Orfeas KyprisUniversity of Oxford | OX · Department of Computer Science
Orfeas Kypris
Ph.D.
About
18
Publications
4,600
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230
Citations
Citations since 2017
Introduction
I am a postdoctoral research associate at the University of Oxford, and I am currently working on the Structalyse (http://structalyse.org) and Mi6Sense (http://mi6sense.org) projects and developing a method for structural health monitoring using low-frequency magnetic fields. I obtained my Ph.D. at Iowa State University, where I developed a method for the non-destructive evaluation of stress in ferromagnetic components using magnetic Barkhausen signals.
Additional affiliations
March 2015 - March 2017
January 2011 - February 2015
Publications
Publications (18)
Smart structures of the future will require a costeffective, easily deployable solution for structural health monitoring. High loads on structures cause stresses that may lead to expansion of gaps, which are of utmost importance when it comes to overall structural health, as they absorb excess stress. Existing methods for direct displacement measur...
Structural health monitoring of critical infrastructure is key to protect large structures from critical and potentially catastrophic failure. It is clear that for long-term and sustained operation, energy must be harvested from some source. Although a large amount of work has considered how to power sensors on the exterior of structures with solar...
In this paper, we analyze the effect of different underground materials on very-low and low frequency magnetic fields used in the contexts of magneto-inductive localization and communication applications, respectively. We calculate the attenuation that these magnetic fields are subject to while passing through most common rocks and minerals. Knowin...
Ferrous and highly conductive materials distort low-frequency magnetic fields and can significantly increase magnetoinductive positioning errors. In this paper, we use the image theory in order to formulate an analytical channel model for the magnetic field of a quasi-static magnetic dipole positioned above a perfectly conducting half-space. The pr...
Magnetic Barkhausen noise analysis (BNA) is an established technique for the characterization of stress in ferromagnetic materials. An important application is the evaluation of residual stress in aerospace components, where shot-peening is used to strengthen the part by inducing compressive residual stresses on its surface. However, the evaluation...
![Figure 2.6: RMS Barkhausen noise voltage vs. stress [33].](profile/Orfeas-Kypris/publication/324031073/figure/fig4/AS:614334728261651@1523480221791/RMS-Barkhausen-noise-voltage-vs-stress-33_Q320.jpg)
In this work, a new, non-destructive method for obtaining stress-depth gradients in ferromagnetic structures was developed, using the information contained within magnetic Barkhausen emissions. A depth- and stress-dependent model for the frequency spectrum of Barkhausen emissions was derived and fitted to measured data obtained from steel samples w...
Ferromagnetic materials occur in single or multi-phase state and furthermore can undergo phase changes during processing or over time during service exposure. These phase changes can be attributed to physical processes or chemical reactions. In this paper, we examine the hysteresis and Barkhausen emission profiles of two-phase magnetic materials. B...
In this study, we conceptually divided a ferromagnetic specimen into layers along its depth. For each layer, we derived a non-linear integral equation that describes the attenuation with frequency and distance of magnetic Barkhausen emissions coming from that layer. We postulate that the Barkhausen spectrum measured at the surface by an induction c...
The effects of design parameters for optimizing the performance of sensors for magnetic Barkhausen emission measurement are presented. This study was performed using finite element analysis. The design parameters investigated include core material, core-tip curvature, core length, and pole spacing. Considering a combination of permeability and satu...
We derive a two parameter multi-exponential model to describe the frequency spectrum of Barkhausen noise in bulk steel under high excitation rates and applied tensile stress. We show how the amplitude and shape of the frequency spectrum depend on two directly measurable quantities, Barkhausen voltage and effective magnetic permeability, respectivel...
This study presents the development of a non-destructive method of detecting stress as a function of depth, useful for inspecting steel structures and components without the need to calibrate against x-ray diffraction data. A new frequency-dependent model for Barkhausen emissions based on the attenuation of emission with frequency and distance is u...
This study presents an experimental validation of a model theory for determining the relationship between a nondestructive measurement parameter and a property of interest. It was found that the reciprocal of the peak envelope amplitude of the Barkhausen emission voltage follows a linear relationship with stress. A linear relationship between stres...
We have recently shown that a linear relationship exists between the
reciprocal peak voltage envelope amplitude 1/Vpeak of the
magnetic Barkhausen signal and elastic stress σ. By applying a
frequency-dependent model [1] to determine the depth of origin of the
Barkhausen emissions in a uniformly stressed steel specimen, this
relationship was found t...
Projects
Projects (3)
The Barkhausen noise method is currently used for detecting the average stress magnitude within a volume of ferromagnetic material. The goal of this project is to develop a non-destructive stress detection method that can identify the depth of origin of Barkhausen emissions, thereby allowing the profiling of stress with depth. This can be accomplished through the Kypris-Jiles model, which models the Barkhausen noise in the frequency domain, assigning a unique spectral signature to emissions emanating from different depth ranges.
mi6sense is a research project that aims to revolutionize the way in which concrete structures are monitored. Smart sensors embedded within structures and foundations precisely measure relative changes in 3-D position and 3-D orientation (hence 6 degrees of freedom). The battery-free devices form a self-organizing mesh and communicate information about structural deformation from deep within the structure. The key technological leap is the use of low frequency magneto-inductive fields which are able to penetrate concrete, soil and rock with minimal loss. This is a patent pending technique developed at the University of Oxford and which has found use in underground animal monitoring and mine rescue.
