scholarly journals Stationary Gas Networks with Compressor Control and Random Loads: Optimization with Probabilistic Constraints

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Martin Gugat ◽  
Michael Schuster
Keyword(s):  
Gas Flow ◽  
Optimization Problems ◽  
Real Data ◽  
Probabilistic Constraints ◽  
Box Constraints ◽  
Random Load ◽  
Existence Of Optimal Solutions ◽  
Random Loads ◽  
Load Vector ◽  
Isothermal Euler Equations

We introduce a stationary model for gas flow based on simplified isothermal Euler equations in a non-cycled pipeline network. Especially the problem of the feasibility of a random load vector is analyzed. Feasibility in this context means the existence of a flow vector meeting these loads, which satisfies the physical conservation laws with box constraints for the pressure. An important aspect of the model is the support of compressor stations, which counteract the pressure loss caused by friction in the pipes. The network is assumed to have only one influx node; all other nodes are efflux nodes. With these assumptions the set of feasible loads can be characterized analytically. In addition we show the existence of optimal solutions for some optimization problems with probabilistic constraints. A numerical example based on real data completes this paper.

Bending fatigue of single-wall and double-wall corrugated paperboards under sinusoidal and random loads

2021 ◽  
pp. 109963622110204
Author(s):  
Zhi-Wei Wang ◽  
Yang-Zhou Lai ◽  
Li-Jun Wang
Keyword(s):  
Energy Dissipation ◽  
Fatigue Failure ◽  
Fatigue Tests ◽  
Stiffness Degradation ◽  
Random Load ◽  
Bending Fatigue ◽  
Random Loads ◽  
Vibration Fatigue ◽  
Sinusoidal Load ◽  
Double Wall

The bending fatigue tests of single-wall and double-wall corrugated paperboards were conducted to obtain the εrms– N curves under sinusoidal and random loads in this paper. The εrms– N equation of corrugated paperboard can be described by modified Coffin–Manson model considering the effect of mean stress. Four independent fatigue parameters are obtained for single-wall and double-wall corrugated paperboards. The εrms– N curve under random load moves left and rotates clockwise compared with that under sinusoidal load. The fatigue life under random load is much less than that under sinusoidal load, and the fatigue design of corrugated box should be based on the fatigue result under random load. The stiffness degradation and energy dissipation of double-wall corrugated paperboard before approaching fatigue failure are very different from that of single-wall one. For double-wall corrugated paperboard, two turning points occur in the stiffness degradation, and fluctuation occurs in the energy dissipation. Different from metal materials, the bending fatigue failure of corrugated paperboard is a process of wrinkle forming, spreading, and folding. The results obtained have practical values for the design of vibration fatigue of corrugated box.

scholarly journals An Effective Couple Method for Reliability-Based Multi-Objective Optimization of Truss Structures with Static and Dynamic Constraints

2019 ◽  
Vol 17 (06) ◽  
pp. 1950016 ◽  
Author(s):  
T. Vo-Duy ◽  
D. Duong-Gia ◽  
V. Ho-Huu ◽  
T. Nguyen-Thoi
Keyword(s):  
Cross Section ◽  
Optimization Problems ◽  
Computational Cost ◽  
Probabilistic Constraints ◽  
Truss Structures ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Dynamic Constraints ◽  
Multi Objective ◽  
Couple Method

This paper proposes an effective couple method for solving reliability-based multi-objective optimization problems of truss structures with static and dynamic constraints. The proposed coupling method integrates a single-loop deterministic method (SLDM) into the nondominated sorting genetic algorithm II (NSGA-II) algorithm to give the so-called SLDM-NSGA-II. Thanks to the advantage of SLDM, the probabilistic constraints are treated as approximating deterministic constraints. And therefore the reliability-based multi-objective optimization problems can be transformed into the deterministic multi-objective optimization problems of which the computational cost is reduced significantly. In these reliability-based multi-objective optimization problems, the conflicting objective functions are to minimize the weight and the displacements of the truss. The design variables are cross-section areas of the bars and contraints include static and dynamic constraints. For reliability analysis, the effect of uncertainty of parameters such as force, added mass in the nodes, material properties and cross-section areas of the bars are taken into account. The effectiveness and reliability of the proposed method are demonstrated through three benchmark-type truss structures including a 10-bar planar truss, a 72-bar spatial truss and a 200-bar planar truss. Moreover, the influence of parameters on the reliability-based Pareto optimal fronts is also carried out.

