An Efficiently Parallelized High-Order Aeroacoustics Solver Using a Characteristic-Based Multi-Block Interface Treatment and Optimized Compact Finite Differencing

This paper presents the development of a fourth-order finite difference computational aeroacoustics solver. The solver works with a structured multi-block grid domain strategy, and it has been parallelized efficiently by using an interface treatment based on the method of characteristics. More importantly, it extends the characteristic boundary condition developments of previous researchers by introducing a characteristic-based treatment at the multi-block interfaces. In addition, most characteristic methods do not satisfy Pfaff’s condition, which is a requirement for any mathematical relation to be valid. A mathematically-consistent and valid method is used in this work to derive the characteristic interface conditions. Furthermore, a robust and efficient approach for the matching of turbulence quantities at the multi-block interfaces is developed. Finally, the implementation of grid metric relations to minimise grid-induced errors has been adopted. The code was validated against a number of benchmark cases, which demonstrated its accuracy and robustness across a range of problem types

Wake-Tailplane Interaction of a Slingsby Firefly Aircraft

This paper presents in-flight measurements of the interaction of the wing wake of a stalled Slingsby T67 Firefly light aircraft with the aircraft tailplane. Tailplane data was recorded by a GoPro360 camera and analyzed using spatial correlation methods. The tailplane movement and corresponding spectra indicate that the aerodynamic wake shedding frequency closely matches the resonant frequency of the tailplane, resulting in a significant excitation of the structure during heavy stall. Large magnitude, lower frequency tailplane movement was also identified by analysis of the pitch attitude from the image data, with results consistent in post-stall behavior reported by previous modelling and measurements

CFD investigation of a core-mounted-target-type thrust reverser, Part 2: reverser deployed configuration

CMTTTR design was proposed by NASA in the second half of the 90's. NASA carried out several experiments at static conditions, and their acquired results suggested that the performance characteristics of the CMTTTR design falls short to comply with the mandatory TR performance criteria, and were therefore regarded as an infeasible design. However, the authors of this paper believe that the results presented by NASA for CMTTTR design require further exploration to facilitate the complete understanding of its true performance potential. This Part2 paper is a continuation from Part1and presents a comprehensive three-dimensional (CFD) analyses of the CMTTTR in deployed configuration; the analyses at forward flight conditions will be covered in Part 3. The key objectives of this paper are: first, to validate the acquired CFD results with the experimental data provided by NASA: this is achieved by measuring the static pressure values on various surfaces of the deployed CMTTTR model. The second objective is to estimate the performance characteristics of the CMTTTR design and corroborate the results with experimental data. The third objective is to estimate the Pressure Thrust (i.e. axial thrust generated due to the pressure difference across various reverser surfaces) and discuss its significance for formulating the performance of any thrust reverser design. The fourth objective is to investigate the influence of kicker plate installation on overall TR performance. The fifth and final objective is to examine and discuss the overall flow physics associated with the thrust reverser under deployed configuration

CFD investigation of a core-mounted-target-type thrust reverser, Part 1: reverser stowed configuration

During the second half of the 90's, NASA performed experimental investigations on six novel Thrust Reverser designs; Core Mounted Target Type Thrust Reverser (CMTTTR) design is one of them. To assess the CMTTTR efficiency and performance, NASA conducted several wind tunnel tests at Sea Level Static conditions. The results from these experiments are used in this paper series to validate the CFD results. This paper is part one of the three-part series; Part 1 and 2 discusses the CMTTTR in stowed and deployed configurations, all analysis in the first two papers are performed at SLS conditions. Part3 discusses the CMTTTR in the forward flight condition. The key objectives of this paper are: first, to perform the 3D CFD analysis of the reverser in stowed configuration; all analyses are performed at SLS condition. The second objective is to validate the acquired CFD results against the experimental data provided by NASA[1]. The third objective is to verify the fan and overall engine net thrust values acquired from the aforementioned CFD analyses against those derived based on 1-D engine performance simulations. The fourth and final objective is to examine and discuss the overall flow physics associated with the CMTTTR under stowed configuration. To support the successful implementation of the overall investigation, full-scale 3DCAD models are created, representing a fully integrated GE90 engine, B777 wing, and pylon configuration. Overall a good agreement is found between the CFD and test results; the difference between the two was less than 5%

Aerodynamics and aeroacoustics of slat tracks

An overview on the progress of the research work on the aerodynamics and aeroacoustics of a three-dimensional multi-element wing with slat track is presented. A two step computational procedure which involves the solution of Navier-Stokes equations in the first-step and an integral surface solution of the Ffowcs-Williams and Hawkings equation in the subsequent step, is used to predict the far-field noise. Two main approaches are used for the computational work in this project. Firstly, the simulations based on the Linearized Euler Equations and the Acoustic Perturbation Equations are used in two- and three-dimensions to understand the effect of the slat track on the acoustic wave propagation. Dipoles and quadrupole are used as the simulated acoustic source. Also the effect of the mean flow in the wave propagation is studies by including a background mean flow in the APE simulations. An integral solution of FW-H equation is used for the far-field acoustic prediction after the source computation. The results are analysed and the future work is proposed

Colorimetric detection on paper analytical device using machine learning

Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. The PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary resulting in less accurate results. Recently, machine learning (ML) assisted models have been used in image analysis. We evaluated a combinations of four ML models - logistic regression, support vector machine, random forest, and artificial neural network, and three image color spaces - RGB, HSV, and LAB for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras, and users for food color and enzyme inhibition assays to create training and test datasets. Prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML model and colorspace combinations. All models better predicted coarse level classification than fine grained concentration labels. ML models using sample color along with a reference color increased the models’ ability in predicting the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration label prediction accuracy obtained for food color was 0.966 when using ANN model and LAB colorspace. The accuracy for enzyme inhibition assay was 0.908 when using SVM model and LAB colorspace. Appropriate model and colorspace combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool

Computational Fluid Dynamics Investigation of a Core-Mounted Target-Type Thrust Reverser - Part 1: Reverser Stowed Configuration

During the second half of the 90 s, NASA performed experimental investigations on six novel thrust reverser (TR) designs; core-mounted target-type thrust reverser (CMTTTR) design is one of them. To assess the CMTTTR efficiency and performance, NASA conducted several wind tunnel tests at sea level static (SLS) conditions. The results from these experiments are used in this paper series to validate the computational fluid dynamics (CFD) results. This paper is part one of the three-part series. Parts 1 and 2 discuss the CMTTTR in stowed and deployed configurations; all analyses in the first two papers are performed at SLS conditions. Part 3 discusses the CMTTTR in the forward flight condition. The key objectives of this paper are: first, to perform the three-dimensional (3D) CFD analysis of the reverser in stowed configuration; all analyses are performed at SLS condition. The second objective is to validate the acquired CFD results against the experimental data provided by NASA (Scott, C. A., 1995, "Static Performance of Six Innovative Thrust Reverser Concepts for Subsonic Transport Applications: Summary of the NASA Langley Innovative Thrust Reverser Test Program," NASA - Langley Research Centre, Hampton, VA, Report No. TM-2000-210300). The third objective is to verify the fan and overall engine net thrust values acquired from the aforementioned CFD analyses against those derived based on one-dimensional (1D) engine performance simulations. The fourth and final objective is to examine and discuss the overall flow physics associated with the CMTTTR under stowed configuration. To support the successful implementation of the overall investigation, full-scale 3D computer aided design (CAD) models are created, representing a fully integrated GE-90 engine, B777 wing, and pylon configuration. Overall, a good agreement is found between the CFD and test results; the difference between the two was less than 5%