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    EXC 3120 BlueMat - Research Topic D3: Hydrovoltaics
    BlueMat aims to revolutionize hydrovoltaics by developing next-generation hydrovoltaic material technologies. Our goal within D3 is to create high-performance, multiscale, porous materials and innovative hydrovoltaic device architectures to efficiently harness electrical energy from humidity variations, imbibition and drying cycles. We will systematically evaluate their energy conversion performance under varying humidity levels, temperatures, and operating conditions. Our research will focus on reducing imbibition–drying cycle times while enhancing energy output by leveraging humidity variations and low-grade waste heat. This interdisciplinary program aims to advance hydrovoltaics through cutting-edge experimentation, addressing key challenges, and unlocking new opportunities for sustainable energy generation from ambient environmental sources. We will develop highly adapted material architectures to optimize power, addressing issues like minimum friction, salt crystallization upon drying, or evaporation using aqueous electrolytes or water itself. The efforts in D3 aim to improve energy conversion efficiency, durability, and scalability of hydrovoltaic applications, paving the way for widespread deployment in diverse environmental settings. The key scientific questions include: How can we exploit novel hydrovoltaic mechanisms to generate energy from cyclic imbibition and desiccation of electrolyte-infused porous media? How do non-equilibrium fluid dynamics, surface chemistry, open-circuit potential, and other fundamental factors, including the interaction of imbibition and drying influenced by pore characteristics and external stimuli, affect the proposed scheme? How can we optimize the performance of the hydrovoltaic scheme using concepts like hierarchically porous electrodes and adaptive pore wall coatings?
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    EXC 3120 BlueMat - Cross Area Modeling
    The characteristic behavior of water-driven materials results to a large extent from complex and as yet often poorly understood interactions across different scales. To unravel the fundamental physics and chemistry of water-driven materials, (1) we will develop novel multiscale modeling methods bridging from the quantum to the device level; (2) machine learning (ML) will help to bridge between the scales and leverage the unique imaging data collected in the CA Imaging. Including physical knowledge into ML architectures will help accelerate the design of water-driven materials by identifying, for example, process-structure-property relationships in a fast and automated manner (physics-informed ML). To achieve these ambitious goals, we will rely on extensive (3) research software engineering, providing a whole ecosystem of cutting-edge open-source research software. Overall, we will create a unique multiscale and multi-purpose open-source software ecosystem for computer simulation and ML to understand, predict and optimize Blue Materials from the quantum level to the device level, laying the foundations to leverage cutting-edge imaging and AI to go from “nature-inspired materials” to materials and functionalities “beyond nature”
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    EXC 3120 BlueMat - Research Topic B1: Tunable Water Condensation and Evaporation
    Evaporative flows at porous surfaces are significantly influenced by the liquid surface exposed to the vapor phase and the vapor pressure of the liquid at the interface, with additional contributions from hydraulic permeability and hydrophilicity within the porous structure. Our goal is to actively control water condensation and evaporation at the vapor-exposed surfaces of structured materials. The key scientific questions are: How can we fabricate 3D structured porous materials systems that – depending on electrical, photonic, or chemical (pH) stimuli – change their evaporative behavior? How can we characterize, understand, and predict water condensation and evaporation (and drying) across scales? How can we achieve tailor-made switching times, imbibition-drying cycles, and minimal energy losses from viscous dissipation?
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    GRK 3068 - Climate-informed Engineering
    Spokesperson: Professor Dr. Shokri Deputy spokesperson: Professor Dr. Smirnova Breakthroughs in computing have led to the development of new generations of Earth Systems Models, which provide detailed information on how our planet may locally respond to the ongoing global warming, with unprecedented spatial and temporal resolutions of 1 km and a few minutes, respectively. This massive climate data may be of little value, if not utilized by engineers who are involved in developing technical solutions for real-world challenges. Engineers stand to benefit from seizing this opportunity and by incorporating climate data in engineering designs, solutions, and practices. This benefit is precisely the key driving force for founding the Research Training Group (RTG) on Climate-informed Engineering (CIE) as an emerging interdisciplinary field of research integrating state-of-the-art climate information with engineering education. A structured training strategy is designed in the RTG featuring a broad range of educational activities to facilitate training and promote early-career researchers who will contribute to developing the next generation of engineering solutions that are adaptive to climate. In doing so, we will integrate a new generation of climate models in our training through the active involvement of the Max-Planck Institute for Meteorology (MPI-M), an internationally renowned organization at the forefront of global efforts on climate models. Furthermore, the RTG offers a joint PhD program between TUHH and the United Nations University Institute for Water, Environment and Health (UNU-INWEH). Hence, the PhD candidates will benefit from the interactions with renowned experts at UNU and the UN on a variety of topics related to United Nations Sustainable Development Goals, which is at the heart of our RTG. The RTG will utilize engineering science and innovative approaches to develop new materials, processes, and predictive capabilities to help people, businesses, and ecosystem in the face of climate change. The RTG will include three main Research Areas, namely CIE for Built Environment, CIE for Process Engineering and CIE for Sustainable Resource Management and Environment. Ten projects are designed in the first funding phase, covering a wide range of topics, spanning from influence of climate on renewable resources and food engineering to developing novel materials for latent heat storage. The projects will couple indoor and outdoor climates based on Internet-of-Things technologies and will develop predictive capabilities for water and food security. All the principal investigators and PhD candidates share the common goal of employing new-generation climate information to devise strategies for mitigating climate change. This interdisciplinary RTG is the first of its kind, ultimately enabling engineers to build infrastructure and to develop new materials and processes that are informed by the climate data, which will be an increasingly important dimension of engineering education in the 21st century.
