27 research outputs found
Weeding Manipulator Exploiting Its Oscillatory Motion for Force Generation: Parameter Optimization of VDP Oscillator using Real-Coded Genetic Algorithm
No full textThis paper addresses parameter optimization of Van der Pol (VDP) oscillator driving a weeding manipulator, which exploits its own oscillatory motion to achieve efficient force generation. The author has confirmed the effectiveness of the exploitation in force generation using simulations. The simulation results have shown that a method using the VDP oscillator attains a superior performance to one using a sinusoidal wave function. In the simulations, however, the parameters of the VDP oscillator have been chosen after several attempts. Therefore, if the parameters are adjusted by an appropriate way, the performance of the force generation method will be improved. In this paper, real-coded genetic algorithm (RCGA), which is an optimization technique, is applied to the parameter optimization of the VDP oscillator because it is easily implementable for nonlinear systems. Simulation results show that RCGA succeeded in finding out sets of better parameters than ones used in the previous study
Weeding Manipulator Exploiting Its Oscillatory Motion for Force Generation: Parameter Optimization of VDP Oscillator using Real-Coded Genetic Algorithm
No full textThis paper addresses parameter optimization of Van der Pol (VDP) oscillator driving a weeding manipulator, which exploits its own oscillatory motion to achieve efficient force generation. The author has confirmed the effectiveness of the exploitation in force generation using simulations. The simulation results have shown that a method using the VDP oscillator attains a superior performance to one using a sinusoidal wave function. In the simulations, however, the parameters of the VDP oscillator have been chosen after several attempts. Therefore, if the parameters are adjusted by an appropriate way, the performance of the force generation method will be improved. In this paper, real-coded genetic algorithm (RCGA), which is an optimization technique, is applied to the parameter optimization of the VDP oscillator because it is easily implementable for nonlinear systems. Simulation results show that RCGA succeeded in finding out sets of better parameters than ones used in the previous study
Synchronisation along quantum trajectories of three coupled VdP oscillators
No full textSynchronisation is the remarkable phenomenon of in-phase movement of coupled oscillators during a prolonged period of time, which even occurs when these have different natural frequencies. This property is used in many fields like physics, biology and chemistry, to model various system behaviours, for instance: circadian rhythms in the chemistry of the eyes \cite{Rompala2007}. \newline\noindent This thesis focuses on synchronisation and entanglement in systems consisting of quantum Van der Pol oscillators. After looking at the exemplary properties of the classical VdP oscillator, it follows the methods of \cite{EshaqiSani2020} to explore the behaviour of two coupled QVdPOs, quantum Van der Pol oscillators, using Monte Carlo simulations for trajectories of this system. The system is then expanded to three-oscillator systems with all-to-all and chain coupling. The validity of the extension of the properties found in two QVdPOs in \cite{EshaqiSani2020} with regard to synchronisation and entanglement is tested. Does an Arnold tongue, the region of parameters for which a system shows synchronisation, still exist and if so has its shape changed? Do synchronisation and strong entanglement still show a positive correlation? \newline\noindent Simulations of the 2-oscillator system gave results which were just slightly different from those obtained in\cite{EshaqiSani2020}, validating the occurrence of synchronisation within the Arnold tongue and showing a positive correlation between synchronisation and entanglement of the system. Both the 3-oscillator systems showed synchronisation as well in their respective Arnold tongues, which were increasingly smaller for the all-to-all coupled and the chain coupled system when compared to the 2-oscillator system. Overall, the amount of synchronisation, shown in three oscillators was very close to the amount shown in two oscillators when strongly coupled and just a little less for weak coupling. The correlation between synchronisation and strong entanglement was also found for three oscillators, though for weaker coupling the chain coupled system showed a little more entanglement than before.Applied Mathematics | Applied Physic
A new look on the “Valid Detection Probability” of PIV Vectors
