Measures describing curvilinear short fiber distributions
نویسندگان
چکیده
منابع مشابه
Describing proofs by short tautologies
Herbrand’s theorem is one of the most fundamental results about first-order logic. In the context of proof analysis, Herbrand-disjunctions are used for describing the constructive content of cut-free proofs. However, given a proof with cuts, the computation of an Herbrand-disjunction is of significant computational complexity, as the cuts in the proof have to be eliminated first. In this paper ...
متن کاملEffects of Fiber Length and Fiber Orientation Distributions on the Tensile Strength of Short-fiber-reinforced Polymers
This paper presents an analytical method considering the effects of fiber length and fiber orientation distributions for predicting the tensile strength (TS) of short-fiber-reinforced polymers (SFRP). Two probability density functions are used for modelling the distributions of fiber length and fiber orientation. The strength of SFRP is derived as a functiolz of fiber length and fiber orientati...
متن کاملMorphing Helicopter Rotor Blade with Curvilinear Fiber Composites
A variable camber morphing rotor blade with curvilinear fiber composite skin is studied in this paper. The benefits of curvilinear fiber (CVF) composites for morphing skin over the linear fiber are investigated, initially. The skin of a morphing blade is modeled as a plate supported between the D-spar and trailing edge of typical rotor blade dimensions. The CVF composite shows about 60 % increa...
متن کاملMeasures and Distributions
Measures A (set) function μ : K → [0,∞], with ∅ ∈ K ⊂ 2 and μ(∅) = 0 is called additive or finitely additive if A,B ∈ K, with A ∪ B ∈ K and A ∩ B = ∅ imply μ(A ∪ B) = μ(A) + μ(B). Similarly, μ is called σ-additive or countably additive if Ai ∈ K, with ⋃∞ i=1 Ai = A ∈ K and Ai ∩ Aj = ∅ for i 6= j imply μ(A) = ∑∞ i=1 μ(Ai). It is clear that if μ is σ-additive then μ is also additive, but the conv...
متن کاملIncorporating visualisation quality measures to curvilinear component analysis
Curvilinear Component Analysis (CCA) is a useful data visualisation method. CCA has the technical property that its optimisation surface, as defined by its stress function, changes during the optimisation according to a decreasing parameter. CCA uses a variant of the stochastic gradient descent method to create a mapping of data. In the optimisation method of CCA, the stress function is only a ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Archive of Applied Mechanics
سال: 2020
ISSN: 0939-1533,1432-0681
DOI: 10.1007/s00419-020-01762-8