scholarly journals Error Analysis and Error Estimates for Co-Simulation in FMI for Model Exchange and Co-Simulation V2.0

2013 ◽  
Vol 60 (1) ◽  
pp. 75-94 ◽  
Author(s):  
Martin Arnold ◽  
Christoph Clauss ◽  
Tom Schierz
Keyword(s):  
System Dynamics ◽  
Error Estimation ◽  
Size Control ◽  
Industrial Applications ◽  
Physical Models ◽  
Modular Structure ◽  
Step Size ◽  
Simulation Tools ◽  
Communication Step ◽  
The Individual

Complex multi-disciplinary models in system dynamics are typically composed of subsystems. This modular structure of the model reflects the modular structure of complex engineering systems. In industrial applications, the individual subsystems are often modelled separately in different mono-disciplinary simulation tools. The Functional Mock-Up Interface (FMI) provides an interface standard for coupling physical models from different domains and addresses problems like export and import of model components in industrial simulation tools (FMI for Model Exchange) and the standardization of co-simulation interfaces in nonlinear system dynamics (FMI for Co-Simulation), see [10]. The renewed interest in algorithmic and numerical aspects of co-simulation inspired some new investigations on error estimation and stabilization techniques in FMI for Model Exchange and Co-Simulation v2.0 compatible co-simulation environments. In the present paper, we focus on reliable error estimation for communication step size control in this framework.

Multi-Swarm Cuckoo Search Algorithm with Q-Learning Model

2020 ◽  
Author(s):  
Juan Li ◽  
Dan-dan Xiao ◽  
Ting Zhang ◽  
Chun Liu ◽  
Yuan-xiang Li ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Size Control ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Local Optimum ◽  
Step Size ◽  
Q Learning ◽  
Fitness Value ◽  
The Individual ◽  
Evolution Effect

Abstract As a novel swarm intelligence optimization algorithm, cuckoo search (CS) has been successfully applied to solve diverse problems in the real world. Despite its efficiency and wide use, CS has some disadvantages, such as premature convergence, easy to fall into local optimum and poor balance between exploitation and exploration. In order to improve the optimization performance of the CS algorithm, a new CS extension with multi-swarms and Q-Learning namely MP-QL-CS is proposed. The step size strategy of the CS algorithm is that an individual fitness value is examined based on a one-step evolution effect of an individual instead of evaluating the step size from the multi-step evolution effect. In the MP-QL-CS algorithm, a step size control strategy is considered as action, which is used to examine the individual multi-stepping evolution effect and learn the individual optimal step size by calculating the Q function value. In this way, the MP-QL-CS algorithm can increase the adaptability of individual evolution, and a good balance between diversity and intensification can be achieved. Comparing the MP-QL-CS algorithm with various CS algorithms, variants of differential evolution (DE) and improved particle swarm optimization (PSO) algorithms, the results demonstrate that the MP-QL-CS algorithm is a competitive swarm algorithm.

scholarly journals A mathematical understanding of how cytoplasmic dynein walks on microtubules

2018 ◽  
Vol 5 (8) ◽  
pp. 171568 ◽  
Author(s):  
L. Trott ◽  
M. Hafezparast ◽  
A. Madzvamuse
Keyword(s):  
Temporal Dynamics ◽  
Cytoplasmic Dynein ◽  
Physical Models ◽  
Variable Step Size ◽  
Mathematical Framework ◽  
Step Size ◽  
Model Framework ◽  
Temporal Behaviour ◽  
Wide Range ◽  
The Individual

Cytoplasmic dynein 1 (hereafter referred to simply as dynein) is a dimeric motor protein that walks and transports intracellular cargos towards the minus end of microtubules. In this article, we formulate, based on physical principles, a mechanical model to describe the stepping behaviour of cytoplasmic dynein walking on microtubules from the cell membrane towards the nucleus. Unlike previous studies on physical models of this nature, we base our formulation on the whole structure of dynein to include the temporal dynamics of the individual subunits such as the cargo (for example, an endosome, vesicle or bead), two rings of six ATPase domains associated with diverse cellular activities (AAA+ rings) and the microtubule-binding domains which allow dynein to bind to microtubules. This mathematical framework allows us to examine experimental observations on dynein across a wide range of different species, as well as being able to make predictions on the temporal behaviour of the individual components of dynein not currently experimentally measured. Furthermore, we extend the model framework to include backward stepping, variable step size and dwelling. The power of our model is in its predictive nature; first it reflects recent experimental observations that dynein walks on microtubules using a weakly coordinated stepping pattern with predominantly not passing steps. Second, the model predicts that interhead coordination in the ATP cycle of cytoplasmic dynein is important in order to obtain the alternating stepping patterns and long run lengths seen in experiments.

Co-Simulation: Error Estimation and Macro-Step Size Control

2020 ◽  
Author(s):  
Tobias Meyer ◽  
Jan Kraft ◽  
Bernhard Schweizer
Keyword(s):  
Error Estimation ◽  
Size Control ◽  
Simulation Models ◽  
Error Estimator ◽  
Step Size ◽  
Error Estimators ◽  
Step Size Control ◽  
Communication Time ◽  
Simulation Error ◽  
Time Grid

Abstract Efficiency and accuracy of a co-simulation may considerable be increased by using a variable communication time grid. Therefore, an error estimator for controlling the macro-step size is required. Here, error estimators are derived and investigated for explicit and implicit co-simulation approaches. The manuscript focuses on mechanical co-simulation models. The basic results may, however, also be applied to arbitrary, non-mechanical co-simulation models.

A Step Size Control Method Improving Estimation Speed in Double Talk Term

2013 ◽  
Vol E96.A (7) ◽  
pp. 1543-1551 ◽  
Author(s):  
Takuto YOSHIOKA ◽  
Kana YAMASAKI ◽  
Takuya SAWADA ◽  
Kensaku FUJII ◽  
Mitsuji MUNEYASU ◽  
...  
Keyword(s):  
Control Method ◽  
Size Control ◽  
Step Size ◽  
Step Size Control

Dioxazine pigments

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Robert Christie ◽  
Adrian Abel
Keyword(s):  
High Performance ◽  
Industrial Applications ◽  
Textile Dye ◽  
Linear Arrangement ◽  
Natural Colorant ◽  
X Ray ◽  
Organic Pigments ◽  
Tyrian Purple ◽  
Violet Color ◽  
The Individual

Abstract This chapter provides an overview of the structural and synthetic chemistry, and the industrial applications, of dioxazine pigments, a small group of high performance organic pigments. The color violet (or purple) has frequently assumed a prominent position in history, on account of its rarity and cost. The natural colorant Tyrian purple and the first synthetic textile dye, Mauveine, are prime examples of this unique historical feature. CI Pigment Violet 23, also referred to as Dioxazine Violet or Carbazole Violet, is one of the most universally used organic pigments, by far the most important industrial pigment in the violet shade area. Dioxazine Violet is also unique as the dominant industrial violet pigment providing a brilliant, intense violet color and an excellent all-round set of fastness properties. The pigment has a polycyclic molecular structure, originally described wrongly as a linear arrangement, and later shown to adopt an S-shaped arrangement on the basis of X-ray structural analysis. Two other dioxazine pigments are of rather lesser importance. The synthesis and manufacturing route to CI Pigment Violet 23 is described in the review. Finally, a survey of the principal current applications of the individual dioxazine pigments is presented.

Sign in / Sign up

Export Citation Format

Share Document