Browse Topic: Containers
Full state feedback offers theoretically guaranteed multi-axis stability, making it superior to conventional PID controllers. There is however one drawback, a full state controller has a mathematical difficulty if the B matrix is not square and thus not invertible. This is the case for helicopters with 6 degrees of freedom and 4 inceptors. Variations of linear quadratic regulators are a work around, however complexity dramatically increases. Best would be a direct solution to the original problem. This is the breakthrough result of this paper. This paper documents an approach which removes the analysis roadblock by partitioning the 6 x 6 system "A" matrix into two groups of 4 x 4 matrices. The 4x4 matrices are individually stabilized with full state gain matrices. One matrix is designated “Driver Matrix” which provides actuator commands. The other matrix is designated "Reference Matrix" which provides references. The two matrices are coupled together by requiring that the driver matrix follow references generated by the reference matrix. With each matrix individually stabilized, the coupled combination is also stabilized. Computation of flight dynamics states (u, v, w, p, q, r) is shared between the matrices. Initial results are very encouraging, showing an originally sluggish, heavy lift helicopter having now concise decoupled responses to pitch and roll commands. Stability derivatives are recomputed during flight allowing coverage over the whole flight envelope. A handling qualities task has been defined to relocate a 40 ft standard seaborne container directed by a pilot in a ground control station. Cooper Harper ratings of this task have demonstrated favorable Level 1 handling qualities if use is made of an automated lateral repositioning command.
The Container trailers are used worldwide to transport goods & materials especially e-commerce applications with valuable materials. These container trailers are presently locked with a mechanical locking system and often broken and unlocked by unauthorized people. During transportation time, the driver stops the vehicle for natural calls, food or any other breakdown, the attempt is made to steal the materials. Many cases were known only after damages are done. It has become a serious issue nowadays in the transportation industry. To avoid these problems, we have designed and developed a system that operates pneumatically with digital locking control. The system is designed to ensure proper safety by rigid mechanical locking. It is actuated by a pneumatic system consisting of Directional control valve & pneumatic cylinders. The lock and unlock inputs are given through digitally and the digital controller provides the appropriate input to solenoid operated direction control valve. Based on the position of Direction control valves, the pneumatic cylinders lock or unlock the mechanical locking system. The safety system is integrated along with the digital pin locking. The system is designed with the concept of fool-proof and fail-safe. In the event of no Pneumatic or electrical supply failure, the system remains in lock condition only. Except the authorized person, others cannot open the containers without the authorization pin. Also, the system provides a technologically improved version of the safety locking system like geo-fencing. We can lock and unlock the container with geological identity. The safety locking system improves confidence among the transporters and ensures safety at the highest level.
Cargo Routing Problem or Container Allocation Problem is key decision-making challenge in the maritime industry at operational level. Existing research focus on static environment or planning decisions, ignoring the dynamic arrival property of shipping request in practical world. In this paper, we introduced the Online Cargo Routing problem and formulation the path-based models under a space-time network. We proposed an online algorithm under the online primal-dual scheme: re-solving strategy. We further conducted simulation experiments under different demand distributions to demonstrate the performance of the proposed algorithm over the offline baselines.
As unmanned vehicular networks become more prevalent in civilian and defense applications, the need for robust security solutions grows in parallel. While ROS 2 offers a flexible platform for robotic operations, its security model lacks the adaptability required for dynamic trust management and proactive threat mitigation. To address these shortcomings, we propose a novel framework that integrates containerized ROS 2 nodes with Kubernetes-based orchestration, a dynamic trust management subsystem, and integrability with simulators for real-time and protocol-flexible network simulation. By embedding trust management directly within each ROS 2 container and leveraging Kubernetes, we overcome ROS 2’s security limitations by enabling real-time monitoring and machine learning-driven anomaly detection (via an autoencoder trained on custom data), facilitating the isolation or removal of suspicious nodes. Additionally, Kubernetes policies allow seamless scaling and enforcement of trust-based security rules, mitigating the static constraints of the default ROS 2 security stack. This approach delivers a robust, scalable, and adaptive platform for unmanned vehicle fleets operating in contested or untrusted domains.
