Evaluation of Microstructural, Mechanical Properties and Corrosion Behavior of AISI Type 316LN Stainless Steel and Modified 9Cr-1Mo Steel Exposed in a Dynamic Bimetallic Sodium Loop at 798 K (525?°C) for 16,000 Hours

Changes occurring in the chemical composition, microstructure, mechanical properties, and carburization behavior of type 316LN stainless steel and modified 9Cr-1Mo steel on exposure to flowing sodium at 798?K (525?°C) for 16,000?hours in a bimetallic loop are discussed in this article. Type 316LN stainless steel revealed a degraded layer of approximately 5???m depth. No significant microstructural changes were observed in the case of modified 9Cr-1Mo steel exposed to sodium. The carburization depth in type 316LN stainless steel was approximately 100???m and the surface carbon concentration was 0.374?wt?pct. In the case of modified 9Cr-1Mo steel, the carbon concentration at the surface was approximately 3.50?wt?pct and the depth of carburization was nearly 75???m. The concentration of nickel and chromium decreased from the bulk to the surface of type 316LN stainless steel, leading to the formation of a ferrite layer. The concentration of these two elements reached the original matrix concentration at around 30???m. Sodium-exposed material indicated an increase in yield strength by 10?pct and reduction in ductility by 34?pct vis-à-vis annealed material. No such changes in strength and ductility were observed in the case of modified 9Cr-1Mo steel. A decrease in impact energy was noticed for sodium-exposed type 316LN stainless steel and modified 9Cr-1Mo steel vis-à-vis as-received material.     

Trust-based Intelligent Scheme for Mitigating Black Hole Attacks in IoT

Internet of Things (IoT) networks are characterized by a multitude of wireless, interconnected devices that can dynamically join or exit the network without centralized administration or fixed infrastructure for routing. While multipath routing in IoT networks can improve data transmission reliability and load balancing by establishing multiple paths between source and destination nodes, these networks are susceptible to security threats due to their wireless nature. Traditional security solutions developed for conventional networks are often ill-suited to the unique challenges posed by IoT environments. In response to these challenges, this paper proposes the integration of the Ad hoc On-demand Multipath Distance Vector (AOMDV) routing protocol with a trust model to enhance network performance. Key findings from this research demonstrate the successful fusion of AOMDV with a trust model, resulting in tangible improvements in network performance. The assessment of trustworthiness bolsters both security and routing capabilities in IoT networks. The trust model plays a crucial role in mitigating black hole attacks in IoT networks by evaluating the trustworthiness of nodes and helping in the identification and avoidance of malicious nodes that may act as black holes. Simulation results validate the efficacy of the proposed trust-based routing mechanism in achieving its objectives. Trust plays a pivotal role in decision-making and in the creation of secure distribution systems. By assessing the trustworthiness of nodes, both network security and routing efficiency can be enhanced. The effectiveness of the proposed trust-based routing mechanism is scrutinized through simulations, offering insights into its potential advantages in terms of improved network security and routing performance in the context of the IoT.