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Record W4391928512 · doi:10.1016/j.energy.2024.130747

Development of a smart powering system with ammonia fuel cells and internal combustion engine for submarines

2024· article· en· W4391928512 on OpenAlexaff
Ibrahim Akgun, İbrahim Dinçer

Bibliographic record

VenueEnergy · 2024
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsInternal combustion engineCombustionFuel cellsAutomotive engineeringEngineeringPropulsionEnvironmental scienceAeronauticsWaste managementAerospace engineeringChemistryChemical engineering

Abstract

fetched live from OpenAlex

The current limitations of hydrogen storage technology in submarines have prompted the need for alternative solutions. One promising option is the use of ammonia, a readily stored fuel. This paper presents an innovative, integrated system that runs on ammonia and does not rely on atmospheric air. The system combines Direct Ammonia Fuel Cell (DAFC) stack and an Internal Combustion Engine (ICE) technology to generate power, freshwater, and cooling. The system also recovers waste heat and utilizes it efficiently to produce these useful outputs. The study further aims to assess the system's performance using energy and exergy analysis methods and to conduct a parametric analysis to examine the impact of parameters and operating conditions on system efficiency. In the developed integrated system, the quantities of net power produced, cooling provided, and freshwater flow rate produced under specified conditions are 4069 kW, 589.5 kW, and 1.269 kg/s, respectively. Its energy and exergy efficiency were found to be 38.58% and 44.77%, respectively. The analysis study also obtains that increasing the ammonia flow rate supplied to the ICE and the steam flow rate provided to the turbine, as well as the reference temperature, could potentially improve both energetic and exergetic efficiencies .

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 score0.033
Threshold uncertainty score0.262

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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

Citations30
Published2024
Admission routes1
Has abstractyes

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