Results 41 to 50 of about 2,760 (198)
Evaluating Energy Absorption Performance of Filled Lattice Structures
Advanced Engineering Materials, EarlyView.Maximum stress must be considered to robustly evaluate energy absorber designs. This approach was applied to compare all types of absorbers in a single Ashby diagram and determine the utility of filling lattice voids with a second material. High‐performance fillers can improve the performance of lattices that are limited by buckling or catastrophic ...
Christian Bonney +2 more
wiley +1 more source
Convergence to the uniform distribution of vectors of partial sums modulo one with a common factor [PDF]
, 2023 Roberta Flenghi, Benjamin Jourdain
openalex +1 more source
Phase Field Failure Modeling: Brittle‐Ductile Dual‐Phase Microstructures under Compressive Loading
Advanced Engineering Materials, EarlyView.The approach by Amor and the approach by Miehe and Zhang for asymmetric damage behavior in the phase field method for fracture are compared regarding their fitness for microcrack‐based failure modeling. The comparison is performed for the case of a dual‐phase microstructure with a brittle and a ductile constituent.
Jakob Huber, Jan Torgersen, Ewald Werner
wiley +1 more source
Two-Sample U-Statistic Processes for Long-Range Dependent Data [PDF]
, 2014 Motivated by some common-change point tests, we investigate the asymptotic distribution of the U-statistic process $U_n(t)=\sum_{i=1}^{[nt]}\sum_{j=[nt]+1}^n h(X_i,X_j)$, $0\leq t\leq 1$, when the underlying data are long-range dependent.
Dehling, Herold +2 more
core
A Topology Optimization Framework for the Inverse Design of Nonlinear Mechanical Metamaterials
Advanced Engineering Materials, EarlyView.This work uses topology optimization to design unit cells for mechanical metamaterials with a prescribed nonlinear stress–strain response. The framework adds contact and postbuckling modeling to synthesize microstructures for three highly nonlinear responses, including pseudoductile behavior, monostable with snap‐through buckling, and bistable ...
Charlie Aveline +2 more
wiley +1 more source
Constructive Matrix Theory for Higher Order Interaction
, 2019 This paper provides an extension of the constructive loop vertex expansion to stable matrix models with interactions of arbitrarily high order. We introduce a new representation for such models, then perform a forest expansion on this representation.
Krajewski, Thomas +2 more
core +3 more sources
Advanced Engineering Materials, EarlyView.This plot compares experimental tensile stress–strain curves (with 4 different strain rates) and corresponding modelled curves (obtained using the optimised sets of Voce and Miller–Norton parameter values shown). The inferred M‐N values, characterizing the creep, are very similar to those obtained via conventional creep testing.
S. Ooi, R. P. Thompson, T. W. Clyne
wiley +1 more source
Advanced Functional Materials, EarlyView.This work explores Li‐substituted P2 layered oxides for Na‐ion batteries by crystallographic and electrochemical studies. The effect of lithium on superstructure orderings, on phase transitions during synthesis and electrochemical cycling and on the interplay of O‐ versus TM‐redox is revealed via various advanced techniques, including semi‐simultaneous
Mingfeng Xu +5 more
wiley +1 more source
Sorelmélet és végtelen sorokkal kapcsolatos egyenlőtlenségek = Theory of series and inequalities concerning infinite series [PDF]
, 2007 2003 óta 29 dolgozatom jelent meg (lásd. Math.Rev. ) és 11 további már közlésre elfogadott. Mind hazai és külföldi ismert folyóirat. Ebben az időszakban számos új osztályát definiáltam számsorozatoknak és ezekre sikerült több klasszikus eredmenyt ...
Leindler, László
core
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source