Inverse heat transfer problem solution of sounding rocket using moving window optimization

Dąbrowski, Adam; Dąbrowski, Leszek

doi:10.1371/journal.pone.0218600

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…Estimation of surface heat flux has been carried out without [15] and with [16] heat conduction and comparison between them shows discrepancies as high as about 27% [17]. Moving window optimization method [18] has been applied to predict the aerodynamic heating in a free-flight of sounding rocket by comparing numerically calculated and measured temperature history. Howard [19] developed a numerical procedure for estimating the heat flux with variable thermal properties using a single embedded thermocouple.…”

Section: Introductionmentioning

confidence: 99%

Influence of Input Parameters on the Solution of Inverse Heat Conduction Problem

Mehta¹

2020

Inverse Heat Conduction and Heat Exchangers

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A one-dimensional transient heat conduction equation is solved using analytical and numerical methods. An iterative technique is employed which estimates unknown boundary conditions from the measured temperature time history. The focus of the present chapter is to investigate effects of input parameters such as time delay, thermocouple cavity, error in the location of thermocouple position and time-and temperature-dependent thermophysical properties. Inverse heat conduction problem IHCP is solved with and without material conduction. A two-time level implicit finite difference numerical method is used to solve nonlinear heat conduction problem. Effects of uniform, nonuniform and deforming computational grids on the estimated convective heat transfer are investigated in a nozzle of solid rocket motor. A unified heat transfer analysis is presented to obtain wall heat flux and convective heat transfer coefficient in a rocket nozzle. A two-node exact solution technique is applied to estimate aerodynamic heating in a free flight of a sounding rocket. The stability of the solution of the inverse heat conduction problem is sensitive to the spatial and temporal discretization.

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Section: Introductionmentioning

confidence: 99%

Influence of Input Parameters on the Solution of Inverse Heat Conduction Problem

Mehta¹

2020

Inverse Heat Conduction and Heat Exchangers

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“…This means that their use in optimisation algorithms can be extremely timeconsuming. An example of this is given in the work of Dabrowski and Dabrowski, where thermocouple data was used alongside a computational model to estimate the heat flux a rocket surface is subjected to [53]. This analysis took over 3 days to complete, with only a single unknown parameter estimated.…”

Section: Surrogate Assisted Optimisationmentioning

confidence: 99%

Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components

Lewis

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Plasma facing components (PFCs) must be designed to routinely withstand the harsh environment of a fusion device, where temperatures at the core of the plasma exceed 150,000,000 °C. The heat by induction to verify extremes (HIVE) experimental facility was established to replicate the thermal loads a PFC is subjected to during normal operation of a fusion device.To maximise its impact on the design of PFCs, HIVE must deliver smarter testing and improved component insight. Currently, the experimental parameters required to deliver a certain response to the component are decided at the point of testing through a combination of previous experience, intuition, and trial & error, which is both time-consuming and unreliable. To assess a PFC’s suitability, knowledge of its mechanical performance while operating at high temperatures is desirable, however HIVE only records pointwise temperature measurements on the component’s surface using thermocouples. Currently, HIVE has no method of inferring a component’s mechanical response using the temperature measure-ments.Both the challenges of smarter testing and improved component insight can be achieved through the identification of inverse solutions. A popular approach to solving engineering inverse problems is surrogate assisted optimisation, where a machine learning model is trained using finite element (FE) simulation data. Much of the work in literature use single value surrogate models on quite simplistic problems, however HIVE is a real-world, multi-physics problem which requires full field (FF) surrogate models to solve its multitude of inverse problems.The development of a method which can easily construct FE data driven FF surrogates would be invaluable for a variety of tasks in engineering, as well as solving inverse problems. In this work, it demonstrates that it can provide a much more robust and comprehensive method of characterising a PFC’s strengths and limitations, enabling more informed decisions to be made during its design cycle.

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Preliminary results from HEDGEHOG REXUS project – A sounding rocket experiment on accelerations, vibrations and heat flow

et al. 2020

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Inverse heat transfer problem solution of sounding rocket using moving window optimization

Cited by 6 publications

References 20 publications

Influence of Input Parameters on the Solution of Inverse Heat Conduction Problem

Influence of Input Parameters on the Solution of Inverse Heat Conduction Problem

Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components

Preliminary results from HEDGEHOG REXUS project – A sounding rocket experiment on accelerations, vibrations and heat flow

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