Random Load Fatigue: Theory and Experiment

1985 ◽  
Vol 199 (3) ◽  
pp. 249-257 ◽  
Author(s):  
J M Tunna
Keyword(s):  
Traditional Method ◽  
Laboratory Tests ◽  
Stress Range ◽  
Constant Amplitude ◽  
Random Load ◽  
Welded Steel ◽  
Random Loads ◽  
Fatigue Design ◽  
Stress Signal ◽  
Theory And Experiment

This paper is concerned with the problem of fatigue design for random loads. A theory is outlined which allows the fatigue life to be predicted from the constant amplitude stress range–life curve and the standard deviation and ruling frequency of the stress signal. Laboratory tests are described which verify the theory for welded steel constructions in a railway environment. The results are also analysed by a more traditional method. Areas where the theory might usefully be extended are identified.

scholarly journals A decoupling algorithm with first-order asymptotic integration for reliability-based design optimization

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879333 ◽  
Author(s):  
Zhiliang Huang ◽  
Tongguang Yang ◽  
Fangyi Li
Keyword(s):  
Design Optimization ◽  
Reliability Analysis ◽  
Optimization Problems ◽  
Asymptotic Integration ◽  
Probabilistic Constraints ◽  
Reliability Based Design Optimization ◽  
First Order Reliability Method ◽  
First Order ◽  
Reliability Based Design ◽  
Reliability Method

Conventional decoupling approaches usually employ first-order reliability method to deal with probabilistic constraints in a reliability-based design optimization problem. In first-order reliability method, constraint functions are transformed into a standard normal space. Extra non-linearity introduced by the non-normal-to-normal transformation may increase the error in reliability analysis and then result in the reliability-based design optimization analysis with insufficient accuracy. In this article, a decoupling approach is proposed to provide an alternative tool for the reliability-based design optimization problems. To improve accuracy, the reliability analysis is performed by first-order asymptotic integration method without any extra non-linearity transformation. To achieve high efficiency, an approximate technique of reliability analysis is given to avoid calculating time-consuming performance function. Two numerical examples and an application of practical laptop structural design are presented to validate the effectiveness of the proposed approach.

scholarly journals New Safe Approximation of Ambiguous Probabilistic Constraints for Financial Optimization Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
NingNing Du ◽  
Yan-Kui Liu ◽  
Ying Liu
Keyword(s):  
Value At Risk ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Random Perturbation ◽  
Probabilistic Constraints ◽  
Optimization Approach ◽  
Chinese Stock Market ◽  
Robust Solution ◽  
Return Rates ◽  
Financial Optimization

In financial optimization problem, the optimal portfolios usually depend heavily on the distributions of uncertain return rates. When the distributional information about uncertain return rates is partially available, it is important for investors to find a robust solution for immunization against the distribution uncertainty. The main contribution of this paper is to develop an ambiguous value-at-risk (VaR) optimization framework for portfolio selection problems, where the distributions of uncertain return rates are partially available. For tractability consideration, we deal with new safe approximations of ambiguous probabilistic constraints under two types of random perturbation sets and obtain two equivalent tractable formulations of the ambiguous probabilistic constraints. Finally, to demonstrate the potential for solving portfolio optimization problems, we provide a practical example about the Chinese stock market. The advantage of the proposed robust optimization method is also illustrated by comparing it with the existing optimization approach via numerical experiments.

scholarly journals A Computer Simulator Model for Generating Sulphuric Acid and Improve the Operational Results, Using Operational Data from a Chemical Plant

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Claudio Leiva ◽  
Víctor Flores ◽  
Carolina Aguilar
Keyword(s):  
Sulphuric Acid ◽  
Acid Production ◽  
Gas Flow ◽  
Production Capacity ◽  
Real Data ◽  
Economic Indicators ◽  
Production Plant ◽  
Interconnected System ◽  
Simulation Based ◽  
Operational Data

The integration of both sensors and simulation in some industrial processes has received a large increase in the recent years, thanks to results such as increase in production indices or improvement in economic indicators. This document describes the development and validation of a simulation-based computer process for generating sulphuric acid. For this process, the data generated by simulation between the fixed beds of a catalytic reactor versus the results obtained using real data from a sulphuric acid production plant in the Antofagasta Region, Chile, have been used. This sulphuric acid production plant is designed for producing 720,000 tons of sulphuric acid annually, with a production capacity of 26 MW, which is used for both its own consumption and the Big North Interconnected System (SING, for its acronym in Spanish) and a sulphur consumption of 240,000 tons/year. For the simulation process, converter input variables such as temperature and gas flow to later observe the oxidation behavior under different operational scenarios were considered. To do it, a working method has been proposed and the software Aspen HYSYS® was used for the simulation. The simulation result was validated using design operational data provided by the company. The real results show a 99.9% of adjustment concerning the values obtained using the simulation. Based on the findings, a new operational scenario was created, and the economic indicators of the simulator implementation were determined: NPV=CLP 161,695,000 and IRR=53% with a 6% monthly production increase.

