Optimization of a plasma instrument design for a ... - IEEE Xplore

Optimization of a plasma instrument design for a CubeSat with space weather research Necmi Cihan Örger, Yiğit Çay, Zerefşan Kaymaz, Turgut Berat Karyot, Melike Nikbay Faculty of Aeronautics and Astronautics İstanbul, Turkey [email protected], [email protected], [email protected], [email protected], [email protected] limitations created a need for miniaturized scientific instruments which can fit into the CubeSats [1]. Today, increasing number of CubeSat missions paves way for the design and development of miniaturized, lightweight instruments with low-power consumption in space environment.

Abstract— This study presents the results of an optimization technique in order to determine the best design parameters of a plasma analyzer with planar geometry. The instrument is considered to be used in PolarBeeSat which is a CubeSat with 4U designed to study the Earth’s space environment. It will carry a magnetometer and a plasma analyzer. The plasma analyzer will be used to measure the energetic electron, ion and neutral particles as PolarBee moves through the polar regions of the magnetosphere Since the design of the instruments is strongly depended upon the characteristics of the region where the spacecraft flies and the mission’s purpose, there is no one detector that is perfectly suitable for all space regions. Therefore, different types of instrument designs are used to study the space environment. In this study we consider two types of plasma analyzer and compare them and decide for the appropriate one to place on PolarBee. Depending on the scientific mission, various criteria are used to determine the type of the instrument is determined. In this work, different design considerations are addressed, and the initial design is optimized by a genetic algorithm used with a particle trajectory tracing program for performance evaluation. The preliminary results from the optimization technique will be presented and discussed for its suitability for the PolarBee’s scientific mission. Optimization results will be used in our future laboratory tests of the instrument as a start and further optimization will be applied according to the necessities in accordance with the environmental factors. Keywords— space environment; plasma; solar wind; CubeSat; plasma analyzer; genetic algorithm; optimization

Space weather refers to the variations on the Sun and their effects on the space environment and spacecraft and ground systems. These variations can be measured by the instruments placed on board the spacecraft. Two of the most basic instruments used to understand the space weather and space environment are the plasma analyzer and the magnetometer. In addition, energetic particles are the most commonly measured parameter to understand the space environment variability. Plasma is described as the collective behavior of the charged particles. The space is filled with the solar wind which is a supersonic plasma with equal number of electrons and protons, i.e. neutral plasma streaming away from the Sun and flows around object which is the Earth’s magnetosphere [2]. As the charges within the solar wind move around, they can alter local concentrations of positive or negative charges that generate electric fields, and hence similarly magnetic fields. These fields in turn affect the motion of the particles which are even farther away [3]. The measurements of the plasma particles has been a long standing issue in space science and yet subjective as it strongly depends on the researcher’s interests. One of the early confirmation of the solar wind was made with a cylindrical curved plate analyzer on Mariner 2 spacecraft, which functions primarily as spectrometer [4]. Also, the Energetic Neutral Particles (ENAs), which are produced within space plasmas through the charge exchange of energetic ions with neutral hydrogen, were discovered in 1970s with solid state detectors [4]. Early versions of the plasma instruments today are subjected to redesign so that it will fit into the CubeSats using advanced instrumentation techniques which is one of our strong motives for this study.

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I. INTRODUCTION A CubeSat is a nano-satellite with a size of 10x10x10 cm which is called one unit or “1U”. Depending on the CubeSat mission purposes and the payload including the instruments the size can be extended into 2U, 3U and so on. CubeSats are inexpensive and use mostly commercial off-the-shelf components for its electronics. Since it was first designed, CubeSats became more and more science oriented in order to understand the structure and variability of the space environment and the phenomena occurring in the space and affecting the spacecraft systems and ground operations. Therefore, CubeSat technologies are highly advanced in order to meet the requirements in space environment. The most critical parameter in a science oriented CubeSat mission is the mass and the volume limitations. As a consequence, these

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Tis study first introduces the current types of plasma analyzers used on interplanetary spacecraft to study the Earth’s magnetic and plasma environment. Characteristics of two of the most commonly used plasma analyzers are chosen and will be discussed for the purpose of optimizing them for PolarBeeSat. The main objective in our study is to present the results of the optimization technique applied to the selected

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plasma analyzer and determine its parameters in order to fit within the PolarBeeSat. This is one of the first studies carried out in our country and results of the optimization and its applicability is important for future design of the plasma analyzers and studies alike in our country. In this presentation, we will also very briefly introduce PolarBeeSat that we intend to place our instrument to study the Earth’s magnetic and plasma environment.

specific energy and angular range. The particles are tracked within both plasma analyzers according to their incident angle. Both designs use electronics to convert and amplify the pulse of a particle incident on the sensors. Below, we give the basic properties of these plasma analyzers and select one of them for further analysis for optimization. According to the results of the optimization the plasma analyzer will be built and tested in our laboratory for use on PolarBee mission to study the environment in the long run.

