The boundary layer equations for plane, incompressible, and steady flow are 

$$\matrix{ {u{{\partial u} \over {\partial x}} + v{{\partial u} \over {\partial y}} = - {1 \over \varrho }{{\partial p} \over {\partial x}} + v{{{\partial ^2}u} \over {\partial {y^2}}},} \cr {0 = {{\partial p} \over {\partial y}},} \cr {{{\partial u} \over {\partial x}} + {{\partial v} \over {\partial y}} = 0.} \cr }$$

Boundary Layer Theory

Sensor networks with battery-powered nodes can seldom simultaneously meet the design goals of lifetime, cost, sensing reliability and sensing and transmission coverage. Energy-harvesting, converting ambient energy to electrical energy, has emerged as an alternative to power sensor nodes. By exploiting recharge opportunities and tuning performance parameters based on current and expected energy levels, energy harvesting sensor nodes have the potential to address the conflicting design goals of lifetime and performance. This paper surveys various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications. The study also discusses the implications of recharge opportunities on sensor node operation and design of sensor network solutions.

Energy Harvesting Sensor Nodes: Survey and Implications

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Energy harvesting generators are attractive as inexhaustible replacements for batteries in low-power wireless electronic devices and have received increasing research interest in recent years. Ambient motion is one of the main sources of energy for harvesting, and a wide range of motion-powered energy harvesters have been proposed or demonstrated, particularly at the microscale. This paper reviews the principles and state-of-art in motion-driven miniature energy harvesters and discusses trends, suitable applications, and possible future developments.

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Energy Harvesting From Human and Machine Motion for Wireless Electronic Devices

Wireless power technology offers the promise of cutting the last cord, allowing users to seamlessly recharge mobile devices as easily as data are transmitted through the air. Initial work on the use of magnetically coupled resonators for this purpose has shown promising results. We present new analysis that yields critical insight into the design of practical systems, including the introduction of key figures of merit that can be used to compare systems with vastly different geometries and operating conditions. A circuit model is presented along with a derivation of key system concepts, such as frequency splitting, the maximum operating distance (critical coupling), and the behavior of the system as it becomes undercoupled. This theoretical model is validated against measured data and shows an excellent average coefficient of determination of 0.9875. An adaptive frequency tuning technique is demonstrated, which compensates for efficiency variations encountered when the transmitter-to-receiver distance and/or orientation are varied. The method demonstrated in this paper allows a fixed-load receiver to be moved to nearly any position and/or orientation within the range of the transmitter and still achieve a near-constant efficiency of over 70% for a range of 0-70 cm.

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Analysis, Experimental Results, and Range Adaptation of Magnetically Coupled Resonators for Wireless Power Transfer

Power quality (PQ) monitoring is an important issue to electric utilities and many industrial power customers This paper presents a DSP-based hardware monitoring system based on a recently proposed PQ classification algorithm The algorithm is implemented with a Texas Instruments (TI) TMS320VC5416 digital signal processor (DSP) with the TI THS1206 12-bit 6 MSPS analog to digital converter A TI TMS320VC5416 DSP starter kit (DSK) is used as the host board with the THS1206 mounted on a daughter card The implemented PQ classification algorithm is composed of two processes: feature extraction and classification The feature extraction projects a PQ signal onto a time-frequency representation (TFR), which is designed for maximizing the separability between classes The classifiers include a Heaviside-function linear classifier and neural networks with feedforward structures The algorithm is optimized according to the architecture of the DSP to meet the hard realtime constraints of classifying a 5-cycle segment of the 60 Hz sinusoidal voltage/current signals in power systems The classification output can be transmitted serially to an operator interface or control mechanism for logging and issue resolution

/pdf/real-time-power-quality-waveform-recognition-with-a-3hrj9gbwvm.pdf

Real-time power quality waveform recognition with a programmable digital signal processor

A real-time software algorithm for calculating dielectric permittivity and conductivity of nondispersive and frequency dispersive insulating materials on the basis of non-destructive measurements is presented. First, the measured response of the interdigital dielectrometry sensor is simulated off-line with two-dimensional finite-element software over the entire range of input parameters. Then, experimental data points are interpolated and material properties are determined without iterative on-line calculations. Offline simulation lends valuable insights into sensitivity of the measurement and the uniqueness of the solution to the inverse problem of material characterization.

/pdf/parameter-estimation-using-an-interdigital-dielectrometry-zhg1xoydbl.pdf

Parameter estimation using an interdigital dielectrometry sensor with finite-element software

Sensitive skin is a highly desired device for biomechanical devices, wearable computing, human-computer interfaces, exoskeletons, and, most pertinent to this paper, for lower limb prosthetics. The measurement of shear stress is very important because shear effects are key factors in developing surface abrasions and pressure sores in paraplegics and users of prosthetic/orthotic devices. A single element of a sensitive skin is simulated and characterized in this paper. Conventional tactile sensors are designed for measurement of the normal stress only, which is inadequate for comprehensive assessment of surface contact conditions. The sensitive skin discussed here is a flexible array capable of sensing shear and normal forces, as well as humidity and temperature on each element.

Simulation of a sensor array for multiparameter measurements at the prosthetic limb interface

Fringing electric field (FEF) sensors are widely used for non-invasive measurement of material properties, such as porosity, viscosity, temperature, hardness, and degree of cure. FEF sensors have also been used to detect the presence of a material or estimate the concentration of a material within the test environment. There are no generic analytical models for FEF sensors. Their design optimization process often involves complex and time-consuming finite element simulations. This paper presents a tool for improvement of the design process through formulating a universal equation for three-electrode concentric FEF sensors. The equation models the effect of sensor geometry and substrate material on sensor output. The model parameters are determined from a 3-D surface fit of finite element simulation results for the most common type of sensor geometry. The variables in the model are non-dimensionalized, which makes the model applicable to a wider range of sensor designs. Based on the model, the terminal capacitance can be estimated for three-electrode concentric sensors of wide range of sizes.

Parametric modeling of concentric fringing electric field sensors

Currently used methods for estimation of moisture content in paper pulp are restricted to levels of moisture concentration under 90%, and also assume that there are no additives in the pulp. This paper presents a technique that uses fringing field interdigital sensors to measure moisture concentration in paper pulp in the presence of calcium carbonate. The method proposed in this paper uses single-sided measurements, offers high sensitivity, and does not require special operating conditions. A self-adapting algorithm used for data extraction is also introduced. The accuracy of the proposed method is also demonstrated.

Alexander Mamishev

Papers

Real-time power quality waveform recognition with a programmable digital signal processor

Parameter estimation using an interdigital dielectrometry sensor with finite-element software

Simulation of a sensor array for multiparameter measurements at the prosthetic limb interface

Parametric modeling of concentric fringing electric field sensors

Estimation of Moisture Content in Paper Pulp Containing Calcium Carbonate Using Fringing Field Impedance Spectroscopy