Propagation of Acoustic Waves Caused by the Accelerations of Vibrating Hand-Held Tools in Viscoelastic Soft Tissues of Human Hands and a Mechanobiological Picture for the Related Injuries

International audienc

Identification of material parameters of thin curvilinear viscoelastic solid layers in ships and ocean structures by sensing the bulk acoustic signals

Ships and other ocean structures have components, which are thin planar or curvilinear viscoelastic solid layers surrounded by air or water. The present work deals with the identification of material parameters of these layers to extend the scope of the real-time structural health monitoring. The work proposes the approach to the parameter identification from passive sensing of acoustic signals resulting from the operational load. The identification is based on the partial integro-differential equation (PIDE) for the non-equilibrium part of the average normal stress. The PIDE is derived in the work. It includes the Boltzmann superposition integral associated with the stress-relaxation function. It is shown that, in the exponential approximation for this function, the PIDE expresses the steady-state solution (with respect to a certain variable) of the corresponding third-order partial differential equation (PDE) of the Zener type. The operat- ors of both the equations are identical. The equations are applicable at all values of the stress-re- laxation time. The roots of the characteristic equation of this operator are consistently analyzed, and the acoustic attenuation coefficient for arbitrary high frequencies is indicated. The approach is exemplified with the identification of the layer-material stress-relaxation time and ratio of the bulk-wave speed to the layer thickness. This identification can be carried out from the acoustic acceleration normal to the layer measured by an acoustic accelerometer attached to the layer surface and is applicable to both planar and curvilinear layers. The identification method presumes the finite-difference calculation of the time derivatives of the measured acoustic acceleration up to the third order and can be computationally efficient

Ice detection for smart de-icing of wind turbines

Icing on a wind turbine rotor blade is a problem in the operation of wind turbines in cold climates. Ice detection is a critical process to get a workable cost-effective wind turbine de-icing system. The paper presents the theoretical models, methods, algorithms, principles, and a demonstrator that are the basis for developing a new technique for detecting icing on rotor blades of a wind turbine based on acoustic wave propagation in composite structures. Two methods have been proposed: guided acoustic wave propagation and bulk acoustic wave propagation in composite structures. Analysis of computer simulations and the results of experimental study obtained by using the developed demonstrator in cold climate lab has shown that the integration of the guided acoustic wave propagation and the bulk acoustic wave propagation methods provides an efficient scientific approach to be used for the design of new ice detection system for wind turbines in cold climate regions. In particular, the guided acoustic wave propagation method makes it possible to detect ice and icing area location on the rotor blades. Several criteria (Icing Index, Frequency Factor Index, others) have been proposed for ice detection of composite structures. Bulk acoustic wave propagation method makes it possible to identify the time-varying spatially heterogeneous “landscapes” over the blade surface for each of the following eight ice parameters: thickness, the volumetric bulk density, bulk and shear moduli, stress relaxation time, porosity, and volume and shear viscosities. These data are necessary for smart, energy-efficient de-icing systems. The identification algorithm is computationally efficient and can be implemented in the real-time mode.A LIDAR (Light Detection And Ranging) for the detection of early ice growth on the wind turbine blades has also designed, tested and evaluated in this project. LIDAR uses laser pulses that emit at two different wavelengths and is capable of distinguishing between a thin layer of ice and water covering the turbine blades. The results of the tests that have been carried out in the project are undeniable. LIDAR detects early ice growth by measuring the difference in reflectivity of a surface by using two different laser wavelengths. The limitation of LIDAR is that it cannot be used to determine the amount of ice on the sheet, only if there is ice or not.The obtained results can be used to develop smart de-icing systems for wind turbines operating in cold climates, and can lead to new future products that are sought after by wind power industry. Since the efficient ice detection systems can increase wind turbine profitability, the results contribute to an increased ability to establish multiple wind turbines in cold regions

Identification of Material Parameters of Thin Curvilinear Viscoelastic Solid Layers in Ships and Ocean Structures by Sensing the Bulk Acoustic Signals

Ships and other ocean structures have components, which are thin planar or curvilinear viscoelastic solid layers surrounded by air or water. The present work deals with the identification of material parameters of these layers to extend the scope of the real-time structural health monitoring. The work proposes the approach to the parameter identification from passive sensing of acoustic signals resulting from the operational load. The identification is based on the partial integro-differential equation (PIDE) for the non-equilibrium part of the average normal stress. The PIDE is derived in the work. It includes the Boltzmann superposition integral associated with the stress-relaxation function. It is shown that, in the exponential approximation for this function, the PIDE expresses the steady-state solution (with respect to a certain variable)of the corresponding third-order partial differential equation (PDE) of the Zener type. The operators of both the equations are identical. The equations are applicable at all values of the stress-re-laxation time. The roots of the characteristic equation of this operator are consistently analyzed, and the acoustic attenuation coefficient for arbitrary high frequencies is indicated. The approach is exemplified with the identification of the layer-material stress-relaxation time and ratio of the bulk-wave speed to the layer thickness. This identification can be carried out from the acoustic acceleration normal to the layer measured by an acoustic accelerometer attached to the layer surface and is applicable to both planar and curvilinear layers. The identification method presumes the finite-difference calculation of the time derivatives of themeasured acoustic acceleration up to the third order and can be computationally efficient

A Scalar Acoustic Equation for Gases, Liquids, and Solids, Including Viscoelastic Media

