A simplified semi-analytical model for the filling and cooling process in plastic molding

2019 ◽  
Vol 31 (6) ◽  
pp. 063105 ◽  
Author(s):  
R. Mollaabbasi ◽  
E. Behzadfar ◽  
S.M. Taghavi
Keyword(s):  
Analytical Model ◽  
Cooling Process ◽  
Plastic Molding

An Inverse, Decision-Based Design Method for Robust Concept Exploration

2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Anand Balu Nellippallil ◽  
Pranav Mohan ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Analytical Model ◽  
Robust Design ◽  
Hot Rolling ◽  
Design Method ◽  
Inverse Design ◽  
Cooling Process ◽  
Robust Solution ◽  
Robust Solutions ◽  
Process Chains ◽  
Inverse Design Method

Abstract In this paper, we extend our previous work on a goal-oriented inverse design method to carry out inverse robust design by managing the uncertainty involved. The extension embodies the introduction of specific robust design goals and new robust solution constraints anchored in the mathematical constructs of Error Margin Indices (EMIs) and Design Capability Indices (DCIs) to determine “satisficing” robust design specifications across analytical model-based process chains. Contributions in this paper include the designer’s ability to explore satisficing robust solution regions when multiple conflicting goals and multiple sources of uncertainty are present. Using the goal-oriented inverse design method, robust solutions are propagated in an inverse manner. We demonstrate the efficacy of the method and the associated robust design functionalities using an industry-inspired hot rolling and cooling process chain example problem for the production of a steel rod. In this example, we showcase the formulation of multiple mechanical property goals for the end product using the robustness metrics and the exploration of satisficing robust solutions for material microstructure after the cooling process using the robust solution constraints. The robust solutions thus identified are communicated in an inverse manner using the design method to explore satisficing robust solutions for the microstructure generated after the hot rolling process. Using the example, we demonstrate the robust co-design of material, product, and associated manufacturing processes. The method and the associated design constructs are generic and support the formulation and inverse robust design exploration under uncertainty of similar problems involving a sequential, analytical model-based flow of information across process chains.

Identification of thermal parameters of a building envelope based on the cooling process of a building object

2019 ◽  
Vol 43 (6) ◽  
pp. 503-527 ◽  
Author(s):  
Iwona Pokorska-Silva ◽  
Artur Nowoświat ◽  
Lidia Fedorowicz
Keyword(s):  
Thermal Conductivity ◽  
Regression Model ◽  
Multiple Regression ◽  
Analytical Model ◽  
Multiple Regression Model ◽  
Building Envelope ◽  
Thermal Parameters ◽  
Cooling Process ◽  
Building Envelopes ◽  
Measurement Results

Thermal properties of building envelopes are often described using thermal conductivity or thermal resistance. And the opposite task involves the identification of thermal parameters of building envelopes based on the measurements of their cooling process. In this article, the authors proposed a method of identifying thermal parameters of a building envelope based on cooling measurements, using a multiple regression model for this purpose. To satisfy the research objectives, two basic experiments were carried out. The first experiment was performed in laboratory conditions. The research model was a cube of the dimensions of 1.1 m × 1.1 m × 1.1 m. The second experiment was carried out in semi-real conditions, and the used model was a small house of the dimensions of 6.0 m × 4.15 m × 5.2 m. The measurement results were also used to calibrate numerical models made in the ESP-r program. The research studies have demonstrated that the model can be used to identify thermal parameters of a building envelope. Based on the measurements and simulations, the cooling equations of the object were determined and the 95% confidence interval for the heat retention index was estimated. On that basis, using the multiple regression model, such parameters of the model as density, specific heat, and thermal conductivity were estimated. It turned out that using the Gauss–Newton approximation, we obtained the correlation of the measurement results and the analytical model with the correlation coefficient of 0.9971 (for the laboratory scale). And the multiple regression improved not only the correlation between the measurement and the analytical model, but it also allowed to obtain “almost identical” results. Similarly, promising results were obtained for the semi-real scale.

Bulk standards for low-temperature biological x-ray microanalysis

1984 ◽  
Vol 42 ◽  
pp. 282-283
Author(s):  
P. Echlin ◽  
M. McKoon ◽  
E.S. Taylor ◽  
C.E. Thomas ◽  
K.L. Maloney ◽  
...  
Keyword(s):  
Phase Separation ◽  
Low Temperature ◽  
Analytical Method ◽  
Salt Solutions ◽  
Absolute Concentration ◽  
Cooling Process ◽  
X Ray ◽  
The Absolute ◽  
Analytical Procedures ◽  
Electrical Conductors

Although sections of frozen salt solutions have been used as standards for x-ray microanalysis, such solutions are less useful when analysed in the bulk form. They are poor thermal and electrical conductors and severe phase separation occurs during the cooling process. Following a suggestion by Whitecross et al we have made up a series of salt solutions containing a small amount of graphite to improve the sample conductivity. In addition, we have incorporated a polymer to ensure the formation of microcrystalline ice and a consequent homogenity of salt dispersion within the frozen matrix. The mixtures have been used to standardize the analytical procedures applied to frozen hydrated bulk specimens based on the peak/background analytical method and to measure the absolute concentration of elements in developing roots.

ANALYTICAL MODEL OF SUPEREXCHANGE

1988 ◽  
Vol 49 (C8) ◽  
pp. C8-911-C8-912
Author(s):  
Yu. V. Rakitin ◽  
V. T. Kalinnikov
Keyword(s):  
Analytical Model

ANALYTICAL MODEL FOR ENVIRONMENTAL IMPACT OF SOLID WASTE DISPOSAL FROM ELECTRICITY GENERATION

2002 ◽  
Vol 4 (1-2) ◽  
pp. 26
Author(s):  
Paulo Fernando Lavalle Heilbron Filho ◽  
Jesus Salvador Perez Guerrero ◽  
Elizabeth May Pontedeiro ◽  
Nerbe J. Ruperti, Jr. ◽  
Renato M. Cotta
Keyword(s):  
Environmental Impact ◽  
Solid Waste ◽  
Analytical Model ◽  
Waste Disposal ◽  
Electricity Generation ◽  
Solid Waste Disposal
Sign in / Sign up

Export Citation Format

Share Document