Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection Curve

A new approach is presented for identifying material model parameters. The approach is based on coupling stochastic nonlinear analysis and an artificial neural network. The model parameters play the role of random variables. The Monte Carlo type simulation method is used for training the neural network. The feasibility of the presented approach is demonstrated using examples of high performance concrete for prestressed railway sleepers and an example of a shear wall failure.

Verification of mathematical model of pressure distribution in artificial knee joint

ArticleThe paper deals with pressure distribution measurement in knee arthroplasty, which is an artificial replacement of human knee joint. The scope of the article is to verify the accuracy of a mathematical model by real measurements. The calculated pressure values basing on the mathematical model are compared with actually measured pressure values in the contact area of the joint. Hereby maximal load the in the contact area, the distribution of the pressure and any potentially dangerous pressure deviations during the walk cycle are checked. To enable accurate pressure distribution measurement without interfering into human’s body, a sophisticated measuring setup was created: the contact area of the joint was equipped with several pressure sensors and a machine simulating the human walk cycle was used. The measured pressure data are finally compared with those from the mathematical model and with the strength limit of the used material, to verify the accuracy of the mathematical model experimentally

Plantograf V18 – new construction and properties

ArticleThe article describes Plantograf V18, a planar tactile transducer, which converts the applied pressure into electric signal and enables a graphical presentation of the measured data; the new version V18 comes with some significant improvements and modifications. The device may be used ev erywhere where the pressure distribution between an object and surface is to be determined, e.g. in medicine or automotive industry. The article contains the detailed description of the transducer design and its electronic control circuits, as well as the yet unpublished measurements of pressure sensitivity with 3.5 mm electrodes

Physics-Informed Polynomial Chaos Expansions

Surrogate modeling of costly mathematical models representing physical systems is challenging since it is typically not possible to create a large experimental design. Thus, it is beneficial to constrain the approximation to adhere to the known physics of the model. This paper presents a novel methodology for the construction of physics-informed polynomial chaos expansions (PCE) that combines the conventional experimental design with additional constraints from the physics of the model. Physical constraints investigated in this paper are represented by a set of differential equations and specified boundary conditions. A computationally efficient means for construction of physically constrained PCE is proposed and compared to standard sparse PCE. It is shown that the proposed algorithms lead to superior accuracy of the approximation and does not add significant computational burden. Although the main purpose of the proposed method lies in combining data and physical constraints, we show that physically constrained PCEs can be constructed from differential equations and boundary conditions alone without requiring evaluations of the original model. We further show that the constrained PCEs can be easily applied for uncertainty quantification through analytical post-processing of a reduced PCE filtering out the influence of all deterministic space-time variables. Several deterministic examples of increasing complexity are provided and the proposed method is applied for uncertainty quantification

The Pauli equation with complex boundary conditions

We consider one-dimensional Pauli Hamiltonians in a bounded interval with possibly non-self-adjoint Robin-type boundary conditions. We study the influence of the spin-magnetic interaction on the interplay between the type of boundary conditions and the spectrum. A special attention is paid to PT-symmetric boundary conditions with the physical choice of the time-reversal operator T.Comment: 16 pages, 4 figure

Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion

The paper presents a novel methodology to build surrogate models of complicated functions by an active learning-based sequential decomposition of the input random space and construction of localized polynomial chaos expansions, referred to as domain adaptive localized polynomial chaos expansion (DAL-PCE). The approach utilizes sequential decomposition of the input random space into smaller sub-domains approximated by low-order polynomial expansions. This allows approximation of functions with strong nonlinearties, discontinuities, and/or singularities. Decomposition of the input random space and local approximations alleviates the Gibbs phenomenon for these types of problems and confines error to a very small vicinity near the non-linearity. The global behavior of the surrogate model is therefore significantly better than existing methods as shown in numerical examples. The whole process is driven by an active learning routine that uses the recently proposed $\Theta$ criterion to assess local variance contributions. The proposed approach balances both \emph{exploitation} of the surrogate model and \emph{exploration} of the input random space and thus leads to efficient and accurate approximation of the original mathematical model. The numerical results show the superiority of the DAL-PCE in comparison to (i) a single global polynomial chaos expansion and (ii) the recently proposed stochastic spectral embedding (SSE) method developed as an accurate surrogate model and which is based on a similar domain decomposition process. This method represents general framework upon which further extensions and refinements can be based, and which can be combined with any technique for non-intrusive polynomial chaos expansion construction

Electronic structure of ferromagnetic semiconductor Ga1-xMnxAs probed by sub-gap magneto-optical spectroscopy

We employ Faraday and Kerr effect spectroscopy in the infrared range to investigate the electronic structure of Ga1-xMnxAs near the Fermi energy. The band structure of this archetypical dilute-moment ferromagnetic semiconductor has been a matter of controversy, fueled partly by previous measurements of the unpolarized infrared absorption and their phenomenological impurity-band interpretation. The infrared magneto-optical effects we study arise directly from the spin-splitting of the carrier bands and their chiral asymmetry due to spin-orbit coupling. Unlike the unpolarized absorption, they are intimately related to ferromagnetism and their interpretation is much more microscopically constrained in terms of the orbital character of the relevant band states. We show that the conventional theory of the disordered valence band with dominant As p-orbital character and coupled by kinetic-exchange to Mn local moments accounts semi-quantitatively for the overall characteristics of the measured infrared magneto-optical spectra.Comment: 4 pages 3 figure

Damage detection in sluice hoist beams subject to excitation at resonance frequency band based on local primary frequency

Cracks of sluice hoist beams due to the load and aging of the material threaten the safety of sluice structural system. As the one of the main methods of damage detection, the non-destructive detection method based on natural frequency is still insensitive to local damage. Therefore, this paper proposes a method for hoist beams damage detection driven by resonance frequency band based on local primary frequency in the local mode. Firstly, the possibility of damage detection based on local primary frequency is discussed and the procedure of determining resonance frequency band is explained. Then the damage identification index based on the change ratio of local primary frequency is provided. Finally, numerical results demonstrate the correctness and effectiveness of the proposed method. The proposed method can provide reference for damage detection of hoist beams and health monitoring of sluice structural system