Browse Topic: Hardware
In the electrical machines, detrimental effects resulted often due to the overheating, such as insulation material degradation, demagnetization of the magnet and increased Joule losses which result in decreased lifetime, and reduced efficiency of the motor. Hence, by effective cooling methods, it is vital to optimize the reliability and performance of the electric motors and to reduce the maintenance and operating costs. This study brings the analysis capability of CFD for the air-cooling of an Electric-Motor (E-Motor) powering on Deere Equipment's. With the aggressive focus on electrification in agriculture domain and based on industry needs of tackling rising global warming, there is an increasing need of CFD modeling to perform virtual simulations of the E-Motors to determine the viability of the designs and their performance capabilities. The thermal predictions are extremely vital as they have tremendous impact on the design, spacing and sizes of these motors.
As mission-critical systems demand more processing power, real-time data movement, and multi-domain interoperability, rugged embedded systems are being transformed. Today's military and aerospace applications increasingly demand the merging of AI computing, enhanced sensor interfaces, and cybersecurity - all under harsh environmental conditions. At the heart of this evolution is the 3U OpenVPX form factor, a modular, compact, and ruggedized hardware standard and increasingly the SOSA aligned subset of the architecture. However, next-generation systems need to go further: supporting higher bandwidth, better thermal efficiency, improved security, while maintaining multi-vendor interoperability and long-term sustainability. We'll discuss some of today's enclosure solutions as well as emerging technologies.
Object detection has many different uses in Command and Control (C2) systems such as autonomous control, target tracking, threat detection, and general surveillance. Graphics Processing Units (GPUs) are the de-facto standard hardware for these types of workloads in datacenter environments. Still, when deploying to an edge environment many considerations are required to ensure an optimized deployment. This paper provides a general overview of how to utilize GPUs for AI inference for object detection at the edge using NVIDIA® HoloScan as well as an overview of the many considerations to account for when selecting the most optimal GPU for any specific ground vehicle solution.
As SAE standard J3400 (also known as NACS, or North American Charging Standard) is being adopted by automakers and deployed on the latest EVs, the standard itself is still evolving. That latest evolution is SAE J3400/2. That extra 2 will make charging quicker, thanks to hardware updates to the port and inlet. As standards are announced, there are elements that result in a standard within the standard. Essentially, J3400 is more of a family of standards that handle everything from the internal technology that allows for compatibility, the hardware specs and testing of adapters that have and will be deployed and, in the case of J3400/2, the hardware itself.
This SAE Aerospace Standard (AS) establishes minimum requirements for eddy current inspection of circular holes in nonferrous, metallic, low conductivity (less than 5% IACS) aircraft engine hardware with fasteners removed. The inspection is intended to be performed at maintenance and overhaul facilities on engine run hardware.
Artificial intelligence (AI) systems promise transformative advancements, yet their growth has been limited by energy inefficiencies and bottlenecks in data transfer. Researchers at Columbia Engineering have unveiled a groundbreaking solution: a 3D photonic-electronic platform that achieves unprecedented energy efficiency and bandwidth density, paving the way for next-generation AI hardware.
Camera-based mirror systems (CBMS) are being adopted by commercial fleets based on the potential improvements to operational efficiency through improved aerodynamics, resulting in better fuel economy, improved maneuverability, and the potential improvement for overall safety. Until CBMS are widely adopted it will be expected that drivers will be required to adapt to both conventional glass mirrors and CBMS which could have potential impact on the safety and performance of the driver when moving between vehicles with and without CBMS. To understand the potential impact to driver perception and safety, along with other human factors related to CBMS, laboratory testing was performed to understand the impact of CBMS and conventional glass mirrors. Drivers were subjected to various, nominal driving scenarios using a truck equipped with conventional glass mirrors, CBMS, and both glass mirrors and CBMS, to observe the differences in metrics such as head and eye movement, reaction time, and
The effect of seat belt misuse and/or misrouting is important to consider because it can influence occupant kinematics, reduce restraint effectiveness, and increase injury risk. As new seatbelt technologies are introduced, it is important to understand the prevalence of seatbelt misuse. This type of information is scarce due to limitations in available field data coding, such as in NASS-CDS and FARS. One explanation may be partially due to assessment complexity in identifying misuse and/or misrouting. An objective of this study was to first identify types of lap-shoulder belt misuse/misrouting and associated injury patterns from a literature review. Nine belt misuse/misrouting scenarios were identified including shoulder belt only, lap belt only, or shoulder belt under the arm, for example, while belt misrouting included lap belt on the abdomen, shoulder belt above the breasts, or shoulder belt on the neck. Next, the literature review identified various methods used to assess misuse
Researchers have combined miniaturized hardware and intelligent algorithms to create a cost-effective, compact powerful tool capable of solving real-world problems in areas like healthcare.
