The calibration of RANS turbulence models is essential for improving predictive accuracy. However, existing approaches are often limited to a single flow scenario that lacks generalizability, or they overfit closure coefficients to a selected set of flow configurations. This study introduces the Reinforced Holistic Calibration (RHC) framework that extends existing calibration strategies through an iterative reinforcement mechanism. RHC identifies cases with the largest prediction errors and recalibrates the RANS model within a multi-case loop, thereby systematically improving model generalizability while mitigating overfitting.The RHC framework was demonstrated by calibrating the k-ω Shear Stress Transport (SST) turbulence model for flows over wall-mounted rectangular prisms at various freestream velocities. Time-resolved particle image velocimetry (TR-PIV) measurements were carried out in a dedicated wind-tunnel campaign, where LEGO-based models enabled systematic and robust variations in prism geometry and spacing. The experimental campaign provided detailed turbulent flow fields for the calibration and validation phases.The optimized closure coefficients showed systematic changes: dissipation-related terms (β1, β2, β∗) were modified to adjust modeled dissipation of k and ω, while the production and stress limiter coefficients (γ1, γ2, a1, b1) were tuned to suppress excessive ω production and limit turbulent viscosity in separation regions. The calibration resulted in elevated turbulent kinetic energy levels within separation and wake zones, elongated reattachment lengths, and intensified after-body interactions in double-block configurations.The RHC framework delivers generalizable calibration, significantly enhancing the k-ω SST model’s predictive fidelity for flow over wall-mounted prisms. Its iterative procedure offers a cost-effective calibration for turbulence models.
The aggregation of alpha-synuclein (αSN) is a key pathological feature of Parkinson’s disease (PD), leading to neural cell death via reactive oxygen species (ROS) overload and activation of downstream neurotoxic pathways. Betanin, a beetroot-derived small molecule, has exhibited antioxidant and neuroprotective properties. In this study, three betaxanthins—Bxn-A, Bxn-B, and Bxn-C—were chemically synthesized from betanin to enhance its therapeutic properties. Betaxanthin Bxn-A effectively reduced intracellular ROS levels without cytotoxicity, even at 500 µM. Additionally, betanin and its derivatives revealed neuroprotective effects, including significant reductions in apoptosis, preservation of mitochondrial membrane potential, modulated autophagy, and enhanced cell viability in PD-model cells. In terms of aggregation inhibition, betaxanthins Bxn-A and Bxn-B significantly reduced αSN aggregation compared to the control after 48 h of incubation. Betaxanthin Bxn-A also triggered disaggregation of existing aggregates and inhibited formation of large, insoluble species. Moreover, αSN aggregation and disaggregation products formed in the presence of betanin or its derivatives exhibited significantly lower cytotoxicity than those formed in their absence. Specifically, cells treated with aggregates formed in the presence of 50 µM betaxanthin Bxn-B showed 100
Root-derived carbon (C) inputs for wheat and maize were estimated using the yield-based allocation functions of Bolinder et al. and Jacobs et al. Comparison with measured root C showed systematic overestimation that becomes larger as predicted C increases. Bolinder gave mean absolute errors (MAE) of 0.4 Mg C ha-1 (wheat) and 1.0 Mg C ha-1 (maize), Jacobs produced larger errors. These results indicate that static, yield-dependent functions inadequately capture root-derived C inputs, highlighting the need for dynamic, environment-specific approaches.
Abstract Fiber-reinforced adhesives are primarily employed in large-scale and highly stressed bonded joints, such as those found in the manufacture of rotor blades for wind energy systems. The subject of the present paper is the usage of non-destructive micro-computed tomography (µ-CT) to gain insights into the causes and effects of the alignment of short glass fibers in structural adhesives resulting from the application process, such as the formation of shell and core zones or the fiber orientation distribution pattern. In order to perform mechanical tests on samples with known specific fiber orientations for investigating adhesive properties that are highly dependent on fiber orientation, the three-dimensional structure of pressed adhesive plates used for sample production was determined beforehand. In that context, results from dynamic fatigue tests on notched tensile specimens with fiber orientations of 0, 30, 60 and 90°, respectively, are presented as well as µ-CT investigations of the notch area with focus on the detection of micro-crack formation. Using prepared adhesive micro-samples from rip plaques at various parameterized application processes as well as laboratory scale adhesive beads, the flow behavior and fiber orientation of the short glass fiber-reinforced adhesive could be determined. In addition, an algorithm for fiber reconstruction was used to determine the fiber orientation and calculate the corresponding orientation tensors components A ij based on CT datasets. Characteristic values from mechanical tests were used as data for numerical investigations by X-FEM simulation with focus on the crack behavior.
Fuel cell electric vehicles represent a zero tailpipe-emission alternative to battery electric vehicles in the long-term replacement of internal combustion engine vehicles. The design of the drive system, including the fuel cell stack, electric machine, and battery, offers a wide range of configuration possibilities. In particular, the characteristics of the fuel cell stack are strongly influenced by multiple design parameters, which directly affect system efficiency. In this paper, we present a fuel cell system synthesis framework for the systematic generation of new fuel cell drive concepts. The synthesis is embedded into a comprehensive drive system synthesis and optimization toolchain, enabling holistic evaluations of complete powertrain architectures. Using this approach, multiple fuel cell system designs are investigated for both a conventional fuel cell electric vehicle and a plug-in fuel cell electric vehicle, under legal driving cycles as well as representative customer operation. The results demonstrate that the optimal fuel cell system configuration differs significantly between the two use cases, highlighting the importance of application-specific system design.
