scholarly journals Evaluating Degradation Coefficients from Existing System Models

2021 ◽  
Vol 2 (1) ◽  
pp. 159-173
Author(s):  
Jude A. Osara ◽  
Michael D. Bryant
Keyword(s):  
Entropy Generation ◽  
Temperature Effects ◽  
Process Analysis ◽  
Energy Systems ◽  
Fatigue Loading ◽  
Thermal Processes ◽  
System Models ◽  
Tests And Measurements ◽  
Frictional Wear ◽  
Empirical System

A generalization of the Degradation-Entropy Generation (DEG) theorem to multi-disciplinary multi-physics system-process analysis via a combination with pre-existing system models is presented in this article. Existing models and the DEG methodology are reviewed, and a method for evaluating degradation coefficients Bi is proposed. These coefficients characterize the system’s transformation based on active dissipative mechanisms, including temperature effects. The consistency of entropy generation in characterizing degradation is then inherited by these often-empirical system models, thereby rendering them more robust and applicable to similar systems without the need for numerous tests and measurements for model corrections. The approach applies to all systems and can quickly analyze and predict a system’s performance and degradation, even in the absence of experimental data (using known properties and material constants). Demonstrated applications herein include mechanically loaded systems (frictional wear, grease shearing, fatigue loading), electrochemical energy systems, thermal processes, and others.

The Crack Length Growth – A Fracture Parameter in a Stainless Steel Influenced by the Loading Test

2016 ◽  
Vol 823 ◽  
pp. 489-494 ◽  
Author(s):  
Valcu Roşca ◽  
Cosmin Mihai Miriţoiu
Keyword(s):  
Stainless Steel ◽  
Mathematical Models ◽  
Process Analysis ◽  
Fatigue Loading ◽  
Polynomial Method ◽  
Fracture Parameter ◽  
Loading Test ◽  
Micro Cracks ◽  
Growth Increase ◽  
Fracture Crack

The defects or micro-cracks that exist in a product mass from the elaboration phase, can extend controlled or not, because of a variable solicitation applied to a product or a sample. The Fracture Mechanics parameter that highlight the crack propagation in time is its rate growth marked as da/dN and represents the crack advancement length during a solicitation cycle. This can be studied based on some mathematical models obtained from some propose models, experimentally determined. In this paper, a propagation process analysis is made of a fracture crack by an axial-eccentric fatigue loading for a 10TiNiCr175 stainless steel. CT type flat samples were loaded with an asymmetry coefficient R= 0.3, for the solicitation temperatures: T= 293K (20°C), T= 253K (-20°C), respectively T= 213 K (-60°C). The crack growth increase was studied by three most used mathematical models: the polynomial method standardized according to ASTM E647, method proposed by Paris and method proposed by Walker.

scholarly journals Causality in Models of Thermal Processes in Ship Engine Rooms with the Use of Bond Graph (BG) Method

2017 ◽  
Vol 24 (s1) ◽  
pp. 32-37 ◽  
Author(s):  
Marian Cichy ◽  
Zbigniew Kneba ◽  
Jacek Kropiwnicki
Keyword(s):  
Energy Systems ◽  
Physical Nature ◽  
Bond Graph ◽  
Thermal Processes ◽  
Electrical System ◽  
Thermodynamic Process ◽  
State Equations ◽  
Simulation Experiments ◽  
Systems Models ◽  
Single Approach

AbstractWith a single approach to modeling elements of different physical nature, the method of Bond Graph (BG) is particularly well suited for modeling energy systems consisting of mechanical, thermal, electrical and hydraulic elements that operate in the power system engine room. The paper refers to the earlier presented [2] new concept of thermal process modeling using the BG method. The authors own suggestions for determining causality in models of thermal processes created by the said concept were given. The analysis of causality makes it possible to demonstrate the model conflicts that prevent the placement of state equations which allows for the direct conduct of simulation experiments. Attention has been drawn to the link between the energy systems models of thermal processes with models of elements of different physical nature. Two examples of determining causality in models of complex energy systems of thermal elements have been presented. The firs relates to the electrical system associated with the process of heat exchange. The second is a model of the mechanical system associated with the thermodynamic process.

