Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Hierarchical gradient metals by additive manufacturing (HIGMAM)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Products and Processes Engineering
Panel description: Product and process design, chemical, civil, environmental, mechanical, vehicle engineering, energy processes and relevant computational methods
Countries involved: Germany
Call: ERC STARTING GRANTS (ERC-2022-STG, 2022)
Start date: 2024-01-01
End date: 2028-12-31
Duration (months): 60.0
Total project cost (EUR): 1492751.0
EU funding (EUR): 1492751.0
Participants: 2
Project status: SIGNED

Over the last decade, the steadily increasing research on gradient-structured metals and alloys has demonstrated great successes of this biological and nature-inspired concept to evade the strength-ductility trade-off dictating in regu-lar engineering materials. However, given the intrinsic limitations of conventional manufacturing methods, the currently engineered structural gradient materials are all featuring a linear pattern, usually from exterior to interior, with only simple structures of grain sizes, twin spaces, lamellae spaces, or combinations. A recent preliminary study of my research team accidentally discovered that additive manufacturing could produce periodic and 3D gradient microstructures with not only simple microstructure features, but also hierarchical ones, from grain size to sub-grain boundaries and even lattice distortions. The hierarchical gradient microstructure keeps the same level of high strength but doubles the failure strain compared to the conventional microstructures. This inspires us to systematically investigate the possibilities and boundaries of these new hierarchical gradient microstructures by additive manufacturing. Due to its extreme complexity across multiple scales and physics laws in correlating process, microstructure, and property of such new materials, we aim to develop a systematic approach for designing hierarchical gradient microstructures by using design of experiments, in-depth and multiscale characterization methods, multiphysics and multiscale numerical models, and data informatics. The intelligent integration of the physics-based and data-driven models will eventually boost the dimensionality, efficiency, and accuracy of the modeling approach for the design of the complicated hierarchical gradient microstructure in 3D. It will provide a powerful, digital and sustainable way for the design of new materials and/or processes and evaluation of material performance.

Keywords: plastic deformation,anisotropy,damage,fracture,multiscale modeling,gradient microstructures,strength ductility trade off
Radically Electrified Cyclic Dividing Wall Column: Systems Integration, Optimization and Control (RE-Cy-DWC)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Postdoctoral Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Netherlands
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2025-04-01
End date: 2027-03-31
Duration (months): 24.0
Total project cost (EUR): 232916.16
EU funding (EUR): 232916.16
Participants: 1
Project status: SIGNED

The RE-Cy-DWC project aims to develop a novel, energy-efficient, and sustainable distillation process by integrating cyclic distillation (Cy-Dist) and dividing wall column (DWC) technologies with process electrification. Distillation is a key process in the chemical industry, but it is highly energy-intensive and relies heavily on fossil fuels. This project addresses the challenge of reducing energy consumption and carbon emissions in distillation processes by combining process intensification (PI) and power-to-heat (PtH) solutions. The primary objectives of the project are to: (i) develop mathematical models for the RE-Cy-DWC process that optimize its energy use and carbon efficiency; (ii) design and implement advanced control systems to maintain operational stability and product quality, even under dynamic conditions; (iii) integrate electrification technologies, such as heat pumps, to further enhance energy savings and reduce CO2 emissions. The project’s approach will lead to a 20% improvement in energy efficiency and a 30% reduction in CO2 emissions compared to conventional distillation processes. The results of the project will directly contribute to the European Union’s goals for industrial decarbonization and net-zero carbon emissions by 2050, aligning with the EU’s Work Programme on climate action and sustainable industries. By advancing low-carbon distillation technologies, the project will provide tangible benefits to industries such as chemicals and energy, enabling a transition towards more sustainable production methods. The knowledge generated will be disseminated through open-access platforms, workshops, and publications, ensuring its broad impact across both academia and industry.

