CAMPO GRAVITICO PDF - Astronomia Os planetas. Sistema solar. É constituido pelo Sol e pelo conjunto dos corpos celestes que se. "O modelo GeodPT08 não é uma superfície equipotencial do campo gravítico da Terra e consequentemente não é verdadeiramente “o geóide”, mas apenas e. PDF | On Oct 11, , Rui Pena Reis and others published IV Curso de mos em Portugal o grupo para o IV Curso de Campo na Bacia Lusitânica. Estes depósitos correspondem a fluxos gravíticos (debris-flow e.

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PDF | Com este curso pretendemos fazer uma introdução geral ao estudo dos movimento de uma partícula material sob a ação do campo gravítico criado por. A estratégia de investigação neste campo une abordagens que, por um lado, . deformação superficial e ao campo gravítico da Terra) podem ser realizadas (e. desaparece na singularidade, mas o campo gravítico, a curvatura do com simetria esférica, após o colapso fica um campo gravítico estático.

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From the most distant lands came famous. Thus, after eg his trials and perils, the renowned son of Shedad was duly married to his darling Abla, and thus he expressed his satisfaction at the consummation of his wishes:. Retrieved 18 February They executed his commands: Many more wonderful exploits were performed by Antar in opposing the enemies of Abs—many illustrious warriors fell beneath the stroke of his irresistible sword Dhami, before he attained the chief desire of his heart.

Al JiwaAncient ArabiaArab world. But should he reach his bride in safety, the people atnar him, and no one attempted to approach him. It formerly acted as the guardians of the Church of the Nativity.

Wikimedia Commons has media related to Antarah ibn Shaddad. Here they conducted the bride, and having seated her on anrar, they said to the bridegroom: Ibn Doreid has him slain by Wasr-ben-Jaber [1] or in battle against the Tai, [2]while according to Antzr Obeida he died a natural death in old age.

Antarah ibn Shaddad Arabic: In her hand she bore a drawn sword, whose lustre dazzled the eyesight. One advantage of the evolutionary algorithms is that they do not require initial solutions they are randomly generated within the problem bounds at the start of the optimization process; other advantage is that they are more likely attracted to a global optimum, contrary to the direct and indirect methods that converge to the local optimum dependant on the initial guess.

The drawback is that there is no guarantee that a minimum has been found; also, there are no necessary conditions for optimality with this approach. All the evolutionary algorithms require that the solution is described by a relatively small set of discrete parameters. In case of a trajectory with impulsive manoeuvres, the set of parameters can be the timing of the impulses, the thrust direction and magnitude. In case of a trajectory with low-thrust, the continuous part of the thrust direction and magnitude has to be parametrized, for example by polynomial equations in time, or with a shape-based method.

In the most basic form of the GA, each solution is described by a finite set of parameters, converted to a string of binary numbers. This sequence of numbers is usually referred to as the chromosome; each 16 37 chromosome can be decoded to yield a trajectory and its associated cost function.

The optimization starts by randomly assigning values to the chromosomes in a population of chromosomes. The various sequences are then improved by selection, combination, and mutation: in the selection step, the worst solutions are removed; in the step of combination, the various chromosomes are put together by combining partial sequences; finally, the mutation takes place, randomly changing a small fraction of the population.

These three steps are repeated until the termination condition is met; it can be either because the process has evolved a fixed number of generations, or because the cost function has reached a plateau. So there is no way to assert if a minimum as been found, and also the chromosomes of the final population may produce infeasible trajectories. The feasibility of the trajectories is usually obtained by introducing a penalty function, that modifies the cost according to the infeasibility of the trajectory the trajectory is feasible if the boundary conditions are met.

A great advantage of using genetic algorithms is that they are easy to implement [2], but they are best used together with a more accurate method direct or indirect, so that the trajectory is feasible. The genetic algorithm has proven successful in generating initial guesses for direct methods[13].

There are several reasons that make this selection the best. The direct methods in general are numerically more robust than the indirect methods [2]; there is also less sensitivity in the convergence when choosing similar initial guesses [4].

In terms of flexibility of the algorithm, the direct transcription method is particularly more easily changed when the problem structure changes [13]; indirect methods need a new analytical derivation of the necessary conditions. This is crucial for our selection of the direct transcription method, because future problems to be solved from the GTOC are global and may require different physical models, new path and terminal constraints, or new objective functions. The main drawback of using a direct method is that by using an optimal control formulation that is simpler that the one derived from variational calculus, the solution obtained is an approximation to the real optimum solution [3].

Another disadvantage, also caused by the discretization of the dynamic variables, is the phenomenon of pseudo-minima [25]; these pseudo-minima satisfy the necessary conditions for optimality but actually are not close to the true optimum. Independently of the numerical method used to solve the indirect formulation, indirect methods suffer from high sensitivity to the initial guess, making it difficult to reach a good optimum solution [1].

