Aerospace

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  • ItemOpen Access
    Data supporting "Set Based Design Techniques for Evolvability Exploration During Conceptual Aircraft Design”
    (Cranfield University, 2024-09-19) Jimeno Altelarrea, Sergio; Riaz, Atif; van Heerden, Albert S.J.; Chen, Xin
  • ItemOpen Access
    The Effect of Rotor Tilt on the Stability and Gust Rejection Properties of VTOL Multirotor Aircraft - MATLAB Codes
    (Cranfield University, 2022-01-04 11:11) Whidborne, James
    Video visualization and MATLAB files and routines to generate figures, video and some results of paper "The Effect of Rotor Tilt on the Stability and Gust Rejection Properties of VTOL Multirotor Aircraft" by James F. Whidborne, Arthur Mendez and Alastair CookeRun the script MultiRotorGust.m to generate all the figures & the video in the paper.
  • ItemOpen Access
    Steady and transient simulation results of air injection into aeroderivative gas turbines for power augmentation and ramp rate improvement
    (Cranfield University, 2020-08-16 16:07) Abudu, Kamal
    The data set includes Turbomatch results for the steady state and transient injection of air into two aeroderivative engines. The engines considered are inspired by the GE LM6000(TS56) and LMS100(TSI118). The steady state simulations data provides engine design specifications and the effect of increased injection on the engines' performance. A ramp up stimulation from 50% of power to full load is also provided for both engines.
  • ItemOpen Access
    Data set for Gas Turbine Minimum Environmental Load Extension with Compressed Air Extraction for Storage
    (Cranfield University, 2020-08-15 09:40) Abudu, Kamal
    The data set contains an emissions model for CO and NOx emissions from a gas turbine with simulation results for air extraction and engine turndown
  • ItemOpen Access
    Dataset for the publication: "Coupon scale Z-pinned IM7/8552 delamination tests under dynamic loading"
    (Cranfield University, 2019-08-27 15:00) Cui, Hao; R. Hallett, Stephen; Mahadik, Yusuf; K. Patridge, Ivana; Allegri, Giuliano; Anusuya Ponnusami, Sathiskumar; Petrinic, Nik
    Datasets for a paper published in Composites Part A: Applied Science and Manufacturinghttps://doi.org/10.1016/j.compositesa.2019.105565The quasi-static tests were done at velocity of around 0.01mm/sDynamic tests were conducted at norminal velocity of around 4m/s, some of the WDCB tests were done at 7m/s.
  • ItemOpen Access
    Stencil selection algorithms publication data
    (Cranfield University, 2019-08-19 14:40) Tsoutsanis, Panagiotis
    Dataset for simulation1) eps images of simulation outputs2) mp4 /mpg of video animation of simulations3) tecplot plt files (use Tecplot, or Paraview for visualisation)
  • ItemOpen Access
    A Benchtop Flight Control Demonstrator - Data and Code
    (Cranfield University, 2019-09-10 09:28) Duran, Joris; Whidborne, James; Pontillo, Alessandro; alejandro Carrizales rodriguez, Martin
    Codes and data for "A benchtop flight control demonstrator" J R Duran, J F Whidborne, M Carrizales Rodriguez, A Pontillo International Journal of Mechanical Engineering Education Published online July 27, 2019 doi: 10.1177/0306419019852688CodesFlight-Desk-Control-Demonstrator-master.zip - GUI on a computer programmed in JAVA - PID Controller for ArduinoExperimental Results- Test open loop.xls- Test P.xls- Test PID.xlsFluent CFD Fileswindtunnel.casWindtunnel.jouWindtunnel_refine.meshdatJavaFoil filescriptJaveFoilMicrosoft Excel Windtunnel design worksheetWind Tunnel Design V2.xlsmCATIA CAD filesCAD-CATIA.zipMSc DissertationDuran JR. Flight Desk Control Demonstrator. MSc dissertation, Cranfield University, Bedfordshire, U.K., 2018. Available at: http://dspace.lib.cranfield.ac.uk/handle/1826/14154
  • ItemOpen Access
    Data supporting: High-order hybrid DG-FV framework for compressible multi-fluid problems on unstructured meshes""
    (Cranfield University, 2024-02-12 17:07) Maltsev, Vadim; Skote, Martin; Tsoutsanis, Panagiotis
    This dataset contains binary output in Tecplot format for the test problems analysed in the "High-order hybrid DG-FV framework for compressible multi-fluid problems on unstructured meshes" JCP paper. Test cases included are: - Gas-water isolated material interface advection - 2D and 3D helium bubble interaction with shock wave - 2D shock driven air bubble collapse in water - 2D and 3D shock driven air bubble array collapse in water - 2D underwater explosion
