Rocket engine experimental data reconstruction based on compressive sensing with MOD dictionary

Compressive sensing has been studied and applied in structural health monitoring for data acquisition and reconstruction, wireless data transmission, structural modal identification, and spare damage identification. The key issue in compressive sensing is finding the optimal solution for sparse optimization. In the past several years, many algorithms have been proposed in the field of applied mathematics. In this article, we propose a machine learning–based approach to solve the compressive-sensing data-reconstruction problem. By treating a computation process as a data flow, the solving process of compressive sensing–based data reconstruction is formalized into a standard supervised-learning task. The prior knowledge, i.e. the basis matrix and the compressive sensing–sampled signals, is used as the input and the target of the network; the basis coefficient matrix is embedded as the parameters of a certain layer; and the objective function of conventional compressive sensing is set as the loss function of the network. Regularized by l1-norm, these basis coefficients are optimized to reduce the error between the original compressive sensing–sampled signals and the masked reconstructed signals with a common optimization algorithm. In addition, the proposed network is able to handle complex bases, such as a Fourier basis. Benefiting from the nature of a multi-neuron layer, multiple signal channels can be reconstructed simultaneously. Meanwhile, the disassembled use of a large-scale basis makes the method memory-efficient. A numerical example of multiple sinusoidal waves and an example of field-test wireless data from a suspension bridge are carried out to illustrate the data-reconstruction ability of the proposed approach. The results show that high reconstruction accuracy can be obtained by the machine learning–based approach. In addition, the parameters of the network have clear meanings; the inference of the mapping between input and output is fully transparent, making the compressive-sensing data-reconstruction neural network interpretable.

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ПАРАМЕТРИЧНІ КОЛИВАННЯ РОТОРУ ТУРБОНАСОСНОГО АГРЕГАТУ НА ШАРИКОПІДШИПНИКАХ

Aerospace technic and technology ◽

10.32620/aktt.2018.7.08 ◽

2018 ◽

pp. 56-61

Author(s):

Яков Николаевич Иванов ◽

Олег Петрович Бадун ◽

Сергей Алексеевич Дешевых ◽

Александр Юрьевич Стрельченко

Keyword(s):

Experimental Data ◽

High Speed ◽

Rocket Engine ◽

Final Analysis ◽

Oscillatory System ◽

Radial Clearance ◽

Engineering Practice ◽

Ball Bearings ◽

Cooling Liquid ◽

Bearing Race

This work is dedicated to the problem of ball bearings capacity on the turbopump assembly rotor. Many publications and books have been devoted to solve this problem. The result of research is benefit for designers, technologists and operatives. However, many important questions, which are concerning of ball bearings work, in engineering practice are not sufficiently disclosed. In favor of this is telling the statistic of development of high-speed rotor assemblies. In the total mass of turbopump assembly defects, which appear at the phase of developmental design, more the half of them are belong to the friction nodes, which are including the ball bearings too. The authors are present their own results of the experimental data, which were accumulated as result of the ball bearings tests on the special installation and as part of the turbopump assembly from a Liquid-Propellant Rocket Engine. The defects of ball bearings parts were described and analyzed. The main factors which are worsening the service life of ball bearings in consisting of the turbopump assembly were examined. The kinematics and force interaction of balls with bearing race were examined.In the final analysis was advanced the version of the cause of increased wear of ball bearings high-speed rotor, which consists of the assumption on that during the operation of the bearing, in the radial clearance mode, axial displacements of the balls occur, under the influence of the axial force generated by the flow of the cooling liquid. Also this article shows how these movements of the balls affect the load distribution between the bearing parts and their deterioration.It is assumed that in this mode of operation, in the system "rotor - ball bearing - body" may arise forced axial oscillations. These oscillations are suitable for the term perametric ocillations, since the energy is introduced to the oscillatory system by the periodically changing parameters of the system. In this case, this is the movement of balls, which is expended the energy of rotor rotation or the energy of the flow of cooling liquid.In support of the advanced version, the experimental data on the state of bearings №207Ю and №205Ю were given after the tests with different values of the flow of cooling liquid.

