Navigating the Property Data Landscape

2021 ◽  
pp. 21-34
Author(s):  
Robert Ciemniak
Keyword(s):  
Property Data

scholarly journals Thermal and Transport Properties of Molten Chloride Salts with Polarization Effect on Microstructure

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 746
Author(s):  
Jianfeng Lu ◽  
Senfeng Yang ◽  
Gechuanqi Pan ◽  
Jing Ding ◽  
Shule Liu ◽  
...  
Keyword(s):  
Heat Capacity ◽  
Polarization Effect ◽  
Chloride Salt ◽  
Coulombic Interaction ◽  
Molten Chloride ◽  
Self Diffusion ◽  
Local Structures ◽  
Property Data ◽  
Chloride Salts ◽  
And Storage

Molten chloride salt is recognized as a promising heat transfer and storage medium in concentrating solar power in recent years, but there is a serious lack for thermal property data of molten chloride salts. In this work, local structures and thermal properties for molten chloride salt—including NaCl, MgCl2, and ZnCl2—were precisely simulated by Born–Mayer–Huggins (BMH) potential in a rigid ion model (RIM) and a polarizable ion model (PIM). Compared with experimental data, distances between cations, densities, and heat capacities of molten chloride slats calculated from PIM agree remarkably better than those from RIM. The polarization effect brings an extra contribution to screen large repulsive Coulombic interaction of cation–cation, and then it makes shorter distance between cations, larger density and lower heat capacity. For NaCl, MgCl2, and ZnCl2, PIM simulation deviations of distances between cations are respectively 3.8%, 3.7%, and 0.3%. The deviations of density and heat capacity for NaCl between PIM simulation and experiments are only 0.6% and 2.2%, and those for MgCl2 and ZnCl2 are 0.7–10.7%. As the temperature rises, the distance between cations increases and the structure turns into loose state, so the density and thermal conductivity decrease, while the ionic self-diffusion coefficient increases, which also agree well with the experimental results.

scholarly journals A Comparative Study of Various Methods of Handling Missing Data in UNSODA

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 727
Author(s):  
Yingpeng Fu ◽  
Hongjian Liao ◽  
Longlong Lv
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Soil Property ◽  
Particle Density ◽  
Organic Matter Content ◽  
Nonparametric Tests ◽  
Matter Content ◽  
Support Vector ◽  
Soil Database ◽  
Property Data

UNSODA, a free international soil database, is very popular and has been used in many fields. However, missing soil property data have limited the utility of this dataset, especially for data-driven models. Here, three machine learning-based methods, i.e., random forest (RF) regression, support vector (SVR) regression, and artificial neural network (ANN) regression, and two statistics-based methods, i.e., mean and multiple imputation (MI), were used to impute the missing soil property data, including pH, saturated hydraulic conductivity (SHC), organic matter content (OMC), porosity (PO), and particle density (PD). The missing upper depths (DU) and lower depths (DL) for the sampling locations were also imputed. Before imputing the missing values in UNSODA, a missing value simulation was performed and evaluated quantitatively. Next, nonparametric tests and multiple linear regression were performed to qualitatively evaluate the reliability of these five imputation methods. Results showed that RMSEs and MAEs of all features fluctuated within acceptable ranges. RF imputation and MI presented the lowest RMSEs and MAEs; both methods are good at explaining the variability of data. The standard error, coefficient of variance, and standard deviation decreased significantly after imputation, and there were no significant differences before and after imputation. Together, DU, pH, SHC, OMC, PO, and PD explained 91.0%, 63.9%, 88.5%, 59.4%, and 90.2% of the variation in BD using RF, SVR, ANN, mean, and MI, respectively; and this value was 99.8% when missing values were discarded. This study suggests that the RF and MI methods may be better for imputing the missing data in UNSODA.

Fitting Weibull Strength Data and Applying It to Stochastic Mechanical Design

1992 ◽  
Vol 114 (1) ◽  
pp. 35-41 ◽  
Author(s):  
C. R. Mischke
Keyword(s):  
Mechanical Design ◽  
Least Square ◽  
Stochastic Methods ◽  
Coordinate Transformations ◽  
Strength Data ◽  
The Third ◽  
Tension Tests ◽  
Property Data ◽  
Simple Tension ◽  
Best Fit

This is the second paper in a series relating to stochastic methods in mechanical design. The first is entitled, “Some Property Data and Corresponding Weibull Parameters for Stochastic Mechanical Design,” and the third, “Some Stochastic Mechanical Design Applications.” When data are sparse, many investigators prefer employing coordinate transformations to rectify the data string, and a least-square regression to seek the best fit. Such an approach introduces some bias, which the method presented here is intended to reduce. With mass-produced products, extensive testing can be carried out and prototypes built and evaluated. When production is small, material testing may be limited to simple tension tests or perhaps none at all. How should a designer proceed in order to achieve a reliability goal or to assess a design to see if the goal has been realized? The purpose of this paper is to show how sparse strength data can be reduced to distributional parameters with less bias and how such information can be used when designing to a reliability goal.

