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Constrained minimization problem extended to necessary conditions for characterizing noninferior points to determine vector-valued criteria in finite dimensional spaces
Quasi-optimal trajectory analysis computer program for optimal spacecraft guidance by iterative techniques
The paper presents the theoretical bases and implementation techniques of sensitivity analyses for efficient structural optimization of large structures, based on finite element static and dynamic analysis methods. The sensitivity analyses have been implemented in conjunction with two methods for optimization, namely, the Mathematical Programming and Optimality Criteria methods. The paper discusses the implementation of the sensitivity analysis method into our in-house software package, AutoDesign.
In this paper, Canadian Nuclear Laboratories (CNL) outlines key tools, strategies and lessons learned from development of their Asset Management Program. Canadian Nuclear Laboratories is a 60-year old site in the midst of a C$1.2B redevelopment that includes construction of six new facilities and demolition of over 120 buildings and structures. The presentation outlines CNL's progress in creating an efficient and effective asset management program that leverages and optimizes current good practices while creating a consistent, disciplined and information-based approach for the maintenance and management of assets. (authors)
The objective is to present results from investigating the integrated use of five computer programs: ANALYZE, ASTROS, NASTRAN, OPTSTAT, and VAASEL for analysis and optimization of a given structure. The structure designated for study purposes was the F-15E vertical tail. The concept of integrated use is limited to the capability of using a NASTRAN structural model to run each of the other specified programs. This was actually accomplished in practice by converting a NASTRAN model of the designated structure into an appropriate analysis model. For optimization purposes, the torque-box of the F-15E vertical tail was used.
Mathematical programming in optimized truss design noting reduction of components under buckling
Documentation for addition to digital computer program for rapid earth-to-space trajectory optimization of orbital rendezvous option
FORTRAN program for radiation shield optimization using steepest descent method
Flame detectors provide an important layer of protection for personnel in petrochemical plants, but effective placement can be challenging. A mixed-integer nonlinear programming formulation is proposed for optimal placement of flame detectors while considering non-uniform probabilities of detection failure. We show that this approach allows for the placement of fire detectors using a fixed sensor budget and outperforms models that do not account for imperfect detection. In this study, we develop a linear relaxation to the formulation and an efficient solution algorithm that achieves global optimality with reasonable computational effort. We integrate this problem formulation into the Python package, Chama, and demonstrate the effectiveness of this formulation on a small test case and on two real-world case studies using the fire and gas mapping software, Kenexis Effigy.
Dynamic programming and multistage decision processes in guidance, flight mechanics, and trajectory optimization
In recent years, optimization has become an engineering tool through the availability of numerous successful nonlinear programming codes. Optimal control problems are converted into parameter optimization (nonlinear programming) problems by assuming the control to be piecewise linear, making the unknowns the nodes or junction points of the linear control segments. Once the optimal piecewise linear control (suboptimal) control is known, a guidance law for operating near the suboptimal path is the neighboring optimal piecewise linear control (neighboring suboptimal control). Research conducted under this grant has been directed toward the investigation of neighboring suboptimal control as a guidance scheme for an advanced launch system.
Generalized disjunctive programming (GDP) models with bilinear and concave constraints, often seen in water network design, are challenging optimization problems. This work proposes quadratic and piecewise linear approximations for nonlinear terms to reformulate GDP models into quadratic GDP (QGDP) models that suitable solvers may solve more efficiently. We illustrate the benefits of the quadratic reformulation with a water treatment network design problem in which nonconvexities arise from bilinear terms in the mixers’ mass balances and concave investment cost functions of treatment units. Given the similarities with water network design problems, we suggest quadratic approximation for the GDP model for the optimal design of a large-scale reverse electrodialysis (RED) process. This power technology can recover energy from salinity differences between by-product streams of the water sector, such as desalination brine mixed with regenerated wastewater effluents. The solver Gurobi excels in handling QGDP problems, but weighing the problem’s precision and tractability balance is crucial. The piecewise linear approximation yields more accurate, yet larger QGDP models that may require longer optimization times in large-scale process synthesis problems.