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Comparison of Reactive Flow Simulations for a LOX/CH4 Multi-element Rocket Engine

MATEC Web of Conferences ◽

10.1051/matecconf/201930407007 ◽

2019 ◽

Vol 304 ◽

pp. 07007

Author(s):

Ainslie D. French ◽

Luigi Cutrone ◽

Antonio Schettino ◽

Marco Marini ◽

Francesco Battista ◽

...

Keyword(s):

Experimental Data ◽

Boundary Conditions ◽

Numerical Simulations ◽

Rocket Engine ◽

Engine Performance ◽

Heat Fluxes ◽

Reactive Flow ◽

Flow Data ◽

Flow Simulations ◽

Experiment Validation

This study details the reactive flow simulations of a LOX/CH4 Multi-element rocket engine. The work has been conducted within the framework of the HYPROB-BREAD project whose main objective is the design, manufacture and testing of a LOX/LCH4 regeneratively cooled ground demonstrator. Numerical simulations have been carried out with both commercial software and CIRA software developed in house. Two sets of boundary conditions, nominal and experimental, have been applied from which a code-to-code validation has been effected with the former and a code-to-experiment validation with the latter. The results presented include both flow data and heat fluxes as well as parameters associated with engine performance, and indicate an excellent agreement with experimental data of a LOX/CH4 Multi-element rocket engine.

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Swirled injector modeling for cavitating multiphase flow

E3S Web of Conferences ◽

10.1051/e3sconf/201912806007 ◽

2019 ◽

Vol 128 ◽

pp. 06007

Author(s):

Bartosz Ziegler ◽

Jędrzej Mosędrżny ◽

Natalia Lewandowska

Keyword(s):

Experimental Data ◽

Nitrous Oxide ◽

Heat Exchange ◽

Multiphase Flow ◽

Equilibrium Model ◽

Rocket Engine ◽

Flow Modeling ◽

Hybrid Rocket ◽

Hybrid Rocket Motor ◽

Significant Magnitude

The goal of this study is to present a comparison between different approaches to multiphase injection modeling of self-pressurized rocket engine propellant. Swirled, tangential orifice injector of nitrous oxide, for an “N” class hybrid rocket motor is the object of the study. A brief descriptionof the injector purpose and geometry is provided, followed by a description of different approaches for flow modeling. Examined techniques range from 0D, Homogeneous Equilibrium Model (HEM) to 3D multiphase with mass and heat exchange between phases. Results of analyses are provided and compared with experimental data. The discrepancies between results are of significant magnitude but expected nature. Co clusions about most feasible approaches for engineering calculations are drawn.

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Experimental Data Processing Using Shift Methods

EPJ Web of Conferences ◽

10.1051/epjconf/202022603014 ◽

2020 ◽

Vol 226 ◽

pp. 03014 ◽

Cited By ~ 1

Author(s):

Andrey Novikov-Borodin

Keyword(s):

Experimental Data ◽

Data Processing ◽

Statistical Data ◽

Data Reconstruction ◽

Uniform Motion ◽

Noise Levels ◽

Experimental Data Processing ◽

Shift Technique ◽

2D Images ◽

Therapy Treatment

The numerical methods of step-by-step and combined shifts are proposed for correction and reconstruction the experimental data convolved with different blur kernels. Methods use a shift technique for the direct deconvolution of experimental data, they are fast and effective for data reconstruction, especially, in the case of discrete measurements. The comparative analysis of proposed methods is presented, inaccuracies of reconstruction with different blur kernels, different volumes of data and noise levels are estimated. There are presented the examples of using the shift methods in processing the statistical data of TOF neutron spectrometers and in planning the proton therapy treatment. The multi-dimensional data processing using shift methods is considered. The examples of renewal the 2D images blurred by uniform motion and distorted with the Gaussian-like blur kernels are presented.

