Halfspace Matching: a Domain Decomposition Method for Scattering by 2D Open Waveguides

We study a scattering problem for the Helmholtz equation in 2D, which involves non-parallel open waveguides, by means of the halfspace matching method. This method has formerly been applied to periodic media and homogeneous anisotropic media, and we extend it to open waveguides. It allows the reformulation of the Helmholtz equation in an exterior domain to a set of equations for particular traces of the solution, reducing the overall dimension of the problem by 1, making it accessible for numerical discretisation. We show the well-posedness of the halfspace matching method for a model problem in the exterior of a triangular domain, assuming the presence of absorption. Furthermore, we introduce a numerical discretisation which allows the realisation of transparent boundary conditions by a system of coupled integral equations. To illustrate the practicality of this method, we study a number of optimisation examples involving junctions of open waveguides by means of material optimisation

Requirements for Sensor Integrating Machine Elements : A Review of Wear and Vibration Characteristics of Gears

For condition monitoring of machines sensor integrating standard machine elements provide advantage in acquiring high-quality, robust data from individual machine elements and reducing effort in signal processing. However, research covering small and inexpensive consumer-grade MEMS sensors with respect to integration and measurement requirements for wear detection is limited. In order to define such requirements, the state of the art of vibration-based condition monitoring of gears is reviewed and summarised. The focus is on the characteristics of progressive wear and how it might show in the vibration signal. The review finds that correlation between wear and vibration characteristics of gears exist, but the interpretation of the vibration signals is challenging and requires purpose-built signal processing methods. The review also concludes that integrated MEMS acceleration sensors are theoretically able to measure the vibration characteristics of gears to detect wear. Important characteristics are the gear mesh acceleration with its frequencies and harmonic multiples (GMFi). Frequency range requirements for the sensors depend on the operating conditions of gears, the upper frequency limit needs to be greater or equal to 1.3 GMF$_{i,max}$. For the measuring range requirements, upper limits of 20 g RMS can be extracted within certain conditions. Data analysis requires a minimum frequency resolution which affects the size of memory needed for an integrated sensor system. However, there is a lack of research whether the sensitivity and internal noise behaviour of available MEMS sensors is good enough to measure relative changes in the vibration signals caused by wear

Design of sensor integrating gears: methodical development, integration and verification of an in-Situ MEMS sensor system

State of the art vibration-based condition monitoring at gearbox housings faces uncertainties in the interpretation of measurement data due to signal transformations and noise. The state of research shows that direct measurements at the source of vibrations with integrated sensors provide higher quality data. Capacitive MEMS sensors seem predestined for integration, but there is limited research covering compactly integrated MEMS sensor systems for condition monitoring by vibration measurement. In this contribution an integrated MEMS sensor system is designed methodically based on VDI 2206. A sensor system is selected based on requirements extracted of previous contributions and verified on a rotational shaker test rig. Afterwards it is integrated on a gear wheel in a gear test bench. Several verification measurements using different principles and locations are performed to verify the measurands. Results show that the gear mesh vibrations including the sidebands can be measured with the integrated sensors which provide superior signal-noise-ratios compared to other locations. This proofs that the sensor integrating gear system is principally able to perform high quality condition monitoring

Sensor-integrating gears: wear detection by in-situ MEMS acceleration sensors [Sensorintegrierende Zahnräder: Verschleißdetektion durch In-situ MEMS Beschleunigungssensoren]

Gear tooth wear is a common phenomenon leading to malfunctions in machines. To detect wear and faults, gear condition monitoring by vibration is established. The problem is that the measurement data quality for detection of wear by vibration is not good enough with currently established measurement methods, caused by long signal paths of the commonly used housing mounted sensors. In-situ sensors directly at the gear achieve better data quality, but are not yet proved in wear detection. Further it is unknown what analysis methods are suited for in-situ sensor data. Existing gear condition metrics are mainly focused on localized gear tooth faults, and do not estimate wear related values. This contribution aims to improve wear detection by investigating in-situ sensors and advance gear condition metrics. Using a gear test rig to conduct an end of life test, the wear detection ability of an in-situ sensor system and reference sensors on the bearing block are compared through standard gear condition metrics. Furthermore, a machine-learned regression model is developed that maps multiple features related to gear dynamics to the gear mass loss. The standard gear metrics used on the in-situ sensor data are able to detect wear, but not significantly better compared to the other sensors. The regression model is able to estimate the actual wear with a high accuracy. Providing a wear related output improves the wear detection by better interpretability

18F-labelled triazolyl-linked argininamides targeting the neuropeptide Y Y1R for PET Imaging of mammary carcinoma

NeuropeptideYY(1) receptors (Y1R) have been found to be overexpressed in a number of different tumours, such as breast, ovarian or renal cell cancer. In mammary carcinoma the highY(1)R density together with its high incidence of 85% in primary human breast cancers and 100% in breast cancer derived lymph node metastases attracted special attention. Therefore, the aim of this study was the development of radioligandsforY(1)R imaging by positron emission tomography (PET) with a special emphasis on imaging agents with reduced lipophilicity to provide a PET ligand with improved biodistribution in comparison with previously published tracers targeting theY(1)R. Three new radioligands based on BIBP3226, bearing an F-18-fluoroethoxy linker (12), an F-18-PEG-linker (13) or an F-18-fluoroglycosyl moiety (11) were radiosynthesised in high radioactivity yields. The new radioligands displayedY(1)R affinities of 2.8 nM (12), 29 nM (13) and 208 nM (11) and were characterised in vitro regarding binding to human breast cancer MCF-7-Y1 cells and slices of tumour xenografts. In vivo, small animal PET studies were conducted in nude mice bearing MCF-7-Y1 tumours. The binding to tumours, solid tumour slices and tumour cells correlated well with theY(1)R affinities. Although 12 and 13 showed displaceable and specific binding toY(1)R in vitro and in vivo, the radioligands still need to be optimised to achieve higher tumour-to-background ratios forY(1)R imaging by PET.Yet the present study is another step towards an optimized PET radioligand for imaging ofY(1)R in vivo