II. POLARBEESAIL MISSION Fig. 1 shows a 3D picture of PolarBeeSail which is a nanosatellite in 4U CubeSat shape and has 4 m x 4 m solar sail providing the satellite small but reasonable thrust levels. The main objective of PolarBeeSail is to investigate the magnetospheric polar regions, namely polar cusps, magnetosheath, magnetopause, and radiation belts, using a plasma analyzer and magnetometer for 11 years of one solar cycle. Each region of the magnetosphere has their own magnetic and plasma characteristics depending on the interaction with the solar wind and IMF (Interplanetary Magnetic Field). The polar cusps of the magnetosphere are characterized by low magnetic fields and plasma mixed with the solar wind plasma and plasma from the magnetosphere and ionosphere. The solar wind is relatively colder with respect to the magnetosheath plasma. The solar electrons are tenuous and energetic while solar wind ions are colder. Spacecraft will cross the magnetopause as well as it will travel through the radiations belts which are populated by the solar energetic protons and electrons and highly energetic cosmic rays. It is desirable that the plasma instrument on board PolarBee will measure these wide range of particle fluxes at wide range of energy levels as it moves along its elliptical trajectory around the Earth’s polar regions.

A. The Cylindrically Symmetric “Top Hat” Analyzer First, the design for the cylindrically symmetric “top hat” analyzer which is illustrated in Fig. 2 was examined. This type of plasma analyzers were used onboard WIND, Cluster, Fast, Mars Observer, Mars Global, and various sounding rockets [5]. Plasma analyzers are designed for a specific plasma population which depends on the mission’s scientific purpose. For the initial design, geometric factor, energy resolution, angular acceptance and analytical expressions are the parameters to be determined first. Then, a computer trajectory tracing program can be used in optimization of input aperture collimator, particle detector location, and other optical elements [5]. Typically, all internal surfaces are blackened with ebanol-C to reduce scattered light that can reach the detector.

Fig. 2. Plasma Analyzer with a Top Hat Geometry Example.

In Figure 2, a test particle (red) is seen to enter the instrument from the right and moves through the aperture and falls upon the sensors on the plate right below it. Particles move through electrostatic field between two closely placed hemispherical plates. Under the influence of this electric field, the positive and negative particles entering from the aperture are deflected in the opposite directions. The motion of the particles through the electrostatic deflection region can be easily calculated since the electric field between the hemispherical plates is inversely proportional with the space between them [5]. If the instrument is designed for ion measurements, electrons will be absorbed by the blackened surfaces or they will not be accepted at all into the instrument. The same is true for the electrons in such case the ions will be absorbed and not measured. If we want to measure both, then we need to have two top hot analyzer on the spacecraft. In addition, this design cannot detect energetic neutral particles. They will pass through the electric field unaffected and will be

Fig. 1. PolarBeeSail.

The optimization process we describe below should take these particle and plasma characteristics into account. In this study, the optimization of the initial design is carried out by genetic algorithm. The result of the optimization will be tested and validated in the laboratory experiments. III. PLASMA ANALYZERS AND SELECTION In this section, we investigate the design of two different types of plasma analyzers closely. In both designs, the geometry and electrical properties of the plasma analyzers are the main parameters to select the plasma particles which have

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absorbed by the blackened surfaces. If we want to measure ENAs as well, then we need to add another instrument. Therefore, the top hat plasma analyzers are not so suitable for multi-purpose particle measurements. As we would like to measure all three types of particle populations, namely electrons, ions, and ENAs, this type of plasma instrument was not chosen for PolarBee.

optimization is the important part of this study which is described below in detail. IV. FORMULIZATION OF THE OPTIMIZATION PROBLEM A. Genetic algortihm A genetic algorithm is an optimization technique that is inspired by biological evolution, and natural evolution occurs by replication, variation and a different probability of each gene set [7]. The algorithm is described as a methodology for studying adaptive systems which are natural and artificial by J. Holland in 1975 [8].

B. Planar Geometry Plasma Instrument with ENA imaging A plasma instrument with ENA imaging capability consists of a collimator, an electrostatic deflector, an attenuator mechanism, a detector, signal processing electronics and structures for mounting on a CubeSat chassis. A basic illustration for this type of analyzer is given in Fig.3.

In the genetic algorithm approach, a combination of features is called a chromosome or a string, and it requires a population of strings and a cost function. The cost function calculates the fitness of each chromosome. Each generation is subjected to operations such as crossover and mutation to create the next one. In the process, the fittest individual population size to survive can be chosen, and the crossover rate and the mutation rate are adjusted for an efficient search. That cycle repeats until all the solutions are efficiently the same and the further iteration seems unnecessary.