The fusion of virtualized base software with simulation technologies has transformed the methods used for development and system testing. This paper examines the architecture, implementation, and advantages of employing virtualization to improve simulation environments. Virtualized base software enables the creation of isolated, scalable, and replicable settings, essential for executing complex simulations that replicate real-world situations. Utilizing virtualization enhances simulations by making them more efficient, flexible, and cost-effective. The study covers the essential elements of virtualized simulation platforms, such as containerization, network abstraction and virtual drivers. It also analyzes how these components collaborate to create a strong framework for simulating diverse applications, ranging from software testing to hardware emulation. This approach offers several benefits, including better resource utilization, quicker deployment times, and the flexibility to easily modify and scale simulations to meet different needs. With Software Defined Vehicle (SDV) becoming today’s reality, the need to continuously verify and validate software components independent from specific target deployments is challenging traditional testing approaches. Virtualization allows for high degree of scalability, enabling Continuous Testing (CT) with fast feedback cycles for verification and validation. Through case studies and experimental results, the paper showcases the practical uses of virtualized simulation, emphasizing its influence on industries like automotive and aerospace. The findings indicate that virtualized base software not only improves the accuracy and reliability of simulations but also substantially cuts down the time and cost compared to traditional testing methods. In conclusion, the integration of virtualization technologies with simulation platforms represents a significant advancement in the field, offering substantial benefits in terms of performance, scalability, and cost-efficiency. Future research directions include exploring more advanced virtualization techniques, improving interoperability between different simulation tools, and expanding the use of virtualized simulations in emerging technologies.
In the medical device production environment, device packaging and sterilization is vital. The same level of rigorous quality controls and regulations that affect the devices themselves are also extended to their packaging. The mechanical and container closure integrity [CCI] evaluations of medical device packaging requires significant testing performed at multiple points throughout the commercialization and production processes.
Testing of ducted fuel injection (DFI) in a single-cylinder engine with production-like hardware previously showed that adding a duct structure increased soot emissions at the full load, rated speed operating point [1]. The authors hypothesized that the DFI flame, which travels faster than a conventional diesel combustion (CDC) flame, and has a shorter distance to travel, was being re-entrained into the on-going fuel injection around the lift-off length (LOL), thus reducing air entrainment into the on-going injection. The engine operating condition and the engine combustion chamber geometry were duplicated in a constant pressure vessel. The experimental setup used a 3D piston section combined with a glass fire deck allowing for a comparison between a CDC flame and a DFI flame via high-speed imaging. CH* imaging of the 3D piston profile view clearly confirmed the re-entrainment hypothesis presented in the previous engine work. This finding suggests that a DFI retrofit for this combustion chamber geometry may at best be load-limited.
Integrated modeling of vehicle, tire and terrain is a fundamental challenge to be addressed for off-road autonomous navigation. The complexities arise due to lack of tools and techniques to predict the continuously varying terrain and environmental conditions and the resultant non-linearities. The solution to this challenge can now be found in the plethora of data driven modeling and control techniques that have gained traction in the last decade. Data driven modeling and control techniques rely on the system’s repeated interaction with the environment to generate a lot of data and then use a function approximator to fit a model for the physical system with the data. Getting good quality and quantity of data may involve extensive experimentation with the physical system impacting developer’s resource. The process is computationally expensive, and the overhead time required is high. High-fidelity simulators coupled with cloud-based containers can help ease the challenge of data ‘quality’ and ‘quantity’. Project Chrono is a multi-physics simulation engine that provides high-fidelity simulation capabilities with emphasis on flow and terrain modeling. With a host of libraries and APIs for industry accepted tools like MATLAB, Simulink and TensorFlow, Project Chrono proves to be a powerful research bed for data-driven modeling and control development for off-road navigation. Containers are lightweight virtual machines that take away repetitive configurations by setting up a computational environment, including all necessary dependencies and libraries. Docker encapsulates an end-to-end platform solution for heavy computation challenges of deep learning applications and allows fast development and testing. The synergy between the high-fidelity simulator and the compute outsourcing capabilities of cloud-based containers proves to be extremely beneficial for continuous integration and continuous deployment (CI/CD) for data driven modeling and control tasks. In the following work, we containerize a high-fidelity simulator (Project Chrono) to develop and validate data driven modeling and control algorithms for off-road autonomous navigation.
Innovative robotic rotational molding technology allows for the use of new materials in manufacturing parts and hard cases for military and aerospace applications. This is creating opportunities for new product geometries, tighter tolerances, and less waste than traditional plastic molding. There are thousands of applications within the aerospace and defense industries using plastic parts and storage containers. Today, the production of specialized parts and protective hard cases for military applications can call for unique materials to meet more stringent standards (flammability, high heat/cold tolerances, airtightness, watertightness, electromagnetic interference shielding, multi-layering, to name a few). Meeting these strict requirements can be challenging when using traditional molding processes such as rotational molding, thermoforming, blow molding, or injection molding.
If only there were some kind of plastic-eating creature that roamed the world devouring our water bottles, food containers, cling film, grocery bags, straws, caps, broken toys, etc., the plastic waste problem filling our landfills, polluting our waterways, and floating through our very bloodstreams, might go away.