No full textFor the reliable estimation of velocity vector field s by means of 2D and 3D particle image velocimetry (PIV), the cross-correlation functions calculated from the signal within each interrogation window must feature a distinct peak that represents the average shift of the particle image ensemble. A high valid detection probability (VDP) of the correct correlation peak is necessary in order to compute valid and accurate velocity field s. The following analysis shows the sensitivity of VDP on flow parameters as well as on evaluation parameters. The most important result is that the so-called effective number of particle images NIFIFO is not suited to predict the VDP in the case of moderate or strong out-of-plane motion. This can be explained by the fact that the VDP depends not only on the number of particle images correctly paired, but also on the number of particle images remaining without partner, which yield spurious correlation peaks. The findings help to better understand the occurrence of false vectors and enable the PIV user to improve the measurement setup as well as the PIV evaluation in order to minimize spurious vectors.Aerodynamic
On the universality of Keane & Adrian's valid detection probability in PIV
No full textFor the reliable estimation of velocity vector fields by means of particle image velocimetry (PIV), the cross-correlation functions calculated from the signal within each interrogation window must feature a distinct peak that represents the average shift of the particle image ensemble. A high valid detection probability (VDP) of the correct orrelation peak is necessary in order to compute valid but also accurate velocity fields. According to Keane and Adrian it is believed that the so-called effective number of particle images NIFIFO must be around 6 to obtain 95% valid detection probability (Keane and Adrian 1992 Appl. Sci. Res. 49 191-215). To prove the findings of Keane and Adrian, this work examines the sensitivity of the VDP on image parameters, flow parameters as well as on evaluation parameters in more detail. The most important result is that the effective number of particle images NIFIFO is not suited to predict the VDP in the case of moderate or strong out-of-plane motion. This can be explained by the fact that the VDP depends not only on the number of particle images correctly paired, but also on the number of particle images remaining without partner, which yield spurious correlation peaks. This point remained unnoticed in the work of Keane and Adrian. The findings of this investigation help to better understand the occurrence of false vectors and enable the PIV user to improve the measurement setup as well as the PIV evaluation in order to minimize spurious vectors.Aerodynamic
A Robust Vitronectin-Derived Peptide Substrate for the Scalable Long-Term Expansion and Neuronal Differentiation of Human Pluripotent Stem Cell (hPSC)-Derived Neural Progenitor Cells (hNPCs)
No full textabstract: Several debilitating neurological disorders, such as Alzheimer's disease, stroke, and spinal cord injury, are characterized by the damage or loss of neuronal cell types in the central nervous system (CNS). Human neural progenitor cells (hNPCs) derived from human pluripotent stem cells (hPSCs) can proliferate extensively and differentiate into the various neuronal subtypes and supporting cells that comprise the CNS. As such, hNPCs have tremendous potential for disease modeling, drug screening, and regenerative medicine applications. However, the use hNPCs for the study and treatment of neurological diseases requires the development of defined, robust, and scalable methods for their expansion and neuronal differentiation. To that end a rational design process was used to develop a vitronectin-derived peptide (VDP)-based substrate to support the growth and neuronal differentiation of hNPCs in conventional two-dimensional (2-D) culture and large-scale microcarrier (MC)-based suspension culture. Compared to hNPCs cultured on ECMP-based substrates, hNPCs grown on VDP-coated surfaces displayed similar morphologies, growth rates, and high expression levels of hNPC multipotency markers. Furthermore, VDP surfaces supported the directed differentiation of hNPCs to neurons at similar levels to cells differentiated on ECMP substrates. Here it has been demonstrated that VDP is a robust growth and differentiation matrix, as demonstrated by its ability to support the expansions and neuronal differentiation of hNPCs derived from three hESC (H9, HUES9, and HSF4) and one hiPSC (RiPSC) cell lines. Finally, it has been shown that VDP allows for the expansion or neuronal differentiation of hNPCs to quantities (>1010) necessary for drug screening or regenerative medicine purposes. In the future, the use of VDP as a defined culture substrate will significantly advance the clinical application of hNPCs and their derivatives as it will enable the large-scale expansion and neuronal differentiation of hNPCs in quantities necessary for disease modeling, drug screening, and regenerative medicine applications.Dissertation/ThesisMasters Thesis Bioengineering 201
Large Scale Expansion and Differentiation of Human Pluripotent Stem Cell-Derived Neural Progenitor Cells (hNPCs)
