An experimental and computational study of tip clearance effects on a transonic turbine stage

This paper describes an experimental and computational investigation into the influence of tip clearance on the blade tip heat load of a high-pressure (HP) turbine stage. Experiments were performed in the Oxford Rotor facility which is a 1½ stage, shroudless, transonic, high pressure turbine. The experiments were conducted at an engine representative Mach number and Reynolds number. Rotating frame instrumentation was used to capture both aerodynamic and heat flux data within the rotor blade row. Two rotor blade tip clearances were tested (1.5% and 1.0% of blade span). The experiments were compared with computational fluid dynamics (CFD) predictions made using a steady Reynolds-averaged Navier–Stokes (RANS) solver. The experiments and computational predictions were in good agreement. The blade tip heat transfer was observed to increase with reduced tip gap in both the CFD and the experiment. The augmentation of tip heat load at smaller clearances was found to be due to the ingestion of high relative total temperature fluid near the casing, generated through casing shear.This work was sponsored by Rolls-Royce plc and the Isle of Man Government.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.ijheatfluidflow.2015.09.00

Korg: fitting, model atmosphere interpolation, and Brackett lines

We describe several updates to Korg, a package for 1D LTE spectral synthesis of FGKM stars. Built-in functions to fit observed spectra via synthesis or equivalent widths make it easy to take advantage of Korg's automatic differentiation. Comparison to a past analysis of 18 Sco shows that we obtain significantly reduced line-to-line abundance scatter with Korg. Fitting and synthesis are facilitated by a rigorously-tested model atmosphere interpolation method, which introduces negligible error to synthesized spectra for stars with $T_\mathrm{eff} \gtrsim 4000\,\mathrm{K}$. For cooler stars, atmosphere interpolation is complicated by the presence of molecules, though we demonstrate an adequate method for cool dwarfs. The chemical equilibrium solver has been extended to include polyatomic and charged molecules, extending Korg's regime of applicability to M stars. We also discuss a common oversight regarding the synthesis of hydrogen lines in the infrared, and show that Korg's Brackett line profiles are a much closer match to observations than others available. Documentation, installation instructions, and tutorials are available at https://github.com/ajwheeler/Korg.jl.Comment: Submitted to AJ, comments welcom

TephraNet : wireless self-organizing network platform for environmental sensing

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, September 2001."August 2001."Includes bibliographical references (leaves 126-127).The growing number of threats to the Earth's environment necessitates the gathering of fine grained environmental sensor data to deepen our understanding of endangered species and assist in their protection. Traditional techniques for gathering environmental data, such as periodic field studies or satellite imaging, do not produce adequately detailed or persistent information. Self-organizing wireless networking provides an ideal way to quickly deploy and gather data from sensor networks in the field. The design of a practical, low-cost, and self organizing wireless sensor network, TephraNet, is examined. This thesis also explores an implementation and deployment of TephraNet in Hawaii Volcanoes National Park to learn about the endangered ground plant Silene hawaiiensis.by Andrew J. Wheeler.M.Eng

Geospatial dimensions of the renewable energy transition — The importance of prioritisation

The renewable energy transition is a priority for many researchers, policy makers, and political leaders because it is projected to stop the dependence of economic growth on increasing fossil fuel use and thus curtail climate change. This study examines how expert judgments affect development decisions to enable the renewable energy transition. Geospatial Multi-Criteria Decision Analyses (MCDA) are frequently used to select offshore wind energy (OWE) sites, however, they are often weak and/or often rely on limited judgement. The Analytical Hierarchy Process is used here with 25 diverse experts to assess the variability in priorities for OWE siting criteria. A geospatial MCDA is implemented using experts' individual priorities, aggregated weights and Monte Carlo simulations. Case study results reveal large variations in expert opinions and bias strongly affecting MCDAs weighted by single decision-makers. A group-decision approach is proposed to strengthen consent for OWE, underpinning the renewable energy transition