Keywords: distillation decarbonization electrification process design and optimization dynamics and control
The Old, the Young, and the Uncertain Future: Using High-Dimensional Stochastic Overlapping-Generations Models to Evaluate Fiscal Policies that Shift Risk and Resources Across Generations (SOLG for Policy)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Individuals, Markets and Organisations
Panel description: Economics, finance , management
Countries involved: Germany
Call: ERC STARTING GRANTS (ERC-2021-STG, 2021)
Start date: 2022-10-01
End date: 2027-09-30
Duration (months): 60.0
Total project cost (EUR): 1483639.0
EU funding (EUR): 1483639.0
Participants: 1
Project status: SIGNED

Policy decisions about government borrowing, taxation, and spending have a differential impact on generations and thus imply substantial intergenerational transfers. The costs and benefits of these policies occur over decades and come with large uncertainty, for instance related to productivity growth, global financial conditions, and demographic trends. Therefore, fiscal policies, whether intentional or not, transfer risk and resources across generations. Assessing their impact on welfare calls for simulating such policies in models that include the relevant demographics and risks, yet also the related risk-premia. The latter lower government borrowing costs relative to the expected return on investments and are thus particularly important for the welfare implications of fiscal policy. SOLG for Policy aims to build stochastic overlapping-generations (SOLG) models that integrate all these elements and are thus able to quantitatively assess the welfare implications of fiscal policy choices involving intergenerational redistribution and risk-sharing. To solve these models despite their high-dimensional nature, SOLG for Policy leverages recent advances in computational economics based on adaptive sparse grids (ASGs) and develops three extensions of this approach. First, using shape-preserving ASGs to facilitate value function iteration. Second, merging ASGs with endogenous grids to avoid compute-intensive optimization steps. Third, parameterizing intergenerational distributions to further alleviate the curse of dimensionality. Policy questions that will be addressed include: Should the debt accumulated in the Covid crisis be paid down through taxation or do low real interest rates make it desirable to leave the debt and its uncertain burden for future taxpayers to deal with? Is it feasible and desirable to make the pension system provide less redistribution yet more insurance across generations, and how can this be achieved?
Flagship advanced solutions for Condition and Health monitoring In Power electronics (FLAGCHIP)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Energy
Destination: Sustainable, secure and competitive energy supply
Action type: Research and Innovation actions
Countries involved: Spain,Norway,France,Switzerland,United Kingdom,Germany,Greece,Netherlands,Cyprus
Call: Condition & Health Monitoring in Power Electronics (PE) - Wide Band Gap PE for the energy sector (HORIZON-CL5-2024-D3-01, 2024)
Start date: 2024-09-01
End date: 2028-02-29
Duration (months): 42.0
Total project cost (EUR): 3989562.75
EU funding (EUR): 3989562.75
Participants: 13
Project status: SIGNED

There is a pressing need to ensure the seamless operation of reliable power converters, prompting a concerted effort to explore new converter topologies, innovative control techniques, and advanced semiconductor materials like wide bandgap and ultra-wide bandgap technologies. To address this challenge, FLAGCHIP will pivot around three pillars of innovation: 1) Power Modules, including innovations at device and package level; 2) Condition and Health Monitoring of such Power Electronics and 3) Cost Efficiency of the proposed solution and the system as a whole. These three pillars will group a total of 13 main innovations that will be developed and tested during the project. In that sense, some of these innovations include strategies for the prediction and prevention of equipment failures minimizing downtime and facilitating cost stress steering and maintenance routines. Also. FLAGCHIP will enhance the efficiency of Power Electronics components, facilitating the creation of a new generation of inverters, converters, and other power equipment for the energy sector. Additionally, the project will reduce space occupancy, with a primary focus on offshore applications, while improving the cost efficiency of power devices and semiconductor fabrication processes. FLAGCHIP's approaches will be demonstrated within the project’s partners research infrastructures, including AC/DC and DC/DC applications at two strategically chosen pilot sites in France (DC/DC MVDC Converter) and Norway (AC/DC Offshore Wind Turbine and Substation Converter architecture ). To achieve that, FLAGCHIP will rely on a competitive consortium formed by 10 partners, from 7 different states, with a strong research group and leading industrial companies on the sector (2 RTOs, 3 Universities, 3 Large Companies and 2 SMEs) that will deliver 13 Key exploitable results by the end of the project.

