Implemented OSIP ideas — November 2022
ESA's Open Space Innovation Platform (OSIP) seeks novel ideas for new space research activities. Campaigns and Channels invite solutions to specific problems or ideas on more general topics, with those run by Discovery & Preparation, including the Open Discovery Ideas Channel, specifically looking for ideas that could be implemented as system studies, early technology developments, or PhD or postdoc research co-funded by ESA and a university.
Open Discovery Ideas Channel
![Depiction of 11 of the ideas implemented through the Open Discovery Ideas Channel in November 2022](/var/esa/storage/images/esa_multimedia/images/2022/11/osip_november_2022_ideas_open_discovery_ideas_channel/24625209-1-eng-GB/OSIP_November_2022_ideas_Open_Discovery_Ideas_Channel_article.png)
In November 2022, the following ideas were implemented through the Open Discovery Ideas Channel.
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3D printed Li-ion batteries in microgravity environment
University of Picardie Jules Verne
It is possible to manufacture mechanical spare parts in microgravity by 3D printing the material PEEK through a process called fused filament fabrication (FFF). This co-funded research project will explore whether it is also possible to manufacture, on demand, replacement energy sources such as lithium-ion batteries using the same type of printer.
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Dynamic 3D mapping with CubeSats
Aarhus University
Read more about this public idea in OSIP
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A new device concept for lightweight, compact and highly sensitive X- and gamma-ray detection in space
Italian Institute of Technology
This co-funded research project will explore whether we can make a lightweight, robust and high sensitivity detector for high-energy photons to be deployed in space, which would be capable of revolutionising X-ray and gamma-ray telescopes.
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A proposed new and innovative technique for rapid assessment of space Li-ion cell fade behaviour
University College London
Batteries degrade over time, but characterising this degradation for space is time consuming and expensive. This co-funded research project will explore whether highly accurate measurement techniques can be developed to allow a battery’s short-term efficiency to be extrapolated to reliable predictions of long-duration space application mission lifetimes.
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Multi-scale microgravity numerical simulations of phase change in CRYOgenics fluids
ISAE SUPAERO
The progress of In Situ Resource Utilisation (ISRU) foresees the production of hydrogen, methane and oxygen on the Moon. This implies the transfer of cryogenic fluids in much larger quantities than any existing technologies have managed. This co-funded research project will provide accurate modelling tools for refuelling of cryogenic propellants. It will explore how we can numerically simulate phase change induced by pressure and temperature variations in cryogenic propellants in micro-gravity, from small (bubble) to large (tank) scales.
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Laser assisted mining of oxygen from regolith
Microelectronica
Oxygen is the most important raw material to support exploration and human activities on the Moon and beyond. This early technology development project intends to demonstrate a new, robust and efficient way to continuously produce oxygen from lunar dust in specific quantities for planned activities on the Moon.
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Adaptive SAR signal compression through artificial intelligence
DLR
Read more about this public idea in OSIP
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Adaptive SAR signal compression through artificial intelligence
Craft Prospect
Read more about this public idea in OSIP
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A water and magnesium propellant deorbit engine
URA Thrusters
Water propulsion has begun to emerge as a sustainable method of powering rockets, but it cannot provide high thrust for a sustained period of time. This early technology development project aims to prove the concept of a water-magnesium hybrid rocket engine, which would be a sustainable alternative propellant combination for deorbit hybrid rocket engines.
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Recycling enhanced additive manufacturing processes under Martian environmental conditions
FOTEC Forschungs- und Technologietransfer
Read more about this public idea in OSIP
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Effect of local geometrical changes and polarisation of labyrinth seal surfaces on the evaporation rate of liquid lubricants in space applications
Brno University of Technology
The field of tribology deals with the processes of wear, friction, and lubrication of contact surfaces. In space technology, the need for lubrication increases due to the extra low ambient pressure, atomic oxygen, hard ultraviolet radiation, or even the state of microgravity. By enhancing our knowledge of space tribology, we can prevent issues with technology and decrease the production of new space debris. This co-funded research project will explore how the surface structure, local geometry, and surface polarisation of a labyrinth seal affect the evaporation intensity of liquid lubricants in the molecular flow regime.
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A highly-scalable radial electron emitter for consumable-less tethers
Dresden University of Technology
This early technology development project will design a new type of electron emitter where electrons are emitted radially on top of a boom, which enables a much more compact emitter and a design that is easily scalable to provide the necessary current. It could be used to deorbit satellites at the end of their lives using a compact and autonomous kit.
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New concepts for onboard software development
![Depiction of one of the ideas implemented through the Campaign for new concepts for onboard software development in November 2022](/var/esa/storage/images/esa_multimedia/images/2022/12/safety_guarantees_for_machine_learning-based_components/24624950-1-eng-GB/Safety_guarantees_for_machine_learning-based_components_article.png)
In November 2022, the following ideas were implemented through the Campaign for new concepts for onboard software development.
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Providing safety guarantees for ML-based components through automated debugging and input generation
University of Luxembourg
Machine learning (ML) automates tasks that are difficult to program, for example semi-automatically driving rovers. It could be extended to a wider range of tasks if we can better ensure its safety. Currently, the safety of ML components is estimated through traditional methods (e.g., accuracy on a test set) rather than evidence supporting safety arguments (e.g., demonstrating that failing scenarios are rare). This co-funded research project proposes a new approach to ML safety that uses clustering algorithms and simulators.
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Neural network software/hardware auto-coding from standard ONNX definition
GMV Aerospace and Defence
Convolutional Neural Networks (CNNs) are being used to solve a wider variety of challenges on the ground, but are also increasingly being use in space, for example in visual-based tasks for rovers. This early technology development project aims to create a tool that helps space sector companies and institutions integrate neural networks into their embedded systems. It will work by automatically generating software and firmware ready to be integrated.
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