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Simulation of shortwave infrared ratio thermometers for the remote opto-thermal characterisation of central external receivers
No full textThe accurate knowledge of the receiver surface temperature Tsurf is important for a safe, efficient and durable power plant operation. Its distribution is typically measured in real time using ground-based longwave infrared (LWIR) thermal cameras. Their calibration requires a priori knowledge of the receiver surface LWIR band emittance LWIR. This parameter can be measured with great effort, using portable reflectometers for on tower optical inspection during periodical power plant maintenance. This paper analyses a new measurement principle, based on passive shortwave infrared (SWIR) ratio thermography, for the simultaneous measurement of surface temperature Tsurf and band emittance SWIR. The first SWIR ratio thermometer combines two narrow bandpass filters centered on water vapor atmospheric absorption bands (1.4/1.9 µm). This thermometer is sensitive to water vapor to block solar radiation, however thermal radiation emitted by the receiver is also attenuated. The applicability of this thermometer is limited for remote opto-thermal characterization. Under favorable operating conditions, it can measure temperature leve[ls above 550 °C with a relative temperature error T/T less than 2 %. The second SWIR ratio thermometer combines two narrow bandpass filters centered on atmospheric windows (1.64/2.09 µm). This thermometer is insensitive to water vapor and suited for remote distances, however it can only operate off-sun when receiver surface temperature is still above 300 °C, for instance during the cool down phase, before molten salts drainage. The relative temperature error T/T is less than 0.5 % for Pyromark 2500 and oxidized Haynes 230, while the absolute band emittance error is less than 2.5 percentage points
Interactive travel experience visualization for mobility providers
No full textFor a shift to higher public transport usage it is necessary to consider traveler’s willingness to switch from individual mobility to public transport. Here, it is important to detect points of collective negative public transport travel experience. Especially intermodal journeys can lead to lower satisfaction due to unmet needs around safety, comfort, accessibility, efficiency, reliability, and information. The project ExperienceAtlas (Erlebensatlas) aims to address these challenges by visualizing collective travel experiences in intermodal journeys and exploring opportunities for improvement.
The project employs a multi-faceted data collection approach, incorporating subjective traveler feedback, cardiac activity, and contextual factors such as weather and traffic conditions. Two data collections in the metropolitan areas of Hamburg and Berlin are planned. Per data collection, it is planned to recruit about 75 volunteers that report their experience along intermodal journeys on 5 to 10 weekdays. Together, these datasets provide a dynamic, real-time perspective of the travel experience, equipping operators to detect service disruptions or discomfort as they occur.
Mobility providers, as primary users of these visualizations, currently lack the means to access real-time insights into traveler experiences. To ensure that these visualizations are both meaningful and actionable, stakeholder interviews with five representatives from a major mobility provider were conducted to identify design and functional requirements. These activities revealed essential insights into the types of data needed and the best ways to visualize them to support service innovation.
This presentation will share the initial findings from these interactions, detailing identified visualization requirements and showcasing methods for visualizing travel experiences, including real-time tracking and post-journey experience aggregation. By offering an in-depth view of traveler experiences, these visualizations provide mobility providers with tools to enhance service offerings, improve customer information strategies, and ultimately foster more satisfying intermodal journeys
Vortrag "A first approach towards an automatized preparation of input data for the agent-based demand model TAPAS"
Get PDFIn order to prepare data needed to simulate a new area using the agent-based demand model TAPAS a large variety of data has to be collected, consolidated, and converted. In the past, this included the purchase of commercial data about the activity places within the area to model, collection of available data about the socio-demographics of the area, and subsequent enrichment of both and the disaggregation of the population. Often, the process of preparing a new scenario could take few months. For allowing a more agile use of TAPAS, we develop a data import and preparation tool that allows preparing a TAPAS representation of an area using open data and needs only minor manual configuration. This paper outlines the methods used by the tool, presents initial results, and gives the next steps to be performed
Development of a directly heated solid media thermal energy storage system with high storage and power density for flexible heat supply in battery electric vehicles
Get PDFProspects for Next-Generation Battery Technologies in the German Passenger Vehicle Market
No full textThis paper analyzes current trends and advancements in battery technologies within the automotive sector and explores the potential impact of future innovations on the passenger vehicle market. It provides an overview of both present and next-generation battery technologies, focusing on key performance indicators such as cost and volumetric energy density. By integrating these technologies into different bottom-up calculated vehicle variants using the VECTOR21 vehicle technology scenario model, they are evaluated against each other within a predefined battery diversification scenario. Understanding the design of next-generation battery electric vehicles is crucial to reduce dependencies on raw mate-rials or supply chains and to assess the economic feasibility of potential technologies. The analysis reveals significant short-term market potential for vehicles equipped with low-cost cell chemistries like lithium iron phosphate or sodium-ion batteries. However, once vehicles with conventional powertrains are unable to meet the more stringent CO2 fleet limits, the sales prospects of models with energy dense Nickel-rich cell chemistries is expected to rise, as agents with demanding range requirements are about to switch to battery electric vehicle options. In order to achieve a relevant market potential for high-performance vehicles with solid-state batteries, cell costs of less than 100 EUR2020/kWh are considered necessary based on the currently implemented willingness-to-pay factors and the expected improvements in conventional batteries with liquid electrolyte
EMBEDDED COGNITIVE TRAINING: ASSESSING COGNITIVE AND OPERATIONAL PERFORMANCE IN A SIMULATION OF MANUAL SPACECRAFT DOCKING
Get PDFOperational tasks such as the manual control of space vehicles or robotic arms place high demands on the
cognitive and sensorimotor performance of astronauts. The success of a mission and the safety of the
crew depend on whether critical skills can be maintained with consistently high reliability over long
periods of time. However, exposure to stressors such as microgravity, sleep deprivation, isolation, and
high workload pose risks to optimal performance. Future long-duration missions to the Moon and Mars in
particular require new methods to allow for autonomous monitoring and training of cognitive and
operational capabilities.
