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Record W1583192711 · doi:10.1109/ccece.2015.7129421

Modeling and control for an integrated thermal hydronic system

2015· article· en· W1583192711 on OpenAlexaff
Arman Zandi Nia, Vladimir Grebenyuk, Ryozo Nagamune

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

This paper presents modeling and control methods for an integrated thermal hydronic system (ITHS) consisting of a solar thermal collector, a thermal storage unit and a thermal booster. Control inputs are: valves activation time, pitch angle, solar pump rate and compressor rate, measurements are: temperatures at critical points, and disturbances are: ambient air temperature, source water temperature and the position of the sun relative to the plane of the solar thermal collector. In this context, the problem of maintaining temperature within the given range of the thermal storage is considered. To address this problem, the model is developed based on lumped-parameter analysis. Using the model, three controllers are tested in simulations, i.e, the on-off controller, the PI controller and the scheduling controller. The modeling will be useful in developing optimal control for high efficiency ITHS.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.994

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)0.0000.000

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 head0.041
GPT teacher head0.245
Teacher spread0.204 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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