Browse Topic: Hazardous materials
With the development of controlled nuclear fusion technology, the tokamak device, as the most promising magnetic confinement fusion reactor for advanced engineering applications, requires remote maintenance of its internal components, which has become a key factor affecting both operational efficiency and safety. As a critical component directly exposed to high-temperature plasma, the divertor target plate needs to be periodically replaced and carefully maintained to ensure stable and reliable reactor operation. However, this region is subject to extreme conditions, including high temperature, high vacuum, and intense radiation, making conventional manual maintenance infeasible. This necessitates the development of intelligent and automated teleoperation systems. To address the automated assembly and disassembly requirements of divertor target plates, this study designs an integrated target plate actuator comprising key functional units: a positioning module, a screwing module, a quick-change module, and a passive compliance structure. The actuator achieves rapid and precise alignment with target plate holes, accommodates bolts of different specifications, and exhibits excellent impact resistance. Furthermore, stiffness and mechanical analyses, supported by finite element simulations, verify the actuator’s safety and reliability under high loads and impact forces. To further enhance operational performance, a segmented disassembly and assembly control strategy based on reinforcement learning is proposed, enabling the actuator to adaptively handle torque variations and ensure precise and stable bolt operations. The results demonstrate that the proposed actuator and control strategy significantly improve the accuracy, stability, and efficiency of target plate operations under complex working conditions, providing a reliable solution for automated divertor maintenance in tokamak devices.
Sealing systems in space applications must perform reliably under demanding conditions in engineering: cryogenic temperatures, vibration, leakage control, ultra-high vacuum, ionizing radiation, abrasive particulates, and repeated thermal cycling. Each factor strains conventional sealing technologies. In combination, they can rapidly cause failure in systems where margins are unforgiving and maintenance is impossible. As spacecraft architectures evolve toward longer operational lifetimes and broader mission profiles, sealing requirements continue to tighten. Launch vehicles, satellites, and exploration platforms now operate across wider temperature ranges and in contact with more aggressive propellants and media. As a result, both metal seals and engineered polymer alternatives are evaluated-and selected-against increasingly specific, measurable performance criteria.
As satellites take on more onboard processing - from Earth imaging to autonomy - spacecraft computing designers are pushing for higher performance under tight thermal and radiation constraints. Here's how suppliers are approaching heat removal, radiation mitigation and production-scale space-grade computing for LEO and beyond.
Just as the world needs the next generation of nuclear reactors to meet climate goals, it needs new options for safely and securely storing radioactive materials. As of 2022, there were approximately 400,000 metric tons of spent fuel being stored globally, with roughly 90,000 tons in the U.S. alone.
Soft robot systems demonstrate exceptional load-bearing capacity and spatial compliance during operation, with transformative potential in disaster response scenarios requiring adaptive morphology and hazardous material manipulation. By integrating the complementary advantages of soft robotics and particle jamming mechanisms, this study proposes a real-time variable-stiffness soft actuator, while systematically investigating its mathematical modeling framework and stiffness modulation principles. A deformation model for the variable stiffness soft actuator is established, followed by static analysis of the variable-stiffness members using particle jamming theory, with theoretical investigation of their stress distributions. Subsequently, a variable-stiffness driver was fabricated via additive manufacturing (3D printing), resulting in a flexible mechanical digit capable of stiffness tuning, A soft mechanical hand grasping test platform was built, and grasping experiments of objects of different shapes and sizes were conducted. Experimental validation confirms the influence of actuator dimensions, particle characteristics, and granule size distribution on both stress states and bending angles at the soft robotic digit’s distal segment. The obtained results establish theoretical foundations and advance variable-stiffness soft robotics research and associated stiffness regulation methodologies.
This study presents a fully integrated, vehicle-level thermal management model for gasoline fuel tanks, designed to predict transient fuel temperatures, tank wall heating, and vapor generation under real-world driving conditions. The model simulates coupled thermal contributions from exhaust radiation, transient underbody airflow, conductive heat transfer, in-tank pump heating, and dynamic changes in fuel composition and level. Validation against on-road measurements shows strong agreement for fuel temperature and vapor flow profiles. Results confirm that exhaust radiative heating is the dominant thermal load, particularly during the post-shutdown heat soak period. A well-designed heat shield reduced peak tank wall temperature by approximately 27 °C, significantly lowering fuel heating and evaporation. Parametric analysis indicates that while fuel Reid Vapor Pressure (RVP) and tank material influence evaporation, their effect is secondary to external heat mitigation. While this model employs simplifications, such as assuming a uniform bulk fuel temperature and using empirically based convective correlations, these assumptions proved adequate for vehicle-level thermal management analysis. This adequacy is supported by the strong correlation between the model’s predictions and experimental field data across realistic driving scenarios. As a practical tool, the model successfully supports the optimization of thermal protection strategies and guides heat shield design decisions. Future work to incorporate measurement uncertainties, localized thermal stratification, and experimental validation of vapor composition would further strengthen predictive accuracy and extend the model's applicability to more detailed design phases.
