Local Heat Transfer Analysis in a Single Microchannel with Boiling DI-Water and Correlations with Impedance Local Sensors

Determination of local heat transfer coefficient at the interface of channel wall and fluid was the main goal of this experimental study in microchannel flow boiling domain. Flow boiling heat transfer to DI-water in a single microchannel with a rectangular cross section was experimentally investigated. The rectangular cross section dimensions of the experimented microchannel were 1050 μm × 500 μm and 1500 μm × 500 μm. Experiments under conditions of boiling were performed in a test setup, which allows the optical and local impedance measurements of the fluids by mass fluxes of 22.1 kg⋅m$^{-2}$⋅s$^{-1}$ to 118.8 kg⋅m$^{-2}$⋅s$^{-1}$ and heat fluxes in the range of 14.7 kW⋅m$^{-2}$ to 116.54 kW⋅m$^{-2}$. The effect of the mass flux, heat flux, and flow pattern on flow boiling local heat transfer coefficient and pressure drop were investigated. Experimental data compared to existing correlations indicated no single correlation of good predictive value. This was concluded to be the case due to the instability of flow conditions on one hand and the variation of the flow regimes over the experimental conditions on the other hand. The results from the local impedance measurements in correlation to the optical measurements shows the flow regime variation at the experimental conditions. From these measurements, useful parameters for use in models on boiling like the 3-zone model were shown. It was shown that the sensing method can shed a precise light on unknown features locally in slug flow such as residence time of each phases, bubble frequency, and duty cycle

A quartz crystal biosensor for measurement in liquids

The detection of anti-human immunodeficiency virus (HIV) antibodies by means of synthetic HIV peptide immobilized on a piezoelectric quartz sensor is demonstrated. The measurement set-up consists of an oscillator circuit, a suitably modified AT-cut thickness-shear-mode quartz crystal with gold electrodes, which is housed in a special reaction vessel, and a computer-controlled frequency counter for the registration of the measured frequency values. The quartz crystal is adapted for a steady operation in liquids at a frequency of 20 MHz. In phosphate-buffered saline solution the oscillator reaches a stability of about 0.5 Hz within a few seconds, of about 2 Hz within 10 min and about 30 Hz within 1 h. The frequency shift due to the adsorption of various proteins to the uncoated sensor surface has been investigated. It can be shown that a stable adsorptive binding of proteins to an oscillating gold surface is feasible and can be used for the immobilization of a receptor layer (e.g. HIV peptide). Specific binding of the anti-HIV monoclonal antibody to the HIV peptide immobilized on the quartz sensor is demonstrated. Control experiments show, however, additional unspecific binding. According to the experiments, the Sauerbrey formula gives a sufficiently accurate value for the decrease of the resonant frequency due to adsorption or binding of macromolecular proteins on the quartz crystal surface

A Comparison of Machine Learning Classifiers for Energy-Efficient Implementation of Seizure Detection

The closed-loop application of electrical stimulation via chronically implanted electrodes is a novel approach to stop seizures in patients with focal-onset epilepsy. To this end, an energy efficient seizure detector that can be implemented in an implantable device is of crucial importance. In this study, we first evaluated the performance of two machine learning algorithms (Random Forest classifier and support vector machine (SVM)) by using selected time and frequency domain features with a limited need of computational resources. Performance of the algorithms was further compared to a detection strategy implemented in an existing closed loop neurostimulation device for the treatment of epilepsy. The results show a superior performance of the Random Forest classifier compared to the SVM classifier and the reference approach. Next, we implemented the feature extraction and classification process of the Random Forest classifier on a microcontroller to evaluate the energy efficiency of this seizure detector. In conclusion, the feature set in combination with Random Forest classifier is an energy efficient hardware implementation that shows an improvement of detection sensitivity and specificity compared to the presently available closed-loop intervention in epilepsy while preserving a low detection delay

Understanding Inconsistencies in Thermohydraulic Characteristics Between Experimental and Numerical Data for Di Water Flow Through a Rectangular Microchannel