  • ItemOpen Access
    Data for Paper "Unsteady Multiphase Simulation of Oleo-Pneumatic Shock Absorber Flow"
    (Cranfield University, 2024-02-21 18:05) Sheikh Al Shabab, Ahmed; Grenko, Bojan; Silva, Paulo; Antoniadis, Antonios; Tsoutsanis, Panagiotis; Skote, Martin
    Dataset for the paper "Unsteady Multiphase Simulation of Oleo-Pneumatic Shock Absorber Flow"
  • ItemOpen Access
    Data for paper "A Modular Multifidelity Approach for Multiphysics Oleo-Pneumatic Shock Absorber Simulations"
    (Cranfield University, 2024-02-21 18:42) Sheikh Al Shabab, Ahmed; Silva, Paulo; Grenko, Bojan; Tsoutsanis, Panagiotis; Skote, Martin
    Dataset for the underlying data discussed in the paper titled "A Modular Multifidelity Approach for Multiphysics Oleo-Pneumatic Shock Absorber Simulations"
  • ItemOpen Access
    Supplementary data: Energy Harvesting Frictionless Brakes for Short-Haul Aircraft: Thermal and Electromagnetic Feasibility of an Axial-Flux Machine for a Landing Gear Drive System
    (Cranfield University, 2023-05-22 09:23) Deja, Jakub; Skote, Martin; Dayyani, Iman; Akbari, Amir; Lowther, David
    Supplementary data for conference paper entitled "Energy Harvesting Frictionless Brakes for Short-Haul Aircraft: Thermal and Electromagnetic Feasibility of an Axial-Flux Machine for a Landing Gear Drive System" presented at AIAA Aviation Forum 2023
  • ItemOpen Access
    SSFs dataset: Self-supervised features dataset of the ultrasonic signals of two-phase flow in an S-shaped riser
    (Cranfield University, 2023-03-09 15:15) Kuang, Boyu
    Title: SSFs dataset: Self-supervised features dataset of the ultrasonic signals of two-phase flow in an S-shaped riser Author: Boyu Kuang, neiil.kuang@cranfiel.ac.uk Time: 09th March 2023 Description: The source data of the proposed SSFs dataset comes from: https://doi.org/10.17862/cranfield.rd.11369379.v1 The Self-supervised features (SSFs) dataset is opened to the community along with our latest journal paper entitled: "Self-supervised learning-based two-phase flow regimen identification using ultrasonic sensors in an S-shape riser". NOTE: the journal DOI will be provided after the acceptance. This dataset is produced using the settings in TABLE I. Here are some details, and please contact me if you got any issues with using the dataset: SSFs_dataset: "the root directory of the dataset" | | -- ex: "the SSFs from the experiment group (ex)" | | | | | -- train: "the training set (70%)" | | | | | -- test: "the testing set (15%)" | | | | | -- valid: "the validation set (the rest)" | | -- ctr-A: "the SSFs from the control group (ctr-A)" | | | | | -- train: "the training set (70%)" | | | | | -- test: "the testing set (15%)" | | | |
  • ItemOpen Access
    Supporting data for "Measuring Airport Service Quality Using Machine Learning Algorithms"
    (Cranfield University, 2022-06-28 15:11) Salih A Homaid, Mohammed; Moulitsas, Irene
    The airport industry is a highly competitive market that has expanded quickly during the last two decades. Airport management usually measures the level of passenger satisfaction by applying the traditional methods, such as user surveys and expert opinions, which require time and effort to analyse. Recently, there has been considerable attention on employing machine learning techniques and sentiment analysis for measuring the level of passenger satisfaction. Sentiment analysis can be implemented using a range of different methods. However, it is still uncertain which techniques are better suited for recognising the sentiment for a particular subject domain or dataset. In this paper, we analyse the sentiment of air travellers using five different algorithms, namely Logistic Regression, XGBoost, Support Vector Machine, Random Forest and Naïve Bayes. We obtain our data set through the SKYTRAX website which is a collection of reviews of around 600 airports. We apply some pre-processing steps, such as converting the textual reviews into numerical form, by using the term frequency-inverse document frequency. We also remove stopwords from the text using the NLTK list of stopwords. We evaluate our results using the accuracy, precision, recall and F1_score performance metrics. Our analysis shows that XGBoost provides the most accurate results when compared with other algorithms.
