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Record W2929084528 · doi:10.1098/rsbm.2018.0024

Dudley Brian Spalding. 9 January 1923—27 November 2016

2019· article· en· W2929084528 on OpenAlexaff
B. E. Launder, Suhas V. Patankar, A. Pollard

Bibliographic record

VenueBiographical Memoirs of Fellows of the Royal Society · 2019
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsQueen's University
FundersUniversity of California, DavisUniversity of Auckland
KeywordsPrincipal (computer security)PassionsOperations researchWork (physics)Computer scienceSubject (documents)Soviet unionLibrary scienceLawManagementPolitical scienceMechanical engineeringMathematicsEngineeringEconomicsArt

Abstract

fetched live from OpenAlex

Over a remarkably productive professional life Brian Spalding largely shaped the development of numerical procedures for computing complex turbulent flows. He created a major software company, CHAM, through which the fruits of his group's research could be made available to industry and other research groups across the globe. Thus, he became the outstanding founding figure in the subject now called computational fluid dynamics (CFD). His contributions were by no means limited to strategies for converting systems of non-linear partial differential equations to forms suitable for computer solution; he also brought notable innovations to the physical modelling of combustion, turbulence and two-phase flows. Besides research, he engaged deeply with the research community in heat and mass transfer, becoming a founding editor of two international journals in these areas, and a principal driver behind the creation of the International Centre for Heat and Mass Transfer in Belgrade. He also served as the inaugural scientific chairman of the European Research Community on Flow, Turbulence and Combustion. He led a protracted and ultimately successful campaign to enable Veniamin Levich to leave the Soviet Union to settle in Israel. Outside of his technical work, his principal passions were poetry and the Russian language, which were intertwined in several published volumes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueBiographical Memoirs of Fellows of the Royal SocietySame topicCombustion and flame dynamicsFrench-language works237,207