gust load analysis and aerostructure control synthesis. This paper presents a comprehensive framework that combines hierarchically composable ROM approaches, the state-consistence enforced autoregressive with exogenous (SCE-ARX) technique, and a genetic algorithm (GA) for automated development of configurable parametric aeroservoelastic-gust (ASEG) ROMs. Our approach is based on the principle of superposition. It simultaneously models the interaction among aerodynamics, control surfaces, gust load, and structural dynamics, representing a significant level of complexity for aerostructural ROM development. Specifically, two hierarchical approaches, top-down and bottom-up, are presented to assemble ROMs from various physics domains, such as ASEG, aeroservoelastic, aeroelastic with gust, and aeroelastic, without the need to regenerate models or training data. The SCE-ARX method is combined with GA-guided ROM hyperparameter optimization to enhance ROM interpolation and accuracy. This framework is used to construct parametric ROM databases at selected flight conditions, which can be interpolated to any interstitial conditions, accommodating ASEG modeling over a broad flight parameter space. The top-down approach with GA-guided SCE-ARX demonstrates the best performance for parametric ROM development.

Uses a genetic algorithm (GA) to find a reduced-order model to approximate a linear system. To test the validity of the technique, the GA is applied to an example system from the literature. The GA's solution is observed to agree closely with the optimal solution. Then, the GA is applied to approximating a large power system, for which the analytic methods become unwieldy. The GA's solution is seen to outperform a commonly used suboptimal method.

[1] An optimal experimental design algorithm is developed to select locations for a network of observation wells that provide maximum information about unknown groundwater pumping in a confined, anisotropic aquifer. The design uses a maximal information criterion that chooses, among competing designs, the design that maximizes the sum of squared sensitivities while conforming to specified design constraints. The formulated optimization problem is non-convex and contains integer variables necessitating a combinatorial search. Given a realistic large-scale model, the size of the combinatorial search required can make the problem difficult, if not impossible, to solve using traditional mathematical programming techniques. Genetic algorithms (GAs) can be used to perform the global search; however, because a GA requires a large number of calls to a groundwater model, the formulated optimization problem still may be infeasible to solve. As a result, proper orthogonal decomposition (POD) is applied to the groundwater model to reduce its dimensionality. Then, the information matrix in the full model space can be searched without solving the full model. Results from a small-scale test case show identical optimal solutions among the GA, integer programming, and exhaustive search methods. This demonstrates the GA's ability to determine the optimal solution. In addition, the results show that a GA with POD model reduction is several orders of magnitude faster in finding the optimal solution than a GA using the full model. The proposed experimental design algorithm is applied to a realistic, two-dimensional, large-scale groundwater problem. The GA converged to a solution for this large-scale problem.

Model order reduction (MOR) is a process of finding a lower order model for the original high order system with reasonable accuracy in order to simplify analysis, design, modeling and simulation for large complex systems. It is desirable that the reduced order model preserves the fundamental properties of the original system. This paper presents a new MOR technique of multi-input multi-output systems utilizing the firefly algorithm (FA) as an artificial intelligence technique. The reduction operation is proposed to maintain the exact dominant dynamics in the reduced order model with the advantage of substructure preservation. This is mainly possible for systems that are characterized as multi-time scale systems. Obtaining the reduced order model is achieved by minimizing the fitness function that is related to the error between the full and reduced order models’ responses. The new approach is compared with recently published work on firefly optimization for MOR, in addition to three other artificial intelligence techniques; namely, invasive weed optimization, particle swarm optimization and genetic algorithm. As a result, simulations show the potential of the FA for the process of MOR.

Reduced Order Modeling is a technique for reducing the computational complexity of a model while preserving the expected fidelity within a controlled error. One of the techniques used to create a Reduced Order Model (ROM) is Artificial Neural Networks (ANN). A successful approach to reducing the variance of ANN model prediction is to train multiple models instead of a single model and to combine the predictions from these models, which is commonly called Ensemble learning. When the predictions from the multiple models are combined using another regression model, it is called Stacking ensemble. This paper studies the effectiveness of using Genetic programming algorithm in taking the outputs of each model as input and attempting to learn how to best combine the input predictions to make a better output prediction.The above-mentioned approach is used to create a ROM for a crossflow heat exchanger steady-state component. There are 6 inputs parameters namely Cold & Hot inlet temperature, Cold & Hot outlet pressure and Cold & Hot inlet flow. There are four outputs namely Hot & Cold outlet temperature and Hot & Cold inlet pressure. A multi-input single output (MISO) ROM is created for each of the outputs. There are 3 different configurations of ANNs used to cover a good range of the Hyperparameter values. The output from each of the ANNs is then combined using Genetic Programming Algorithm. The Overall model has an R2 value of above 95% for each of the outputs. The ROM thus created can run simulations at a much faster rate. The ROM of the HX component is a black box and can be shared with third party without any concerns over propriety information loss.

The widespread application of electric machines across different industries have a large impact on the operation cost and energy usage. This has driven research to improve the performance of electric machines in terms of the power density, efficiency and reliability. A comprehensive method of thermal management integrating the design, monitoring and control of electric machines is proposed in this research. The method is applied to two permanent magnet motors - a high power axial flux motor and a high precision linear motor. Firstly, a two stage optimization technique is applied to the design of the linear motor. A first stage global search using Genetic Algorithm followed by a second stage Branch and Bound method is a systematic way of searching for the optimal feasible solution. It resulted in an improved design which is more compact in size and produces a higher thrust force (39.9%) while reducing the heat generation (26.2%) when compared to an initial design. The design optimization takes into consideration the multi-physics interactions using a reduced order model. This resulted in a computation time saving of 80% over a commercial software optimization package while modelling accuracy is maintained through an output space mapping technique. During a continuous 5 hour cyclic positioning application, a model based compensation method is applied to the linear motor for real time thermal disturbance rejection. It resulted in improved positioning accuracy with a final mean unidirectional position deviation of −0.2μm and repeatability of ±0.7μm. Effective disturbance rejection is achieved through accurate disturbance modelling while using minimal sensor measurements. The minimal realization of the compensation model is achieved through model identification. In addition, a Modified Kalman Filter is proposed which led to a reduction in the number of temperature sensors required. Lastly, an experimentally determined lumped parameter thermal model is used for condition monitoring of the high power motor. Components of the electromagnetic losses are derived from a parameter estimation method using temperature measurement as input to the model. The method is able to detect input current fluctuations during a drive cycle which makes it possible to identify faults like a short circuit. Moreover, the model is useful for real time temperature monitoring which provides thermal protection against transient overloads. The modelling accuracy is improved by using a model identification technique to determine the thermal parameters.