Robust topology optimization for continuum structures with random loads

2018 ◽  
Vol 35 (2) ◽  
pp. 710-732 ◽  
Author(s):  
Jie Liu ◽  
Guilin Wen ◽  
Qixiang Qing ◽  
Fangyi Li ◽  
Yi Min Xie
Keyword(s):  
Topology Optimization ◽  
Optimization Method ◽  
Computational Time ◽  
Optimization Approach ◽  
Random Load ◽  
Content Type ◽  
Continuum Structures ◽  
Robust Topology Optimization ◽  
Random Loads ◽  
Expansion Technique

Purpose This paper aims to tackle the challenge topic of continuum structural layout in the presence of random loads and to develop an efficient robust method. Design/methodology/approach An innovative robust topology optimization approach for continuum structures with random applied loads is reported. Simultaneous minimization of the expectation and the variance of the structural compliance is performed. Uncertain load vectors are dealt with by using additional uncertain pseudo random load vectors. The sensitivity information of the robust objective function is obtained approximately by using the Taylor expansion technique. The design problem is solved using bi-directional evolutionary structural optimization method with the derived sensitivity numbers. Findings The numerical examples show the significant topological changes of the robust solutions compared with the equivalent deterministic solutions. Originality/value A simple yet efficient robust topology optimization approach for continuum structures with random applied loads is developed. The computational time scales linearly with the number of applied loads with uncertainty, which is very efficient when compared with Monte Carlo-based optimization method.

scholarly journals Data Pooling in Stochastic Optimization

2021 ◽  
Author(s):  
Vishal Gupta ◽  
Nathan Kallus
Keyword(s):  
Stochastic Optimization ◽  
Inventory Management ◽  
Large Scale ◽  
Optimization Problems ◽  
Lot Sizing ◽  
A Priori ◽  
Real Data ◽  
Data Pooling ◽  
Combining Data ◽  
A Chain

Managing large-scale systems often involves simultaneously solving thousands of unrelated stochastic optimization problems, each with limited data. Intuition suggests that one can decouple these unrelated problems and solve them separately without loss of generality. We propose a novel data-pooling algorithm called Shrunken-SAA that disproves this intuition. In particular, we prove that combining data across problems can outperform decoupling, even when there is no a priori structure linking the problems and data are drawn independently. Our approach does not require strong distributional assumptions and applies to constrained, possibly nonconvex, nonsmooth optimization problems such as vehicle-routing, economic lot-sizing, or facility location. We compare and contrast our results to a similar phenomenon in statistics (Stein’s phenomenon), highlighting unique features that arise in the optimization setting that are not present in estimation. We further prove that, as the number of problems grows large, Shrunken-SAA learns if pooling can improve upon decoupling and the optimal amount to pool, even if the average amount of data per problem is fixed and bounded. Importantly, we highlight a simple intuition based on stability that highlights when and why data pooling offers a benefit, elucidating this perhaps surprising phenomenon. This intuition further suggests that data pooling offers the most benefits when there are many problems, each of which has a small amount of relevant data. Finally, we demonstrate the practical benefits of data pooling using real data from a chain of retail drug stores in the context of inventory management. This paper was accepted by Chung Piaw Teo, Special Issue on Data-Driven Prescriptive Analytics.

scholarly journals Fast and reliable transient simulation and continuous optimization of large-scale gas networks

2022 ◽  
Author(s):  
Pia Domschke ◽  
Oliver Kolb ◽  
Jens Lang
Keyword(s):  
Large Scale ◽  
Gas Flow ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Gas Pipeline ◽  
Physical Models ◽  
Active Set ◽  
Simulation And Optimization ◽  
Pipeline Systems ◽  
Adjoint Approach

AbstractWe are concerned with the simulation and optimization of large-scale gas pipeline systems in an error-controlled environment. The gas flow dynamics is locally approximated by sufficiently accurate physical models taken from a hierarchy of decreasing complexity and varying over time. Feasible work regions of compressor stations consisting of several turbo compressors are included by semiconvex approximations of aggregated characteristic fields. A discrete adjoint approach within a first-discretize-then-optimize strategy is proposed and a sequential quadratic programming with an active set strategy is applied to solve the nonlinear constrained optimization problems resulting from a validation of nominations. The method proposed here accelerates the computation of near-term forecasts of sudden changes in the gas management and allows for an economic control of intra-day gas flow schedules in large networks. Case studies for real gas pipeline systems show the remarkable performance of the new method.

scholarly journals APPLICATION OF GLOBAL OPTIMIZATION TO PREDICT STRAINS IN RC COMPRESSED COLUMNS

2021 ◽  
Vol 61 (1) ◽  
pp. 242-252
Author(s):  
Marek Lechman ◽  
Andrzej Stachurski
Keyword(s):  
Least Squares Method ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Trust Region ◽  
Global Solutions ◽  
Optimization Methods ◽  
Box Constraints ◽  
Particle Swarm Method ◽  
Global And Local Optimization ◽  
Global And Local

In this paper, the results of an application of global and local optimization methods to solve a problem of determination of strains in RC compressed structure members are presented. Solutions of appropriate sets of nonlinear equations in the presence of box constraints have to be found. The use of the least squares method leads to finding global solutions of optimization problems with box constraints. Numerical examples illustrate the effects of the loading value and the loading eccentricity on the strains in concrete and reinforcing steel in the a cross-section.Three different minimization methods were applied to compute them: trust region reflective, genetic algorithm tailored to problems with real double variables and particle swarm method. Numerical results on practical data are presented. In some cases, several solutions were found. Their existence has been detected by the local search with multistart, while the genetic and particle swarm methods failed to recognize their presence.

Sign in / Sign up

Export Citation Format

Share Document