Calibration of reactive burn and Jones-Wilkins-Lee parameters for simulations of a detonation-driven flow experiment with uncertainty quantification

Garno, Joshua; Ouellet, Frederick; Bae, Sangjune; Jackson, T. L.; Kim, Nam-Ho; Haftka, Raphael T.; Hughes, Kyle; Balachandar, S.

doi:10.1103/physrevfluids.5.123201

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…Several methods have been proposed to address this issue and we refer the reader to [19] for a review. GSA has been successfully applied to many engineering problems and as just a few examples we can cite piston shape design [104], liquid rocket injector shape design [196], bluff body shape optimization [140], optimization of an integrated thermal protection system [79], design of high lift airfoil [143], the identification of material properties based on full field measurements [138], stream flow modeling [36], performance characterization of plasma actuators [33], uncertainty quantification in multi-phase detonations [65].…”

Section: Variance Based Techniquesmentioning

confidence: 99%

Surrogate modeling: tricks that endured the test of time and some recent developments

Viana

Gogu

Goel³

2021

Struct Multidisc Optim

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Tasks such as analysis, design optimization, and uncertainty quantification can be computationally expensive. Surrogate modeling is often the tool of choice for reducing the burden associated with such data-intensive tasks. However, even after years of intensive research, surrogate modeling still involves a struggle to achieve maximum accuracy within limited resources. This work summarizes various advanced, yet often straightforward, statistical tools that help. We focus on four techniques with increasing popularity in the surrogate modeling community: (i) variable screening and dimensionality reduction in both the input and the output spaces, (ii) data sampling techniques or design of experiments, (iii) simultaneous use of multiple surrogates, and (iv) sequential sampling. We close the paper with some suggestions for future research.

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Section: Variance Based Techniquesmentioning

confidence: 99%

Surrogate modeling: tricks that endured the test of time and some recent developments

Viana

Gogu

Goel³

2021

Struct Multidisc Optim

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“…Using the mixture definitions and the above assumptions, the BN model reduces to the five-equation multiphase flow model [28,29]. The five-equation model has been used successfully for reactive burn [20] and shock-to-detonation transition [17] simulations. The resulting simplified multiphase governing equation, including species mass transport with chemical reactions, is…”

Section: Governing Equationsmentioning

confidence: 99%

“…However, RB models require a calibrated set of input parameters for the reactant equation of state (EOS), product EOS, and reaction rate. Calibration of the reactant EOS and reaction rate requires significant empirical input [19][20][21], which are unavailable for many materials. RB models require full numerical resolution of the reaction zone of the detonation, which is undesirable and impractical for afterburning applications.…”

Section: Introductionmentioning

confidence: 99%

“…The SBM is built on the five-equation compressible multiphase model [28,29], which has been demonstrated in recent reactive burn [20] and SDT simulations [17]. This paper discusses the development of the SBM and the numerical methods used, shows examples of its use, and then compares it with traditional reaction models used in programmed burn.…”

Section: Introductionmentioning

confidence: 99%

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A simplified burn model for simulating explosive effects and afterburning

Houim

2021

Shock Waves

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A simplified burn model (SBM) was developed for use in simulating the effects of explosive detonations. The goal of SBM is to provide a subgrid model for detonation propagation on grids that are many times coarser than the physical width of the reaction zone. Similar to traditional programmed burn (PB), SBM provides accurate enough initial conditions for simulating largescale blasts and afterburning processes resulting from the detonation. The governing equations are based on the five-equation compressible multiphase flow model that describes reaction and post-detonation processes similar to reactive burn models. The model uses highly simplified reactant equations of state and reaction rate laws. The reaction model is based on burning rates from traditional PB approaches and is designed to spread the reaction zone over a small number of grid points regardless of the mesh resolution. The detonation velocity and state are independent of the input parameters for the reactant equation of state and reaction model, provided the inputs are physically realistic. The single input parameter for the equation of state must be selected such that the reactant and product Hugoniot curves do not cross, otherwise unphysical weak detonations result. Numerical experiments show that the velocity, state, and bulk structure of Chapman-Jouguet detonations can be reproduced by the model. The resulting blast profiles, pressure-time traces, and detonation velocity for multidimensional simulations are relatively independent on the input parameters, again, provided the inputs are physically reasonable. The simplified burn model shows great potential for simulating afterburning processes with detailed reaction models or explosive particle dispersal. The SBM is relatively straightforward to implement in compressible multiphase flow codes.

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“…To do this, he suggested a very original idea of conducting experiments on trusses with replaceable members; he utilized evidence theory of uncertainty in paper by Soundappan et al (2004), resorted to limited number of samples in paper by Chaudhuri et al (2015a, b), applied very cleverly Monte Carlo simulation in the paper by Dodwell et al (2020), and participated in NASA's uncertainty quantification challenge in the paper by Chaudhuri et al (2015a, b). There are papers in press (Garno et al 2020;Zhang et al 2020) on uncertainty analysis. He did not neglect any of the uncertainty techniques but used them according to the pragmatic needs.…”

Section: R T Haftka's Contributions To Uncertainty Quantificationmentioning

confidence: 99%

Review of uncertainty quantification, associated parables, and Raphael Tuvia Haftka’s contributions

Elishakoff

2021

Struct Multidisc Optim

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Two quotes, although unusual for the abstract, immediately appear to be called for in a way of describing what this paper is about; the first one is by Alfred Freudenthal (1968) in his classic paper: "… it seems absurd to strive for more and more refinement of methods of stress-analysis of the structural elements if, in order to determine the dimensions of the structural elements, its results are subsequently compared with so-called 'working stresses,' derived in a rather crude manner by dividing the values of somewhat dubious material parameters obtained in conventional material tests by still more dubious empirical numbers called 'safety factors'." His equally classic paper by Giuseppe Grandori in Meccanica 26:17-21 (1990) resonates, as it were, with the above quote, stressing: "The probabilistic approach to structural safety is today a well-established paradigm. As to the current state of this paradigm, one can notice a dissymmetry similar to that observed by Freudenthal in the traditional approach. An overwhelming part of the research effort, in fact, has been and still is devoted to estimating failure probabilities. By contrast, only sporadic research deals with the problem of choosing an acceptable risk of failure. It is true that the adoration of the probabilistic approach is in any case a progress, even in the case when acceptable risk levels are conveniently defined, because it allows us to treat different structures with homogeneous criteria. However, the concept of structural safety will not leave the 'realm of metaphysics' unless we devise a method for justifying the choice of risk acceptability levels." As we see, the first quote criticizes the deterministic approach in engineering; the other one questions the validity of a probabilistic approach. Here we are concerned with the "mother of all problems," namely how to deal with uncertainty, which methodology to choose and why and how. Hereinafter, the author's musings on uncertainty quantification are presented, via some parables. Finally, connection will be made with the philosophy adopted by late Professor Raphael Tuvia Haftka in his researches on uncertainty.

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Calibration of reactive burn and Jones-Wilkins-Lee parameters for simulations of a detonation-driven flow experiment with uncertainty quantification

Cited by 5 publications

References 22 publications

Surrogate modeling: tricks that endured the test of time and some recent developments

Surrogate modeling: tricks that endured the test of time and some recent developments

A simplified burn model for simulating explosive effects and afterburning

Review of uncertainty quantification, associated parables, and Raphael Tuvia Haftka’s contributions

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