Keywords: mosfet,wbg,uwbg,package,candhm,stress steering,fbg,fom,scbg,tcad,tsep,tj,cost efficiency,cooling,mvdc,hvdc,converter,maintenance,operation
Up-Skilling for Industry 5.0 Roll-Out (Up-Skill)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: Digital
Destination: A human-centred and ethical development of digital and industrial technologies
Action type: Research and Innovation actions
Countries involved: Sweden,United Kingdom,Italy,Spain,Germany
Call: Workforce skills for industry 5.0 (RIA) (HORIZON-CL4-2021-HUMAN-01, 2021)
Start date: 2022-09-01
End date: 2026-02-28
Duration (months): 42.0
Total project cost (EUR): 2948250.25
EU funding (EUR): 2948250.25
Participants: 12
Project status: SIGNED

The 4th Industrial Revolution/Industry 4.0 has enabled reduction of production costs, improved consistency of product quality and enabled mass customisation by merging the physical and digital worlds. The transition is still ongoing - Industry 4.0 is a general-purpose technology, adding value across all industrial sectors. However, the perception of Industry 4.0 at a human level has not all been positive. It has been plagued by fear of job cuts and in some sectors completely replacing the human workforce. Automation projects have often failed due to omitting the critical skilled human elements in business success with unintended consequences including reduced customer satisfaction, poorer product quality and lower process efficiency. Automation alone clearly cannot be a source of sustained competitive advantage. I5.0 will address the balance between humans and technology, focussing on the collaborative relationship between skilled workers and automation. The intent is reinstate skilled craftsmanship at the centre of production processes where people add unique value and competitive advantage, augmented by intelligent, data-driven technology emerging from Industry 4.0. In the Up-Skill project, we will address the implications of Industry 5.0, in particular the relationship between automation, skilled work and organisational systems. Our research will establish how the relationship between automation and human input plays out in a range of industrial settings, creating comparative case studies to capture effective implementation strategies. We will address under-explored strategic spaces in production - where automation adds value to skilled and artisanal work, and where further automation risks undermining product value. This research will identify the shifting organisational characteristics that are needed to ensure technology advancements are implemented within companies while ensuring sustainable, added value for man, machine, and organisation.

Keywords: industry 5 0,collaborative robots,augmentation,skilled craftsman,decent work,blended production,automation,industry 4 0,digitisation,value of labour,mass personalisation,up skilling
Research Exchanges in the Mathematics of Deep Learning with Applications (REMODEL)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Staff Exchanges
Action type: Marie Skłodowska-Curie actions
Panel: Mathematics
Countries involved: Norway,Japan,United States,Netherlands,Canada,United Kingdom
Call: MSCA Staff Exchanges 2022 (HORIZON-MSCA-2022-SE-01, 2022)
Start date: 2024-01-01
End date: 2027-12-31
Duration (months): 48.0
Total project cost (EUR): 473800.0
EU funding (EUR): 473800.0
Participants: 7
Project status: SIGNED

The subject of this proposal is “mathematical aspects of deep learning algorithms and their applications”. We will address several questions related to the mathematical foundations of neural networks and set up an interdisciplinary team to aidthe design of test problems and validate the research results obtained. The impact of neural networks and deep learning in recent years has been profound and unprecedented. But in the wake of the vast progress in this area, several questions and concerns have been raised about the robustness, reliability, accuracy, reproducibility and feasibility of neural networks. It is widely recognised that the mathematical sciences, are a key enabling technology in many aspects of machine learning, not the least to resolve some of the above mentioned concerns. Mathematical language and formalism can bring morerigour and precision to the understanding of the deep learning methodology. Recently, deep learning methods have been applied to physical simulations, and to discover the underlying mathematical model. Most of the work in this area has been limited to proof-of-concept and has not been applied to practical problems. An alternative approach is to make use of reduced order modelling, and this can also be combined with machine learning methods. The aim of this project is to understand, study, prove, and test the properties of deep learning algorithms using ideas from dynamical systems, geometry and optimisation. The research objectives are three-fold. The first pertains to understandingthe general properties of neural networks and their impact on a range of applications. The second is about the use of neural networks for investigating dynamical systems, and their applications to physical models. Finally we establish a new and complementary network of mathematicians from European and third countries for studying neural networks and the methods of deep learning with connections to a range of application areas through staff exchanges.

