Time-temperature and time-stress correspondence in nonlinear creep. Experimental behaviour of amorphous polymers and quantitative modelling approaches

e-Polymers ◽  
2004 ◽  
Vol 4 (1) ◽  
Author(s):  
José R. S. André ◽  
José J. C. Cruz Pinto
Keyword(s):  
Nonlinear Creep ◽  
Predictive Capability ◽  
Kinetic Formulation ◽  
Time Stress ◽  
Relative Contribution ◽  
Proposed Model ◽  
Fully Coupled ◽  
Experimental Behaviour ◽  
Modelling Approach

Abstract Non-linear creep is described by a non-simulative, analytical, dynamic molecular modelling approach. Elementary, molecular-scale, process-relevant frequencies are derived by adequate kinetic formulation. They follow almost exactly an Arrhenius-like behaviour with a range of activation enthalpies. Their relative contribution to the overall macroscopic behaviour of the materials is quantified to account for the materials’ retardation time spectra and final non-Arrhenius behaviour. A new creep compliance equation is derived, yielding a fully coupled timetemperature- stress formulation, with long-term predictive capability. Experimental data for poly(methyl methacrylate) are analysed to identify the extent to which timetemperature and time-stress correspondence relationships may be valid, and it is shown that they are approximations (especially the latter), limited to narrow ranges of experimental variables, in contrast to the proposed model, which more reasonably fits the experimental behaviour.

Creep Behavior of Building Silicone Sealant under Long-Term External Loading and its Time-Stress Equivalence

2014 ◽  
Vol 633 ◽  
pp. 451-454
Author(s):  
Xiao Gen Liu ◽  
Yi Wang Bao ◽  
Xiu Fang Wang
Keyword(s):  
Creep Curve ◽  
Creep Behavior ◽  
External Loading ◽  
Nonlinear Creep ◽  
Creep Tests ◽  
Time Stress ◽  
Silicone Sealant ◽  
Term Creep ◽  
Term Performance

The main mechanics behavior character of building silicone sealant is its time dependence£¬which lies in the existence of the interior timepiece or the characteristic time. The creep behaviors were fundamental to evaluating the long-term performance of the building silicone sealant under long-term external loading. A series of creep tests of building silicone sealant were conducted in the laboratory under different combinations of external loads, the characteristics of the creep curve were obtained. In this work, the creep behaviors of building silicone sealant were also investigated under various temperatures at invariable stress. The test measurements show that the creep behavior of building silicone sealant are nonlinear, Nonlinear creep behavior is analyzed by means of the equivalence principle of time-stress, the main creep curve under reference stress was obtained by fitting with the viscoelastic rheological model. So the long-term creep behavior of the building silicone sealant under lower stress can be predicted by short-term creep behavior under higher stress.

Investigation of a New Approach to Predict Water Quality Extremes with a Case Study of Chemical Determinands in Stream Water

1991 ◽  
Vol 24 (6) ◽  
pp. 25-33
Author(s):  
A. J. Jakeman ◽  
P. G. Whitehead ◽  
A. Robson ◽  
J. A. Taylor ◽  
J. Bai
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Probability Distributions ◽  
Management Strategies ◽  
Hybrid Modelling ◽  
Trend Model ◽  
Environmental Extremes ◽  
Modelling Approach

The paper illustrates analysis of the assumptions of the statistical component of a hybrid modelling approach for predicting environmental extremes. This shows how to assess the applicability of the approach to water quality problems. The analysis involves data on stream acidity from the Birkenes catchment in Norway. The modelling approach is hybrid in that it uses: (1) a deterministic or process-based description to simulate (non-stationary) long term trend values of environmental variables, and (2) probability distributions which are superimposed on the trend values to characterise the frequency of shorter term concentrations. This permits assessment of management strategies and of sensitivity to climate variables by adjusting the values of major forcing variables in the trend model. Knowledge of the variability about the trend is provided by: (a) identification of an appropriate parametric form of the probability density function (pdf) of the environmental attribute (e.g. stream acidity variables) whose extremes are of interest, and (b) estimation of pdf parameters using the output of the trend model.

