2025-10-03 –, Coffee room Language: English
At IENAI SPACE, we adopted Julia 3 years ago to develop our mission analysis tools for simulation and optimization of small-satellite space missions. Thanks to high-level productivity, strong performance and a vibrant open-source ecosystem with strong foundations (highlighting DifferentialEquations.jl and SatelliteToolbox.jl in particular), we are quickly achieving our objectives with a small team. We will discuss strengths, pain points, and highlight some advanced analyses we perform with them
This talk aims to describe how Julia and its ecosystem has enabled IENAI SPACE to quickly develop an cutting-edge mission analysis toolbox with a small team.
Background
We are a NewSpace startup based on the Madrid metropolitan area that aims to address the mobility gap in small satellites through both hardware solutions (in the form of the Athena electrospray thruster) and software solutions (such as IENAI 360, the main subject of this talk) that allow satellite and constellation operators to efficiently perform analysis and design of small-satellite missions; we have a particular focus on low-thrust propulsion systems, whose operational implications often demand specific analysis solutions and methodologies that are not as easily covered by traditional tools.
Structure
1) Introduction: we will describe our mission (helping to solve the problem of small-satellite propulsion and the design of their mobility operations) and how a pillar of our solutions is the development of accurate, performant, versatile and effective mission analysis software.
2) History and ecosystem foundations - in other words, why and when did we choose Julia? We explain how the assessed benefits, costs, and risks were a good fit for a startup such as us. In particular, even though the use of a newer programming language of smaller industrial popularity entails some risks and drawbacks, we considered that they were outweighed by the combination of high productivity, good performance, good versatility, and a very strong ecosystem for our specific context. In particular, we will discuss some critical enablers for us, such as the existence of the DifferentialEquations.jl suite (and the SciML ecosystem in general) and the SatelliteToolbox.jl library, which covers many pillars and models in the space analysis field.
3) Solutions and analysis examples - at this stage, we will discuss the main features and architecture of our modular mission analysis solution, 360, whose core computational engine is written in Julia. We will direct particular emphasis to how multiple dispatch enables powerful abstraction patterns, which allow us to implement a versatile panoply of functionalities without sacrificing performance when it matters. We will discuss deterministic and uncertain simulation use cases, parametric analysis, and multi-objective optimization.
4) Practical aspects of working with Julia: we will conclude with some commentary on the benefits, drawbacks and pain points we encountered and encounter in our Julian journey, focusing not just on the purely technical aspects but also on the socio-laboral implications of this choice from our experience.
Daniel González Arribas is a control & automation engineer specializing in computational modeling, simulation and optimization of aerospace system. He obtained his PhD on Aerospace Engineering in 2019 at Universidad Carlos III de Madrid, where he performed research on aircraft trajectory optimization and optimal flight planning under meteorological uncertainty. He is now at IENAI SPACE, a NewSpace startup, where he leads the development of the computational engine of IENAI's mission analysis tools.