No full textabstract: Neurodegenerative diseases such as Alzheimer’s Disease, Parkinson’s Disease and Amyotrophic Lateral Sclerosis are marked by the loss of different types of neurons and glial cells in the central nervous system (CNS). Human Pluripotent Stem Cell (hPSC)-derived Neural Progenitor Cells (hNPCs) have the ability to self-renew indefinitely and to differentiate into various cell types of the CNS. HNPCs can be used in cell based therapies and have the potential to reverse or arrest neurodegeneration and to replace lost neurons and glial cells. However, the lack of completely defined, scalable systems to culture these cells, limits their therapeutic and clinical applications. In a previous study, a completely defined, robust, synthetic peptide- a Vitronectin Derived Peptide (VDP) that supports the long term expansion and differentiation of various embryonic and induced pluripotent stem cell (hESC/hIPSC) derived hNPC lines on two dimensional (2D) tissue culture plates was identified. In this study, the culture of hNPCs was scaled up using VDP coated microcarriers (MC). VDP MC were able to support the long term expansion of hESC and hiPSC derived hNPCs over multiple passages and supported higher fold changes in cell densities, compared to VDP coated 2D surfaces. VDP MC also showed the ability to support the neuronal differentiation of hNPCs, and produced mature neurons expressing several neuronal, neurotransmitter and cortical markers. Additionally, alzheimer’s disease (AD) relevant phenotypes were studied in patient hIPSC derived hNPCs cultured on laminin MC to assess if the MC culture system could be used for disease modelling and drug screening. Finally, a microcarrier based bioreactor system was developed for the large scale expansion of hNPCs, exhibiting more than a five-fold change in cell density and supporting more than 100 million hNPCs in culture. Thus, the development of a xeno-free, scalable system allows hNPC culture under standard and reproducible conditions in quantities required for therapeutic and clinical applications.Dissertation/ThesisMasters Thesis Bioengineering 201
Velocity dependent potential effects on two-electron quantum dot in plasmas
No full textIn this study, for the first time, the effects of the velocity-dependent potential (VDP) on the energies of a two-electron parabolic quantum dot (TEPQD) in Debye and quantum plasma environments depicted by a more general exponential cosine screened Coulomb (MGECSC) potential are taken into consideration. The Schrödinger equation is modified by combining the MGECSC potential and VDP, solving numerically via the asymptotic iteration method. The Schrödinger equation with VDP is basically another type of one with position-dependent mass. The effects of VDP on two interacting electrons inside the parabolic quantum dot in plasmas are probed by considering the isotropic form factor with the harmonic (? (r) = ? 0 r 2) and constant (? (r) = ? 0) form. The alternativeness of the plasma shielding parameters to each other, the confinement parameter of the quantum dot, and the VDP parameters on energies and possible radiations of TEPQD are also discussed. © 2019 Author(s).Türkiye Bilimsel ve Teknolojik Araştirma KurumuThis work has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK) in the framework of the Project No. 117R001
Discrete-Time Nonlinear Reduced-Order Models for Aeroelastic Analysis: Linear System Idenfitifcation Methods and a Polynomial Nonlinear Model Investigation
No full textIn computational aeroelasticity, unsteady aerodynamics is computationally expensive compared to the structural calculation. This problem becomes more severe for prediction of flutter or Limit-Cycle oscillation (LCO), which requires multiple runs of simulations at various flow conditions. The former can be captured by linear computational-aerodynamic-fluid (CFD) solver, while the prediction of the latter requires nonlinear CFD solver. Reduced-order modelling (ROM) techniques for unsteady aerodynamics have been investigated extensively. Among these ROMs, Auto-Regression with eXogeneous variables (ARX) has been applied to predict the flutter behaviour successfully for relatively complicated test cases. Motivated by the nice performance of ARX, whether other linear system identification methods, such as Auto-Regression Moving Average with eXogeneous inputs (ARMAX), Output-Error (OE) and Box-Jenkins (BJ), are effective as linear ROMs is investigated. When it comes to the nonlinear ROM, the linear information is expected to use. A polynomial-based state-space model, extended from linear system identification, is defined to combine the linear part with nonlinear functions of the state and input. A direct requirement for the linear method is the representation in state-space form. The nonlinear functions compose of polynomials of degree equal or greater than . The coefficients of this nonlinear model are obtained by solving an