The Audio system is an important part of the design of a vehicle cabin. In the vehicle development process, the audio system needs to be tuned for optimal acoustic performance. Traditionally, this process is performed physically on vehicles. In this paper, a methodology is developed to numerically simulate the acoustic performance of the audio system across the full audible frequency range. To provide validation of the method, the p/v acoustic transfer functions (ie., the sound pressure p at the passengers’ ears divided by the voltage inputs v) are measured for different speakers in a production vehicle. As the sound perceived by the passengers depends on both the source and the path, the method development is split into two parts: (a) characterization of parameters that describe the loudspeaker as a source and (b) representation of the vehicle cabin as a path. The speaker parameters are characterized from sound radiation data measured in a 2pi chamber. To represent the vehicle cabin, a hybrid BEM-SEA model is utilized in which the cabin is fully deterministic below 1kHz and is statistical between 1 kHz and 20 kHz. The speaker model is then integrated into the cabin model in order to predict the acoustic transfer functions. This model accounts for two-way coupling between the speakers and the cabin. The results show that the predicted transfer functions are in very good agreement with the data from acoustic measurements. Therefore, performing the audio tuning virtually by numerical simulation is a feasible solution for the industry.
A newly developed tool could enable more control over how energetic materials function throughout manufacturing processes. Purdue University, West Lafayette, IN Much like baking the perfect cake involve s following a list of ingredients and instructions, manufacturing energetic materials - explosives, pyrotechnics and propellants - requires precise formulations, conditions and procedures to ensure they are safe and perform as intended. Because any small tweaks or environmental changes can dramatically alter how energetic materials function, Purdue University engineer Monique McClain is developing state-of-the-art tools and methods to control these materials' behavior throughout the manufacturing process and down to the particle level.
To reduce its environmental impact and pollution, the shipping industry is investigating methods to construct more lightweight ships. One potential method is using adhesive bonding techniques to replace traditional welding and riveted joints on ships to fabricate lighter ships with smaller carbon footprints. However, adhesives age and deteriorate when exposed to moisture, high temperatures, and ultraviolet light. This makes it necessary to understand how they age in maritime environments to determine whether they can truly replace traditional welding techniques.
Much like baking the perfect cake involves following a list of ingredients and instructions, manufacturing energetic materials — explosives, pyrotechnics and propellants — requires precise formulations, conditions and procedures to ensure they are safe and perform as intended.
A team of researchers at Penn State have devised a new, streamlined approach to design metasurfaces, a class of engineered materials that can manipulate light and other forms of electromagnetic radiation with just their structures. This rapid optimization process could help manufacture advanced optical systems like camera lenses, virtual reality headsets, holographic imagers and more, the team said.
The HVAC (Heating, Ventilation, and Air conditioning) system is designed to fulfil the thermal comfort requirement inside a vehicle cabin. Human thermal comfort primarily depends upon an occupant’s physiological and environmental condition. Vehicle AC performance is evaluated by mapping air velocity and local air temperature at various places inside the cabin. There is a need to have simulation methodology for cabin heating applications for cold climate to assess ventilation system effectiveness considering thermal comfort. Thermal comfort modelling involves human manikin modeling, cabin thermal model considering material details and environmental conditions using transient CAE simulation. Present study employed with LBM (Lattice-Boltzmann Method) based PowerFLOW solver coupled with finite element based PowerTHERM solver to simulate the cabin heat up. Human thermal comfort needs physiological modelling; thus, the in-built Berkeley human comfort library is used in simulation. Human thermal modelling includes metabolic rate of heat production with effects of clothing in external ambient conditions. Once human thermal modelling in a controlled environment stabilized, LBM-based solver used to predict the convective heat transfer phenomenon. Thereafter, conduction and radiation effects were solved using a coupling approach in PowerTHERM. Physical tests conducted in a controlled environment of climate chambers. Simulation results obtained correlated with experimental data. Occupants’ thermal comfort evaluated using the Berkeley comfort model. The current process further highlights the impact of heater capacity variation on in-cabin air temperature and passenger comfort level. The proposed method is helpful in thermal comfort prediction for passenger vehicles at cold ambient comfort requirements, heater capacity, and airflow delivery system effectiveness. Current process is found more effective where heater capacity and thermal comfort balance prediction are sensitive to two heaters, discussed in this paper.
