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Modeling Process Forces in CFRP Grinding: Influence of Cutting Materials and Coolant on Process Force Behavior
No full textCarbon Fibre Reinforced Polymer (CFRP) is favoured for its high strength to weight ratio, excellent directional mechanical and thermal properties, and the ability to be optimized in the direction of stress or heat flow. These properties make it ideal for power transmission applications. Meeting the high-quality requirements in this area requires a precise grinding process and a thorough understanding of cutting forces, which are influenced by different factors e.g. coolant usage, or cutting material. However, machining unidirectional CFRP is challenging due to its anisotropic behaviour, resulting in different machining forces for identical parameters with different fibre orientations.
A universal process-independent model was recently developed to describe the engagement conditions during oblique cutting of unidirectional CFRP by introducing the spatial fibre cutting angle θ0 and the spatial engagement angle φ0. Using this description, an universal mechanistic machining force model for grinding of CFRP was developed.
In the paper, an extension of the model of oblique cutting for grinding is extended and experimentally verified, taking into account additional parameters e.g. coolant and cutting material. Therefore, the process forces were measured as a function of the spatial fibre cutting angles for different cutting materials, both with and without the use of coolant
Numerical modeling of granular bulk cargo
No full textTo study the behavior of unsaturated granular materials on vessels, a monolithic multi-physics approach is implemented within a Finite Volume framework. This includes constitutive models representing various cargo failure mechanisms which are applied to 2D parameter studies that provide comprehensive insights into cargo behavior under diverse conditions. The models include a rigid-perfectly plastic soil model, a porous media approach for rigid materials, an incompressible elastic model, and a coupling of the rigid-perfectly plastic model with the porous media. Thorough validation and verification of all implemented models are presented, along with a proof-of-concept study demonstrating a complete three-dimensional simulation of a loaded bulk carrier in waves.Um das Ladungsverhalten von ungesättigten granularen Schüttgütern auf Massengutfrachtern zu untersuchen, werden mehrere Materialmodelle, mit denen unterschiedliche Versagensmechanismen der Ladung dargestellt werden können, monolithisch in einen Finite Volumen Löser implementiert. Die implementierten Modelle beinhalten ein starr-ideal-plastisches Bodenmodell, einen Ansatz für starre poröse Materialien, ein inkompressibles elastisches Materialmodell und eine Kopplung des ideal-plastischen Modells mit dem Ansatz für poröse Medien. Es werden eine gründliche Validierung und Verifizierung aller implementierten Modelle sowie eine Konzeptstudie einer vollständigen dreidimensionalen Simulation eines beladenen Massengutfrachters in Wellen beschrieben
Large components in the subcritical Norros-Reittu model
No full textThe Norros-Reittu model is a random (multi-)graph with n vertices and i.i.d. weights assigned to them. The number of edges between any two vertices follows an independent Poisson distribution whose parameter is increasing in the weights of the two vertices. Choosing a suitable weight distribution leads to a power-law behaviour of the degree distribution as observed in many real-world complex networks. We study this model in the subcritical regime, i.e. in the absence of a giant component. For each component, we count all its vertices to determine the component sizes and show convergence of the corresponding point process of (rescaled) component sizes to a Poisson process. More generally, one can also count only specific vertices per component, like leaves. From this one can deduce asymptotic results on the size of the largest component or the maximal number of leaves in a single component. The results also apply to the Chung-Lu model and the generalised random graph
CO₂e Life-Cycle Assessment: Twin Comparison of Battery-Electric and Diesel Heavy-Duty Tractor Units with Real-World Data
No full textIn 2023, the EU set the target to reduce greenhouse gas (GHG) emissions by 55% until 2030 compared to 1990. The European Transport Policy sees battery–electric vehicles as a key technology to decarbonize the transport sector, so governments support the adoption through dedicated funding programs. Battery–electric trucks hold great potential to decarbonize the transport sector, especially for high-impact, heavy-duty trucks. Theoretical life-cycle assessments (LCA) predict a lower CO2e emission impact from battery–electric trucks compared to conventional diesel trucks. Yet, one concern repeatedly mentioned by potential users is the doubt about the ecological advantage of battery–electric vehicles. This is rooted in the problem of a much higher CO2e impact of the lithium-ion batteries production process. As heavy-duty trucks have a much larger battery, the hypothec in the construction phase of the vehicle is significantly higher, which must be regained during the use phase. Although theoretical assessments exist, CO2e evaluations using real-world application data are almost nonexistent, as the technology is at the very start of the adoption curve. Exemplary is the fact that there were only 72 registered battery–electric heavy-duty tractor trucks throughout the whole of Germany at the start of 2023. This paper aims to deliver one of the first real-world quantifications using operational data for the actual reduction impact of battery–electric heavy-duty trucks compared to diesel trucks. This study uses the methodology of the life-cycle assessment approach according to ISO 14040/14044 to gain a systematic and holistic technology comparison. For this LCA, the system boundaries are considered from cradle to cradle. This includes the production of raw materials and energy, the manufacturing of the trucks, the use phase, and the recycling afterward. The research objects of this study are battery–electric and diesel Volvo FM trucks, which have been in use by the German freight company Nord-Spedition GmbH since May 2023. The GREET® database is used to assess the emission impact of the material production and manufacturing process. The Volvo tractor trucks resemble a critical case, as the vehicles have a battery size of 540 kWh—around 11 times larger than a usual passenger car. The operation data is directly provided by the logistics company to observe fuel/electricity consumption. Other factors are assessed through company interviews as well as a wide literature research. Finally, a large question mark concerning total emissions lies in the cradle-to-cradle capabilities of large-scale lithium-ion batteries and the electricity grid mix. Different scenarios are being considered to assess potential disposal or recycling paths as well as different electricity grid developments and their impact on the overall balance. The findings estimate the total emissions reduction potential to range between 34% and 69%, varying with assumptions on the electricity grid transition and recycling opportunities. This study displays one of the first successful early-stage integrations of battery–electric heavy-duty trucks into the daily operation of a freight company and can be used to showcase the ecological advantage of the technology
