Hetero-agglomeration of micron- and submicron-sized particles enables the formation of all-solid-state battery cathode materials with enhanced safety and energy density. This study investigates a turbulent pipe flow process for mixing lithium iron phosphate (LiFePO4, LFP), lithium indium chloride (Li3InCl6, LIC), and carbon black (CB) to form three-component hetero-agglomerates. The analysis aims to identify suitable pipe dimensions and evaluate how different mixing configurations affect agglomerate size, composition, and structure. The investigation employs a dedicated simulation framework utilizing a mesh-free representation of turbulent flow fields derived from direct numerical simulation datasets. Lagrangian particle tracking incorporates an improved discrete random walk model, while stochastic agglomerate structure simulations account for various turbulent collision mechanisms, hydrodynamic interactions, and breakup events. Prior to simulations, virtual CB aggregates were stereologically reconstructed from scanning electron microscopy images, the particle size distributions were determined using laser light diffraction, and the surface energies of all materials were determined from contact angle measurements using the Owens-Wendt-Rabel-Kaelble method. Simulations examined parallel mixing and sequential premixing configurations (LFP-CB, LIC-CB, and LFP-LIC) across various pipe diameters. For parallel mixing under experimental process conditions, an 18 mm pipe diameter proved optimal, achieving maximum particle capture in agglomerates at a downstream distance of 2.73 m. Premixing diameters were selected based on mixing length and deposition. The mixing configuration significantly influences the agglomerate composition, material-specific mixing quality measures, and the contact-pair distribution. The findings provide concrete recommendations for experimental process design regarding pipe dimensions and mixing strategies, while the influence on electrochemical properties requires further investigation.
Aircraft turnaround sits on the critical path between an inbound arrival and the next departure, and a late inbound consumes the scheduled ground time that would otherwise absorb it. A large literature predicts turnaround duration and delay, and a parallel literature optimises ground operations and models network delay propagation, but the two are rarely joined: prediction studies report accuracy, while optimisation and recovery studies assume clean inputs. What is missing is a measurement, on real data, of how the quality of operational information changes a constrained turnaround decision. We provide one. From United States Bureau of Transportation Statistics on-time records we derive 2.52 million turnarounds across five months spanning 2022 to 2026, and we frame a transparent decision: flag the next departure as likely to leave more than 15 min late so that an operator can act. We validate what that outcome measures against the carrier-reported causal delay fields, against the scheduled buffer, and across operational conditions: the outcome occurs in 93.5% of turnarounds where the inbound delay exceeds the scheduled ground time against 18.6% where it fits inside, and a propagation-specific variant confirmed by the carrier's own attribution behaves the same way. We then degrade the inbound-status signal and measure the decision, holding the alarm budget fixed so that operating-point shifts are not mistaken for information loss. The central result is an asymmetry between availability and precision. Coarse inbound status — four buckets instead of an exact figure — costs 0.009 of area under the ROC curve, while losing the signal entirely costs 0.156 and leaves 57.9% of the delay unflagged at an equal alarm budget. Additive noise degrades precision at a fixed threshold and recall at a fixed budget, but never as much as absence. A ladder of decision points shows the alert can be raised 144 min earlier, at the inbound aircraft's own off-block, for 0.032 of discrimination. Decision-curve analysis shows the signal is worth most precisely where false alarms are costly. The predictor transfers across airports and four years, but local retraining buys nothing in discrimination and recalibration improves calibration rather than catch rate. A European ADS-B arm is reported as descriptive evidence only; an apparent propagation signal in it does not survive controls for route and for non-consecutive legs. The study shows that what governs this decision is whether the inbound signal is present, not how precise it is.
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-coShear 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 (/i1, /i2, /i & lowast;) were modified to adjust modeled dissipation of k and co, while the production and stress limiter coefficients (y1, y2, a1, b1) were tuned to suppress excessive coproduction 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-coSST model's predictive fidelity for flow over wall-mounted prisms. Its iterative procedure offers a cost-effective calibration for turbulence models.
This paper interfaces combinatorial integral approximation strategies with the inherent robustness properties of conventional model predictive control with stabilizing terminal conditions to establish practical asymptotic stability results for finite control set and mixed-integer model predictive control. We examine the impact of sequential control rounding on the closed-loop performance in terms of stability and optimality. Sum-up rounding takes its decision on the basis of the integrated control approximation error and represents one instance of a sequential integer control reconstruction approach in combinatorial integral approximation. We first apply outer convexification and relaxation and then show how to embed the class of sequential rounding algorithms into the step-by-step operation of model predictive control. Numerical experiments serve to illustrate the importance of an advanced rounding strategy in the context of mixed-integer model predictive control.
Due to the integration of mechanical, electronical and software functions in modern products, virtual product development is becoming increasingly important. In this context, virtual prototyping in particular can benefit from the visualization of CAD models of a system in virtual reality (VR) simulations, as this can support activities such as ergonomic assessments and design reviews in the early stages of development. Furthermore, managing the growing complexity of products can be supported by creating consistent system models following the Model-Based Systems Engineering (MBSE) approach. By combining system models, created by SysML modeling syntax, CAD models, and VR simulations, benefits for the virtual model-based product development can be generated and information as well as model consistency can be improved. This article introduces a tool for the integration, simultaneously visualization, and automated linking of SysML and CAD models in an interactive VR environment. The tool can support design review and validation processes by displaying CAD objects and the associated information of the system model in one virtual environment at the same time. The focus of the exemplary tool application in this article is the validation of system requirements defined in a SysML model through the examination of a true-to-scale CAD model in VR. As a build-in feature, the validation results can be documented in the VR simulation and returned back to the SysML modeling tool so that they can be taken into account in further system development. The tool validation demonstrates potential for virtual reviews, early requirement validation, and bidirectional data exchange.
