Multilayer radiation shield for satellite electronic components protection

In this paper, various multi-layer shields are designed, optimized, and analyzed for electron and proton space environments. The design process is performed for various suitable materials for the local protection of sensitive electronic devices using MCNPX code and the Genetic optimization Algorithm. In the optimizations process, the total ionizing dose is 53.3% and 72% greater than the aluminum shield for proton and electron environments, respectively. Considering the importance of the protons in the LEO orbits, the construction of the shield was based on designing a proton source. A sample shield is built using a combination of Aluminum Bronze and molybdenum layers with a copper carrier to demonstrate the idea. Comparisons of radiation attenuation coefficient results indicate a good agreement between the experimental, simulation, and analytical calculations results. The good specifications of the proposed multi-layer shield prove their capability and ability to use in satellite missions for electronic device protection.

Performance improvement of decentralized control for bidirectional converters in a DC micro-grid

In this paper, using a neural controller and a genetic optimization algorithm to control the voltage as well as, control the frequency of the grid along with the management of the reactive power of the micro-grid to control the output power during islanding using Simultaneous bilateral power converters with voltage/frequency droop strategy and optimization of PI coefficients of parallel power converters by genetic-neural micro-grid algorithm to suppress AC side-current flow that increases stability and improvement of conditions frequency and voltage are discussed. Given the performance of the micro-grid in two simulation scenarios, namely transition from on-grid to off-grid, the occurrence of a step change in load in island mode as well as return to working mode is connected. The ability to detect the robust performance and proper performance of two-level neural controller. The controller performance time was also very good, indicating the appropriate features of the method used to design the controller, namely two-level neural, genetics. The main advantage of this method is its simplicity of design. The method used is also efficient and resistant to changes in the system, which results from the simulations.

LBPSGORA: Create Load Balancing with Particle Swarm Genetic Optimization Algorithm to Improve Resource Allocation and Energy Consumption in Clouds Networks

Due to the purpose of this study that reducing power consumption in the cloud network is based on load balancing, the fitness function measures the load balance between cloud network and servers (the hosts). This technique is appropriate for handling the resource optimization challenges, due to the ability to convert the load balancing problem into an optimization problem (reducing imbalance cost). In this research, combining the results of the particle swarm genetic optimization (PSGO) algorithm and using a combination of advantages of these two algorithms lead to the improvement of the results and introducing a suitable solution for load balancing operation, because in the proposed approach (LBPSGORA), instead of randomly assigning the initial population in the genetic algorithm, the best result is procured by putting the initial population. The LBPSGORA method is compared with PSO, GA, and hybrid GA-PSO. The execution cost, load balancing, and makespan have been evaluated and our method has performed better than similar methods.

Optimization of Direct Laser Deposition of a Martensitic Steel Powder (Metco 42C) on 42CrMo4 Steel

In this study, the deposition of martensitic stainless-steel (Metco 42C) powder on 42CrMo4 structural steel by direct laser deposition (DLD) was investigated. Clads were produced by varying the laser power, scanning speed, feed rate, and preheating. The effect of these processing variables on the microstructure and microhardness of the clads was analyzed, as well as their soundness, yield (measured by dilution), and geometric characteristics (height, width, and depth). The complex interaction of the evaluated processing variables forced the application of complex parameters to systematize their effect on the clads. A genetic optimization algorithm was performed to determine the processing conditions warranting high-quality clads, that is, sound clads, metallurgically bonded to the substrate with required deposition yield.

Dynamic Analysis and Parametric Optimization of Telescopic Tubular Mast Applied on Solar Sail

The Telescopic Tubular Mast (TTM) has excellent performance and is widely used in aerospace. Reasonable parameter design and optimization can shorten development cycle and improve performance for TTM. This paper designed a TTM driven by the bistable carbon reeled composite boom. The equivalent model of the TTM is established and simulated, which can be used as ex-tending structure for the solar sail. The work flow of the solar sail with the TTM is introduced. The natural frequency of the equivalent model and the segmented model is solved respectively using ABAQUS. The TTM under six different load conditions is analyzed. The influence of different factors on the vibration characteristics of the TTM is analyzed and the sensitivity analysis is carried out. Parameters including stiffness, natural frequency, mass and extension ratio are optimized using the multi-objective genetic optimization algorithm. According to the optimization results, the prototype was processed, and the experiment was completed with the equivalent load of solar sail. It provides a reference for the parametric design of the TTM.