optimisation problem with a Levenberg-Marquardt (LM) algorithm. Furthermore, we assume that the linear method is capable of capturing the flutter and keep the linear matrices constant during the nonlinear optimisation to reduce computational cost. As for the test case for LCO, an analytical Van-der-pol (VDP) oscillator is selected. The nonlinear ROM is built to replace the nonlinear term in VDP. Coefficients of the nonlinear ROM are obtained by solving an optimisation problem and the validation is carried out by reproducing the VDP oscillation. Another important factor for a fairly accurate ROM is the training signal. Different training signals are examined to reproduce the VDP oscillation. After checking the theoretical representation and the numerical solution algorithm, ARX and ARMAX are selected as linear ROMs for two reasons: the construction of state-space representation is explicit; ARMAX model is a variation from ARX by adding averaged error terms without changing the stability of the system. The comparison is carried out in two test cases. In the analytical test case, ARX and ARMAX can reproduce the training signal very well after an order selection and capture the flutter boundary. For the second test case, using CFD data, ARX and ARMAX match training signals well, but for test signals, ARMAX shows a lower fitness caused by overestimation of error contribution. For the nonlinear training, the optimiser can follow the nonlinear behaviour of the reference output, which demonstrates significant error reduction compared to the linear model. The validation of the ROM is examined for different training signals: chirps, random phase multi-sine, sequential sinusoids and multi-chirps. Chirps and sequential sinusoids fail to reproduce VDP. For most cases, random phase multi-sine is unable to follow VDP oscillation. Although the bounded oscillation is predicted for certain cases, repetitive tests are not consistent. For the multi-chirps, the optimiser is changed to accustom the cost function. With a couple of tests, this signal can deterministically reproduce the bounded oscillation, but the accuracy is not high. Another assumption that typical frequencies at flutter and LCO are not far apart is considered. Sequential sinusoids with narrow frequency band and wide amplitude range are applied to construct the nonlinear ROM in both random and deterministic cases. The frequencies and amplitudes are randomly chosen with the frequencies predetermined in the random case, while frequencies and amplitudes in the deterministic case are scattered uniformly. The LCO behaviour is predicted quite well in terms of amplitude and frequency in the bounded phase but not in transitional stage for both the cases. Repetitive tests are required for the random case. This study applied ARX and ARMAX as the linear ROM for flutter prediction and built the polynomial-based nonlinear ROM for predicting LCO. The observation that narrowing frequency range and widening amplitude range increase the chance of capturing LCO can be used for nonlinear training signal design.Aerospace EngineeringAerodynamics, Wind Energy & Propulsio
An Integrated Biomanufacturing Platform for the Large-Scale Expansion and Differentiation of Neural Progenitor Cells
No full textabstract: Neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, or amyotrophic lateral sclerosis are defined by the loss of several types of neurons and glial cells within the central nervous system (CNS). Combatting these diseases requires a robust population of relevant cell types that can be employed in cell therapies, drug screening, or patient specific disease modeling. Human induced pluripotent stem cells (hiPSC)-derived neural progenitor cells (hNPCs) have the ability to self-renew indefinitely and differentiate into the various neuronal and glial cell types of the CNS. In order to realize the potential of hNPCs, it is necessary to develop a xeno-free scalable platform for effective expansion and differentiation. Previous work in the Brafman lab led to the engineering of a chemically defined substrate—vitronectin derived peptide (VDP), which allows for the long-term expansion and differentiation of hNPCs. In this work, we use this substrate as the basis for a microcarrier (MC)-based suspension culture system. Several independently derived hNPC lines were cultured on MCs for multiple passages as well as efficiently differentiated to neurons. Finally, this MC-based system was used in conjunction with a low shear rotating wall vessel (RWV) bioreactor for the integrated, large-scale expansion and neuronal differentiation of hNPCs. Finally, VDP was shown to support the differentiation of hNPCs into functional astrocytes. Overall, this fully defined and scalable biomanufacturing system will facilitate the generation of hNPCs and their derivatives in quantities necessary for basic and translational applications.Dissertation/ThesisMasters Thesis Biomedical Engineering 201