The adjoint vectors need to be estimated, and often they do not have a obvious physical interpretation [2]. Another disadvantage occurs when there are path inequalities; it is necessary to first guess the sequence of constrained and unconstrained sub-arcs before the optimization can be performed [2].

The advantage of the indirect methods is that when found, they produce the real solution, not the approximate solution obtained from the discretization used in direct methods. However, it is difficult to 17 38 obtain convergence in indirect methods; moreover the inflexibility due to the necessity of analytically deriving conditions makes the indirect approach less adequate for global problems. It is important to note that, although there are no good or bad solutions but only true optimal ones, a good optimal solution is understood as a trajectory that satisfies all the constraints and produces a cost function J that is lower or higher when considering maximization than all the previously known trajectories for a specific problem.

This is the case of global trajectory optimization; the huge dimension of the phase space of the trajectories makes it impossible to assess if a given trajectory produces or not a global minimum.

The high dimension of the problem usually caused by large launch windows and numerous possible sequences of events demands a wide search on the state space, so that the probability of finding the global optimum increases. The search is commonly performed by automating the generation of initial guesses. The global search methods generally use some sort of approximation for the trajectories, as it has been seen in the global methods discussed in Section 1.

The cause for the approximation is that, for wide searches to be feasible, there should be a rapid computation of the trajectories. Two common practices are to exhaustively search the approximate state space or use an evolutionary algorithm to perform the stochastic search; it is noted that the evolutionary algorithm still needs an approximate way to evaluate each trajectory, or computational times become infeasible [13]. From the two common approaches for generating an initial guess globally search an approximate state space or use an evolutionary algorithm, the first was implemented in this work the planar shapebased method.

It performs an exhaustive search on the approximate state space. By using only four constants to define a trajectory, trajectories cannot be defined to match both departure and arrival position and velocity rendevouz problem , so Wall and Conway [22] investigated exponential sinusoids with two additional constants, but the results found were not satisfactory: the computational times were slow and the resulting trajectories were far from being optimal.

Although the exponential sinusoid considered has not the sufficient parameters to solve a rendezvous problem, the method is used for its purposes: approximate the optimal low-thrust transfer in a rapid and efficient way, permitting a broad search.

This two-dimensional shape-based method, used together with a local direct transcription optimizer has proven very successful in global optimization problems [27]; and has achieved first place in three editions of the GTOC competition. The user first sets as input the sequence of flybys, the launch window and information related to the type of legs; from this input the shape-based method finds thousands of possible trajectories that intersect the flyby bodies; the trajectories found with the shape-based method are then transcribed in the third block traj2input to the format needed for the local optimization method.

Figure 3. The input information format and the global search method are both presented in section 3. The program runs through a user-defined launch window and sequence of flyby bodies and exhaustively finds the 19 40 trajectories that intersect them. The leg type leg is the trajectory between two bodies , sequence is also specified a priori: the legs can be either thrust, coast no thrust or mixed. In the case of mixed legs the switch radius must be defined; the switch radius defines the point from where the thrust is turned off for the rest of the leg.

The constant k 1 is called the dynamic range parameter and controls the ratio of the apoapsis to the periapsis; k 2 is the winding parameter and controls the number of revolutions around the central body. Figures 3. The program steps through the launch window defined by the user, and for each departure time t 0 the trajectories that reach the next body in the sequence are computed.

The hyperbolic velocity v, in case 21 42 of a launch, is defined by the user and may point in any direction; in the case of a flyby the hyperbolic velocity turn angle is constrained by altitude.

The program then steps through the range of possible turn angles for the outgoing v. For a departure time t 0, the intersections of the spacecraft with the planet are searched in function of the constant k 2.

Calculating Intersections For each turn angle, the k 2 values that yield intersections of the shape with the target body are found; having determined the point where the intersection occurs, the specific value of k 2 that results in a correct time of flight must be calculated. For the spacecraft pursuing a exponential sinusoid, the time of flight has to be numerically calculated.

Knowing both the spacecraft and planet position, the miss angle heliocentric angle between the spacecraft and the 22 43 planet can be calculated, and a search is made over k 2 to find the zero miss angle, that is, the k 2 that produces a flyby is searched. The search for the zero miss angle is performed both along and across the curves.

If two points on a curve straddle a zero, a linear extrapolation is performed over k 2 until the new zero miss angle is below the tolerance defined 10 12 , then the k 2 is retained; this process is made over the entire turn angle curve. For the next contour computed, each value is compared to the previous contour. If two k 2 points in two different curves straddle a zero miss angle, a linear extrapolation is made over the turn angle; with the extrapolated turn angle value the k 2 is extrapolated until a zero is found.

Thus this root-finding scheme needs to store in memory two turn angle vs k 2 curves, the previous curve and the one that is being computed. The mixed legs consist of a exponential sinusoid patched with a coast leg; a switch radius must be defined so that from there the thrust is turned off until the next intersection. The determination of the intersections for these types of legs is similar to that of the exponential sinusoid: first the orbit intersection is computed, then, after the time of flight is calculated, the miss angle 23 44 is obtained.

