1,285 research outputs found
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yet unpublished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
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
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