Data-Driven Flight Load Prediction using Modal Decomposition Techniques

2022 ◽  
Author(s):  
Stephan Koschel ◽  
Robert Carrese ◽  
Michael Candon ◽  
Haytham Fayek ◽  
Pier Marzocca ◽  
...  
Keyword(s):  
Data Driven ◽  
Decomposition Techniques ◽  
Load Prediction ◽  
Modal Decomposition

scholarly journals Predicting specific impulse distributions for spherical explosives in the extreme near-field using a Gaussian function

2021 ◽  
pp. 204141962199349
Author(s):  
Jordan J Pannell ◽  
George Panoutsos ◽  
Sam B Cooke ◽  
Dan J Pope ◽  
Sam E Rigby
Keyword(s):  
Scaling Laws ◽  
Near Field ◽  
Specific Impulse ◽  
Gaussian Function ◽  
Data Driven ◽  
Blast Load ◽  
Structural Deformation ◽  
Load Prediction ◽  
Validation Data ◽  
Wide Range

Accurate quantification of the blast load arising from detonation of a high explosive has applications in transport security, infrastructure assessment and defence. In order to design efficient and safe protective systems in such aggressive environments, it is of critical importance to understand the magnitude and distribution of loading on a structural component located close to an explosive charge. In particular, peak specific impulse is the primary parameter that governs structural deformation under short-duration loading. Within this so-called extreme near-field region, existing semi-empirical methods are known to be inaccurate, and high-fidelity numerical schemes are generally hampered by a lack of available experimental validation data. As such, the blast protection community is not currently equipped with a satisfactory fast-running tool for load prediction in the near-field. In this article, a validated computational model is used to develop a suite of numerical near-field blast load distributions, which are shown to follow a similar normalised shape. This forms the basis of the data-driven predictive model developed herein: a Gaussian function is fit to the normalised loading distributions, and a power law is used to calculate the magnitude of the curve according to established scaling laws. The predictive method is rigorously assessed against the existing numerical dataset, and is validated against new test models and available experimental data. High levels of agreement are demonstrated throughout, with typical variations of <5% between experiment/model and prediction. The new approach presented in this article allows the analyst to rapidly compute the distribution of specific impulse across the loaded face of a wide range of target sizes and near-field scaled distances and provides a benchmark for data-driven modelling approaches to capture blast loading phenomena in more complex scenarios.

Data-driven modal decomposition of transient cavitating flow

2021 ◽  
Vol 33 (11) ◽  
pp. 113316
Author(s):  
Yunqing Liu ◽  
Jincheng Long ◽  
Qin Wu ◽  
Biao Huang ◽  
Guoyu Wang
Keyword(s):  
Cavitating Flow ◽  
Data Driven ◽  
Modal Decomposition

scholarly journals Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Fisnik Dalipi ◽  
Sule Yildirim Yayilgan ◽  
Alemayehu Gebremedhin
Keyword(s):  
Machine Learning ◽  
Heat Load ◽  
District Heating ◽  
Heating System ◽  
Partial Least Square ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Support Vector ◽  
Load Prediction ◽  
District Heating System

We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS). Even though ML has been used as an approach to heat load prediction in literature, it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific. For that reason, we compared and evaluated three ML algorithms within a framework on operational data from a DH system in order to generate the required prediction model. The algorithms examined are Support Vector Regression (SVR), Partial Least Square (PLS), and random forest (RF). We use the data collected from buildings at several locations for a period of 29 weeks. Concerning the accuracy of predicting the heat load, we evaluate the performance of the proposed algorithms using mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient. In order to determine which algorithm had the best accuracy, we conducted performance comparison among these ML algorithms. The comparison of the algorithms indicates that, for DH heat load prediction, SVR method presented in this paper is the most efficient one out of the three also compared to other methods found in the literature.

Study of Dynamical Instabilities in Siemens Liquid Spray Injectors Using Complementary Modal Decomposition Techniques

2019 ◽  
Author(s):  
Adesile Ajisafe ◽  
Midhat Talibi ◽  
Andrea Ducci ◽  
Ramanarayanan Balachandran ◽  
Nishant Parsania ◽  
...  
Keyword(s):  
High Speed ◽  
Dynamical Behaviour ◽  
Operating Conditions ◽  
Dynamic Mode Decomposition ◽  
Fuel Spray ◽  
Chamber Pressure ◽  
Dynamic Mode ◽  
Decomposition Techniques ◽  
Planar Imaging ◽  
Modal Decomposition

Abstract Liquid fuel spray characterisation is essential for understanding the mechanisms underlying fuel energy release and pollutant formation. Careful selection of operating conditions can promote flow instabilities in the fuel spray which can enhance atomisation and fuel mixing, thereby resulting in more efficient combustion. However, the inherent instabilities present in the spray could have adverse effect on the combustor dynamics. Hence, it is important to better understand the dynamical behaviour of the spray, and particularly at representative operating conditions. This work describes an experimental investigation of dynamical behaviour of pressure-swirl atomisers used in Siemens industrial gas turbine combustors, at a range of chamber pressures and fuel injection pressures, using high speed laser planar imaging. Two modal decomposition techniques — Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) — are applied and compared to assess the spray dynamics. Results indicate that both POD and DMD are able to capture periodic structures occurring in the spray at different spatial length scales. The characteristic frequencies estimated from both the methods are in good agreement with each other. Both techniques are able to identify coherent structures with variable size, shape and level of staggering, which are observed to be dependent on the pressure difference across the atomiser and the chamber pressure. The spatio-temporally resolved data and the results could be used for spray model development and validation. Furthermore, the methods employed could be applied to other fuel atomisers, and more complicated conditions involving cross flow and higher chamber temperatures.

scholarly journals Towards reduced order modelling for predicting the dynamics of coherent vorticity structures within wind turbine wakes

2017 ◽  
Vol 375 (2091) ◽  
pp. 20160108 ◽  
Author(s):  
M. Debnath ◽  
C. Santoni ◽  
S. Leonardi ◽  
G. V. Iungo
Keyword(s):  
Boundary Layer ◽  
Velocity Field ◽  
Atmospheric Boundary Layer ◽  
Wind Turbine ◽  
Turbine Blades ◽  
Dynamic Mode ◽  
Decomposition Techniques ◽  
Modal Decomposition ◽  
Tip Vortices ◽  
Reduced Order

The dynamics of the velocity field resulting from the interaction between the atmospheric boundary layer and a wind turbine array can affect significantly the performance of a wind power plant and the durability of wind turbines. In this work, dynamics in wind turbine wakes and instabilities of helicoidal tip vortices are detected and characterized through modal decomposition techniques. The dataset under examination consists of snapshots of the velocity field obtained from large-eddy simulations (LES) of an isolated wind turbine, for which aerodynamic forcing exerted by the turbine blades on the atmospheric boundary layer is mimicked through the actuator line model. Particular attention is paid to the interaction between the downstream evolution of the helicoidal tip vortices and the alternate vortex shedding from the turbine tower. The LES dataset is interrogated through different modal decomposition techniques, such as proper orthogonal decomposition and dynamic mode decomposition. The dominant wake dynamics are selected for the formulation of a reduced order model, which consists in a linear time-marching algorithm where temporal evolution of flow dynamics is obtained from the previous temporal realization multiplied by a time-invariant operator. This article is part of the themed issue ‘Wind energy in complex terrains’.

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