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Implementation of fiber optic temperature sensors for enhanced performance in fuel cell systems
No full textProton exchange membrane (PEM) fuel cells represent a forefront technology in the realm of sustainable mobility, characterized by their impressive efficiency and minimal emissions. Nonetheless, internal thermal gradients significantly impact their performance and durability. Localized hotspots can accelerate material degradation and reduce electrochemical efficiency. Conventional temperature sensors often lack the spatial resolution required to detect these crucial variations. This study addresses this limitation by integrating distributed fiber optic temperature sensors into the gas channels of a PEM fuel cell stack to enable high resolution thermal monitoring. Eight U shaped optical fiber loops are embedded within the bipolar plate, enabling temperature measurements with sub millimeter spatial resolution. A Python based data processing algorithm was developed to calibrate raw sensor output using reference RTD measurements conducted during an oven test. The tool achieved a reduction in temperature error from 17.86 °C to 0.41 °C. The corrected dataset showed a noise reduction of 96.7%, with 84.67% of the values falling within ±0.30 °C of the RTD reference and a mean absolute error of 0.41 °C. Post-processing steps include isolating thermally relevant gas channel regions and removing invalid data near structural interfaces. Temperature fields that have been validated are mapped onto a finite element mesh and visualized with ParaView, resulting in better 3D thermal profiles of the stack. To assess the effects of integration on fluid dynamics, computational fluid dynamics (CFD) simulations were conducted in ANSYS Fluent for three designs: a reference model, a previous integration layout, and an optimized geometry. The results demonstrate that the improved sensor configuration preserves flow uniformity and reduces turbulence,low pressure loss, maintaining efficient reactant distribution at test conditions corresponding to a flow velocity of 10 meters per second. This integrated framework combining calibrated fiber optic sensing, automated correction, and simulation validation offers a robust platform for advanced thermal diagnostics in PEM fuel cells. It supports future developments in real time monitoring, intelligent thermal control, and digital twin technologies, enabling more efficient and reliable fuel cell systems
Cast Polyamide 6 Molds as a Suitable Alternative to Metallic Molds for In Situ Automated Fiber Placement
Get PDFThermoplastic in situ Automated Fiber Placement (AFP) is an additive manufacturing method currently investigated for its suitability for the production of aerospace-grade composite structures. A considerable expense in this process is the manufacturing and preparation of a mold in which a composite part can be manufactured. One approach to lowering these costs is the use of a 3D-printable thermoplastic mold. However, AFP lay-up on a 3D-printed mold differs from the usage of a traditional metallic mold in various aspects. Most notable is a reduced stiffness of the mold, a lower thermal conductivity of the mold, and the need for varied process parameters of the AFP process. This study focuses on the investigation of the difference in mechanical and morphological characteristics of laminates produced on metallic and polymeric molds. To this end, the tensile strength and the interlaminar shear strength of laminates manufactured on each substrate were measured and compared. Additionally, morphological analysis using scanning electron microscopy and differential scanning calorimetry was performed to compare the crystallinity in laminates. No statistically significant difference in mechanical or morphological properties was found. Thus, thermoplastics were shown to be a suitable material for non-heated molds to manufacture in situ AFP composites
Estimating Turbulence Modeling Uncertainty for Turbomachinery Flows Using Physics-Constrained Eigenspace Perturbations of the Reynolds Stress Tensor
Get PDFThe synthesis of RANS (Reynolds-averaged Navier-Stokes) equations and the required modeling of turbulence entails certain uncertainties in CFD (Computational Fluid Dynamics). Currently, engineers employ safety factors to address these uncertainties in turbomachinery design processes, which lead to overly conservative designs and result in missed opportunities to optimize the performance. Therefore, understanding the
uncertainties in CFD and establishing methods to analyze the credibility of numerical simulations is crucial to achieve robust designs for future turbomachinery components and to create virtual certification processes.