The work deals with a mathematical model for real-time acoustic monitoring of material parameters of media in multi-state viscoelastic engineering systems continuously operating in irregular external environments (e.g., wind turbines in cold climate areas, aircrafts, etc.). This monitoring is a high-reliability time-critical task. The work consistently derives a scalar wave PDE of the Stokestype for the non-equilibrium part (NEP) of the average normal stress in a medium. The explicit expression for the NEP of the corresponding pressure and the solution-adequateness condition are also obtained. The derived Stokes-type wave equation includes the stress relaxation time and is applicable to gases, liquids, and solids

Passive acoustic signal sensing approach to detection of ice on the rotor blades of wind turbines

In cold seasons, irregular layers of atmospheric ice (AI) are usually accreted on the rotor blades of wind turbines. These layers can cause unexpected downtimes and increase the maintenance cost reducing the efficiency. AI presents an unpredictable mixture of crystalline and amorphous ices including such forms as dense snow frozen to the surface, soft rime, hard rime, clear ice, and glaze. The parameters of the AI-layer e.g., the thickness, mass volumetric density, porosity, elastic moduli, viscosities, and stress relaxation time, vary significantly, from a half on order to a few orders, depending on the parameter and type of AI.To solve the icing problem for wind turbines, the ice-detection and de-icing systems are needed. The ice detection systems (IDSs) should not only detect the AI-layer on the blade skin but also provide the data allowing identification of the AI-layer parameters, which are sufficient for the cost-efficient de-icing. The identification method is, thus, in the focus of the IDS development, which deals with the following main features. (1) The operational load in a blade creates irregular space-time distributions of acoustic variable (e.g., strain, stress, and displacement) which depend on the acceleration, deceleration, and speed of rotation of the rotor, the blade-pitch angle, the wind, the presence of the AI layer on the skin, and other factors. The corresponding experimental data are well documented. (2) The blade skin is a layer of a complex, curvilinear shape, which, in the course of the turbine operation, varies in space and time. This feature is also well documented. (3) The AI stress-relaxation time can be in an interval of a few orders. (4) The AI-layer parameters should be identified by means of an appropriate acoustic model from the data of the sensors, which are located on the inner surface of the blade skin and wirelessly controlled in the real-time mode by a computer and gateways. The present work develops an acoustic model and method for identification of four of the AI-layer parameters: the thickness, mass density, bulk-wave speed, and stress-relaxation time. Due to Point (1), the identification method presumes passive rather than active sensing. The method is based on measurements of the acoustic accelerations at different points on the inner surface of the skin. The challenge in Point (2) is met by the generalizing the thin-planar-disk approximation from a single solid layer to the system of the blade-skin/AI layers. The proposed identification method is computationally efficient and suitable for the use indicated in Point (4). It extends the scope of the structural health monitoring techniques

Passive acoustic signal sensing approach to detection of ice on the rotor blades of wind turbines

In cold seasons, irregular layers of atmospheric ice (AI) are usually accreted on the rotor blades of wind turbines. These layers can cause unexpected downtimes and increase the maintenance cost reducing the efficiency. AI presents an unpredictable mixture of crystalline and amorphous ices including such forms as dense snow frozen to the surface, soft rime, hard rime, clear ice, and glaze. The parameters of the AI-layer e.g., the thickness, mass volumetric density, porosity, elastic moduli, viscosities, and stress relaxation time, vary significantly, from a half on order to a few orders, depending on the parameter and type of AI.To solve the icing problem for wind turbines, the ice-detection and de-icing systems are needed. The ice detection systems (IDSs) should not only detect the AI-layer on the blade skin but also provide the data allowing identification of the AI-layer parameters, which are sufficient for the cost-efficient de-icing. The identification method is, thus, in the focus of the IDS development, which deals with the following main features. (1) The operational load in a blade creates irregular space-time distributions of acoustic variable (e.g., strain, stress, and displacement) which depend on the acceleration, deceleration, and speed of rotation of the rotor, the blade-pitch angle, the wind, the presence of the AI layer on the skin, and other factors. The corresponding experimental data are well documented. (2) The blade skin is a layer of a complex, curvilinear shape, which, in the course of the turbine operation, varies in space and time. This feature is also well documented. (3) The AI stress-relaxation time can be in an interval of a few orders. (4) The AI-layer parameters should be identified by means of an appropriate acoustic model from the data of the sensors, which are located on the inner surface of the blade skin and wirelessly controlled in the real-time mode by a computer and gateways. The present work develops an acoustic model and method for identification of four of the AI-layer parameters: the thickness, mass density, bulk-wave speed, and stress-relaxation time. Due to Point (1), the identification method presumes passive rather than active sensing. The method is based on measurements of the acoustic accelerations at different points on the inner surface of the skin. The challenge in Point (2) is met by the generalizing the thin-planar-disk approximation from a single solid layer to the system of the blade-skin/AI layers. The proposed identification method is computationally efficient and suitable for the use indicated in Point (4). It extends the scope of the structural health monitoring techniques

What Stochastic Mechanics is Relevant to the Study of Living Systems?

Biologists have identified many features of living systems, which cannot be studied by the application of fundamental statistical mechanics (FSM). The present work focuses on some of these features. By discussing all of the basic approaches of FSM, the work formulates the extension of the kinetic-theory paradigm (based on the reduced one-particle distribution function) that possesses all of the considered properties of the living-systems. This extension appears to be a model within the generalised kinetic theory developed by N. Bellomo and his co-authors. In connection with this model, the work also stresses some other features necessary for making the model relevant to living systems. An example is discussed, which is a generalised kinetic equation coupled with the probability-density equation representing the varying component content of a living system. The work also suggests directions for future research