Environmental pollution is one of the growing concerns of our society. As vehicle emissions are a major contributor to air pollution, emission control is a primary goal of the Automotive industry. Vehicle emissions are higher due to improper combustion, which leads to toxic gases being generated from the exhaust system. Unburnt fuel is one of the leading causes of toxic pollutants such as Carbon Monoxide, Nitric Oxides (NOx) and Hydrocarbons. The catalytic converter converts these gases into less toxic substances such as Carbon Dioxide, Nitrogen, and water vapor. The catalytic converter performs efficiently after reaching its “Light Off” temperature, after which the catalyst becomes active. Hence, elevated temperature of the exhaust gases aids in efficient conversion. Presently, the gases from the exhaust system are approximately at a temperature of 300°C-600°C. This paper outlines the concept of a Peltier (Thermoelectric) Module - based system, which helps maintain the high temperature of the exhaust gases prior to entering the catalytic converter. Peltier Modules are thermoelectric devices well-known for their usage in heating/cooling applications. The proposed system includes a chamber in which the Peltier Module is embedded. As the gases flow through the chamber, the embedded Peltier Module, which is powered by the battery, increases the temperature inside the chamber. Therefore, with this concept, the components required to heat the catalytic converter could be potentially reduced, since the exhaust gases will be maintained at the targeted temperature required for better emission control. Moreover, the Peltier Module is also known to be used for electricity generation. Consequently, by generating electricity through heat utilization on the surface of the chamber, we provide an added benefit of this proposed concept. This can be achieved by mounting the Peltier Module on the hot surface of the chamber. The other side of the Peltier Module is exposed to ambient air and thereby a potential difference is created through the Seebeck Effect.
Researchers are exploring new ways to utilize microwave technology in monitoring and assessing health conditions. The results of experiments conducted with realistic models are promising. Bras that detect breast cancer, leg sleeves that identify blood clots, and a helmet that monitors the effects of radiation therapy offer a glimpse into what future healthcare might look like.
In order to ensure the safety of urban rail transit, X-ray baggage inspection systems have been widely used. However, at the current, baggage inspection mainly relies on manual judgment, which has the problems of high labor cost, low efficiency and lack of objectivity. This paper aims to realize intelligent and automated baggage inspection and studies and designs a dangerous goods detection method based on spatial feature transformation network. This method adopts target detection technology based on Anchor mechanism, and transforms high-level semantic features spatially spatial transformation network layer. The feature pyramid fusion approach is employed to integrate the transformed high-level semantic features with the low-level detailed features, resulting in the generation of multi-scale features that are rich in semantic information. This innovative method enables accurate prediction of the categories and precise localization of various hazardous items within X-ray images, thereby significantly enhancing the detection accuracy.It proves the feasibility of using intelligence algorithms for automatic security inspection, and provides strong support for the implementation of automatic monitoring of urban rail transit baggage inspection.
In order to meet the demand for the transformation of traditional manufacturing industries into intelligent manufacturing, a virtual monitoring system for the production workshops of nuclear - key products has been built. There are problems such as poor environment, long distance and remote collaborative office in this production workshop, and managers lack information tools to master the workshop status in real time. In order to minimize the harm of nuclear radiation to the human body, in view of the problems of low transparency, poor real - time performance and low data integration in traditional two - dimensional forms, configuration software and video monitoring, a remote monitoring system for virtual workshops driven by digital models has been developed. This system realizes the remote dynamic display of real - time information in the workshop based on data collection and three - dimensional modeling technologies. Virtual monitoring technology improves the management efficiency of the workshop and provides new kinetic energy for the digital and intelligent development of the production workshop.
Plastic materials are used for a wide variety of spacecraft applications including seals, bearings, fasteners, electrical insulators, thermal isolators, and radomes. Selecting plastics for use in space is complex due to wide operating temperature ranges, vacuum conditions, and exposure to radiation and atomic oxygen. Additionally, some spacecraft applications require sealing flammable propellants such as hydrogen and oxygen. This article will present some design considerations when selecting plastics for use in spacecraft. It will provide rich data on the performance characteristics of plastics as well as examples of successful spacecraft applications.
The increased functionality of today’s medical devices is astounding. Optical devices, for example, analyze chemicals, toxins, and biologic specimens. Semiconductor devices sense, analyze, and communicate. Microelectromechanical system (MEMS) devices utilize inertial methods to detect motion, direct light, and move components over short distances. Radiofrequency (RF) devices communicate wirelessly to other devices directly and remotely over the Internet. Handheld acoustic devices scan the body and build a virtual 3D model that shows conditions in the body. The innovation currently happening in the medical device industry is staggering, limited only by imagination and finding technical methods to implement the vision.
Researchers have developed a new type of sensor platform using a gold nanoparticle array. The sensor is made up of a series of gold disk-shaped nanoparticles on a glass slide. When an infrared laser is pointed at a precise arrangement of the particles, they start to emit unusual amounts of ultraviolet (UV) light.
This study presents a novel methodology for optimizing the acoustic performance of rotating machinery by combining scattered 3D sound intensity data with numerical simulations. The method is demonstrated on the rear axle of a truck. Using Scan&Paint 3D, sound intensity data is rapidly acquired over a large spatial area with the assistance of a 3D sound intensity probe and infrared stereo camera. The experimental data is then integrated into far-field radiation simulations, enabling detailed analysis of the acoustic behavior and accurate predictions of far-field sound radiation. This hybrid approach offers a significant advantage for assessing complex acoustic sources, allowing for quick and reliable evaluation of noise mitigation solutions.
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