Simulation of laser-driven,ablated plasma flows in collisionless shock experiments on OMEGA and the NIF

Experiments investigating the physics of interpenetrating, collisionless, ablated plasma flows have become an important area of research in the high-energy-density field. In order to evaluate the feasibility of designing experiments that will generate a collisionless shock mediated by the Weibel instability on the National Ignition Facility (NIF) laser, computer simulations using the Center for Radiative Shock Hydrodynamics (CRASH) radiation-hydrodynamics model have been carried out. This paper reports assessment of whether the experiment can reach the required scale size while maintaining the low interflow collisionality necessary for the collisionless shock to form. Comparison of simulation results with data from Omega experiments shows the ability of the CRASH code to model these ablated systems. The combined results indicate that experiments on the NIF are capable of reaching the regimes necessary for the formation of a collisionless shock in a laboratory experiment.     

Plasma production by helicon and slow waves

The observation of slow-wave sustained (SW) discharge in a whistler- or helicon-wave range of frequency is made using high-frequency and very-high-frequency bands of rf. The SW discharge occurs at an extremely low rf power and plasma density, which are lower than a capacitive-coupling discharge region.     

A multi-objective lead time control problem in multi-stage assembly systems using genetic algorithms

In this paper, we develop a multi-objective model to optimally control the lead time of a multi-stage assembly system, using genetic algorithms. The multi-stage assembly system is modelled as an open queueing network. It is assumed that the product order arrives according to a Poisson process. In each service station, there is either one or infinite number of servers (machines) with exponentially distributed processing time, in which the service rate (capacity) is controllable. The optimal service control is decided at the beginning of the time horizon. The transport times between the service stations are independent random variables with generalized Erlang distributions. The problem is formulated as a multi-objective optimal control problem that involves four conflicting objective functions. The objective functions are the total operating costs of the system per period (to be minimized), the average lead time (min), the variance of the lead time (min) and the probability that the manufacturing lead time does not exceed a certain threshold (max). Finally, we apply a genetic algorithm with double strings using continuous relaxation based on reference solution updating (GADSCRRSU) to solve this multi-objective problem, using goal attainment formulation. The results are also compared against the results of a discrete-time approximation technique to show the efficiency of the proposed genetic algorithm approach.     

An achievement rate approach to linear programming problems with an interval objective function

In this paper, we focus on a treatment of a linear programming problem with an interval objective function. From the viewpoint of the achievement rate, a new solution concept, the maximin achievement rate solution, is proposed. Nice properties of this solution are shown: a maximin achievement rate solution is necessarily optimal when a necessarily optimal solution exists, and if not, then it is still a possibly optimal solution. An algorithm for a maximin achievement rate solution is proposed based on a relaxation procedure together with a simplex method. A numerical example is given to demonstrate the proposed solution algorithm.     

An approximate solution of linear multicriteria control problems through the multicriteria simplex method

This paper deals with multicriteria, linear, continuous optimal control problems through the application of the multicriteria simplex method. Since the system is linear, the state variables at a given time can be expressed in terms of definite integrals. Upon using standard numerical integration formulas, the definite integrals are approximately expressed by means of weighted sums of the integrands. Then, after introducing some suitable auxiliary variables, approximate linear multicriteria programming problems are formulated. Illustrative numerical examples are provided to indicate the efficiency of the proposed method.The author is indebted to Professor Y. Sawaragi of Kyoto University for his constant encouragement. The author also wishes to thank T. Suwa for his cooperation in preparing the programming for the digital computer.     

An interactive fuzzy satisficing method for random fuzzy multiobjective integer programming problems through probability maximization with possibility

This paper considers multiobjective integer programming problems where each coefficient of the objective functions is expressed by a random fuzzy variable. A new decision making model is proposed by incorporating the concept of probability maximization into a possibilistic programming model. For solving transformed deterministic problems, genetic algorithms with double strings for nonlinear integer programming problems are introduced. An interactive fuzzy satisficing method is presented for deriving a satisficing solution to a decision maker by updating the reference probability levels. An illustrative numerical example is provided to clarify the proposed method.     

Spontaneous vesicle formation of single chain and double chain cationic surfactant mixtures

The concentration vs composition diagram of aggregate formation of the dodecyltrimethylammonium bromide (DTAB) and didodecyldimethylammonium bromide (DDAB) mixture in aqueous solution at rather dilute region was constructed by analyzing the surface tension, turbidity, and electrical conductivity data and inspected by cryo-TEM images and dynamic light scattering data. Although the aqueous solution of DTAB forms only micelles, the transition from monomer to small aggregates and then to vesicle was found at 0.1 < X2 相似文献   

Stackelberg solutions for fuzzy random two-level linear programming through level sets and fractile criterion optimization

This paper considers Stackelberg solutions for two-level linear programming problems under fuzzy random environments. To deal with the formulated fuzzy random two-level linear programming problem, an α-stochastic two-level linear programming problem is defined through the introduction of α-level sets of fuzzy random variables. Taking into account vagueness of judgments of decision makers, fuzzy goals are introduced and the α-stochastic two-level linear programming problem is transformed into the problem to maximize the satisfaction degree for each fuzzy goal. Through fractile criterion optimization in stochastic programming, the transformed stochastic two-level programming problem can be reduced to a deterministic two-level programming problem. An extended concept of Stackelberg solution is introduced and a numerical example is provided to illustrate the proposed method.     

Local convergence of an algorithm for solving optimal control problems

In this paper, a method is proposed for the numerical solution of optimal control problems with terminal equality constraints. The multiplier method is employed to deal with the terminal equality constraints. It is shown that a sequence of control functions, which converges to the optimal control, is obtained by the alternate update of control functions and multipliers.The authors wish to thank Dr. N. Fujii for his most valuable comments and suggestions.