ENHANCED 155 MM ARTILLERY PROPELLING CHARGE DEVELOPMENT WITH NUMERICAL MODELLING AND A DEEP-LEARNING APPROACH

Since the start of the war in Ukraine, one can realize the emphasis put on long-range artillery fires. Based on this observation, the EURENCO Group is developing new artillery propelling charges with improved interior ballistics performances and range. Developing and testings these charges may present a certain level of uncertainty because the dynamic behaviors of a new propellant and charge is difficult to accurately predict from closed vessel experiments and 0D numerical calculations. EURENCO is building models to tackle these issues in particular using a deep-learning based surrogate model coupled with EURENCO proprietary 0D internal ballistics numerical modelling code in order to increase numerical calculations representativeness. These modelling codes were used to support a long-range propelling charge development process. They allowed to determine optimal propellant grain dimensions and the velocities (deep-learning based model) and pressures (0D model) were fairly well predicted. Thus, development time and safety management were greatly improved.

Authors

Publication Details

Published
2025-05-05
DOI
https://doi.org/10.12783/ballistics25/37132
Primary Topic
Rocket and propulsion systems research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ENHANCED 155 MM ARTILLERY PROPELLING CHARGE DEVELOPMENT WITH NUMERICAL MODELLING AND A DEEP-LEARNING APPROACH

Bonthoux Sébastien, Théophane Nicolas, Fabien Arnaud, Sébastien Cauët
Rocket and propulsion systems research
article

ENHANCED 155 MM ARTILLERY PROPELLING CHARGE DEVELOPMENT WITH NUMERICAL MODELLING AND A DEEP-LEARNING APPROACH

Bonthoux Sébastien, Théophane Nicolas, Fabien Arnaud, Sébastien Cauët
article en

Abstract

Since the start of the war in Ukraine, one can realize the emphasis put on long-range artillery fires. Based on this observation, the EURENCO Group is developing new artillery propelling charges with improved interior ballistics performances and range. Developing and testings these charges may present a certain level of uncertainty because the dynamic behaviors of a new propellant and charge is difficult to accurately predict from closed vessel experiments and 0D numerical calculations. EURENCO is building models to tackle these issues in particular using a deep-learning based surrogate model coupled with EURENCO proprietary 0D internal ballistics numerical modelling code in order to increase numerical calculations representativeness. These modelling codes were used to support a long-range propelling charge development process. They allowed to determine optimal propellant grain dimensions and the velocities (deep-learning based model) and pressures (0D model) were fairly well predicted. Thus, development time and safety management were greatly improved.

Openalex Percentile: Top 87%
Rocket and propulsion systems research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.