The Use of Decomposition for the Large Scale Synthesis/Design Optimization of Highly Coupled, Highly Dynamic Energy Systems: Part II—Applications

2000 ◽  
Author(s):  
Jules R. Muñoz ◽  
Michael R. von Spakovsky
Keyword(s):  
Global Convergence ◽  
Design Optimization ◽  
Large Scale ◽  
Environmental Control ◽  
Linear Optimization ◽  
Mixed Integer ◽  
Decomposition Approach ◽  
Independent Variables ◽  
Synthesis Design ◽  
Non Linear

Abstract An application of the Iterative Local-Global Optimization (ILGO) decomposition approach developed in an accompanying paper (Muñoz and von Spakovsky, 2000b) is presented. The synthesis / design optimization of a turbofan engine coupled to an environmental control system for a military aircraft was carried out. The problem was solved for a given mission (i.e. the load / environmental profile) composed of fifteen segments. The number of decision (independent) variables used for this highly non-linear optimization problem is one hundred fifty-three, some of which are integer. Both thermodynamic and physical (weight and volume) simulations use state-of-the art tools. Two objective functions were investigated: take-off gross weight and mission fuel consumption, and no observable differences were found in the final results. In addition to the mathematical foundations for global convergence of the proposed decomposition approach presented in Muñoz and von Spakovsky (2000b), numerical support for this convergence was found by solving the entire mixed-integer non-linear programming (MINLP) problem without decomposition using a subset of the independent variables. The constant value of the marginal costs (or linear behavior of the Optimum Response Surface — OSR) played a major role in the global convergence of the ILGO.

Optimal Test Assembly in Practice

2013 ◽  
Vol 221 (3) ◽  
pp. 190-200 ◽  
Author(s):  
Jörg-Tobias Kuhn ◽  
Thomas Kiefer
Keyword(s):  
Particle Physics ◽  
Large Scale ◽  
Block Design ◽  
Linear Optimization ◽  
Mixed Integer ◽  
Optimal Test ◽  
Automated Test Assembly ◽  
Test Assembly ◽  
Block Level ◽  
Mixed Integer Linear Optimization

Several techniques have been developed in recent years to generate optimal large-scale assessments (LSAs) of student achievement. These techniques often represent a blend of procedures from such diverse fields as experimental design, combinatorial optimization, particle physics, or neural networks. However, despite the theoretical advances in the field, there still exists a surprising scarcity of well-documented test designs in which all factors that have guided design decisions are explicitly and clearly communicated. This paper therefore has two goals. First, a brief summary of relevant key terms, as well as experimental designs and automated test assembly routines in LSA, is given. Second, conceptual and methodological steps in designing the assessment of the Austrian educational standards in mathematics are described in detail. The test design was generated using a two-step procedure, starting at the item block level and continuing at the item level. Initially, a partially balanced incomplete item block design was generated using simulated annealing, whereas in a second step, items were assigned to the item blocks using mixed-integer linear optimization in combination with a shadow-test approach.

COMBINING FLUX AND ENERGY BALANCE ANALYSIS TO MODEL LARGE-SCALE BIOCHEMICAL NETWORKS

2006 ◽  
Vol 04 (06) ◽  
pp. 1227-1243 ◽  
Author(s):  
WILLIAM J. HEUETT ◽  
HONG QIAN
Keyword(s):  
Energy Balance ◽  
Large Scale ◽  
Heat Dissipation ◽  
Linear Optimization ◽  
Biochemical Networks ◽  
Programming Algorithm ◽  
Optimization Approach ◽  
Stoichiometric Matrix ◽  
Non Linear ◽  
Detailed Kinetics

Stoichiometric Network Theory is a constraints-based, optimization approach for quantitative analysis of the phenotypes of large-scale biochemical networks that avoids the use of detailed kinetics. This approach uses the reaction stoichiometric matrix in conjunction with constraints provided by flux balance and energy balance to guarantee mass conserved and thermodynamically allowable predictions. However, the flux and energy balance constraints have not been effectively applied simultaneously on the genome scale because optimization under the combined constraints is non-linear. In this paper, a sequential quadratic programming algorithm that solves the non-linear optimization problem is introduced. A simple example and the system of fermentation in Saccharomyces cerevisiae are used to illustrate the new method. The algorithm allows the use of non-linear objective functions. As a result, we suggest a novel optimization with respect to the heat dissipation rate of a system. We also emphasize the importance of incorporating interactions between a model network and its surroundings.

scholarly journals VIMO: A Visual-Inertial-Magnetic Navigation System Based on Non-Linear Optimization

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4386
Author(s):  
Jingzhe Wang ◽  
Leilei Li ◽  
Huan Yu ◽  
Xunya Gui ◽  
Zucheng Li
Keyword(s):  
Large Scale ◽  
Linear Optimization ◽  
Position Estimation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Non Linear ◽  
Outdoor Environments ◽  
Novel Method ◽  
And Performance ◽  
Inertial Measurements

Visual-inertial navigation systems are credited with superiority over both pure visual approaches and filtering ones. In spite of the high precision many state-of-the-art schemes have attained, yaw remains unobservable in those systems all the same. More accurate yaw estimation not only means more accurate attitude calculation but also leads to better position estimation. This paper presents a novel scheme that combines visual and inertial measurements as well as magnetic information for suppressing deviation in yaw. A novel method for initializing visual-inertial-magnetic odometers, which recovers the directions of magnetic north and gravity, the visual scalar factor, inertial measurement unit (IMU) biases etc., has been conceived, implemented, and validated. Based on non-linear optimization, a magnetometer cost function is incorporated into the overall optimization objective function as a yawing constraint among others. We have done extensive research and collected several datasets recorded in large-scale outdoor environments to certify the proposed system’s viability, robustness, and performance. Cogent experiments and quantitative comparisons corroborate the merits of the proposed scheme and the desired effect of the involvement of magnetic information on the overall performance.

