The Experts below are selected from a list of 22305 Experts worldwide ranked by ideXlab platform
Milan Jirásek - One of the best experts on this subject based on the ideXlab platform.
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Damage-plastic model for concrete failure
International Journal of Solids and Structures, 2006Co-Authors: Peter Grassl, Milan JirásekAbstract:AbstractThe present paper deals with the combination of plasticity and damage applied to modeling of concrete failure. First, the local uniqueness conditions of two types of combinations of stress-based plasticity and strain-driven scalar damage are studied. Then a triaxial damage-plastic model for the failure of concrete is presented. The plasticity part is based on the effective stress and the damage model is driven by the plastic strain. The implementation of the model in the form of a fully implicit integration scheme is discussed and the corresponding algorithmic stiffness matrix is derived. The constitutive response is compared to a wide range of experimental results. Finally, the model is applied to the structural analysis of reinforced concrete columns. A regularized version of this model with weighted Spatial Averaging of the damage-driving variable is published in a separate paper
Kostianko Anna - One of the best experts on this subject based on the ideXlab platform.
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Bi-Lipschitz Mané projectors and finite-dimensional reduction for complex Ginzburg-Landau equation
'The Royal Society', 2020Co-Authors: Kostianko AnnaAbstract:We present a new method of establishing the finitedimensionality of limit dynamics (in terms of bi- Lipschitz Mané projectors) for semilinear parabolic systems with cross diffusion terms and illustrate it on the model example of 3D complex Ginzburg- Landau equation with periodic boundary conditions. The method combines the so-called Spatial-Averaging principle invented by Sell and Mallet-Paret with temporal Averaging of rapid oscillations which come from cross-diffusion terms
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Bi-Lipschitz Mane projectors and finite-dimensional reduction for complex Ginzburg-Landau equation
2020Co-Authors: Kostianko AnnaAbstract:We present a new method of establishing the finite-dimensionality of limit dynamics (in terms of bi-Lipschitz Mane projectors) for semilinear parabolic systems with cross diffusion terms and illustrate it on the model example of 3D complex Ginzburg-Landau equation with periodic boundary conditions. The method combines the so-called Spatial-Averaging principle invented by Sell and Mallet-Paret with temporal Averaging of rapid oscillations which come from cross-diffusion terms
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Inertial manifolds via Spatial Averaging revisited
2020Co-Authors: Kostianko Anna, Li Xinhua, Sun Chunyou, Zelik SergeyAbstract:The paper gives a comprehensive study of inertial manifolds for semilinear parabolic equations and their smoothness using the Spatial Averaging method suggested by G. Sell and J. Mallet-Paret. We present a universal approach which covers the most part of known results obtained via this method as well as gives a number of new ones. Among our applications are reaction-diffusion equations, various types of generalized Cahn-Hilliard equations, including fractional and 6th order Cahn-Hilliard equations and several classes of modified Navier-Stokes equations including the Leray-$\alpha$ regularization, hyperviscous regularization and their combinations. All of the results are obtained in 3D case with periodic boundary conditions
Unku Moon - One of the best experts on this subject based on the ideXlab platform.
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an oversampling stochastic adc using vco based quantizers
IEEE Transactions on Circuits and Systems I-regular Papers, 2018Co-Authors: Hyuk Sun, Kazuki Sobue, Koichi Hamashita, Unku MoonAbstract:An oversampling stochastic analog-to-digital converter is presented. This stochastic converter Spatially averages quantization errors in multiple voltage-controlled oscillator (VCO)-based quantizers. Unlike other stochastic converters, this proposed architecture does not require an inverse Gaussian cumulative density function estimator. The digital adder becomes an ideal estimator due to uncorrelated quantization errors, which can be modeled as a uniformly distributed random variable. Because of the simple open-loop structure, this stochastic converter can easily be synthesized and reconfigured. The proof-of-concept prototype is implemented in a 0.18 $\mu \text{m}$ CMOS process. The prototype employs eight VCO-based quantizers and validates the extra 9 dB signal to quantization noise ratio improvement from quantization error Spatial Averaging. The measurements further demonstrate 54.2 dB and 45.4 dB SNDR for 50 MHz and 100 MHz bandwidths, while dissipating 116 mW of power.
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a 50 mhz bandwidth 54 2 db sndr reference free stochastic adc using vco based quantizers
Asian Solid-State Circuits Conference, 2016Co-Authors: Hyuk Sun, Jason Muhlestein, Spencer Leuenberger, Kazuki Sobue, Koichi Hamashita, Unku MoonAbstract:A reference-free stochastic ADC is proposed by utilizing both Spatial Averaging and oversampling noise-shaping schemes. By implementing multiple VCO-based quantizers in parallel, stochastic Spatial Averaging for quantization errors is inherently obtained. In addition, 1st-order noise shaping of a VCO-based quantizer is achieved in an open-loop oversampling configuration. By resolving a faster conversion rate, this open-loop structure eliminates biasing, loop filter, sample-and-hold, and external reference, and it consists of only delay cells and digital logic. The proof-of-concept prototype which includes eight VCO-based quantizers and Spatial Averaging estimator is implemented in a 0.18 μm CMOS process, demonstrating 54.2 dB and 45.4 dB SNDR for 50 MHz and 100 MHz bandwidths, with 116 mW power consumption. Measurement results reveal that the eight channel stochastic ADC provides an average 9 dB SQNR improvement due to the Spatial Averaging.
Yi Gao - One of the best experts on this subject based on the ideXlab platform.
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development of temperature evaluation of pure rotational coherent anti stokes raman scattering rcars spectra influenced by Spatial Averaging effects
Proceedings of the Combustion Institute, 2015Co-Authors: Yi Gao, Thomas Seeger, Alfred LeipertzAbstract:Abstract The issue of Spatial Averaging effect arises in CARS temperature measurements when the probe volume contains temperature gradients, a situation frequently encountered in turbulent reacting flows where flow patterns are irregular and temperature gradients are typically steep. In present work, a new dual-temperature fitting model is developed to deal with the Spatial Averaging effect and then applied to evaluate the measured pure rotational CARS spectra performed on a premixed flame of a Wolfhard–Parker slot burner. Comparing with the traditional single-temperature fitting model, an improved spectral fitting and therefore, temperature and concentration evaluation by using the dual-temperature fitting model, is demonstrated. Finally, the feasibility of the dual-temperature model is shown in turbulent spray flame.
Peter Grassl - One of the best experts on this subject based on the ideXlab platform.
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Damage-plastic model for concrete failure
International Journal of Solids and Structures, 2006Co-Authors: Peter Grassl, Milan JirásekAbstract:AbstractThe present paper deals with the combination of plasticity and damage applied to modeling of concrete failure. First, the local uniqueness conditions of two types of combinations of stress-based plasticity and strain-driven scalar damage are studied. Then a triaxial damage-plastic model for the failure of concrete is presented. The plasticity part is based on the effective stress and the damage model is driven by the plastic strain. The implementation of the model in the form of a fully implicit integration scheme is discussed and the corresponding algorithmic stiffness matrix is derived. The constitutive response is compared to a wide range of experimental results. Finally, the model is applied to the structural analysis of reinforced concrete columns. A regularized version of this model with weighted Spatial Averaging of the damage-driving variable is published in a separate paper