  • ItemOpen Access
    Presentation: A Kriging Approach to Model Updating for Damage Detection
    (Cranfield University, 2022-07-04 01:10) Dessena, Gabriele
    This are the presentation slides for the presentation delivered on the 4.7.2022 at the 10th European Workshop on Structural Health Monitoring in Palermo, Italy. The presentation refers to the "A Kriging Approach to Model Updating for Damage Detection" paper that can be found at https://doi.org/10.1007/978-3-031-07258-1_26
  • ItemOpen Access
    Data: Modeling and performance evaluation of sustainable arresting gear energy recovery system for commercial aircraft
    (Cranfield University, 2023-08-07 11:48) Deja, Jakub; Skote, Martin; Dayyani, Iman
    Datasets showing the system performance for different aircraft
  • ItemOpen Access
    Data for Paper "Numerical Investigation of Oleo-Pneumatic Shock Absorber: A Multi-Fidelity Approach"
    (Cranfield University, 2023-08-25 14:35) Sheikh Al Shabab, Ahmed; Grenko, Bojan; Vitlaris, Dimitrios; Tsoutsanis, Panagiotis; Antoniadis, Antonios; Skote, Martin
    Raw data of simulations used in the ECCOMAS 2022 paper titled: Numerical Investigation of Oleo-Pneumatic Shock Absorber: A Multi-Fidelity Approach
  • ItemOpen Access
    Experimental data for the dynamic inter-fibre failure of composite laminates with through-thickness reinforcement
    (Cranfield University, 2019-04-10 12:14) Cui, Hao
    Experimental data as published in "Dynamic inter-fibre failure of unidirectional composite laminates with through-thickness reinforcement" DOI: 10.1016/j.compscitech.2019.04.004
  • ItemOpen Access
    Graphic abstract
    (Cranfield University, 2022-06-07 12:41) Deja, Jakub; Dayyani, Iman; Skote, Martin
    Modelling and Performance Evaluation of Sustainable ArrestingGear Energy Recovery System for Commercial Aircraft
  • ItemOpen Access
    Surface line integral convolution-based vortex detection using computer vision
    (Cranfield University, 2022-02-22 21:24) Ashor Amran Abolholl, Hazem
    Vortex cores in fluid mechanics are easy to visualise, yet difficult to detect numerically. Precise knowledge of these allow fluid dynamics researchers to study the underlying complex flow structures with greater precision and allow for a better understanding of the turbulence transition process and the development and evolution of flow instabilities, to name but a few relevant areas. Various approaches such as the Q, delta and swirling strength criterion have been proposed to visualise vortical flows and these approaches can be used as well to detect vortex core locations. Using these methods will detect spurious vortex cores and the number of false positives and negatives need to be balanced through a threshold criterion, making these methods lack robustness. To overcome this shortcoming, we propose a new approach using convolutional neural networks to detect flow structures directly from streamline plots, using the line integral convolution method. We show that our computer vision-based approach is able to reduce the number of false positives and negatives entirely while removing the need to calibrate user-defined parameters which are flow problem-specific. We validate our approach for the well-known Taylor-Green vortex problem where we extract line integral convolution-based streamline plots on the centre planes of the domain which are then used to train our convolutional neural network. We show that with an increasing number of images used for training, we are able to monotonically reduce the number of false positives and negatives. We then apply our trained network to a different flow problem and show that we are able to detect vortices irrespective of the flow case. Thus, our study presents a convolutional neural network approach that allows for reliable vortex core detection that only needs to be trained once but is applicable to a wide range of flow scenarios.
  • ItemOpen Access
    Mesh PSP robin mod7 - pitch 7deg -OGE
    (Cranfield University, 2023-12-12 14:48) Silva, Paulo
    This file is the mesh file for the PSP full helicopter for collective angle 7deg.