Factors Affecting Nanoindentation Test Data

2011 ◽  
pp. 77-104 ◽  
Author(s):  
Anthony C. Fischer-Cripps
Keyword(s):  
Test Data ◽  
Nanoindentation Test ◽  
Factors Affecting

scholarly journals Evaluation of installation effects on set-up of field displacement piles in sand

2019 ◽  
Vol 56 (4) ◽  
pp. 461-472 ◽  
Author(s):  
Ivana Anusic ◽  
Barry M. Lehane ◽  
Gudmund R. Eiksund ◽  
Morten A. Liingaard
Keyword(s):  
Test Data ◽  
Impact Frequency ◽  
Short Term ◽  
Factors Affecting ◽  
Installation Effects ◽  
Field Displacement ◽  
Small Influence ◽  
Set Up ◽  
Shaft Friction

The paper presents results from a new series of tests on displacement piles in sand, involving different installation modes, and combines these with results from previous tests at the same site as well as with test data at two other well-investigated sand sites to provide fresh insights into factors affecting “short-term” capacity and set-up of shaft friction. It is shown that the shaft capacity measured shortly after installation reduces systematically with the logarithm of the number of impact blows or jacking increments per unit shaft area imparted during installation. However, the degree of set-up of shaft friction for piles increases with an increase in the number of blows, and piles installed using a large number of blows can attain highest “long-term” shaft capacities, despite having the lowest short-term capacity. The tests indicated that the driving impact frequency had a relatively small influence on shaft friction, while piles installed by vibration attain short-term capacities comparable to driven impact piles, but showed negative set-up.

Mechanical Properties of Ion-Irradiated Stainless Steel Determined by Nanoindentation Tests and Finite Element Analyses

2020 ◽  
Author(s):  
Jae Min Sim ◽  
Yoon-Suk Chang ◽  
Byeong Seo Kong ◽  
Changheui Jang
Keyword(s):  
Mechanical Properties ◽  
Finite Element ◽  
Test Data ◽  
Radiation Effects ◽  
Ion Irradiation ◽  
Nanoindentation Test ◽  
Finite Element Analyses ◽  
Stress Strain ◽  
Term Operation ◽  
Constitutive Parameters

Abstract While austenitic stainless steels (ASSs) have been widely adopted for reactor vessel internals because of their excellent material properties, diverse ageing-related degradation may occur due to high temperature, corrosive and neutron radiation environments during operation. In particular, since the change of mechanical properties is a major concern in long-term operation but it is difficult to prepare and handle standard specimens influenced by neutrons, most of experimental researches for enhanced understanding of the radiation effects have been focused on high-energetic ion-irradiation and tests of small specimens. In this study, systematic finite element analyses were carried out to quantify changing mechanical properties based on both virgin and ion-irradiated nanoindentation test data of typical ASS material. First of all, numerical analysis was carried out to obtain unirradiated material constitutive parameters by using trial set along the miniature specimen and comparing test data, and then indentation stress-strain (ISS) curve was derived. Subsequently, ISS was converted into uniaxial stress-strain response taking into account simple correlation. Finally, with regard to the irradiated material, similar analytical procedures were established. 304 SS was irradiated with 2 MeV proton and radioactivity is being measured. Comparison between analysis result and experimental one will be carried out, of which details and key findings will be discussed.

Credit-worthiness Prediction in Energy-Saving Finance Using Machine Learning Model

2021 ◽  
Vol 39 (10) ◽  
Author(s):  
Eka Sudarmaji ◽  
Noer Azam Achsani ◽  
Yandra Arkeman ◽  
Idqan Fahmi
Keyword(s):  
Machine Learning ◽  
Energy Saving ◽  
Test Data ◽  
Life Cycle Cost ◽  
Multinomial Logistic Regression ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Factors Affecting ◽  
Positive Rate ◽  
Sensitivity Rate

Companies can form their own "ESCO model" with their capitals. Unfortunately, customer's creditworthiness was becoming more crucial for ESCO. Machine learning was used to predict the creditworthiness of clients in ESCO financing processes. This research aimed to develop a scoring model to leverage a machine learning and life cycle cost analysis (LCCA) to evaluate alternative financing for Energy Saving in Indonesia. The results of calculations using multinomial logistic regression showed that the accuracy value of prediction data with test data was 88.3562 %. The prediction rate result that refers to the percentage of correct predictions among all test data was 91.67%, and False Positive Rate (FPR) was 39.44%. The True Positive Rate was called Recall or 'Sensitivity Rate' as it was defined as several positive cases that were correctly identified (TPR) was 92.20%. We found the machine learning methods for creditworthiness prediction in retrofitting projects were fresh and worth a shot. It was hoped that this new practice would grow in popularity and become standard among ESCOs. Unfortunately, current machine-learning-based creditworthiness scoring practices lacked explain ability and interpretability. Unfortunately, ESCO must penalize the retrofitting project. As a result, since retrofitting was a new industry, the credit approval process was challenging to communicate to consumers. The most important thing for ESCO to deal with the project was to have a friendship and know-how with the client. Research from these case studies led to a clearer understanding of the factors affecting all parties' decisions to implement and continue with their ESCO project.

Availability Analysis for Airborne ECM System Automatic Test System

2013 ◽  
Vol 333-335 ◽  
pp. 1617-1620
Author(s):  
Min Fan ◽  
Hai Xia Zhang ◽  
Wei Fan Liu
Keyword(s):  
Test Data ◽  
Evaluation Method ◽  
Test System ◽  
Calculation Model ◽  
Fuzzy Mathematics ◽  
Automatic Test ◽  
Factors Affecting ◽  
Automatic Test System ◽  
Availability Analysis ◽  
Main Factors

In this paper, the composition and characteristics of airborne ECM system automatic test system is fully acquainted, the main factors affecting the availability is analysed, the availability evaluation method of fuzzy mathematics system is studied, the corresponding availability evaluation method is proposed and the availability calculation model is established in this paper. Based on the relevant test data and the detail calculation evaluation for the airborne ECM system automatic test system, the automatic test system is verified available.

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