Hi!
Is there any SIMPLE (direct) way to obtain effective properties of laminates in Abaqus?
Thanks in advance.
Kamil
Hi!
Is there any SIMPLE (direct) way to obtain effective properties of laminates in Abaqus?
Thanks in advance.
Kamil
Dear all,
I have a question which seems quite simple but has troubled me for quite some time:
I have a composite tube modeled using layered solid elements C3D8, and I want to output radial, circumferential, and axial stress components averaged at preselected nodes (defined as a node set) in Abaqus data file (*.dat). Nevertheless, I would like to have those stress components expressed in a cylindrical coordinate system, rather than in local material coordinate system. I would appreciate it a lot if anyone knows whether it is possible to do so. Thanks.
Normal
0
false
false
false
EN-US
X-NONE
AR-SA
The new blog post discusses yet another application of the Linear Assignment Problem; in this case the use of the Hungarian algorithm for finding an optimal list of close data points to a set of point generated using Latin Hypercube sampling. See http://www.parresianz.com//sampling/hungarian_algorithm/
-- Biswajit
The attached notes are written for a course on plasticity. When I update the notes, I will post a link on my twitter account: https://twitter.com/zhigangsuo.
Rheology is the science of deformation. This science poses a question for every material: Given a history of stress, how do we predict the history of strain, or the other way around?
Large numerical simulations lead to large data sets that typically need some sampling to make analysis tractable. Check out my upcoming series of posts on the topic at http://www.parresianz.com//sampling/latin_hypercube_sampling/
-- Biswajit
The attached notes are written for a course on plasticity. When I update the notes, I will post a link on my twitter account: https://twitter.com/zhigangsuo.
I am taking a course in Bayesian statistics, which is off my field. In the lecture notes, the instructor showed
E[X^2n] = (2n-1) σ^2n
and E[X^3 . Y] = E[Y^3 . X] = 3.ρ.σ^4
where σ = variance
E = expectation
X is a random variable.
ρ = cor(XY) = E[XY]/σ^2
Can anyone kindly explain how these equations were derived or atleast point me towards some text where I can understand this? Is this to do with the moment generating function?
I was solving the following question and I derived the Auto correlation function and proved that it is a WSS process. However, I am not sure how to go about finding the Marginal probability density function. Should I find joint probability of a and b and then find marginal probability of each random variable?