This cumulative dissertation investigates a framework to account for the uncertainty stemming from the turbulence closure model, a primary source of the overall uncertainty in RANS simulations. Although there are a variety of approaches to assess the uncertainty associated with turbulence models, the appropriate estimation of the epistemic uncertainty inherent in turbulence models is demanding. Therefore, the establishment of a methodology capable of reasonably assessing this model-form uncertainty in the context of RANS-based design of turbomachinery components is a major objective of the present thesis.
The EPF (Eigenspace Perturbation Framework), designed to add physics-constrained perturbations to the eigenspace of the Reynolds stress tensor and provide a systematic approach to quantifying its inherent uncertainty, is implemented in the CFD solver TRACE and evaluated in the scope of this thesis. The current research includes an extensive verification of the numerical implementation as well as a scrutiny of the conceptual idea of
the EPF. This thesis proposes enhancements related to the consistency and applicability of the methodology, while exploring its general capabilities and limitations. Additionally, the introduction of machine learning to reduce user-defined input and prevent overly conservative estimations of the turbulence modeling uncertainty is discussed and results are presented. Moreover, the eigenvalue perturbation is applied to the TUDa compressor stage, presenting the first application of the EPF to multi-row turbomachinery flows. Finally, the ability to quantify, analyze and interpret the uncertainties associated with turbulence closure models, provides a valuable starting point for next-generation enhancements in modeling turbulent effects for complex configurations and flow phenomena. Overall, by refining the EPF and critically assessing its capabilities, this dissertation contributes to the development of reliable methods to consider the uncertainties in CFD
High-resolution air pollutant emissions from thermal power plants in EU decarbonisation scenarios
Get PDFAir pollutant emissions from thermal power plants (e.g. coal, gas, biomass) have significant negative
impacts on human health. Given the ambitious decarbonisation objectives stated by the countries of
the European Union (EU) by 2030 and the increase in renewable energy capacities, the quantity,
spatial and temporal distribution of power plant emissions are expected to change considerably and
dynamically. Projections of these changes are required in order to assess their potential impact on air
quality.
This work aims at estimating air pollutant emissions from thermal power plants in the EU for the year
2030 considering different decarbonisation scenarios. Using the energy system model framework
REMix, we model power plant activities with high temporal and spatial resolution. We calculate
emissions based on the modelled activities, primarily using plant-specific emission factors derived
from EU-reported data. In addition, we account for higher emissions due to inefficient plant
operation (e.g. from frequent startups or part load conditions).
Our results show that emissions from inefficient plant operation could make up more than 25% of
total emissions in 2030
TSP measurements on a heated wedge model in hypersonic flows
No full textIn this work, the europium-doped phosphor (Sr,CaAlSiN₃:Eu²⁺) is tested for its applicability in a Ludwieg tube wind tunnel. The hypersonic wind tunnel (HPG) located in Göttingen was used for the first time to advance research into high-temperature TSP (thermographic phosphors) applied to a wedge model
Full-Scale Particle Image Velocimetry in Aircraft Related Flows Under Open-Air Conditions
No full textParticle Image Velocimetry (PIV) is a well-established tool for flow field measurements in laboratory experiments as well as in industrial wind tunnel environments. However, real-scale aircraft investigations are still a challenging task especially when performed under open-air conditions. In this publication main problems and possible solutions are highlighted in short reviews. Moreover, an engine intake flow field measurement within an aircraft ground test is presented in more detail
Post-COVID-19 Condition in Track and Field Master Athletes: Severity, Symptoms, and Associations With Quality of Life and C-Reactive Protein Levels
Get PDFHere, we assessed the prevalence of post-COVID-condition (PCC, also known as long-COVID) and investigated its associations with health-related quality of life and immune-related biomarkers in track and field masters athletes (MAs). A total of 216 MAs (114 males, 102 females; age: 58.3±11.9 vs. 56.6±11.7 years; BMI: 23.6 [22.2–24.8] vs. 21.3 [20.0–23.6] kg/m2) reported their post-COVID-conditions via the Post-COVID Syndrome Questionnaire (PCSQ). In a subgroup of 108 MAs, fasting blood samples were collected to assess C-reactive protein (CRP) levels as a biomarker of immune status (MAs-CRP)
