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Record W4415760462 · doi:10.1016/j.aeaoa.2025.100389

Pressure-liquefied ammonia jet dispersion: Multi-model intercomparison using Desert Tortoise and FLADIS field data

2025· article· en· W4415760462 on OpenAlexfundno aff
Simon Gant, Joseph Chang, Steven R. Hanna, Gemma Tickle, Tom Spicer, Sun McMasters, Shannon Fox, Ron Meris, Scott Bradley, Sean Miner, Matthew R. King, S. Simpson, Thomas Mazzola, Alison McGillivray, Harvey Tucker, Oscar Björnham, Bertrand Carissimo, L. Fabbri, Maureen Heraty Wood, Mike Harper, Frank Hart, Thomas Vik, Anders Helgeland, J.E. Howard, Lorenzo Mauri, Shona Mackie, A. Mäck, Jean-Marc Lacôme, Stephen Puttick, Derek Miller, Seshu Dharmavaram, Amy Shen, Stéphane Burkhart, Chris Dixon, Sandra Nilsen, Robert W. Bradley, Hans Langva Skarsvåg, Eirik Holm Fyhn, Ailo Aasen

Bibliographic record

VenueAtmospheric Environment X · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsnot available
FundersNorges ForskningsrådCanada Excellence Research Chairs, Government of CanadaDefense Threat Reduction AgencyDanmarks Tekniske UniversitetCalifornia Student Aid Commission
KeywordsRange (aeronautics)TortoiseDispersion (optics)Desert (philosophy)Atmospheric dispersion modelingPlumePrecipitationJet (fluid)

Abstract

fetched live from OpenAlex

This paper presents the findings of an international model inter-comparison exercise that was undertaken in the period 2021-2024 to assess the performance of atmospheric dispersion models for simulating releases of pressure-liquefied ammonia. The exercise used data from ammonia field trials dating from the 1980s and 1990s: the Desert Tortoise and the FLADIS trials. Concentration data from two arcs of sensors in the Desert Tortoise trials and three arcs of sensors in the FLADIS trials were used. Twenty-one independent modelling teams from North America and Europe participated in the exercise and provided in total twenty-seven sets of results from a range of different models, including empirically-based nomograms, integral, Gaussian puff, Lagrangian particle, and Computational Fluid Dynamics (CFD) models. The work is novel in presenting the results from such a large cohort of models, examining specifically the dispersion behaviour of ammonia. This is particularly relevant at the current time, given the growing international interest in using ammonia as a clean energy vector and shipping fuel. The study found that the agreement between model predictions and measurements (as determined by performance measures such as geometric mean bias and geometric variance) varied between different models. At any downwind distance, the range in predicted plume arc-max concentrations spanned a range of up to one or two orders of magnitude about the measurements. Several modelling teams used the same models and, in most cases, their predictions differed. Given appropriate inputs, most models generally predicted concentrations that agreed with the data within commonly-used model acceptance criteria. There was no single class of model that provided superior predictions to others; predictions from several empirically-based nomograms, integral, Gaussian puff, Lagrangian particle, and CFD models were all in close agreement with the data (as defined by the model acceptance criteria). The findings of the exercise are being used to help plan a programme of future ammonia experiments in the USA, called the Jack Rabbit III trials. The results are also useful for assessing the performance of models that may be applied to assess risks at ammonia facilities, and for emergency planning and response. • Study assessed capabilities of dispersion models for simulating ammonia releases • Model intercomparison exercise used data from the Desert Tortoise and FLADIS trials • 21 participating organisations and 27 sets of results from range of different models • Most models predicted acceptable arc-max concentrations • Findings useful for risk assessments, emergency response and planning future trials

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 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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.090
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.028
GPT teacher head0.268
Teacher spread0.241 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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