The STEIN (SupraThermal Electrons, Ions and Neutrals) instrument is investigated and taken as an example in this part. STEIN is designed by UC Berkeley to be used on CINEMA mission to measure ~2-100 keV particles, and it has ability to classify the measured particles as electrons, ions and neutrals up to ~20 keV [6]. The electrons and ions are deflected to opposite sides between the electrostatic deflector plates, where they are measured by the two farther edge sensors on the detector while neutrals and higher energy particles are measured by the center pixels.

By using the new population, the evaluation of the fitness is repeated for each chromosome. During the calculation, the best genes are kept, and the best one is chosen as the solution after the convergence of the optimization problem. The particles which are needed to be classified are taken and used in the evaluation of the fitness process where the accuracy is calculated. The optimization objective is maximizing the accuracy in the particle detection during the experiments. The parameters of the plasma analyzer are optimized by the genetic algorithm which uses the parameter blocks as chromosomes. The scheme of the genetic algorithm used for the analyzer is given in Fig. 4.

Fig. 3. Planar Geometry Plasma Instrument.

The silicon semiconductor detector where the particles fall upon in the back is sensitive to UV radiation and the visible light as well as charged particles. Therefore all mechanical surfaces are blacken with Ebanol-C, and it has a collimator to reduce the incident and scattered light striking the detector. Also, the deflector edges will be closed; therefore, the fringing electric fields, from one plate to another while extending away from the electrostatic plates, will not affect the particle trajectories. After close examination of these two plasma analyzers, STEIN type has been found appropriate for our scientific purposes and chosen to place onboard PolarBee. Thus the optimization technique has been applied for this design and the parameters were determined thorough the optimization will be used on PolarBee after the laboratory tests. Since the size of the plasma analyzer need to be small to fit in a CubeSat, the applied optimization technique and its results are essential for a successful laboratory test. Therefore a careful

Fig. 4. The Genetic Algorithm for the Plasma Analyzer Optimization.

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B. Formulization The optimization is constrained to symmetric design only, and the total design space is reduced by half for this reason. At first, the test design is initially optimized by a simple approximation of a constant electric field between parallel electrostatic deflector plates and simulating particle trajectories. These parameters can be listed as x component of the collimator x1, the length of the parallel deflector plates x2,the distance between the parallel deflector plates and sensors x3, y component of the collimator x4, the distance between the parallel deflector plates x5 and the deflection voltage value between parallel plates x6 as seen in Fig.5.

data are used for evaluation after the calculation of the particle motion inside the analyzer. The pixels of the sensors are numbered from 1 to 4. A computer code for the trajectory-tracing was written for the initial design of the plasma analyzer, and the design details were optimized for measuring specific plasma populations as desired. After we determine the primary mechanical properties, using these optimization results, we want to perform tests using an electron and an ion source with 15 keV charged particles in our laboratory. For the initial basic design, effects such as electron scattering, energy resolution, radiation shielding and contamination etc. were not considered. They may be added once the basic design successfully built and run. The basic design will be modified and improved according to other additional environmental factors. According to the desired values, a particle population creation function is written in Matlab to be used in the evaluation of the each design generation. According to the energy range of each particle type, this function creates the desired particle population to be used in particle trajectory tracing program, and the population contains high energy ions, low energy ions, high energy electrons, low energy electrons and energetic neutral particles within desired angle of attack limits.

Fig. 5. Optimization Parameters for the Analyzer

The objective function can be given as:

Initial particle data are created containing energy of the particle, position on y axis at the entrance and angle of attack. Then, evaluation column is added according to the particle trajectory tracing program and the particle type. For instance, low energy electrons will have 1 in their evaluation column if the particle trajectory tracing program finds that it hits the first sensor surface, and 0 for other sensor surfaces, or it hits another surface and is absorbed. Similarly, high energy electrons require striking the second sensor surface, ENAs requires striking the second and third sensor surfaces, low energy ions require striking the fourth sensor surface, and high energy ions require striking the third sensor surfaces to have 1 in their evaluation column. Otherwise, evaluation column got 0 in their value.

(1) where

(2)

(3) The constraints for the problem can be given as: (4) (5) (6)

Particle data at the end of optimization contain energy of the particle, position on y axis at entrance, angle of attack, evaluation and incidence angle. The size of the data set is (Nx5) where N is the number of the chosen particle data. Finally, the accuracy of each genetic algorithm iteration, where k is iteration number, is calculated by using evaluation column of the output.

