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Record W2623203820 · doi:10.18260/1-2--2980

Pc Based Measurement Of The Heat Of Combustion Of A Solid Fuel Using Oxygen Bomb Calorimeter

2020· article· en· W2623203820 on OpenAlexaff
Ramesh Prasad, Ryan Munro

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicThermal and Kinetic Analysis
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsCalorimeter (particle physics)CombustionNuclear engineeringCalorimeter constantHeat of combustionData acquisitionUSBSolid fuelAdiabatic processCombustion chamberProcess engineeringPyrometerAutomotive engineeringMechanical engineeringMaterials scienceEngineeringTemperature measurementChemistryElectrical engineeringComputer scienceThermodynamicsSoftwarePhysics

Abstract

fetched live from OpenAlex

The paper describes an experimental system developed for measurement of the heat of combustion of a sample of solid fuel. The system is set up to use an Oxygen Bomb Calorimeter together with a temperature sensor. A data acquisition system is used to accurately record temperature versus time response before, during and after the combustion of the fuel sample. The data acquisition system includes an analog to digital converter (IOtech Personal DAQ 3005 with USB connection) a standard software package (DASYLab) to obtain the measurement under program control to determine the observed temperature rise of the system following combustion of a carefully weighed sample of solid fuel. The data processing includes several corrections to the measured temperature rise in order to determine the heating value of the fuel. The oxygen bomb calorimeter can be operated as an adiabatic system to eliminate the heat gain/loss during the experiment. However, the experiment is carried out in a non-adiabatic system to allow a greater insight in this experiment and to enhance its pedagogical value. This experimental system has been developed for an undergraduate laboratory in thermodynamics for Mechanical/Chemical Engineering students.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.248
Teacher spread0.187 · 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 designBench or experimental
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

Citations0
Published2020
Admission routes1
Has abstractyes

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