Kerbala Journal for Engineering Sciences

A new attempt development of new hybrid composite materials has been studied. Increasing
the mechanical and physical properties of composite materials to lower their weight and cost is the
most frequent problem in engineering projects. This study's goal is to enhance the mechanical and
physical characteristics of composite materials. Also due to this, hybrid composite materials have
received recent attention. In this work, hybrid composite materials were created to enhance mechanical
and physical characteristics. In this investigation, glass fiber and mica fiber were both employed as
synthetic and natural fibers, respectively. The influence of mica as natural fiber with glass fiber as
synthetic reinforcement by weight percent (wt%) on the mechanical properties of hybrid composite
materials were investigated. Epoxy resin (thermosetting polymer) reinforced by various wt % of glass
fiber/mica fiber, such as (0-0, 15-0, 10-5, 7.5-7.5, 5-10, 0-15 %), as well as samples created by hand
lay-up method. In addition, glass fiber and mica fiber in long fiber unidirectional form are utilized;
mechanical properties such as tensile, flexural and impact strength have been tested. As the results of
this study, both glass fiber and mica fiber increased mechanical properties, although glass fiber has a
greater effect than mica fiber but also using mica fiber make great improvement between 26% to 67%.
Composites reinforced with 15% glass fiber had a better value of mechanical properties compared to
others. Additionally, the hybrid composite made of glass fiber and mica fiber performed well in all
tests, enhanced mechanical characteristics, which decreased the cost of making composites.

Early detection of fires plays a crucial role in minimizing their impact and preventing them from
spreading. Every year, the repetition of fires results in the loss of human life, animal life, and plant life.
Fire detection has become increasingly desirable and significant in surveillance systems, where
traditional methods of detecting smoke relied on smoke sensors. Therefore, this method is ineffective in
open and large buildings, and outdoor areas. As a result, this study suggests using computer vision
systems to detect smoke in open spaces by using a static camera. To reduce the data size while preserving
important details, the input video is framed and decomposed using the Integer Haar Lifting Wavelet
Transform (IHLWT). Then, for smoke color detection, a new method called the multi-threshold
International Commission on Illumina (CIE) Lab color space is used, which took into account the smoke
colors' change from whitish gray to blackish gray. In addition, the Frame Differences (FD) technique is
used to detect motion and thus reduce false alarms. The smoke color detection is combined with frame
difference techniques. The small pixels are removed via a morphological operation that represents noise.
According to the experimental findings, the approach precision for offline videos is greater than 94.7%
for eleven videos, while the average detection reaches 92.8% for online (real time) videos. It also reduces
false alarms significantly. According to the trials and comparisons, the suggested smoke detection
algorithm performs better than the traditional algorithms in many scenarios. It is also simple, efficient,
and low in complexity.

An autonomous or robotic car is commonly known as a self-driving car. This vehicle can
sense its surroundings, navigate, and meet human transportation needs without any human intervention,
which is a significant step forward in the advancement of future technologies. Self-driving cars use GPS,
cameras, lidar, radar, and navigational paths to perceive their environment. The benefits of autonomous
cars, such as increased reliability, fewer traffic collisions, increased roadway capacity, reduced traffic
police, reduced traffic congestion, and care insurance, are compelling for the development of
autonomous vehicles. However, issues such as software reliability, cybersecurity, liability for damage,
and loss of driver-related jobs must be overcome. This study aimed to investigate local and global path
planning for self-driving cars using two algorithms, namely A* and the potential field algorithm. The
objective was to determine the effectiveness of each algorithm and explore how they could be combined
to achieve optimal results. This article proposes a path-planning approach for a self-driving car in an
environment with obstacles. The path planner is based on the strategy of using both global and local
planners. The global planner is designed using the A* algorithm, which is used to generate an initial
global path that provides an efficient way to guarantee the shortest path to the goal in an environment.
The local planner is implemented using the potential field algorithm, which is used to adjust the path in
real-time based on local obstacles and other dynamic factors. The intention of using a potential function
is based on its safety, simplicity, and low computational cost. The proposed approach is evaluated in a
simulated environment and shows promising results in providing an efficient way to guarantee the
shortest path to the goal in an environment with obstacles. The combination of global and local planning
techniques is expected to enhance the robustness and safety of autonomous vehicles in real-world
scenarios.

The use of joint technologies to improve the heat transfer process in engineering and industrial
applications enhances the heat transfer coefficient and Nusselt number, thus increasing its thermal
efficiency and performance for these applications for example (air conditioning systems, solar energy
systems, storage tanks, and heat exchangers). This paper presents a numerical simulation using (the
k-epsilon turbulence) model to investigate the influence of forced convection heat transfer and fluid
flow for a channel (square cross-section) of a length (50 cm), width of (12 cm), and height (12 cm)
partially filled with height layer (8 cm) of the porous medium (Glass Spheres of diameter 5mm) with
the addition of hybrid nanofluids (HNF) to the base fluid (Engine Oil) as a combined technique to
obtain the best improvement, where the three- dimensional (3D) test model was designed using the
commercial code COMSOL Multiphysics 6.0 program in two cases, the first does not contain any
addition to the improvement, and the second contains the joint techniques (porous media and HNF),
the governing equations for the turbulent flow of the fluid (mass conservation equation, momentum
conservation equation, energy conservation equation) were solved. The results showed that the
temperature, velocity, and pressure distribution of the base fluid in a test section increases
significantly using these techniques and enhances the heat transfer coefficient compared to the results
for the test section without these additions. In addition, the temperature distribution increases with
the increase in the axial length of the fluid flow in the test section of the channel. The results of this
study also showed that the porous material leads to obstruction of the fluid flow, and thus the
thickness of the boundary layer decreases, and the shape of the temperature and velocity distribution
changes along the test section.