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Record W4322005320 · doi:10.5194/egusphere-egu23-7812

HETV2: An update the vectorized inorganic chemistry solver HETV to include Na+-Cl--Ca2+-K+-Mg2+ in the metastable state option based on ISORROPIA II algorithms

2023· preprint· en· W4322005320 on OpenAlexaff
Stefan Miller, Paul A. Makar, Colin Lee

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsFortranSubroutineComputer scienceAlgorithmVectorization (mathematics)SolverChemistryComputational scienceParallel computingProgramming language

Abstract

fetched live from OpenAlex

Inorganic heterogeneous chemistry (the reactions taking place between inorganic components of the gas-particle system) is one of the most complex and computationally demanding parts of atmospheric chemistry models. Accurate and highly computationally efficient algorithms for carrying out these calculations are essential for these models. Here we present a revised and updated approach for carrying out these calculations, called HETV2.HETV2 updates the original HETV metastable state subroutines (Makar et al., 2003) expanding the aerosol system to include base cations (Mg2+, K+, Ca2+, Na+), and partitioning between chlorine, ammonium, and nitrate ions and HCl, NH3 and HNO3 gases. HETV2 is based on the algorithms of ISORROPIA II (Fountoukis and Nenes, 2007), with several key improvements for accuracy and computational efficiency of the calculations. First, the accuracy and stability of polynomial roots have been improved by using a Taylor series expansion of the quadratic formula, for times when the coefficients differ by orders of magnitude. Second, the new algorithms in HETV2 enforce mass conservation for cases where all species are present and the ratio of total base cations to sulfate is between 1.0 and 2.0. Third, the code has been optimized using a “vectorization by gridpoint” approach, allowing a single call to each subroutine for n sets of input conditions, reducing the subroutine call factor overhead. Fourth, the code has been optimized to remove unnecessary calculations, and the programming language has been updated from Fortran 77 to Fortran 90. Fifth, all subroutines that require bisection to obtain an equilibrium solution (i.e., the ‘major systems’) have had their root-finding method updated to the ‘Interpolate, Truncate and Project (ITP)’ method (Oliveria et al., 2021); the ITP method can obtain superlinear convergence, and therefore may significantly reduce the number of iterations, and hence the computational time, required to obtain the same result as ISORROPIA II. The new algorithms significantly improve both the computational speed and accuracy for inorganic heterogeneous chemistry calculations relative to ISORROPIA II. In this talk, we will describe the inorganic heterogeneous chemistry systems that are solved, the improvements to the algorithms, and compare the computational speed of ISORROPIA II to the new HETV2 code (depending on the chemical subspace examined, the new code is up to 2x faster than ISORROPIA II).References Fountoukis, C., & Nenes, A., 2007. ISORROPIA II: A computationally efficient thermodynamic equilibrium model for Aerosols. Atmospheric Chemistry and Physics, 7(17), 4639–4659.Makar, P. A., Bouchet, V. S., & Nenes, A., 2003. Inorganic Chemistry calculations using HETV—a vectorized solver for the SO42−–NO3−–NH4+ system based on the ISORROPIA algorithms. Atmospheric Environment, 37(16), 2279–2294.Oliveira, I. F., & Takahashi, R. H., 2021. An enhancement of the bisection method average performance preserving Minmax optimality. ACM Transactions on Mathematical Software, 47(1), 1–24.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0040.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0200.007

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 head0.026
GPT teacher head0.247
Teacher spread0.220 · 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
GenreSoftware

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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