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Impact of Oxygen/Nitrogen Oxide Partial Gas Pressures on 310 N Stainless Steel Corrosion in Solar Salt at 600 °C
Get PDFThis study examines how oxygen and nitrogen oxide gases affect the corrosion of 310N stainless steel in Solar Salt at 600 °C. A controlled atmosphere with ≥ 5 vol% O₂ and ≥ 400 ppm NO stabilizes the salt by suppressing nitrite and oxide ions, leading to dense, protective Fe–oxide/Cr2O3 layers and stable corrosion rates. The results show that O2 + NO gas management enables more durable and higher-temperature operation of molten salt thermal energy storage systems
AMIRIS - the open Agent-based Electricity Market Model: Assessing Market Perspectives for Energy Technologies
Get PDFWe present the Agent-based Market model for the Investigation of Renewable and Integrated energy Systems AMIRIS which can be used, e.g., to
1) investigate future electricity market dynamics (e.g. prices),
2) assess economic perspectives of electricity generation technologies and flexibility options
3) evaluate electricity market designs and policies.
AMIRIS has been actively developed since 2008. It represents all major actors in the electricity system, models their assets and decision strategies, and enables its users to observe emerging market dynamics. To this end, AMIRIS includes agents for market places, trading, power plant operation, storage operation, demand-side flexibility, and policy instruments. Back-testing demonstrates the high quality of AMIRIS results. To enable smooth integration of AMIRIS in other workflows, we follow the FAIR4RS principles and provide a full metadata description of AMIRIS model parameters, linking to the Open Energy Ontology wherever possible
Predicting onflow parameters using transfer learning for domain and task adaptation
Get PDFDetermining onflow parameters is crucial from the perspectives of wind tunnel testing and regular flight and wind turbine operations. These parameters have traditionally been predicted via direct measurements which might lead to challenges in case of sensor faults. Alternatively, a data-driven prediction model based on surface pressure data can be used to determine these parameters, which requires complex system representations including nonlinearities. It is essential that such predictors achieve close to real-time learning as dictated by practical applications such as monitoring wind tunnel operations or learning the variations in aerodynamic performance of aerospace and wind energy systems. To overcome the challenges caused by changes in the data distribution as well as in adapting to a new prediction task, we propose a transfer learning methodology to predict the onflow parameters, specifically angle of attack and onflow speed. It requires first training a convolutional neural network (ConvNet) model offline for the core prediction task, then freezing the weights of this model except the selected layers preceding the output node, and finally executing transfer learning by retraining these layers. A demonstration of this approach is provided using steady CFD analysis data for an airfoil for i) domain adaptation where transfer learning is performed with data from a target domain having different data distribution than the source domain and ii) task adaptation where the prediction task is changed. Further exploration on the influence of noisy data, performance on an extended domain, and trade studies varying sampling sizes and architectures are provided. Results successfully demonstrate the potential of the approach for adaptation to changing data distribution, domain extension, and task update while the application for noisy data is concluded to be not as effective
How to construct hyperuniform systems from fair partitions
No full textThe talk showed how one can apply recent theoretical results to generate a multitude of examples of hyperuniform point processes. Further, the examples were supplemented by numerical simulations of world-record system sizes
The effect of pressure and temperature on exergy efficiency in power-to-methane: A comparison of steam and co-electrolysi
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Regenerative Cooling System Investigation and Calorimetric Design for Heat-Transfer Characterisation in a Hydrogen–Oxygen Rotating Detonation Combustor
No full textRotating Detonation Engines (RDEs) offer a pressure-gain combustion cycle capable of increasing rocket engine efficiency within compact geometries. While theoretical analyses predict meaningful performance advantages, experimental validation at high pressure remains limited. To support this transition from short-duration to sustained operation, the German Aerospace Center (DLR) developed a hydrogen–oxygen Rotating Detonation Combustor (RDC) and performed several test campaigns. As these evolve towards long-duration firings, active cooling becomes essential. The main challenge lies in achieving reliable heat removal and accurate calorimetric measurement while maintaining structural integrity and manufacturability. This thesis addresses that challenge through two complementary developments at different levels of maturity. The first focuses on the long-term path towards flight-relevant configurations: a regenerative cooling analysis tool was created to assess the feasibility of actively cooled RDE liners at larger scales. The model predicts wall heat fluxes, coolant-side temperature evolution, and material temperature margins under representative operating conditions. After benchmarking against published datasets, it was applied to both single- and dual-coolant architectures to explore geometric and operational trade-offs, ultimately defining a feasible regenerative design concept suitable for DLR test benches. In parallel, a near-term experimental design was developed to characterise wall heat fluxes on small-scale, water-cooled hardware. A calorimetric inner body was conceived to enable spatially resolved measurements under relevant boundary conditions. The proposed hybrid concept combines additively manufactured internal routing with conventionally machined copper interfaces, balancing cost, manufacturability, and diagnostic access. Conjugate heat-transfer simulations defined the thermal envelope, while cold-flow testing quantified hydraulic losses and verified flow uniformity ahead of hot-fire application. Together, the regenerative cooling study and the calorimetric hardware form a coherent methodology for RDE thermal characterisation: a rapid predictive framework for design exploration, anchored by experimental measurement capability. This combination strengthens the foundation for the progressive development of regeneratively cooled, flight-relevant rotating detonation engines
CLEVER: Stream-based Active Learning for Robust Semantic Perception from Human Instructions
Get PDFWe propose CLEVER, an active learning system for robust semantic perception with Deep Neural Networks (DNNs). For data arriving in streams, our system seeks human support when encountering failures and adapts DNNs online based on human instructions. In this way, CLEVER can eventually accomplish the given semantic perception tasks. Our main contribution is the design of a system that meets several desiderata of realizing the aforementioned capabilities. The key enabler herein is our Bayesian formulation that encodes domain knowledge through priors. Empirically, we not only motivate CLEVER's design but further demonstrate its capabilities with a user validation study as well as experiments on humanoid and deformable objects. To our knowledge, we are the first to realize stream-based active learning on a real robot, providing evidence that the robustness of the DNN-based semantic perception can be improved in practice
Feeling jet-lagged? Effects of flying in an unknown state of acclimatization on crew fatigue
No full textSleepiness and fatigue due to long working hours and circadian disruption is a potential risk for aviation safety. The presenters represent an international research consortium in a large EU funded project (2021-2025) which aims to review the effectiveness of the current flight and duty time limitations and rest requirements in the EU. We have collected extensive survey and field data from eight European airlines including both pilots and cabin crew. The analyses we are conducting focus on outcomes measuring sleep, fatigue and workload in selected duties of special interest for European Union Aviation Safety Agency (EASA): long flight duty periods, duties including high number of sectors (i.e., flights), duties in an unknown state of acclimatization, standby duties and utilization of Controlled Rest (CR). We will present the results in five presentations based on the selected duties and define the study aim as well as wrap up the conclusions and recommendations for the regulator, airlines, and flight crew at both ends of the symposium. We aim to increase participants’ understanding of the diverse effects of extended and intermittent work-rest periods in this safety-critical industry, as well as promote discussion on the effectiveness of current regulation in civil aviation in a global perspective