Keywords: deep learning,neural networks,numerical analysis,hyperspectral image analysis,inverse problems,equivariance,computer vision,model order reduction,dynamics discovery,accelerated simulations
Electrification of industrial processes using induction and microwaves technologies (JOULIA)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Energy
Destination: Efficient, sustainable and inclusive energy use
Action type: Innovation actions
Countries involved: Spain,Italy,Greece,Portugal,Belgium,Poland,Sweden
Call: Alternative heating systems for efficient, flexible and electrified heat generation in industry (HORIZON-CL5-2024-D4-01, 2024)
Start date: 2024-12-01
End date: 2028-11-30
Duration (months): 48.0
Total project cost (EUR): 6521506.95
EU funding (EUR): 5298114.39
Participants: 16
Project status: SIGNED

JOULIA aims to develop two innovative electric-heating processes to maximize their flexibility, energy savings and RES integration into two mid-temperature manufacturing processes. To this end, induction and microwave technologies will be demonstrated in the rubber and plastic sector for rubber vulcanization (rubber production) and glue thermal activation (multilayer pipes production) respectively. In this vein, processes configuration will be optimised and monitored, using the developed digital models and process simulations to fine-tune the whole industrial process enabling their control, adaptability and cost optimization. Health and security requirements will be implemented supported by a predictive maintenance tool. Both contactless systems will be validated at TRL7, including technical and economic studies (TEA and LCA/LCC) using previously developed models. Additionally, JOULIA solutions' replicability potential will be assessed in other industrial sectors (food and ceramics), addressing future upscaling plans to be aligned with new market opportunities and stakeholder engagement. General public awareness of electrified technologies will be improved while new business models and training materials will be developed. JOULIA will contribute to decarbonizing a sector that heavily depends on fossil fuel combustion, trying to maximize its energy efficiency, sustainability and economic benefits, improving European industries competitiveness and supporting their clean energy transition. To achieve these objectives, JOULIA's consortium consists of 16 partners from 7 EU countries, with diversified expertise and knowledge of the addressed processes and access to the required lab and pilot infrastructure. At the end of the project, JOULIA will increase the resilience and security of European industrial, as well as minimize its dependence on imported fossil fuels.

Keywords: induction,microwaves,rubber and plastics,electromagnetic technologies,process flexibility,energy efficiency,ceramics,food and beverages,industrial decarbonization
NOVEL ELECTRODE COATINGS AND INTERCONNECT FOR SUSTAINABLE AND REUSABLE SOEC (NOUVEAU)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: Industry
Destination: Increased Autonomy in Key Strategic Value Chains for Resilient Industry
Action type: Research and Innovation actions
Countries involved: Belgium,Germany,France,Netherlands,Poland,Spain,Switzerland
Call: Safe- and sustainable-by-design metallic coatings and engineered surfaces (RIA) (HORIZON-CL4-2021-RESILIENCE-01, 2021)
Start date: 2022-09-01
End date: 2025-11-30
Duration (months): 39.0
Total project cost (EUR): 3880640.75
EU funding (EUR): 3880638.75
Participants: 14
Project status: SIGNED

The NOUVEAU project will develop solid oxide cells (SOCs) with innovative La- and PMG-free electrode materials, solid electrolyte and interconnects with an overall reduced amount of REE (30%), recycled Y (50-70%) and Cr (20%). To this end, advanced coating methodologies and modelling will be employed in combination with sustainable-by-design and recycling approaches. Integrated models will be adapted and developed to predict physicochemical properties/toxicity endpoints in real life scenarios, including multiscale models; data-based modelling (SHF, SPF); user-ready modelling for industrial deployment (SSbD tools); standardisation and regulatory compliance (REACH updates). By addressing resource and energy efficiency through material design and waste management, NOUVEAU will create opportunities for increased circularity of raw materials, lower climate impact and decreased criticality of solid oxide cells materials. To validate the NOUVEAU project's objectives and their economic, commercial and environmental impact, a comprehensive set of assessment techniques will be used, including life-cycle analysis, cost analysis, and social and eco-efficiency life-cycle analysis. The assessment results will guide the project's efforts towards optimised resource efficiency and SOC upscaling with improved stability benchmarked against the reference state-of-the ones. More specifically, NOUVEAU will benefit from the complementarity and scalability of the green inks development in combination with spray printing, slot die coating and convection and radiation drying. The NOUVEAU project draws on the complementary expertise of applied research centres and innovation driven companies, including Marion Technologies, Coatema, Fiaxell and QSAR Lab, in the field of materials design, SOC engineering and multi-scale modelling, including in silico methodologies (machine learning, artificial intelligence).

Keywords: la free electrodes yttrium recycling low cr steel green inks solid oxide cells slot die coating spray coating sustainable by design shf spf model ssbd tool social and life cycle analysis