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Efficient estimation of phase-response curves via compressive sensing

Journal of Neurophysiology ◽

10.1152/jn.00919.2011 ◽

2012 ◽

Vol 108 (7) ◽

pp. 2069-2081 ◽

Cited By ~ 5

Author(s):

Sungho Hong ◽

Quinten Robberechts ◽

Erik De Schutter

Keyword(s):

Experimental Data ◽

Compressive Sensing ◽

Degrees Of Freedom ◽

Goodness Of Fit ◽

Phase Response Curve ◽

Phase Changes ◽

Phase Response ◽

Efficient Estimation ◽

Estimation Methods ◽

High Dimensional

The phase-response curve (PRC), relating the phase shift of an oscillator to external perturbation, is an important tool to study neurons and their population behavior. It can be experimentally estimated by measuring the phase changes caused by probe stimuli. These stimuli, usually short pulses or continuous noise, have a much wider frequency spectrum than that of neuronal dynamics. This makes the experimental data high dimensional while the number of data samples tends to be small. Current PRC estimation methods have not been optimized for efficiently discovering the relevant degrees of freedom from such data. We propose a systematic and efficient approach based on a recently developed signal processing theory called compressive sensing (CS). CS is a framework for recovering sparsely constructed signals from undersampled data and is suitable for extracting information about the PRC from finite but high-dimensional experimental measurements. We illustrate how the CS algorithm can be translated into an estimation scheme and demonstrate that our CS method can produce good estimates of the PRCs with simulated and experimental data, especially when the data size is so small that simple approaches such as naive averaging fail. The tradeoffs between degrees of freedom vs. goodness-of-fit were systematically analyzed, which help us to understand better what part of the data has the most predictive power. Our results illustrate that finite sizes of neuroscientific data in general compounded by large dimensionality can hamper studies of the neural code and suggest that CS is a good tool for overcoming this challenge.

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Design Comments and Experimental Results for Cavitation-Resistant Inducers up to 40,000 Suction Specific Speed

Journal of Engineering for Power ◽

10.1115/1.3677571 ◽

1964 ◽

Vol 86 (2) ◽

pp. 176-180

Author(s):

M. K. Wright

Keyword(s):

Experimental Data ◽

Rocket Engine ◽

Geometric Design ◽

Experimental Results ◽

Cavitation Resistance ◽

Trial And Error ◽

Error Type ◽

General Correlation ◽

Design Considerations ◽

Specific Speed

General geometric design considerations for cavitation resistant inducers are discussed, particularly those related to the “trial and error” type not covered by the textbook approach. A general correlation of four dimensionless cavitation parameters is developed. Experimental data for typical rocket engine pumps are given where suction specific speed values up to 40,000 are obtained. Finally, several schemes are given for augmenting the cavitation resistance.

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The relevance vector machine for seismic Bayesian compressive sensing

Geophysics ◽

10.1190/geo2019-0200.1 ◽

2020 ◽

Vol 85 (4) ◽

pp. WA279-WA292

Author(s):

Georgios Pilikos

Keyword(s):

Compressive Sensing ◽

Seismic Data ◽

Ad Hoc ◽

Relevance Vector Machine ◽

Basis Functions ◽

Irregular Sampling ◽

Seismic Survey ◽

Data Reconstruction ◽

Reconstruction Accuracy ◽

Acquisition Processes

Missing traces in seismic surveys create gaps in the data and cause problems in later stages of the seismic processing workflow through aliasing or incoherent noise. Compressive sensing (CS) is a framework that encompasses data reconstruction algorithms and acquisition processes. However, CS algorithms are mainly ad hoc by focusing on data reconstruction without any uncertainty quantification or feature learning. To avoid ad hoc algorithms, a probabilistic data-driven model is used, the relevance vector machine (RVM), to reconstruct seismic data and simultaneously quantify uncertainty. Modeling of sparsity is achieved using dictionaries of basis functions, and the model remains flexible by adding or removing them iteratively. Random irregular sampling with time-slice processing is used to reconstruct data without aliasing. Experiments on synthetic and field data sets illustrate its effectiveness with state-of-the-art reconstruction accuracy. In addition, a hybrid approach is used in which the domain of operation is smaller while, simultaneously, learned dictionaries of basis functions from seismic data are used. Furthermore, the uncertainty in predictions is quantified using the predictive variance of the RVM, obtaining high uncertainty when the reconstruction accuracy is low and vice versa. This could be used for the evaluation of source/receiver configurations guiding seismic survey design.

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