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Record W4251063260 · doi:10.1017/cbo9781316226926.001

Preface

2016· book-chapter· en· W4251063260 on OpenAlexaff
John H. S. Lee

Bibliographic record

VenueCambridge University Press eBooks · 2016
Typebook-chapter
Languageen
FieldMathematics
TopicGas Dynamics and Kinetic Theory
Canadian institutionsMcGill University
Fundersnot available
KeywordsShock wavePartial differential equationCompressible flowShock (circulatory)CompressibilityFlow (mathematics)Gas dynamicsMethod of characteristicsApplied mathematicsConservation lawPhysicsNumerical integrationMechanicsClassical mechanicsComputer scienceMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Gas dynamics of explosions is a subject that deals with the non-steady propagation of shock waves. The theoretical description of non-steady shock propagation requires, in general, the solution of the non-linear partial differential equations of compressible flow that govern the unsteady flow behind the shock. This requires the numerical integration of the conservation equations. However, there exist various analytical methods that can give approximate solutions and provide a useful alternative to the more involved numerical integration of the gas dynamics equations. Analytical methods can also render the physics of the problem more transparent. The material in this book is largely based on a course on shock dynamics that the author gave periodically since the 1970s. The objective of that course is to discuss the fundamentals of the non-steady gas dynamics and shock waves, where relatively few books on the subject are available. The choice of topics is that of the author and emphasis is placed on presenting the basics of gas dynamics. Thus, relatively few practical problems and numerical results are given, and sample problems are only used to serve as illustrations of the method. Although the works of numerous authors are reviewed and developed upon, the book neither gives a comprehensive review of the extensive literature on the subject nor provides a detailed bibliography. It is felt that references can readily be obtained via an internet search. The few references that are given are mostly limited to the few early studies where the method was developed. The author is extremely grateful to Dr. K. Ramamurthi who made valuable contributions and carried a very thorough reading of the manuscript. Dr. P. Thibault and Mr. M. Gaug also carried out proof reading of the manuscript. Drs. Qin Hui and Li Jian carried out the difficult task of typing the manuscript from the author's handwritten draft.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.400
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.4000.251

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.217
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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