Numerical Simulations of Seeded Batch Crystallization Demonstrating the Effect of Stochastic Nucleation on Crystal Product Quality

2021 ◽  
Vol 54 (7) ◽  
pp. 380-386
Author(s):  
Joi Unno ◽  
Izumi Hirasawa
Keyword(s):  
Numerical Simulations ◽  
Product Quality ◽  
Batch Crystallization ◽  
Crystal Product

Virtually-Guided Certification With Uncertainty Quantification Applied to Die Casting

2018 ◽  
Author(s):  
Shantanu Shahane ◽  
Soham Mujumdar ◽  
Namjung Kim ◽  
Pikee Priya ◽  
Narayana Aluru ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Product Quality ◽  
Uncertainty Quantification ◽  
Process Parameters ◽  
High Speed ◽  
Prediction Models ◽  
Die Casting ◽  
Stochastic Variation ◽  
Complex Process ◽  
Mechanical Properties Prediction

Die casting is a type of metal casting in which liquid metal is solidified in a reusable die. In such a complex process, measuring and controlling the process parameters is difficult. Conventional deterministic simulations are insufficient to completely estimate the effect of stochastic variation in the process parameters on product quality. In this research, a framework to simulate the effect of stochastic variation together with verification, validation, and uncertainty quantification is proposed. This framework includes high-speed numerical simulations of solidification, micro-structure and mechanical properties prediction models along with experimental inputs for calibration and validation. Both experimental data and stochastic variation in process parameters with numerical modeling are employed thus enhancing the utility of traditional numerical simulations used in die casting to have a better prediction of product quality. Although the framework is being developed and applied to die casting, it can be generalized to any manufacturing process or other engineering problems as well.

scholarly journals Multiobjective Dynamic Optimization of Ampicillin Batch Crystallization: Sensitivity Analysis of Attainable Performance vs Product Quality Constraints

2019 ◽  
Vol 58 (40) ◽  
pp. 18756-18771 ◽  
Author(s):  
Antonios Dafnomilis ◽  
Samir Diab ◽  
Alistair D. Rodman ◽  
Andreas G. Boudouvis ◽  
Dimitrios I. Gerogiorgis
Keyword(s):  
Sensitivity Analysis ◽  
Product Quality ◽  
Dynamic Optimization ◽  
Batch Crystallization

Simulations of Die Casting With Uncertainty Quantification

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Shantanu Shahane ◽  
Soham Mujumdar ◽  
Namjung Kim ◽  
Pikee Priya ◽  
Narayana R. Aluru ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Product Quality ◽  
Uncertainty Quantification ◽  
Process Parameters ◽  
High Speed ◽  
Prediction Models ◽  
Die Casting ◽  
Stochastic Variation ◽  
Complex Process ◽  
Mechanical Properties Prediction

Die casting is a type of metal casting in which a liquid metal is solidified in a reusable die. In such a complex process, measuring and controlling the process parameters are difficult. Conventional deterministic simulations are insufficient to completely estimate the effect of stochastic variation in the process parameters on product quality. In this research, a framework to simulate the effect of stochastic variation together with verification, validation, and uncertainty quantification (UQ) is proposed. This framework includes high-speed numerical simulations of solidification, microstructure, and mechanical properties prediction models along with experimental inputs for calibration and validation. Both experimental data and stochastic variation in process parameters with numerical modeling are employed, thus enhancing the utility of traditional numerical simulations used in die casting to have a better prediction of product quality. Although the framework is being developed and applied to die casting, it can be generalized to any manufacturing process or other engineering problems as well.

Determination of crystallization kinetics from batch experiments II

1983 ◽  
Vol 48 (10) ◽  
pp. 2767-2776 ◽  
Author(s):  
Jaroslav Nývlt ◽  
Stanislav Žáček
Keyword(s):  
Crystal Growth ◽  
Size Distribution ◽  
Crystallization Kinetics ◽  
Kinetic Data ◽  
Batch Experiments ◽  
Aluminium Sulphate ◽  
Batch Crystallization ◽  
Relative Kinetic ◽  
Crystal Product

Relations have been derived for the size distribution of crystal product of batch crystallization with constant and variable supersaturation, applicable where the distribution cannot be approximated by the relation holding for continuous MSMPR crystallizers. In the cases considered, relative kinetic data, but not the kinetic exponents of nucleation and crystal growth, can be evaluated for model experiments involving any variation of supersaturation with time. Application of the relationships is illustrated by treating data for potassium aluminium sulphate precipitation.

Saturation mechanism and generated viscosity in gravito-turbulent accretion disks

2020 ◽  
Vol 640 ◽  
pp. A53
Author(s):  
L. Löhnert ◽  
S. Krätschmer ◽  
A. G. Peeters
Keyword(s):  
Growth Rate ◽  
Numerical Simulations ◽  
Accretion Disks ◽  
Gravitational Instability ◽  
Turbulent Viscosity ◽  
Radial Distance ◽  
Maximum Growth ◽  
Wave Number ◽  
Radiative Cooling ◽  
Mixing Length

Here, we address the turbulent dynamics of the gravitational instability in accretion disks, retaining both radiative cooling and irradiation. Due to radiative cooling, the disk is unstable for all values of the Toomre parameter, and an accurate estimate of the maximum growth rate is derived analytically. A detailed study of the turbulent spectra shows a rapid decay with an azimuthal wave number stronger than ky−3, whereas the spectrum is more broad in the radial direction and shows a scaling in the range kx−3 to kx−2. The radial component of the radial velocity profile consists of a superposition of shocks of different heights, and is similar to that found in Burgers’ turbulence. Assuming saturation occurs through nonlinear wave steepening leading to shock formation, we developed a mixing-length model in which the typical length scale is related to the average radial distance between shocks. Furthermore, since the numerical simulations show that linear drive is necessary in order to sustain turbulence, we used the growth rate of the most unstable mode to estimate the typical timescale. The mixing-length model that was obtained agrees well with numerical simulations. The model gives an analytic expression for the turbulent viscosity as a function of the Toomre parameter and cooling time. It predicts that relevant values of α = 10−3 can be obtained in disks that have a Toomre parameter as high as Q ≈ 10.

Sign in / Sign up

Export Citation Format

Share Document