Thermophysical Properties of Fluids Measured Under Microgravity Conditions

1998 ◽  
Vol 551 ◽  
Author(s):  
H.-J. Fecht ◽  
R.K. Wunderlich
Keyword(s):  
Thermophysical Property ◽  
Thermophysical Properties ◽  
European Space Agency ◽  
Space Station ◽  
Space Flights ◽  
Space Agency ◽  
Property Data ◽  
Microgravity Conditions ◽  
Key Industries ◽  
Sufficient Precision

AbstractThe analysis of nucleation and growth processes relies mostly on circular arguments since basic thermophysical properties necessary, such as the Gibbs free energy (enthalpy of crystallization, specific heat), the density, emissivity, thermal conductivity (diffusivity), diffusion coefficients, surface tension, viscosity, interfacial crystal / liquid tension, etc. are generally unknown with sufficient precision and therefore often deduced from insufficient linear interpolations from the elements. The paucity of thermophysical property data for commercial materials as well as research materials is mostly a result of the experimental difficulties arising from the unwanted convection and reactions of melts with containers at high temperatures. An overview will be given on the results of thermophysical property measurements during several different space flights using containerless processing methods. Furthermore, a perspective on a future measurement program of thermophysical properties supported by the European Space Agency is described. In this regard, the International Space Station is considered as the ideal laboratory for high precision measurements of thermophysical properties of fluids which help to improve manufacturing processes for a number of key industries.

Electronic Access to Factual Materials Information: The State of the Art

MRS Bulletin ◽  
1995 ◽  
Vol 20 (8) ◽  
pp. 40-48 ◽  
Author(s):  
J.H. Westbrook ◽  
J.G. Kaufman ◽  
F. Cverna
Keyword(s):  
State Of The Art ◽  
The State ◽  
Easy Access ◽  
End User ◽  
Cd Rom ◽  
Component Design ◽  
Property Data ◽  
On Line ◽  
Electronic Access ◽  
Access To Data

Over the past 30 years we have seen a strong but uncoordinated effort to both increase the availability of numeric materials-property data in electronic media and to make the resultant mass of data more readily accessible and searchable for the end-user engineer. The end user is best able to formulate the question and to judge the utility of the answer for numeric property data inquiries, in contrast to textual or bibliographic data for which information specialists can expeditiously carry out searches.Despite the best efforts of several major programs, there remains a shortfall with respect to comprehensiveness and a gap between the goal of easy access to all the world's numeric databases and what can presently be achieved. The task has proven thornier and therefore much more costly than anyone envisioned, and computer access to data for materials scientists and engineers is still inadequate compared, for example, to the situation for molecular biologists or astronomers. However, progress has been made. More than 100 materials databases are listed and categorized by Wawrousek et al. that address several types of applications including: fundamental research, materials selection, component design, process control, materials identification and equivalency, expert systems, and education. Standardization is improving and access has been made more easy.In the discussion that follows, we will examine several characteristics of available information and delivery systems to assess their impact on the successes and limitations of the available products. The discussion will include the types and uses of the data, issues around data reliability and quality, the various formats in which data need to be accessed, and the various media available for delivery. Then we will focus on the state of the art by giving examples of the three major media through which broad electronic access to numeric properties has emerged: on-line systems, workstations, and disks, both floppy and CD-ROM. We will also cite some resources of where to look for numeric property data.

The European Union Mg-Engine Project - Generation of Material Property Data for Four Die Cast Mg-Alloys

2006 ◽  
Author(s):  
Per Bakke ◽  
Andreas Fischersworring-Bunk ◽  
Isabelle de Lima ◽  
Hans Lilholt ◽  
Ingemar Bertilsson ◽  
...  
Keyword(s):  
European Union ◽  
Material Property ◽  
Mg Alloys ◽  
The European Union ◽  
Property Data ◽  
Die Cast ◽  
Material Property Data
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