Supporting security and adequacy in future energy systems: The need to enhance long-term energy system models to better treat issues related to variability

2014 ◽  
Vol 39 (3) ◽  
pp. 377-396 ◽  
Author(s):  
Manuel Welsch ◽  
Mark Howells ◽  
Mohammad Reza Hesamzadeh ◽  
Brian Ó Gallachóir ◽  
Paul Deane ◽  
...  
Keyword(s):  
Energy Systems ◽  
Energy System ◽  
System Models ◽  
Term Energy

scholarly journals The Open Energy Modelling Framework (oemof) - A novel approach in energy system modelling

2017 ◽  
Author(s):  
Simon Hilpert ◽  
Cord Kaldemeyer ◽  
Uwe Krien ◽  
Stephan Günther ◽  
Clemens Wingenbach ◽  
...  
Keyword(s):  
Energy Systems ◽  
Maximum Level ◽  
Energy System ◽  
System Modelling ◽  
System Models ◽  
Modelling Framework ◽  
Novel Approach ◽  
Model Complex ◽  
Energy Modelling ◽  
Development Approach

Energy system models have become indispensable to shape future energy systems by providing insights into different trajectories. However, sustainable systems with high shares of renewable energy are characterised by growing crosssectoral interdependencies and decentralised structures. To capture important properties of increasingly complex energy systems, sophisticated and flexible modelling environments are needed. This paper presents the Open Energy Modelling Framework (oemof) as a novel approach in energy system modelling, representation and analysis. The framework forms a structured set of tools and sub-frameworks to construct comprehensive energy system models and has been published open source under a free licence. Using a collaborative development approach and extensive documentation on different levels, the framework seeks for a maximum level of transparency. Based on a generic graph based description of energy systems it is well suited to flexibly model complex crosssectoral systems ranging from a distributed or urban to a transnational scale. This makes the framework a multi-purpose modelling environment for strategic planning of future energy systems.

Assessment of soft and hard linking approaches of integrated water-energy simulation

2020 ◽  
Author(s):  
Mikiyas Etichia ◽  
Eduardo Alejandro Martinez ◽  
Julien Harou ◽  
Mathaios Panteli
Keyword(s):  
Renewable Energy Sources ◽  
Computational Cost ◽  
Energy Systems ◽  
Energy System ◽  
Domestic Water ◽  
Sustainable Solutions ◽  
System Models ◽  
Advantages And Disadvantages ◽  
Operation Rules ◽  
Trade Offs

<p>The strong synergies between water and energy use are becoming increasingly evident nowadays. It is becoming more and more apparent that significant benefits can be gained if both resources are managed in an integrated manner, which can be critical to improve efficiencies, reduce trade-offs, and find better and more sustainable solutions to future energy and water resources scarcity problems. Two types of approaches have drawn attention to integrate water and power system models, namely soft-link and hard-link approaches. Soft-linking approaches involve iterations, wherein the two system models are simulated independently, and their outputs (e.g., water available for hydropower generation) are passed to the other model until convergence is reached. In hard-link approaches, both the water and power systems are simulated with a single optimization model. More research to understand better the implications of different water-energy linking approaches, their computational cost, flexibility, and scalability are critically needed.</p><p>In this work water and energy system network models are linked with varying levels of integration (i.e., gradually moving from soft to hard link approaches) to demonstrate the advantages and disadvantages of the different types of links. The water and energy model includes multi-purpose storage reservoirs, irrigation, and domestic water users, renewable energy sources, and conventional power generators. Results show that soft linking approaches are more suitable for water-energy systems with fixed reservoir operation rules. Hard linking approaches are proven to be more suitable for cases with well established water and energy markets and can be computationally cheaper than soft linking approaches. Better joint simulation will help investigate better ways to manage and invest in water-energy systems.</p>

scholarly journals A Review on Time Series Aggregation Methods for Energy System Models

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 641 ◽  
Author(s):  
Maximilian Hoffmann ◽  
Leander Kotzur ◽  
Detlef Stolten ◽  
Martin Robinius
Keyword(s):  
Time Series ◽  
Input Data ◽  
Renewable Energy Sources ◽  
Energy Systems ◽  
Energy System ◽  
Machine Learning Techniques ◽  
Future Research ◽  
System Models ◽  
Number Of Publications ◽  
The Impact

Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.

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