Evaluasi Desain Terminal Penumpang Bandar Udara Sentani Jayapura Provinsi Papua

Sentani Airport serves public using air transportation with increasing people mobility conditions because Jayapura is the National Activity Center, so it causes frequent accumulation of passengers in the terminal area especially during peak hours. To determine the effectiveness of the terminal in accommodating the flow of passenger movement, it is necessary to evaluate the terminal design of Sentani Airport. The method used is to calculate the number of PWS with passenger distribution patterns, calculate the standard area of terminal capacity using references from SNI 03-7046-2004 and SKEP 77/VI/2005, calculate the terminal LOS with IATA standards, measure the airport service standards based on PM 178 of 2015, processing passengers’ perceptions with IPA and QFD methods and forecasting with simple linier regression. From the analysis, Sentani Airport terminal gives passengers mobility with low service performance value, so it is rated as less optimal. In addition, the limited availability of several facilities causes the passengers feeling less comfortable. Thus, it is necessary to re-design the passenger terminal area and re-management the facilities according to Angkasa Pura standards.

Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing

The incorporation of unmanned aircraft terminal operations into the scope of Detect and Avoid systems necessitates analysis of the safety performance of those systems—principally, an assessment of how well those systems prevent loss of well clear from and collision with other aircraft. This type of analysis has typically been conducted by Monte Carlo simulation with synthetic, statistically representative encounters between aircraft drawn from an appropriate encounter model. While existing encounter models include terminal airspace classes, none explicitly represents the structure expected while engaged in terminal operations, e.g., aircraft in a traffic pattern. The work described herein is an initial model of such operations, scoped at this time specifically for assessment of unmanned aircraft landings and encounters with other aircraft either landing or taking off. The model shares the Bayesian network foundation of other MIT Lincoln Laboratory encounter models but tailors those networks to address structured terminal operations, i.e., correlations between trajectories and the airfield and each other. This initial model release is intended to elicit feedback from the standards-writing community.

Innovation in Managing Leak Repair and Crude Oil Lifting Schedule to Respond to Pipeline Leak in Offshore Terminal System

Offshore terminal usually performs crude oil lifting process regularly every 7-8 days. However, three repetitive leaks in crude oil subsea pipeline 36" MOL SPM-1 to SPM-2 occurred in the offshore terminal area had stopped the natural oil lifting process. Due to complex conditions, leak repair needs a longer duration and some future crude oil lifting schedules facing cancelation possibilities. By simulating the leak parameter, more than 60 bbl. of crude oil would release from the leaking pipeline in 48 hours crude oil lifting operation. An innovative approach is built by designing a new tool to contain oil spills from the source using a pyramid shape tank and safely transport to the temporary storage tank on the diving vessel to keep crude oil lifting process execution still possible to hold while the subsea pipeline repair by installing subsea clamp is undergo. New tools have successfully eliminated oil spill spreading during crude oil lifting takes place schedule. Six crude lifting schedules have been safely delivered with nearly 30,000 liters of crude oil spill have been evacuated and transferred back to processing facilities. Further implementation would possibly be held in pipeline preservation program and diver less application, which can increase leak response time.

CACLA-Based Trajectory Tracking Guidance for RLV in Terminal Area Energy Management Phase

This paper focuses on the trajectory tracking guidance problem for the Terminal Area Energy Management (TAEM) phase of the Reusable Launch Vehicle (RLV). Considering the continuous state and action space of this guidance problem, the Continuous Actor–Critic Learning Automata (CACLA) is applied to construct the guidance strategy of RLV. Two three-layer neuron networks are used to model the critic and actor of CACLA, respectively. The weight vectors of the critic are updated by the model-free Temporal Difference (TD) learning algorithm, which is improved by eligibility trace and momentum factor. The weight vectors of the actor are updated based on the sign of TD error, and a Gauss exploration is carried out in the actor. Finally, a Monte Carlo simulation and a comparison simulation are performed to show the effectiveness of the CACLA-based guidance strategy.