A multi-perspective framework to address manufacturing and transportation challenges in green hydrogen supply chains
No full textThe strategic changeover towards green hydrogen supply chains entails a large number of challenges in determining strategic manufacturing, transportation, and supply chain decisions such as the locations for the generation and storage or the transport mode for the transfer of green hydrogen. Such decisions face three dedicated obstacles interesting for research analysis. First, such decisions in a greenfield setting have in many cases never been made before, reducing the access to historical information about manufacturing and transportation volumes, aggravated by new levels of uncertainty in many global manufacturing and transportation contexts. Second, at the same time, new data sources and computation capabilities become available in a digital Industry 4.0 setting, creating big data and data lake settings where traditional decision support methodologies have limited access and use in this regard. Third, an integrated evaluation of economic, environmental, and social sustainability aspects together with a resilience perspective is required as trade-offs between these different performance dimensions have to be incorporated. This paper outlines and addresses the three specific challenges by deriving a concept of how to integrate the three standard sustainability perspectives with the added resilience perspective and how to tailor this towards decision-making in a strategic, data-driven and digital setting of emerging green hydrogen supply chains
Space-efficient parameterized algorithms on graphs of low shrubdepth
No full textDynamic programming on various graph decompositions is one of the most fundamental techniques used in parameterized complexity. Unfortunately, even if we consider concepts as simple as path or tree decompositions, such dynamic programming uses space that is exponential in the decomposition’s width, and there are good reasons to believe that this is necessary. However, it has been shown that in graphs of low treedepth it is possible to design algorithms which achieve polynomial space complexity without requiring worse time complexity than their counterparts working on tree decompositions of bounded width. Here, treedepth is a graph parameter that, intuitively speaking, takes into account both the depth and the width of a tree decomposition of the graph, rather than the width alone.
Motivated by the above, we consider graphs that admit clique expressions with bounded depth and label count, or equivalently, graphs of low shrubdepth. Here, shrubdepth is a bounded-depth analogue of cliquewidth, in the same way as treedepth is a bounded-depth analogue of treewidth.
We show that also in this setting, bounding the depth of the decomposition is a deciding factor for improving the space complexity. More precisely, we prove that on n-vertex graphs equipped with a tree-model (a decomposition notion underlying shrubdepth) of depth d and using k labels,
- Independent Set can be solved in time 2O(dk) ·nO(1) using O(dk2logn) space; and
- Max Cut can be solved in time nO(dk) using O(dklogn) space; and
- Dominating Set can be solved in time 2O(dk) · nO(1) using nO(1) space via a randomized
algorithm.
We also establish a lower bound, conditional on a certain assumption about the complexity of Longest Common Subsequence, which shows that at least in the case of Independent Set the exponent of the parametric factor in the time complexity has to grow with d if one wishes to keep the space complexity polynomial
A globular protein exhibits rare phase behavior and forms chemically regulated orthogonal condensates in cells
No full textProteins with chemically regulatable phase separation are of great interest in the fields of biomolecular condensates and synthetic biology. Intrinsically disordered proteins (IDPs) are the dominating building blocks of biomolecular condensates which often lack orthogonality and small-molecule regulation desired to create synthetic biomolecular condensates or membraneless organelles (MLOs). Here, we discover a well-folded globular protein, lipoate-protein ligase A (LplA) from E. coli involved in lipoylation of enzymes essential for one-carbon and energy metabolisms, that exhibits structural homomeric oligomerization and a rare LCST-type reversible phase separation in vitro. In both E. coli and human U2OS cells, LplA can form orthogonal condensates, which can be specifically dissolved by its natural substrate, the small molecule lipoic acid and its analogue lipoamide. The study of LplA phase behavior and its regulatability expands our understanding and toolkit of small-molecule regulatable protein phase behavior with impacts on biomedicine and synthetic biology
Multi-fidelity Gaussian process surrogate models for numerical acoustic design in frequency domain
No full textWave drift force and moment in deep and shallow water
No full textTo attain a low Energy Efficiency Design Index (EEDI), large ships possibly lack the necessary propulsion power to avoid stranding in case of strong adverse wind and wave conditions. To estimate this danger, here, the longitudinal and transverse drift force and the yaw drift moment caused by regular waves of arbitrary frequency and direction are computed using a 3-dimensional Rankine panel method. In many cases, drift forces are larger in shallow than in deep water. Therefore, the theory for computing drift force and moment is extended to shallow water. As published results for shallow water are lacking, the method is verified only for deep water by comparisons with results of model experiments and CFD computations for three ships. For one of them, the dependence of non-dimensional coefficients of longitudinal and transverse drift force and of the drift yaw moment on wave frequency, wave angle, water depth and ship speed is shown. The source files of the programs used for these computations may be obtained from the author if an adequate fee is donated to the Medecins Sans Frontieres or to the author