Program Synthesis of FEA Constitutive Behavior Modules Through Data Driven Design Optimization

2002 ◽  
Author(s):  
J. G. Michopoulos
Keyword(s):  
Finite Element ◽  
Design Optimization ◽  
Polymer Matrix Composites ◽  
Linear Optimization ◽  
Program Synthesis ◽  
Constitutive Behavior ◽  
Dissipated Energy ◽  
Material System ◽  
Custom Made ◽  
Non Linear

Design optimization in the context of finite element modeling (FEM) and analysis (FEA) has been traditionally used to help designers determine optimal structural geometry and/or material property parameters according to objective functions of interest and necessary constraints. In the present paper it is attempted to generalize the design optimization methodology into a program synthesis technique for determining the code necessary to encapsulate the constitutive behavior of the material system required for generalized FEA applications. The core concept behind the methodology followed by our group in the past, has been the experimental identification of a dissipated energy density (DED) function for polymer matrix composites (PMCs) through a non-linear optimization scheme for determining the free coefficients of the sum of the basis functions that are used to construct the DED function and is based on the energy balance of the specimen under testing. The utilized testing generated massive amounts of experimental data that would be produced by exposing PMC specimens to multidimensional loading paths with the help of custom made multi-axial computer-controlled testing machines. The variety of custom environments utilized to implement the analytical and numerical details has often created difficulties in transferring our technology to end users in the design and material communities. The present implementation was greatly enabled by recent advances in finite element techniques and “of the shelf” design optimization integration technologies along with the parallel hardware and software evolution. The program synthesis lies on a process that automatically generates the code of a user material subroutine through minimization of the error between measured and simulated specimen behavior. The generated code can be subsequently used with any geometry and loading specification definable within the limits of the non-linear element library in commercial codes such as ANSYS and ABAQUS.

Solving an Integrated Sales-Leasing Problem With Remanufacturing and Inventory Shortage Using Differential Evolution

2018 ◽  
Vol 9 (3) ◽  
pp. 1-26
Author(s):  
Masoud Rabbani ◽  
Sina Keyhanian ◽  
Mojtaba Aryaee ◽  
Esmat Sangari
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
High Sensitivity ◽  
Third Party ◽  
Mixed Integer ◽  
Heuristic Solution ◽  
Optimal Amount ◽  
Solution Approach ◽  
De Algorithm ◽  
Non Linear

In this article, an integrated sales and leasing company is considered. This company remanufactures leased products at the end of operating lease contracts to make them as good as new ones and sell them to the customers. In order to satisfy customers' demand, required products are provided from a third-party when the company meets inventory shortage. Non-linear competitive demand functions are used which are sensitive to manufacturer suggested retail price (MSRP) and inflation rate. A mixed integer non-linear mathematical model (MINLP) is developed to determine optimal price of selling products, optimal amount of monthly payments in leasing contracts, and optimal inventory control planning, i.e. the optimal amount of manufacturing and remanufacturing products and optimal inventory levels. The main objective is to maximize net profit of the company. Small, medium and large-scale sizes of the model are solved to show the applicability of the model. To solve the large-scale problem, differential evolution (DE) algorithm is applied as a meta-heuristic solution approach. Numerical results show high sensitivity of model to demands. Also, optimal trend behaviors of some main variables of the problem seem similar to the competitive behavior of demands.

Optimal Synthesis/Design of a Pem Fuel Cell Cogeneration System for Multi-Unit Residential Applications–Application of a Decomposition Strategy

2004 ◽  
Vol 126 (1) ◽  
pp. 30-39 ◽  
Author(s):  
Borja Oyarza´bal ◽  
Michael R. von Spakovsky ◽  
Michael W. Ellis
Keyword(s):  
Fuel Cell ◽  
Design Optimization ◽  
Large Scale ◽  
Unit Cost ◽  
Pem Fuel Cell ◽  
Energy System ◽  
Cell System ◽  
Proton Exchange ◽  
Fuel Cell System ◽  
Synthesis Design

The application of a decomposition methodology to the synthesis/design optimization of a stationary cogeneration proton exchange membrane (PEM) fuel cell system for residential applications is the focus of this paper. Detailed thermodynamic, economic, and geometric models were developed to describe the operation and cost of the fuel processing sub-system and the fuel cell stack sub-system. Details of these models are given in an accompanying paper by the authors. In the present paper, the case is made for the usefulness and need of decomposition in large-scale optimization. The types of decomposition strategies considered are conceptual, time, and physical decomposition. Specific solution approaches to the latter, namely Local-Global Optimization (LGO) are outlined in the paper. Conceptual/time decomposition and physical decomposition using the LGO approach are applied to the fuel cell system. These techniques prove to be useful tools for simplifying the overall synthesis/design optimization problem of the fuel cell system. The results of the decomposed synthesis/design optimization indicate that this system is more economical for a relatively large cluster of residences (i.e. 50). Results also show that a unit cost of power production of less than 10 cents/kWh on an exergy basis requires the manufacture of more than 1500 fuel cell sub-system units per year. Finally, based on the off-design optimization results, the fuel cell system is unable by itself to satisfy the winter heat demands. Thus, the case is made for integrating the fuel cell system with another system, namely, a heat pump, to form what is called a total energy system.

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