Welding is a dominant joining process employed in fabrication industries, especially in critical areas such as boiler, pressure vessels, and marine structure manufacturing. Online monitoring of welding processes using sensors and intelligent models is increasingly used in industries for predicting weld conditions. Studies are conducted in a Shielded Metal Arc Welding (SMAW) process using sound, current, and voltage sensors to predict the weld conditions. Sensor signatures are acquired from the good weld and defective weld conditions established in this study. Signal processing is carried out, and time-domain statistical features are extracted. Statistical features are also extracted from the power waveform derived from the current and voltage data for all the weld conditions. Classification And Regression Tree (CART) and Support Vector Machine (SVM) algorithms are used to build the statistical models to predict the weld conditions. SVM algorithm with Quadratic Kernel function trained using power signature features predicts weld conditions considered in this study with an accuracy of 99%.
Air Cargo is one of the major modes of cargo transportation in the world. It is helping to transport goods swiftly across the globe during emergencies like pandemic, evacuation, and natural calamities etc. It plays a key role in economy of a country by exporting and importing goods across the globe. This business is growing every YOY with increase in demand for e-Commerce and globalization. It is also important to keep up the efficiency of the system as the business demand grows. This paper focuses on Artificial Intelligence (AI) implementation can reduce the inefficiency and inconsistency due to the manual intervention in cargo operation in different areas. The major Implementation study area of AI in this paper include implementing in Cargo load planning to reduce the human dependency and error, ground handling with the help of autopiloting vehicle which can operate in any weather condition, sequence of loading Unit Load Devices (ULD’s) based on priority, operating control unit to move ULD in the Cargo deck , fixing ULD’s when it is stuck during operation and implementation of AI based predictive maintenance for the Cargo electrical and mechanical components and AI based design decision making in cargo LRU’s. The required data for AI implementation for the ground handling and cargo operation is collected from the existing system and Subject matter experts. It also generates more data after the implementation which can be continuously fed to model for the improvement.
A unique shore-based facility and flight tests designed to investigate rotor/obstacle aerodynamic interactions under low wind speed conditions were planned and carried out in 2020 at Naval Air Station (NAS) Patuxent River. A temporary elevated fixed platform (EFP) sized to represent the aft half of an LPD-17 flight deck was built out of stacked CONEX shipping containers on a closed taxiway. The EFP walls were instrumented with ultrasonic anemometers to gather velocity flow field measurements as various rotorcraft executed simulated recovery profiles to and hover ladders near the EFP. The EFP was subsequently reconfigured to conduct a confined landing area investigation. The simultaneous acquisition of aircraft performance data and flow field data will be invaluable for the validation of the Navy's shipboard operations modeling and simulation tools, maximizing the Navy's return on investment in building the temporary EFP facility.
Ducted fuel injection (DFI), a concept that utilizes fuel injection through ducts, was implemented in a constant pressure High Temperature Pressure Vessel at 60 bar ambient pressure, 800-1000 K ambient temperature, and 21 % oxygen. The ducts were 14 mm long and placed 3-4.7 mm from the orifice exit. The duct diameters ranged from 1.6-3.2 mm and had a rounded inlet and a tapered outlet. Diesel fuel was used in single-orifice fuel injectors operating at 250 MPa rail pressure. The objective of this work was to study soot reduction for various combinations of orifice and duct diameters. A complete data set was taken using the 150 μm orifice. A smaller data set was acquired for a 219 μm orifice, showing similar trends. Soot reduction peaked at an optimal duct diameter of 2-2.25 mm, corresponding to an 85-90 % spray area reduction for the 150 μm orifice. Smaller or larger duct diameters were less effective. Duct diameter had a minimal effect on ignition delay. Ambient temperature had an apparent strong effect on soot reduction. At 800 K, where the reaction rates were slower, DFI reduced soot by more than 94 % via longer lift-off lengths regardless of the duct geometry. At 1000 K, where the lift-off length was much shorter, apparent DFI soot reduction peaked at 42 %. For the larger orifice diameter, duct effectiveness was less sensitive to duct diameter.
Innovators have developed an RFID-based system for sensing the angular position of rotating systems. The RFID-Based Rotary Position Sensor can be used as a position/orientation sensor or implemented in a controller to interpolate and refine the rotation angle of a rotating system. The sensor is part of a suite of RFID-based technologies developed to monitor and manage inventory based on passive RFID sensors. NASA's RFID sensors can wirelessly track either bulk levels or discrete quantities of materials within a container without having to attach RFID tags to each item.
Human-machine interface (HMI) software is continually improving, now providing IT and operations technology (OT) capabilities. Once confined to the role of machine and process visualization and control, modern unified HMI software now delivers better user interfaces, containerization, and remote device management — all wrapped up in a cybersecure package.