The search for the intersection is also made by a Newton s method with 3. In the case of the mixed legs, the same situation applies for the initial conditions of the exponential sinusoid; the constraint relationship 3. For the coast leg the orbital parameters are readily obtained from the initial state r, v 0 and also used in the 3.

The search for the zero miss angle is performed as for the exponential sinusoid, with two curves stored in memory and searching for zeros both along and across the curves. Multiple Gravity-Assist Search Algorithm The shape-based search is performed for a user-defined sequence of gravity-assisted flybys, from departure to arrival.

Each leg must be specified: it can either be a thrust, coast or mixed leg. The user also specifies the launch window interval and the departure hyperbolic velocity range. The program then finds sequences of intersections according to the algorithm of figure 3. First the departure time is stepped through the launch window; for each instant of departure, the search for intersections with the next body in the sequence of flybys is made; when a trajectory that reaches the next body is found, the gravity-assist equations of section are used to provide the range of turn angles for the v after the flyby.

This process is repeated recursively for all the legs in the sequence; 24 45 when a trajectory finds its way through the sequence up to the arrival planet, that trajectory is stored; each solution is then evaluated by the amount of propellant used. For example, 616 trajectories found for a Earth-Mars-Ceres mission with two thrust arcs take up to approximately 1. An example of the input format, representing a multiple gravity-assist trajectory from Earth to Uranus, with intermediate flybys at Jupiter and Saturn is presented in figure 3.

Input format example mga. The legs are all thrust, the departure hyperbolic velocity magnitude is 5. Each trajectory leg is then divided into N segments, and the V of each segment is obtained integrating the thrust profile, as it will be explained in section 3.

This will serve as the input of the direct transcription method. The optimal control problem of low-thrust multiple gravity-assists trajectories is transcribed to a non-linear programming problem. The transcription is made by dividing the trajectory into legs that begin and end at control nodes, and each node is usually associated with a celestial body. On each leg there is a match point; the trajectory is propagated forward from the initial control node to the match point and backward from the final node to the match point.

The low-thrust is modelled as a series of impulses; the V impulses are applied along each of the N segments that divide each leg. Typically 10 segments are used for a simple interplanetary transfer leg. The resulting trajectory structure is represented in figure 3.

This formulation leads to a constrained non-linear optimization problem [4]. The variables of optimization are the state r, v, m and corresponding epoch at each control node, the V impulses, and the flyby turn angle at each intermediate flyby.

The state at the control node is generally a dependent variable because the position is usually set to be equal to the flyby planet, unless the control node is set to be a fixed point in space, in which case the variable is independent.

The equality constraint of the optimization is the continuity of the spacecraft state on the match point. To assure that the V i on each segment is feasible, an inequality constraint is placed on every segment for the magnitude of the impulse V i. Any objective function or other constraints can be placed on the trajectory, depending on the problem, provided that they can be expressed by the variables of the trajectory. Local Optimization Input To optimize the trajectories with the transcription model, the solutions obtained from the global search must be transformed to the format needed: the legs between flyby bodies have to be segmented in time and the V accumulated over the segment has to be calculated to serve as an input for the optimization.

In the direct transcription method used, instead of the V i, the engine throttles are used equivalently as the variable for optimization. Python is a widely used programming language that supports object-oriented, imperative and functional programming.

The language automatically deals with the memory management and has an extensive standard library, including scientific packages with mathematical and plotting functionalities similar to those found in the MATLAB computing environment. The main reason for selecting Python as the language to implement both the shape-based and direct transcription algorithms was the existence of the ESA s PyGMO library for Python.

PyGMO employs a paradigm called the generalized island model, for the parallelization of optimization algorithms. The solutions for a given problem are represented as individuals; a group of individuals is called a population, and such a population over which an algorithm acts to improve the solutions is called an island. Finally, various islands grouped together in an archipelago can be formed in a defined topology and share their solutions.

The algorithm that acts on the island can be coded by the user; but it was preferred to use the existing implemented non-linear algorithms in the PyGMO library, already known to be efficient. The island where the individual lives is then initialized by assigning to it the problem and the algorithm. The initialization of the island automatically generates a random population sized by the user; in my case a population of one individual was used for the optimization.

The individual decision vector obtained by the global shape-based method is then attributed to the individual, and the algorithm is performed on the island. The decision vector D may have simultaneously a continuous dimension n and an integer dimension m; it is this decision vector that describes each solution also known as individual.

Each component of the decision vector, whether real or integer, is allowed to vary only between the lower and upper bounds specified by lb and ub respectively. These compact and efficient lights are gradually replacing the standard bulky solar arrays and banks of car batteries currently used to power marine navigation lights. Seeowners manual on line for additional flash codes. Self-contained and low maintenance: Carmanah initially marketed lighting solutions to the carmanan navigation sector, designing durable, reliable products for harsh and variable marine weather conditions.

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