The 6df simulation developed by the German Aerospace Center (DLR) facilitates to acquire and maintain
the skills required for the manual control of a space vehicle with six degrees of freedom of motion. Aim
of the newly developed Embedded Cognitive Training is to train and monitor operational docking
performance against a background of simultaneous high cognitive demand in various domains. The
supplementary cognitive tasks were developed in such a way that they fit plausibly into the docking
scenario and cover a variety of potentially sensitive cognitive domains, i.e. working memory, visual
attention, logical and numerical reasoning.
We present the embedded training concept and first performance results from a spaceflight analog
environment. In the SANS-CM bed rest studies at the DLR Institute of Aerospace Medicine in Cologne,
47 participants took part in the 6df Embedded Cognitive Training. Participants spent 30 days in 6° headdown tilt bed rest to simulate the physiological effects of microgravity. Lower body negative pressure (N
= 12) and lying ergometer cycling with veno-occlusive thigh cuffs (N = 12) were tested as
countermeasures against spaceflight-associated neuro-ocular syndrome and compared with daily upright
sitting (N = 11) and a strict bed rest control group (N = 12). After achieving sufficient skills in controlling
six degrees of freedom with the learning program of the 6df simulation, participants completed docking
sessions that included embedded cognitive tasks twice a week during the bed rest phase and the two-week
regeneration period.
Embedded Cognitive Training is intended to provide operators with more comprehensive feedback on
their own operational as well as cognitive performance and individualized training to maintain
performance, especially for long-duration missions. In contrast to conventional test procedures,
operational tasks are less affected by motivation effects due to their direct relation to the working
environment. Further developments are planned to enable training adaptations contingent on the
operator’s status and thereby support a growing crew autonom
From Research to Application: Advanced Methods on the Move.
No full textPresentation about the application of advanced CFD Methods (LES, DDES) an Machine Learning Methods in Turbomachinery with the TRACE flow solve
Harmonized tropospheric NO2 column monitoring for LEO and GEO constellations
No full textOver the past few decades, the global distribution and trends of atmospheric NO2 have been monitored from space by low-earth-orbiting (LEO) satellite instruments, such as the Global Ozone Monitoring Experiment (GOME), SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY), Ozone Monitoring Instrument (OMI), Global Ozone Monitoring Experiment-2 (GOME-2), and Tropospheric Monitoring Instrument (TROPOMI). These LEO measurements have significantly enhanced our understanding of global tropospheric NO2 levels and their long-term trends, but are limited to provide observations only once per day at specific local times. To address this limitation, a constellation of geostationary orbiting satellite sensors (GEO) is being deployed, including the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia, Tropospheric Emissions: Monitoring of Pollution (TEMPO) over North America, and two Sentinel-4 (S4) missions over Europe. Although the GEO monitoring system has limited spatial coverage, it provides multiple observations per day, enabling detailed monitoring of diurnal variations in NO2 due to changes in emissions and chemical reactions throughout the day.
As described, LEO (global daily monitoring) and GEO (regional hourly monitoring) each have distinct advantages and limitations. Therefore, by complementing each other and combining their strengths, a synergetic effect can be achieved in monitoring atmospheric compositions including NO2. However, obtaining consistent data on total, stratospheric and tropospheric NO2 columns from LEO and GEO satellite missions remains challenging since their current operational algorithms use different retrieval approaches (e.g. stratosphere-troposphere separation, cloud corrections) and auxiliary datasets.
In this study, we aim to develop a harmonized retrieval system for tropospheric NO2 columns applicable to both LEO and GEO satellite measurements. The DLR NO2 retrieval algorithm for LEO and GEO constellations (NO2_LGC) consists of three key steps: (1) the spectral retrieval of total NO2 slant columns, (2) the separation of slant columns into stratospheric and tropospheric contributions using the Copernicus Atmosphere Monitoring Service (CAMS) forecast model data, and (3) the conversion of tropospheric slant columns into vertical columns using air mass factors (AMFs). Based on Seo et al. (2024), a consistent retrieval approach is applied across current LEO (e.g. TROPOMI) and GEO (e.g. GEMS and TEMPO) observations, using identical forecast model, cloud corrections, and auxiliary data. This harmonized retrieval algorithm for the LEO+GEO constellation provides more consistent NO2 data records from TROPOMI, GEMS and TEMPO and will be extended to future GEO and LEO missions including Sentinel-4 and Sentinel-5. In addition, we demonstrate the advantages of harmonized LEO and GEO NO2_LGC retrievals, particularly for analyzing NO2 in regions with high pollution levels and downwind pollution outflows