Modeling of unforced demand response programs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hassan Jalili ◽  
Pierluigi Siano
Keyword(s):  
Demand Response ◽  
Load Shifting ◽  
Response Models ◽  
Temperature Changes ◽  
Proposed Model ◽  
Equipment Failures ◽  
Demand Response Programs ◽  
Simultaneous Implementation ◽  
Long Term Studies

Abstract Demand response programs are useful options in reducing electricity price, congestion relief, load shifting, peak clipping, valley filling and resource adequacy from the system operator’s viewpoint. For this purpose, many models of these programs have been developed. However, the availability of these resources has not been properly modeled in demand response models making them not practical for long-term studies such as in the resource adequacy problem where considering the providers’ responding uncertainties is necessary for long-term studies. In this paper, a model considering providers’ unavailability for unforced demand response programs has been developed. Temperature changes, equipment failures, simultaneous implementation of demand side management resources, popular TV programs and family visits are the main reasons that may affect the availability of the demand response providers to fulfill their commitments. The effectiveness of the proposed model has been demonstrated by numerical simulation.

scholarly journals Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Recurrent Neural Network ◽  
Machine Tools ◽  
Prediction Performance ◽  
Sequential Data ◽  
Diagnosis And Prognosis ◽  
Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

A Global Socio-economic-medico-legal Model for the Sustainability of Longitudinal Electronic Health Records

2006 ◽  
Vol 45 (03) ◽  
pp. 240-245 ◽  
Author(s):  
A. Shabo
Keyword(s):  
Electronic Health Records ◽  
Healthcare Providers ◽  
Financial Assets ◽  
Healthcare Organizations ◽  
Health Records ◽  
Record Keeping ◽  
Proposed Model ◽  
Legal Model ◽  
Electronic Health

Summary Objectives: This paper pursues the challenge of sustaining lifetime electronic health records (EHRs) based on a comprehensive socio-economic-medico-legal model. The notion of a lifetime EHR extends the emerging concept of a longitudinal and cross-institutional EHR and is invaluable information for increasing patient safety and quality of care. Methods: The challenge is how to compile and sustain a coherent EHR across the lifetime of an individual. Several existing and hypothetical models are described, analyzed and compared in an attempt to suggest a preferred approach. Results: The vision is that lifetime EHRs should be sustained by new players in the healthcare arena, who will function as independent health record banks (IHRBs). Multiple competing IHRBs would be established and regulated following preemptive legislation. They should be neither owned by healthcare providers nor by health insurer/payers or government agencies. The new legislation should also stipulate that the records located in these banks be considered the medico-legal copies of an individual’s records, and that healthcare providers no longer serve as the legal record keepers. Conclusions: The proposed model is not centered on any of the current players in the field; instead, it is focussed on the objective service of sustaining individual EHRs, much like financial banks maintain and manage financial assets. This revolutionary structure provides two main benefits: 1) Healthcare organizations will be able to cut the costs of long-term record keeping, and 2) healthcare providers will be able to provide better care based on the availability of a lifelong EHR of their new patients.

scholarly journals Automatic Generation of Meta-Path Graph for Concept Recommendation in MOOCs

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1671
Author(s):  
Jibing Gong ◽  
Cheng Wang ◽  
Zhiyong Zhao ◽  
Xinghao Zhang
Keyword(s):  
Auxiliary Information ◽  
Automatic Generation ◽  
Information Network ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Online Study ◽  
Path Graph ◽  
Proposed Model ◽  
Meta Path

In MOOCs, generally speaking, curriculum designing, course selection, and knowledge concept recommendation are the three major steps that systematically instruct users to learn. This paper focuses on the knowledge concept recommendation in MOOCs, which recommends related topics to users to facilitate their online study. The existing approaches only consider the historical behaviors of users, but ignore various kinds of auxiliary information, which are also critical for user embedding. In addition, traditional recommendation models only consider the immediate user response to the recommended items, and do not explicitly consider the long-term interests of users. To deal with the above issues, this paper proposes AGMKRec, a novel reinforced concept recommendation model with a heterogeneous information network. We first clarify the concept recommendation in MOOCs as a reinforcement learning problem to offer a personalized and dynamic knowledge concept label list to users. To consider more auxiliary information of users, we construct a heterogeneous information network among users, courses, and concepts, and use a meta-path-based method which can automatically identify useful meta-paths and multi-hop connections to learn a new graph structure for learning effective node representations on a graph. Comprehensive experiments and analyses on a real-world dataset collected from XuetangX show that our proposed model outperforms some state-of-the-art methods.

Simulating the long-term dynamics of slug populations: a process-based modelling approach for pest control

2001 ◽  
Vol 38 (2) ◽  
pp. 401-411 ◽  
Author(s):  
Mark D.F. Shirley ◽  
Stephen P. Rushton ◽  
Andrew G. Young ◽  
Gordon R. Port
Keyword(s):  
Pest Control ◽  
Modelling Approach
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