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Record W2083501222 · doi:10.1518/001872000779698213

Why Fluid Dynamics Matters for Display Design in Process Control: Commentary on Bennett and Malek

2000· letter· en· W2083501222 on OpenAlexafffund
Kim J. Vicente, C. Ross Ethier

Bibliographic record

VenueHuman Factors The Journal of the Human Factors and Ergonomics Society · 2000
Typeletter
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProcess (computing)Control (management)Dynamics (music)Human factors and ergonomicsPoison controlPsychologyComputer scienceHuman–computer interactionArtificial intelligenceMedical emergencyMedicine

Abstract

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INTRODUCTION As its name implies, cognitive engineering requires attention to properties of both human cognition and engineering systems. Bennett and Malek (this issue) have done an exemplary job of investigating properties of human cognition that are pertinent to design of animated mimic displays for process control systems. In so doing, however, they may have inadvertently overlooked some potentially vital properties of real engineering systems (e.g., how fluid flows in a piping network under normal and, especially, abnormal situations). A closer look at fluid mechanical factors reveals that using animation to depict flow rate will sometimes provide operators with misleading feedback that could negatively affect plant safety. FLOW PATTERNS IN A PIPING NETWORK Implications for Animated Mimic Displays Bennett and Malek (this issue) motivated their research on animated mimic displays by need to support fault management behavior. In their discussion authors return to this issue, stating that the inclusion of animation in mimic displays could ... improve detection and diagnosis of faults (p. 448). However, two studies conducted evaluated performance on a quantitative psychophysical task, not a fault management task. It is not clear how results from an elemental task of quantitative judgments of velocity generalize to more complex relational task of fault management. Therefore, it is of interest to consider implications of using an animated mimic display in a complex piping network, typical of that found in real-world applications. Although usability of a design can only be assessed empirically, its usefulness can be evaluated analytically by identifying control requirements associated with a problem (Rouse, 1990). In this case these requirements can be examined by reviewing how fluid flows in a piping network. We initially consider simplest case (shown in Figure la) of fluid flowing through a pipe controlled by a valve (VA), resulting in a sensed flow rate (FA). Breakdown of Normal Expectations about Flow Rate Conservation of mass requires that instantaneous flow rate of an incompressible fluid in a rigid pipe be same at every location along a pipe segment. A pipe segment is defined as any continuous length of pipe uninterrupted by branch or feeder pipes. Clearly, flow rate will change across a branch or feeder point, as fluid will leave or enter pipe at such a location. Under normal operating conditions, it is therefore appropriate to represent flow in an entire pipe segment based on output of a single flow sensor. A display of type advocated by Bennett and Malek (this issue) would be effective in this case. However, consider more critical and demanding case of a pipe break or leak. Depending on location and severity of leak or break, flow rate along pipe could change drastically as a function of spatial location. For example, flow rate downstream of flow sensor location could be much less than that at flow sensor itself because of leaking fluid. However, an animated mimic display like that shown in Figure 1b would erroneously suggest that fluid is flowing at same flow rate all along pipe segment. It would do so because display extrapolates from flow datum collected at a single location to create a very compelling and attention-grabbing animated representation of what is normally true (i.e., constant flow rate all along pipe). This normal relationship is an inference because we do not have flow sensors all along pipe. During some faults, this inference is incorrect, and animated display may discourage operators from entertaining valid hypotheses about where pipe break or leak might be. The resulting situation is analogous to that observed in Three Mile Island (TMI) control room (Rubinstein, 1979). …

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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)0.021
metaresearch head score (Gemma)0.078
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.053
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.078
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0070.018
Scholarly communication0.0080.013
Open science0.0110.004
Research integrity0.0530.077
Insufficient payload (model declined to judge)0.0050.004

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 head0.028
GPT teacher head0.301
Teacher spread0.272 · 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
GenreCommentary

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

Citations2
Published2000
Admission routes2
Has abstractyes

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