Wind-assisted, electric, and pure wind propulsion - the path towards zero-emission RoRo ships

Electrical and wind propulsion, together with energy stored in batteries and renewable energies harnessed onboard, can lead the way towards zero-emission ships. This study compares wind propulsion solutions and battery storage possibilities for a RoRo ship operating in the Baltic Sea. The ship energy systems simulation model ShipCLEAN is used to predict the performance of the zero-emission ship in real-life operating conditions. The study showcases how ships can be transferred from a conventional, diesel-powered to a zero-emission ship. For the zero-emission ship, all energy needed for auxiliaries and propulsion is taken from renewable sources onboard or from batteries. Challenges and opportunities, as well as necessary adaptions of the route and logistics, are discussed. Results of the study present which wind propulsion technology is the most suitable for the example RoRo ship, and how the installation of suitably sized battery packs for zero-emission operation affects the cargo capacity of the ship

Retrofitting WASP to a RoPax vessel—design, performance and uncertainties

Wind-assisted propulsion (WASP) is one of the most promising ship propulsion alternatives\ua0that radically reduce greenhouse gas emissions and are available today. Using the example of a\ua0RoPax ferry, this study presents the performance potential of WASP systems under realistic weather\ua0conditions. Different design alternatives and system layouts are discussed. Further, uncertainties in\ua0the performance prediction ofWASP systems are analyzed. Included in the analysis are the sail forces\ua0as well as the aero- and hydrodynamic interaction effects, i.e., the sail–sail and sail–deck interaction as\ua0well as the drift and yaw of the ship. As a result, this study provides guidelines on the most important\ua0parameters when designing and modeling aWASP ship. Finally, the study presents an analysis of the\ua0expected accuracy of the employed empirical/analytical performance prediction model ShipCLEAN

Development of a ship performance model for power estimation of inland waterway vessels

A ship performance model is an important factor in energy-efficient navigation. It formulates a speed–power relationship that can be used to adjust the engine loads for dynamic energy optimisation. However, currently available models have been developed for sea-going vessels, where the environmental conditions are significantly different from those experienced on inland waterways. Inland waterway shipping has great potential to become a mode of transport that can both improve safety and reduce emissions. Therefore, this paper presents the development of an energy performance model specifically for inland waterway vessels (IWVs). The holistic ship energy system model is based on empirical methods, from resistance to engine performance prediction, established in a modular code architecture. The resistance and propulsion prediction in confined waterways are captured by a newly developed method, considering a superposing of shallow water and bank effect. Verification against model tests and high-fidelity simulations indicate that the selected empirical methods achieved good accuracy for predicting ship performance. The resistance prediction error was 5.2% for single vessels and 8% for pusher-barge convoys based on empirical methods. The results of a case study investigating the performance of a self-propelled vessel under dynamic waterway data, indicate that the developed model could be used for onboard power monitoring and energy optimisation during operation

Propulsive performance of a rigid wingsail with crescent-shaped profiles

Wind-assisted ship propulsion is considered an effective method for reducing greenhouse gas emissions. This paper presents numerical analyses of the aerodynamics of a single rigid wingsail conducted using the unsteady Reynolds-averaged Navier–Stokes (uRANS) equations. The wingsail is designed with a new sectional profile: a crescent-shaped foil. This new profile and the classical NACA 0015 profile were compared. Simulations were performed in two and three dimensions, with a focus on key physical quantities such as the external loads on the wingsail, the flow field, and the propulsive performance. It is concluded that the wingsail with the crescent-shaped section has higher propulsion efficiency than the NACA 0015. However, stronger flow separation was detected for the crescent-shaped section. As the separation deteriorates, the flow unsteadiness, challenges the strength and stability of the wingsail structure. The three-dimensional simulations of both profiles, particularly NACA 0015, show that the tip vortices induced from the side edge of the wingsail account for substantial negative effects on the propulsion performance. A case study revealed that installing a wingsail with a crescent-shaped profile reduced fuel consumption by 9% compared with no wingsail

Fehlertoleranz bei Prozessabläufen: Mit Anwendungen bei akustischen Unterwassernetzwerken

Wie koordinieren sich Robotorteams, wenn die Absprache im Medium stark gestört ist? Dieser Beitrag zur Kooperation autonomer Teams unter Wasser ist als Gemeinschaftsarbeit der Verfasser aus einem Bachelorpraktikum hervorgegangen und untersucht aus der Sicht der angehenden Wirtschaftsinformatiker die notwendigen Nachrichteninhalte und minimalsten Prozessabläufe. Das Ziel sind fehlertolerante und damit verlässliche Netzwerke. Doch wie können Prozesse hierfür entwickelt und validiert werden? Wann ist das mobile Ad-hoc-Netzwerk verlässlich? Die Basisfähigkeiten hierzu werden mittels S-BPM (Subject-orientated Business Process Managment) modelliert und erste Kooperationen abgebildet sowie auf Fehler hin untersucht

High power, single-frequency, monolithic fiber amplifier for the next generation of gravitational wave detectors