Facing discrepancies between numerical simulation, experimental measurement and theory is common in studies of fluid flow and heat transfer in microchannels. The cause of these discrepancies is often linked to the transition from the macro-scale to the micro-scale, where the flow dynamics might be expected to deviate due to possible change in dominant forces. In this work, an attempt is made to achieve agreement between experiment, numerical simulation and theoretical description within the usual framework of laminar flow theory. For this purpose, the pressure drop, friction factor, and Poiseuille number under isothermal conditions and the temperature profile, heat transfer coefficient, Nusselt number, and thermal performance index under diabatic conditions (heating power of 10 W) in a heat sink with a stainless steel microchannel with a hydraulic diameter of 850 µm were investigated numerically and experimentally for mass flow rates between 1 and 68 g/min. The source of inconsistencies in pressure drop characteristics is found to be linked to the geometrical details of the utilized microchannel, e.g. the design of inlet/outlet manifolds, the artefacts of manufacturing technique and other features of the experimental test rig. For the heat transfer characteristics, it is identified, that an appropriate estimation of the outer boundary condition for the numerical simulation remains the crucial challenge to obtain a reasonable agreement. The manuscript presents a detailed overview on how to consider these details to mitigate the discrepancies and to establish a handshake between experiments, numerical simulations and theory

A Comparison of Energy-Efficient Seizure Detectors for Implantable Neurostimulation Devices

INTRODUCTION: About 30% of epilepsy patients are resistant to treatment with antiepileptic drugs, and only a minority of these are surgical candidates. A recent therapeutic approach is the application of electrical stimulation in the early phases of a seizure to interrupt its spread across the brain. To accomplish this, energy-efficient seizure detectors are required that are able to detect a seizure in its early stages. METHODS: Three patient-specific, energy-efficient seizure detectors are proposed in this study: (i) random forest (RF); (ii) long short-term memory (LSTM) recurrent neural network (RNN); and (iii) convolutional neural network (CNN). Performance evaluation was based on EEG data (n = 40 patients) derived from a selected set of surface EEG electrodes, which mimic the electrode layout of an implantable neurostimulation system. As for the RF input, 16 features in the time- and frequency-domains were selected. Raw EEG data were used for both CNN and RNN. Energy consumption was estimated by a platform-independent model based on the number of arithmetic operations (AOs) and memory accesses (MAs). To validate the estimated energy consumption, the RNN classifier was implemented on an ultra-low-power microcontroller. RESULTS: The RNN seizure detector achieved a slightly better level of performance, with a median area under the precision-recall curve score of 0.49, compared to 0.47 for CNN and 0.46 for RF. In terms of energy consumption, RF was the most efficient algorithm, with a total of 67k AOs and 67k MAs per classification. This was followed by CNN (488k AOs and 963k MAs) and RNN (772k AOs and 978k MAs), whereby MAs contributed more to total energy consumption. Measurements derived from the hardware implementation of the RNN algorithm demonstrated a significant correlation between estimations and actual measurements. DISCUSSION: All three proposed seizure detection algorithms were shown to be suitable for application in implantable devices. The applied methodology for a platform-independent energy estimation was proven to be accurate by way of hardware implementation of the RNN algorithm. These findings show that seizure detection can be achieved using just a few channels with limited spatial distribution. The methodology proposed in this study can therefore be applied when designing new models for responsive neurostimulation

Energy Income Estimation for Solar Cell Powered Wireless Sensor Nodes

Solar cells are one common choice to power energy-autonomous wireless sensor nodes (WSNs). There are different approaches to improve their service and reliability via solar energy prediction algorithms, to allow the WSN to “know” about the future energy income All these algorithms require information on the energy income of the sensor node obtained e.g., via separate measurements of light intensity or via monitoring the current flow to the WSNs energy storage. Here, we present a method to determine the energy income via temporarily switching the PV cell’s input current to a capacitor for determining the energy income by the accumulated charge. The data are compared to measurements with a pyranometer. This system provides advantages with respect to the consideration of the multiple different losses in the WSNs power management