Uncertainty analysis with a reduced set of input uncertainties selected using pathway analysis

Koelman, P.M.J.; Yordanova, Danka; Graef, Wouter; Mousavi, Samaneh Tadayon; Dijk, Jan van

doi:10.1088/1361-6595/ab0738

Cited by 8 publications

(9 citation statements)

References 52 publications

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“…Then, Kustova et al have developed multi-temperature models for CO 2 . Finally, in the work of Koelman et al pathway analysis has been proposed as a fast method to identify important mechanisms in CO 2 plasma chemistry. Although able to describe several features of plasma chemistry, none of these methods can fully depict the vibrational distribution function (VDF).…”

Section: Introductionmentioning

confidence: 99%

Validation of the Fokker–Planck Approach to Vibrational Kinetics in CO₂ Plasma

et al. 2019

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The Fokker–Planck (FP) approach to describe vibrational kinetics numerically is validated in this work. This approach is shown to be around 1000 times faster than the usual state-to-state (STS) method to calculate a vibrational distribution function (VDF) in stationary conditions. Weakly ionized, nonequilibrium CO2 plasma is the test case for this demonstration, in view of its importance for the production of carbon-neutral fuels. VDFs obtained through the resolution of an FP equation and through the usual STS approach are compared in the same conditions, considering the same kinetic data. The demonstration is shown for chemical networks of increasing generality in vibrational kinetics of polyatomic molecules, including V–V exchanges, V–T relaxation, intermode V–V′ reactions, and excitation through e–V collisions. The FP method is shown to be accurate to describe the vibrational kinetics of the CO2 asymmetric stretching mode, while being much faster than the STS approach. In this way, the quantitative validity of the FP approach in vibrational kinetics is assessed, making it a fully viable alternative to STS solvers, that can be used with other processes, molecules, and physical conditions.

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Section: Introductionmentioning

confidence: 99%

Validation of the Fokker–Planck Approach to Vibrational Kinetics in CO₂ Plasma

et al. 2019

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“…There is increasing awareness about the importance of sensitivity analysis in the assessment of LTP-related data (see [265] and references within), but this analysis is not yet being systematically adopted in the efforts of model validation. Future challenges will probably include the use of machine learning algorithms to ease the computational burden related with sensitivity analysis and the reduction of kinetic schemes (also within real-geometry CFD codes), aiming for the definition of 'reaction mechanisms': sets of reactions and rate coefficients validated against benchmark experiments.…”

Section: Advances In Science and Technology To Meet Challengesmentioning

confidence: 99%

The 2022 Plasma Roadmap: low temperature plasma science and technology

Adamovich

Agarwal

Ahedo

et al. 2022

J. Phys. D: Appl. Phys.

266

139

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The 2022 Roadmap is the next update in the series of Plasma Roadmaps published by Journal of Physics D with the intent to identify important outstanding challenges in the field of low-temperature plasma (LTP) physics and technology. The format of the Roadmap is the same as the previous Roadmaps representing the visions of 41 leading experts representing 21 countries and five continents in the various sub-fields of LTP science and technology. In recognition of the evolution in the field, several new topics have been introduced or given more prominence. These new topics and emphasis highlight increased interests in plasma-enabled additive manufacturing, soft materials, electrification of chemical conversions, plasma propulsion, extreme plasma regimes, plasmas in hypersonics, data-driven plasma science and technology and the contribution of LTP to combat COVID-19. In the last few decades, LTP science and technology has made a tremendously positive impact on our society. It is our hope that this roadmap will help continue this excellent track record over the next 5–10 years.

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“…Also the method of Pathway Analysis (PWA) [659] has seen renewed interest [659,660], and has been applied applied to large plasma chemistries, see for example Refs. [661,662]. More recently, graph theory and machine learning are being used to extract information from complex chemistries [663,664].…”

Section: H Reduction Of Chemical Reaction Modelsmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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Uncertainty analysis with a reduced set of input uncertainties selected using pathway analysis

Cited by 8 publications

References 52 publications

Validation of the Fokker–Planck Approach to Vibrational Kinetics in CO₂ Plasma

Validation of the Fokker–Planck Approach to Vibrational Kinetics in CO₂ Plasma

The 2022 Plasma Roadmap: low temperature plasma science and technology

2022 Review of Data-Driven Plasma Science

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Uncertainty analysis with a reduced set of input uncertainties selected using pathway analysis

Cited by 8 publications

References 52 publications

Validation of the Fokker–Planck Approach to Vibrational Kinetics in CO2 Plasma

Validation of the Fokker–Planck Approach to Vibrational Kinetics in CO2 Plasma

The 2022 Plasma Roadmap: low temperature plasma science and technology

2022 Review of Data-Driven Plasma Science

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Product

Resources

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Validation of the Fokker–Planck Approach to Vibrational Kinetics in CO₂ Plasma

Validation of the Fokker–Planck Approach to Vibrational Kinetics in CO₂ Plasma