(7) C. Particle Data Creation and Evaluation The thermal velocities are obtained using particle energies since the analyzer optimization code uses particle velocity rather than bulk velocities for initialization. This can be calculated by using: 1eV = 11600 K (8)

V. THE RESULTS The optimization is performed for different scenario by changing the particle population such as particle types, their energy and angle range, position on y axis at entrance, and it affects the evaluation criteria at each optimization step since the objective function is based on particle population evaluation, and each type of particle requires different surfaces to strike. It must be noted that it is not desired to have one

(9)

This velocity is used to find y and x components of the particle with an initial angle of attack. Then, the final particle

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optimal solution for each scenario instead, a human expert can decide which is more suitable to be produced by viewing a population of the best solutions.

In order to test the instrument’s sensitivity on angle of attack, optimization procedure was run for -4/+4 degree angle of attack and results are given in Fig. 8 and Fig. 9 for comparison. The results are found to be very sensitive on the angle of attack such that even if a particle population created with the same energy, different angle of attack from the previous one in the same tests will alter the instrument’s capability.

Production capabilities are important to be considered, and the sensors are also sensitive to the UV radiation, X-rays and the visible light; therefore, a longer deflection plate and smaller distance between the plates can be more desirable. On the other hand, this will decrease the geometric factor that is related to the velocity space volume. Fig. 6 below gives the maximum performance (vertical axis) obtained from the optimization process versus the generation number (horizontal axis) for -2/+2 degree angle of attack.

Fig. 8. Maximum Performance for Each Generation for -4/+4 degree angle of attack. Fig. 6. Maximum Performance for Each Generation for -2/+2 degree angle of attack.

As seen in Fig.6, the initial population had a design point with maximum performance of 95%; however, the genetic algorithm found a new configuration with 100% performance value after 4 iterations. The main objective here was maximizing the performance value for the plasma analyzer, which is calculated by using the particle evaluation process.

Fig. 9. Average Performance of Each Generation for -4/+4 degree angle of attack.

Fig.10 is made using the optimized parameters for -2/+2 degree angle of attack and illustrates the optimized view of the analyzer in the cross-sectional view. The optimized design given in Fig. 10 is performed for 15 keV charged particles coming within the angle of attack between -2/+2 degree. The final length of the electrostatic deflector plates are taken as 17.6 mm, and the distance between the plates are taken as 3.7 mm from a set of design configurations. Also, the length of the collimator is taken as 6.4 mm, and the distance between the edges of the plates and the sensor surfaces is taken as 26 mm. Total lengths are found as 50 mm which is one of the design constraints of the project. Finally, the deflection voltage is optimized at 900V that means 450V for each plate.

Fig. 7. Average Performance for Each Generation for -2/+2 degree angle of attack.

As seen in Fig.7, the average performance value increased with each generation most of the time; however, this value decreased between seventh and eighth generations. It shows that mutation rates must be given very carefully since the design parameters are very sensitive to alterations. Even 1 millimeter change can decrease the performance from %95 to zero, and this is also valid for the deflection voltage value.

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the laboratory tests such that additional parameters of the space environment may be added. The genetic algorithm results points out that there are several configurations providing the same performance and that the small differences in the optimized parameters decreased the overall performance drastically. During the initial tests, we have seen there are other design choices with 100% performance with longer deflection plates for example. The ones with longer deflection plates will help protecting the sensors from light during the laboratory tests. Also, it will help protecting the sensors from UV radiation and X-rays since the sensors are also sensitive to them. For this preliminary study, several factors are not considered in the optimization process such as sensitivity, precision, stability, noise levels, hardware options, and particle-detector interactions. Future designs should take these factors into account as well for a most successful and efficient mission.

Fig. 10. Cross-Sectional View of the Optimized Plasma Analyzer.

Fig. 11 gives a comparison of the optimized plasma analyzer with respect to the 3U CubeSat structure for comparison.

Acknowledgment This work has been supported by TÜBİTAK project, Project No: 114Y382 and carried out in Upper Atmosphere and Space Weather Laboratory.

References [1] [2] [3] Fig. 11. Cross-Section View of the Optimized Plasma Analyzer and a 3U CubeSat Structure .

[4]

VI. CONCLUSION [5]

In this study, we compared two commonly used plasma analyzers to be placed in PolarBeeSail mission which is a CubeSat: top hat plasma analyzer and planar geometry plasma analyzer. A planar geometry plasma analyzer with ENA imaging was chosen since the scientific purpose of the PolarBee mission is to be able to measure both negative and positive charged particles as well as the energetic neutral atoms in the Earth’s magnetic environment. We determined the parameters of the plasma analyzer by applying genetic algorithm technique and obtained first initial optimized design parameters for this type of plasma analyzer for a CubeSat. This was the main purpose of the study to present the preliminary results before we move on to the laboratory tests. It will be optimized further according to the needs imposed by

[6]

[7]

[8]

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