Low noise, high power single-frequency lasers and amplifiers are key components of interferometric gravitational wave detectors. One way to increase the detector sensitivity is to increase the power injected into the interferometers. We developed a fiber amplifier engineering prototype with a pump power limited output power of 200 W at 1064 nm. No signs of stimulated Brillouin scattering are observed at 200 W. At the maximum output power the polarization extinction ratio is above 19 dB and the fractional power in the fundamental transverse mode (TEM00) was measured to be 94.8 %. In addition, measurements of the frequency noise, relative power noise, and relative pointing noise were performed and demonstrate excellent low noise properties over the entire output power slope. In the context of single-frequency fiber amplifiers, the measured relative pointing noise below 100 Hz and the higher order mode content is, to the best of our knowledge, at 200 W the lowest ever measured. A long-term test of more than 695 h demonstrated stable operation without beam quality degradation. It is also the longest single-frequency fiber amplifier operation at 200 W ever reported. © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT

Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning~(DL)-based methods were introduced, outperforming conventional denoising algorithms on this task due to their high model capacity. However, for the transition of DL-based denoising to clinical practice, these data-driven approaches must generalize robustly beyond the seen training data. We, therefore, propose a hybrid denoising approach consisting of a set of trainable joint bilateral filters (JBFs) combined with a convolutional DL-based denoising network to predict the guidance image. Our proposed denoising pipeline combines the high model capacity enabled by DL-based feature extraction with the reliability of the conventional JBF. The pipeline's ability to generalize is demonstrated by training on abdomen CT scans without metal implants and testing on abdomen scans with metal implants as well as on head CT data. When embedding two well-established DL-based denoisers (RED-CNN/QAE) in our pipeline, the denoising performance is improved by $10\,\%$/$82\,\%$ (RMSE) and $3\,\%$/$81\,\%$ (PSNR) in regions containing metal and by $6\,\%$/$78\,\%$ (RMSE) and $2\,\%$/$4\,\%$ (PSNR) on head CT data, compared to the respective vanilla model. Concluding, the proposed trainable JBFs limit the error bound of deep neural networks to facilitate the applicability of DL-based denoisers in low-dose CT pipelines

A gradient-based approach to fast and accurate head motion compensation in cone-beam CT

Cone-beam computed tomography (CBCT) systems, with their portability, present a promising avenue for direct point-of-care medical imaging, particularly in critical scenarios such as acute stroke assessment. However, the integration of CBCT into clinical workflows faces challenges, primarily linked to long scan duration resulting in patient motion during scanning and leading to image quality degradation in the reconstructed volumes. This paper introduces a novel approach to CBCT motion estimation using a gradient-based optimization algorithm, which leverages generalized derivatives of the backprojection operator for cone-beam CT geometries. Building on that, a fully differentiable target function is formulated which grades the quality of the current motion estimate in reconstruction space. We drastically accelerate motion estimation yielding a 19-fold speed-up compared to existing methods. Additionally, we investigate the architecture of networks used for quality metric regression and propose predicting voxel-wise quality maps, favoring autoencoder-like architectures over contracting ones. This modification improves gradient flow, leading to more accurate motion estimation. The presented method is evaluated through realistic experiments on head anatomy. It achieves a reduction in reprojection error from an initial average of 3mm to 0.61mm after motion compensation and consistently demonstrates superior performance compared to existing approaches. The analytic Jacobian for the backprojection operation, which is at the core of the proposed method, is made publicly available. In summary, this paper contributes to the advancement of CBCT integration into clinical workflows by proposing a robust motion estimation approach that enhances efficiency and accuracy, addressing critical challenges in time-sensitive scenarios.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Calibration by differentiation – Self‐supervised calibration for X‐ray microscopy using a differentiable cone‐beam reconstruction operator

High‐resolution X‐ray microscopy (XRM) is gaining interest for biological investigations of extremely small‐scale structures. XRM imaging of bones in living mice could provide new insights into the emergence and treatment of osteoporosis by observing osteocyte lacunae, which are holes in the bone of few micrometres in size. Imaging living animals at that resolution, however, is extremely challenging and requires very sophisticated data processing converting the raw XRM detector output into reconstructed images. This paper presents an open‐source, differentiable reconstruction pipeline for XRM data which analytically computes the final image from the raw measurements. In contrast to most proprietary reconstruction software, it offers the user full control over each processing step and, additionally, makes the entire pipeline deep learning compatible by ensuring differentiability. This allows fitting trainable modules both before and after the actual reconstruction step in a purely data‐driven way using the gradient‐based optimizers of common deep learning frameworks. The value of such differentiability is demonstrated by calibrating the parameters of a simple cupping correction module operating on the raw projection images using only a self‐supervisory quality metric based on the reconstructed volume and no further calibration measurements. The retrospective calibration directly improves image quality as it avoids cupping artefacts and decreases the difference in grey values between outer and inner bone by 68–94%. Furthermore, it makes the reconstruction process entirely independent of the XRM manufacturer and paves the way to explore modern deep learning reconstruction methods for arbitrary XRM and, potentially, other flat‐panel computed tomography systems. This exemplifies how differentiable reconstruction can be leveraged in the context of XRM and, hence, is an important step towards the goal of reducing the resolution limit of in vivo bone imaging to the single micrometre domain