Pressure vessels are critical equipment used in industries for storing liquids or gases at a pressure significantly different from ambient conditions. Porosity is one of the major weld defects in pressure vessels that leads to failure during inspection and as well as during its service. Gas Metal Arc Welding (GMAW) process is widely used in industries to fabricate pressure vessels using carbon steel “IS 2062 E250BR” material for storing compressed air. The main objective of this article is to reduce the porosity defect in the longitudinal seam (LS) welding of the pressure vessels. Detailed analysis is carried out to identify the parameters which are influencing the porosity defect. Central Composite Design (CCD) and Response Surface Methodology (RSM) approaches are used to find the optimum value of the weld parameters which produce weld without porosity or any major defects in the pressure vessel. An experimental setup has been established and welding experiments have been conducted under a controlled environment. Experiments were conducted without any external disturbances ensuring clean weld surface and filler wire without any moisture, rust, oil, and the presence of any organic materials. For all the weld specimens, visual and radiography examinations were carried out to identify the severity of porosity. A porosity index is proposed in this study for conducting statistical analysis. Statistical analysis shows current, travel speed, gas flow rate, and torch angle have a linear relationship and stickout distance has a nonlinear relationship with porosity. In square term, stickout distance has a significant influence on porosity defect. In two-way interaction studies, current and gas flow rate, current and torch angle, and travel speed and torch angle have a significant influence on porosity. Confirmatory tests were carried out to validate the optimum weld parameters obtained in this study.
Fire is a dramatic issue in aircraft nowadays, especially for composite air craft. An additional issue is the dangerous use of flammable Li-Ion batteries in many devices. To minimize fire issues, it is proposed to produce aircraft interiors, fire doors, cargo bay walls, and cargo containers that are able to contain a fire inside them, with our ceramic composite called TOUGHCERAM®. It is low-cost, ceramic, damage tolerant, and flexible between minus 100°C and plus 350°C. TOUGHCERAM® poly-crystalizes between 20°C and 110°C and can be reinforced with fibers (carbon or basalt). It will survive 90 minutes under direct contact with a propane torch of 1900°C. TOUGHCERAM® does not burn or smoke. This paper will explain how it is possible to develop a fully mineral-ceramic offering with unique mechanical, fire, and blast containment properties.
The present study deals with the reduction of fluid vibrations by dissipating the kinetic energy in a closed vibrating container partly filled using vertical slotted obstacles. The effect of the barriers on the liquid vibration inside a closed container exposed to a harmonic excitation is numerically studied. A single vertical slotted barrier (SVSB) and multivertical slotted barrier (MVSB) systems are considered for different liquid levels. The 3D liquid domain with the tank and the barrier as boundaries is modelled and solved numerically using ANSYS-CFX software. The reduction in pressures on the walls and the ceiling of the tank due to the influences of the slot size and numbers were evaluated to optimize the size and the numbers of the slots. The numerical approach shows an ability to simulate the nonlinear behavior of the liquid vibration when using vertical slotted barriers (VSB). The obtained results show that the SVSB is more efficient than the MVSB to decrease the dangers of dynamic impacts of the liquid vibrations inside the container. The system-damping factor depends mainly on the slot size and reaches a greatest value at a relative slot size equal to 0.25 for all liquid depth ratios. The presented VSB combines the orifice hydrodynamic action and the integrity of the fluid bulk, which may be relevant for any liquid level. The present model findings agree well with the available analytical and numerical results.
In order to guarantee the effectiveness of enforcement action, Brazilian National Petroleum Agency (ANP) has published Resolution n°9/2007, which establishes the sampling of two liters of fuel, one being a test sample and another as a control sample. In this way, it is essential that the container used for this purpose maintains the physical-chemical parameters of the sample. In an attempt to evaluate possible alternatives to the current container used by ANP, this work deals with the application of aluminum bottle containers for the storage of the ethanol fuel, E27 gasohol and B10 and B15 diesel fuel blends. Approximately 15 liters of each fuel, except diesel fuel blends, were sampled on retail stations. B10 and B15 diesel fuel blends were formulated from diesel and biodiesel obtained on distribution base, being thoroughly homogenized and portioned on one-liter aluminum containers. Three samples of each fuel were used to characterize the fuel in the beginning of the work. For each condition (ambient, 30 °C and 40 °C), three samples of each fuel were kept for 60 days and other three were analyzed as control samples, which were stored at 0 °C also for 60 days. The samples had their main parameters analyzed using the standard methodologies established in the respective specifications and the results analyzed according to the limits specified by ANP. Ethanol fuel samples had presented no significative variation on the parameters analyzed, when they were compared with the characterization samples. E27 gasohol samples had shown differences in density, ethanol, olefin, saturated and aromatic content, while diesel fuel samples had presented variations on water content, oxidation stability and lubricity. With respect to the temperature, it was possible to identify trends in most of the parameters that presented variations.
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