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    How much water evaporates from the world’s rivers and streams? Mechanistic modeling of evaporative losses and ecological consequences
    The global network of rivers and streams spans over 80 million kilometers and is the key to human settlement, supporting diverse aquatic ecosystems, and transporting sediment and carbon into the oceans. Evaporative losses from these important aquatic arteries affect not only water availability but may influence chemical and biological processes of river ecosystems through changes in energy budget and water temperature. Despite progress in quantifying evaporation from other terrestrial compartments, estimates of evaporation from rivers remain largely local and empirical lacking predictability to varying fluvial and climatic conditions at different scales. Specifically, effects of turbulence and mixing of these dynamic evaporating surfaces, aerodynamic interactions with overlying air, variable radiative regimes and flow characteristics have not been systematically incorporated into evaporation models across a hierarchy of scales. In contrast with Penman-type approaches that build on empirical surface exchange coefficients, this project seeks to develop a physically-based framework for constraining river and stream evaporation considering a scale-appropriate energy partitioning scheme. A model of a characteristic river/stream profile (1-D) will be developed to include salient features of mixing and turbulence while accounting for variations in radiative energy inputs and flow characteristics. We hypothesize that the ingredients included in this simplified representation capture the primary processes affecting evaporative losses and water temperatures in a scalable manner while reducing empiricism. Our specific objectives are to: i) develop a mechanistic framework that incorporates hydro-aerodynamic characteristics with radiative energy partitioning and heat budget of characteristic river profiles to quantify evaporative losses over stream stretches, ii) conduct systematic laboratory and field experiments to identify dominant fluvial characteristics and environmental factors that govern evaporation dynamics to guide model evaluation and upscaling, and iii) classify spatio-temporal characteristics of global fluvial network to guide upscaling of the theoretical approach. The project will harness laboratory and field equipment and expertise at Desert Research Institute (USA), Jülich Research Centre (Germany), Utrecht University (Netherlands), and Boston University (USA). The quantitative framework will enable assessment of future climate effects on stream characteristics (early snowmelt, increasing air temperature, changes in rainfall) with important ecological consequences. The project will provide improved estimates of evaporative losses from global fluvial network towards addressing knowledge gaps concerning water balance and availability in arid regions; and improving the science underlying regional and transboundary water transfer and water rights.
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    Accelerating collection and use of soil health information using AI technology to support the Soil Deal for Europe and EU Soil Observatory
    The objective of AI4SoilHealth is to co-design, create and maintain an open access European-wide digital infrastructure, compiled using state-of-the-art Artificial Intelligence (AI) methods combined with new and deep soil health understanding and measures. The AI-based data infrastructure functions as a Digital Twin to the real-World biophysical system, forming a Soil Digital Twin. This can be used for assessing and continuously monitoring Soil Health metrics by land use and/or management parcel, supporting the Commission’s objective of transitioning towards healthy soils by 2030. The project is divided into seven (7) work-packages including: (WP2) Policy and stakeholder engagement - networking and synchronising with EU and national programmes, (WP3) Soil health methodology and standards - developing/testing methodology to be used by WPs 4-6, (WP4) Soil health in-situ monitoring tools and data - developing field and laboratory solutions for Observations & Measurements, (WP5) Harmonised EU-wide soil monitoring services - developing the final suite of tools, data and services, (WP6) Multi-actor engagement pilots - organizing field-works and collect users' feedback, (WP7) Soil literacy, capacity building and communication - organizing public campaigns and producing educational materials. Key deliverables include: 1) Coherent Soil Health Index methodology 2) Rapid Soil Health Assessment Toolbox 3) AI4SoilHealth Data Cube for Europe 4) Soil-Health-Soil-Degradation-Monitor 5) AI4SoilHealth API and Mobile phone App. Produced tools will be exposed to target-users (including farmer associations in >10 countries), so their feedback is used to improve design/functionality. Produced high-resolution pan-European datasets will be distributed under an Open Data license, allowing easy access by development communities. AI4SoilHealth will provide an effective Soil Health Index certification system to support landowners and policy makers under the new Green Deal for Europe.
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