Honeywell International Inc.

Emc IP Holding Company LLC

Avinash Kumar

Elango Chockalingam

Mahesh Reddy Appireddygari Venkataramana

Theodore S Brockett

Srinivas Chinthapally

Anil Kumar Thokala

Milind Khare

Venkatarao Ganji

Jayanth Hirebelaguly Shivaprakash

Pavankumar Kadam

Akhil Thampi

Manjunath Subbarayachar

Rama Sateesh Venkata Kandula

Krishna Mohan Kumbham

Gnanaraj Devadoss

Jayanth Hs

A bearing assembly includes an inner race to receive a rotating component having an axis of rotation, and at least one bearing element coupled to the inner race. The at least one bearing element is to rotate with the inner race along the axis of rotation. The bearing assembly includes an outer race coupled to the at least one bearing element, and a bearing housing coupled to the outer race and to be coupled to a static structure. The bearing housing includes a first frangible portion to release upon receipt of a moment load greater than a moment threshold and a second frangible portion to release upon receipt of a radial load greater than a radial threshold.

Bearing assembly with load isolation for gas turbine engine

Spacers, structural support systems that includes the spacers, and method of coupling components therewith are provided. The spacers may include a body having oppositely disposed ends, cylindrical sidewalls extending therebetween, and radially outward extending flanges at the ends. Inner surfaces of the sidewalls define a longitudinal bore with openings at each of the ends. The bore is configured to receive a shank of a fastener such that the body is secured between a head of the fastener and a structure to which the fastener is secured. The spacer is formed from a super-elastic shape memory alloy that allows for recoverable deformation without failing when at or above a transformation temperature thereof. The sidewalls have an initial shape prior to undergoing deformation, undergo the recoverable deformation when a load thereon exceeds a first threshold, and recover back to the initial shape once the load drops below a second threshold.

Spacers, structural support systems, and methods of use thereof

A frangible strut for a gas turbine engine includes a first end to couple to a first structure of the gas turbine engine, and a second end opposite the first end. The second end is to couple to a second structure of the gas turbine engine. The second end includes an outer portion coaxial with and spaced radially apart from an inner portion. The inner portion is to receive a fastening device to couple the second end to the second structure. The outer portion is coupled to a body of the frangible strut. The inner portion is interconnected to the outer portion by a plurality of frangible members that are spaced apart about a perimeter of the inner portion, and each of the frangible members are configured to release the inner portion from the outer portion. The frangible strut includes the body interconnecting the first end and the second end.

Frangible strut for gas turbine engine

A method for performing a backup operation includes obtaining a backup request, wherein the backup request specifies a virtual machine (VM) set, and, in response to the backup request: identifying a first set of virtual machines (VMs), wherein the first set of VMs comprises a first portion of the VM set, performing a criticality analysis on the first set of VMs using a machine learning algorithm and a trained machine learning model to obtain an ordered list of critical VMs, performing a non-critical VM prioritization on a second set of VMs using the machine learning algorithm and the trained machine learning model to obtain an ordered list of non-critical VMs, consolidating the ordered list of critical VMs and the ordered list of non-critical VMs to obtain a final list, and initiating a backup of virtual machines using the final list.

System and method to achieve virtual machine backup load balancing using machine learning

A method for performing a restoration operation includes obtaining a restoration request, wherein the restoration request specifies a virtual machine (VM) set to be restored, and in response to the restoration request: identifying a first set of virtual machines (VMs), performing a critical VM prioritization on the first set of VMs using a trained machine learning model to obtain an ordered list of critical VMs, performing a non-critical VM prioritization on a second set of VMs using the trained machine learning model to obtain an ordered list of non-critical VMs, consolidating the ordered list of critical VMs and the ordered list of non-critical VMs to obtain a final list, and initiating a restoration of virtual machines using the final list.

System and method for a machine learning based smart restore mechanism

A turbine engine has an engine case with an inlet and an interior of the engine. A bearing is disposed on a portion of the engine case. A fan is disposed within the engine and rotates with operation of the engine. The fan includes a plurality of fan blades, and each fan blade is secured at a first end to a disk. A second end of each fan blade is disposed adjacent to the bearing. The portion is oriented relative to the fan such that a reaction force of a fan blade impacting the bearing is directed axially or away from the inlet.

Engine case for fan blade out retention

Exemplary embodiments are provided to reduce the high transient mount load in an aircraft engine mounting system under extreme loading condition. The exemplary embodiments reduce the impact of the snubbing phenomenon without adding to the weight or space claim of the engine mounting system.

Growing community of inventors

Bengaluru, India

Avinash Kumar