To meet the requirements of high-precision and stable positioning for autonomous driving vehicles in complex urban environments, this paper designs and develops a multi-sensor fusion intelligent driving hardware and software system based on BDS, IMU, and LiDAR. This system aims to fill the current gap in hardware platform construction and practical verification within multi-sensor fusion technology. Although multi-sensor fusion positioning algorithms have made significant progress in recent years, their application and validation on real hardware platforms remain limited. To address this issue, the system integrates BDS dual antennas, IMU, and LiDAR sensors, enhancing signal reception stability through an optimized layout design and improving hardware structure to accommodate real-time data acquisition and processing in complex environments. The system’s software design is based on factor graph optimization algorithms, which use the global positioning data provided by BDS to constrain
In an era where technological advancements are rapid and constant, the U.S. Army will need a more agile and efficient approach to modernizing systems on succeeding generations of Army vehicles. Legacy platforms like Abrams, Stryker, and Bradley vehicles use multiple mission computers tied to individual sensors that often required the addition of “boxes” to accommodate new capabilities, which could take years to deploy and drove sustainment costs up due to vendor lock. In addition, this antiquated approach doesn’t leverage data to converge effects across the formation in a multi-domain environment. Centralized, common computing as detailed in GCIA would help solve this problem, potentially linking all major subsystems and providing higher-speed processing to assess large datasets in real time with AI and ML algorithms. By using a common, open architecture computer, the Army will be able to rapidly integrate new capabilities inside one box, versus adding multiple boxes. This pivotal
In an era where technological advancements are rapid and constant, the U.S. Army will need a more agile and efficient approach to modernizing systems on succeeding generations of Army vehicles. Legacy platforms like Abrams, Stryker, and Bradley vehicles use multiple mission computers tied to individual sensors that often required the addition of “boxes” to accommodate new capabilities, which could take years to deploy and drove sustainment costs up due to vendor lock. In addition, this antiquated approach doesn't leverage data to converge effects across the formation in a multi-domain environment. Centralized, common computing as detailed in GCIA would help solve this problem, potentially linking all major subsystems and providing higher-speed processing to assess large datasets in real time with AI and ML algorithms. By using a common, open architecture computer, the Army will be able to rapidly integrate new capabilities inside one box, versus adding multiple boxes. This pivotal
LIDAR-based autonomous mobile robots (AMRs) are gradually being used for gas detection in industries. They detect tiny changes in the composition of the environment in indoor areas that is too risky for humans, making it ideal for the detection of gases. This current work focusses on the basic aspect of gas detection and avoiding unwanted accidents in industrial sectors by using an AMR with LIDAR sensor capable of autonomous navigation and MQ2 a gas detection sensor for identifying the leakages including toxic and explosive gases, and can alert the necessary personnel in real-time by using simultaneous localization and mapping (SLAM) algorithm and gas distribution mapping (GDM). GDM in accordance with SLAM algorithm directs the robot towards the leakage point immediately thereby avoiding accidents. Raspberry Pi 4 is used for efficient data processing and hardware part accomplished with PGM45775 DC motor for movements with 2D LIDAR allowing 360° mapping. The adoption of LIDAR-based AMRs
The next-gen 15-liter diesel engine meets all 2027 EPA emissions regulations while boosting fuel efficiency. Cummins provided extensive details of the design and engineering efforts involved in developing the new HELM version of its X15 diesel engine. The company says its new engine will offer up to a 7% improvement in fuel economy compared to the current EPA 2024-certified X15 while also meeting all 2027 emissions targets. Truck & Off-Highway Engineering was invited to tour the company's headquarters in Columbus, Indiana, where journalists were given a comprehensive update on the hardware powering the latest X15.
This paper proposes a novel approach to the design of a Hardware Abstraction Layer (HAL) specifically tailored to embedded systems, placing a significant emphasis on time-controlled hardware access. The general concept and utilization of a HAL in industrial projects are widespread, serving as a well-established method in embedded systems development. HALs enhance application software portability, simplify underlying hardware usage by abstracting its inherent complexity and reduce overall development costs through software reusability. Beyond these established advantages, this paper introduces a conceptual framework that addresses critical challenges related to debugging and mitigates input-related problems often encountered in embedded systems. This becomes particularly pertinent in the automotive context, where the intricate operational environment of embedded systems demands robust solutions. The HAL design presented in this paper mitigates these issues. The design is structured as a
The transition from ICE to electric power trains in new vehicles along with the application of advanced active and passive noise reduction solutions has intensified the perception of noise sources not directly linked to the propulsion system. This includes road noise as amplified by the tire cavity resonance. This resonance mainly depends on tire geometry, gas temperature inside the tire and vehicle speed and is increasingly audible for larger wheels and heavier vehicles, as they are typical for current electrical SUV designs. Active technologies can be applied to significantly reduce narrow band tire cavity noise with low costs and minimal weight increase. Like ANC systems for ICE powertrains, they make use of the audio system in the vehicle. In this paper, a novel low-cost system for road induced tire cavity noise control (RTNC) is presented that reduces the tire cavity resonance noise inside a car cabin. The approach is cheap in terms of computational effort (likewise ICE order
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
Dramatic video of the first flight of the Space Launch System (SLS), from the initial blastoff to rocket-booster separation, gave NASA essential information about the performance of the Artemis I flight. It also proved the capabilities of a new rugged video camera mounted on the exterior of the core rocket stage. The camera, developed using patented NASA hardware and agency expertise, survived the heat of blastoff and the cold of space, and it’s now ready for extreme conditions on Earth.
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