The preliminary results of a test program to optimize a neutralizer design for 30 cm xenon ion thrusters are discussed. The impact of neutralizer geometry, neutralizer axial location, and local magnetic fields on neutralizer performance is discussed. The effect of neutralizer performance on overall thruster performance is quantified, for thruster operation in the 0.5-3.2 kW power range. Additionally, these data are compared to data published for other north-south stationkeeping (NSSK) and primary propulsion xenon ion thruster neutralizers.
A computer program is described which represents a comprehensive analytical tool providing the capability for predicting the output power and temperature profile of an arbitrary radioisotope thermoelectric generator (RTG) design in the presence of time-dependent operating conditions. The approach taken involves the merging of three existing computer programs - namely, an RTG weight optimization design program, a thermoelectric analysis program, and a nodal heat-transfer computer program. A total of seven transient conditions are included in the computer program as the principal transients affecting long- and short-term performance characteristics of RTGs. This computer program is unique in that it designs an optimum RTG, generates a thermal model or analog and performs heat-transfer analysis of the RTG under user-specified transient conditions.
Compilers transform program source code to machine executable code. During this transformation, they perform a number of compiler optimizations to improve the performance of the generated executable code. Importantly, applying those optimizations depends on the source code structure, such as the parallel programming model used to parallelize an algorithm. Often, implementations of the same algorithm with different programming models have vastly different performance because the compiler optimized them differently. We create FAROS, a framework to structure and automate the analysis of compiler optimizations on programs. FAROS automates the building process, execution profiling, and analysis of compiler optimization of programs, through a configuration interface. It outputs compiler optimization reports to show which optimizations applied to which line of source code, leveraging compilation remarks output by the compiler. Also, FAROS supports benchmarking performance of different program versions by collecting execution time results. In this first release of FAROS, we provide a configuration file to analyze compiler optimization differences for sequential vs OpenMP compilation, including 38 programs consisting of HPC proxy/mini/large applications, and NAS and Rodina kernels for analysis.
In order to reduce the air traffic delay in the terminal area, an immediate remedy is to increase airport capacity by an expansion of the existing runway system. The runway expansion program is often limited by budgetary constraints; the expensive facilities for a long-term improvement cannot be built at once. When a runway improvement strategy is being considered for a longer planning horizon, the investiment decision depends upon the interrelations of its composite periods. The problem, therefore, is to determine how time factor and investment decisions interact to yield an optimal improvement scheme that meets demand at a minimum cost. With this objective in mind, a dynamic programming methodology is employed to determine the optimal planning scheme. Also, an example runway improvement problem is tested to illustrate how a dynamic programming model is practical in actual application.
Presentation of means for achieving statistically optimal measurement and/or feedback gain programs for midcourse guidance
Nonlinear mathematical-programming-based design optimization can be an elegant method. However, the calculations required to generate the merit function, constraints, and their gradients, which are frequently required, can make the process computational intensive. The computational burden can be greatly reduced by using approximating analyzers derived from an original analyzer utilizing neural networks and linear regression methods. The experience gained from using both of these approximation methods in the design optimization of a high speed civil transport aircraft is the subject of this paper. The Langley Research Center's Flight Optimization System was selected for the aircraft analysis. This software was exercised to generate a set of training data with which a neural network and a regression method were trained, thereby producing the two approximating analyzers. The derived analyzers were coupled to the Lewis Research Center's CometBoards test bed to provide the optimization capability. With the combined software, both approximation methods were examined for use in aircraft design optimization, and both performed satisfactorily. The CPU time for solution of the problem, which had been measured in hours, was reduced to minutes with the neural network approximation and to seconds with the regression method. Instability encountered in the aircraft analysis software at certain design points was also eliminated. On the other hand, there were costs and difficulties associated with training the approximating analyzers. The CPU time required to generate the input-output pairs and to train the approximating analyzers was seven times that required for solution of the problem.