High-resolution 3D mapping of cold-water coral reefs using machine learning

Structure-from-Motion (SfM) photogrammetry is a time and cost-effective method for high-resolution 3D mapping of cold-water corals (CWC) reefs and deep-water environments. The accurate classification and analysis of marine habitats in 3D provide valuable information for the development of management strategies for large areas at various spatial and temporal scales. Given the amount of data derived from SfM data sources such as Remotely-Operated Vehicles (ROV), there is an increasing need to advance towards automatic and semiautomatic classification approaches. However, the lack of training data, benchmark datasets for CWC environments and processing resources are a bottleneck for the development of classification frameworks. In this study, machine learning (ML) methods and SfM-derived 3D data were combined to develop a novel multiclass classification workflow for CWC reefs in deep-water environments. The Piddington Mound area, southwest of Ireland, was selected for 3D reconstruction from high-definition video data acquired with an ROV. Six ML algorithms, namely: Support Vector Machines, Random Forests, Gradient Boosting Trees, k-Nearest Neighbours, Logistic Regression and Multilayer Perceptron, were trained in two datasets of different sizes (1,000 samples and 10,000 samples) in order to evaluate accuracy variation between approaches in relation to the number of samples. The Piddington Mound was classified into four classes: live coral framework, dead coral framework, coral rubble and sediment and dropstones. Parameter optimisation was performed with grid search and cross-validation. Run times were measured to evaluate the trade-off between processing time and accuracy. In total, eighteen variations of ML algorithms were created and tested. The results show that four algorithms yielded f1-scores >90% and were able to discern between the four classes, especially those with usually similar characteristics, e.g., coral rubble and dead coral. The accuracy variation among them was 3.6% which suggests that they can be used interchangeably depending on the classification task. Furthermore, results on sample size variations show that certain algorithms benefit more from larger datasets whilst others showed discrete accuracy variations (<5%) when trained in datasets of different sizes

Fully convolutional neural networks applied to large-scale marine morphology mapping

In this study we applied for the first time Fully Convolutional Neural Networks (FCNNs) to a marine bathymetric dataset to derive morphological classes over the entire Irish continental shelf. FCNNs are a set of algorithms within Deep Learning that produce pixel-wise classifications in order to create semantically segmented maps. While they have been extensively utilised on imagery for ecological mapping, their application on elevation data is still limited, especially in the marine geomorphology realm. We employed a high-resolution bathymetric dataset to create a set of normalised derivatives commonly utilised in seabed morphology and habitat mapping that include three bathymetric position indexes (BPIs), the vector ruggedness measurement (VRM), the aspect functions and three types of hillshades. The class domains cover ten or twelve semantically distinct surface textures and submarine landforms present on the shelf, with our definitions aiming for simplicity, prevalence and distinctiveness. Sets of 50 or 100 labelled samples for each class were used to train several U-Net architectures with ResNet-50 and VGG-13 encoders. Our results show a maximum model precision of 0.84 and recall of 0.85, with some classes reaching as high as 0.99 in both. A simple majority (modal) voting combining the ten best models produced an excellent map with overall F1 score of 0.96 and class precisions and recalls superior to 0.87. For target classes exhibiting high recall (proportion of positives identified), models also show high precision (proportion of correct identifications) in predictions which confirms that the underlying class boundary has been learnt. Derivative choice plays an important part in the performance of the networks, with hillshades combined with bathymetry providing the best results and aspect functions and VRM leading to an overall deterioration of prediction accuracies. The results show that FCNNs can be successfully applied to the seabed for a morphological exploration of the dataset and as a baseline for more in-depth habitat mapping studies. For example, prediction of semantically distinct classes as “submarine dune” and “bedrock outcrop” can be precise and reliable. Nonetheless, at present state FCNNs are not suitable for tasks that require more refined geomorphological classifications, as for the recognition of detailed morphogenetic processes