Abstract: Space Radiation has become one of the major factors in successful long duration space exploration. Exposure to space radiation not only can affect the health of astronauts but also can disrupt or damage materials and electronics. Hazards to materials include degradation of properties, such as, modulus, strength, or glass transition temperature. Electronics may experience single event effects, gate rupture, burnout of field effect transistors and noise. Presently aluminum is the major component in most of the space structures due to its lightweight and good structural properties. However, aluminum is ineffective at blocking space radiation. Therefore, most of the past research involved studying at polymers which contain large amounts of hydrogen. Again, these materials are not structural materials and would require large amounts of material to achieve the structural properties needed. One of the materials to alleviate this problem is polymeric composite materials, which has good structural properties and use polymers that contained large amounts of hydrogen. This paper presents steps involved in fabrication of multi-functional hybrid sandwich panels that can provide beneficial radiation shielding as well as structural strength. Multifunctional hybrid sandwich panels were manufactured using vacuum assisted resin transfer molding process and were subjected to radiation treatment. Study indicates that various nanoparticles including Boron Nano powder, Boron Carbide and Gadolinium nanoparticles can be successfully used to block the space radiation without sacrificing the structural integrity.
Abstract: The main goal in this paper is to quantify the quality of
different techniques for radiation treatment plans, a back-propagation
artificial neural network (ANN) combined with biomedicine theory
was used to model thirteen dosimetric parameters and to calculate
two dosimetric indices. The correlations between dosimetric indices
and quality of life were extracted as the features and used in the ANN
model to make decisions in the clinic. The simulation results show
that a trained multilayer back-propagation neural network model can
help a doctor accept or reject a plan efficiently. In addition, the
models are flexible and whenever a new treatment technique enters
the market, the feature variables simply need to be imported and the
model re-trained for it to be ready for use.