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Mixed convection flow over heat source modules mounted on a horizontal plate has been studied numerically. The present analysis is valid when the buoyancy force effects are small compared to forced convection effects. The mixed convection flow problem is formulated by two-dimensional incompressible flow with the buoyancy term represented by the Boussinesq approximation. The governing equations are solved in the stream function and vorticity formulation using high accuracy finite difference schemes. Results are reported for single and two heat source modules mounted on a horizontal plate with and without thickness. The effects of induced velocity, heat input, thickness, and location of heat source modules on heat transfer rate and thermal field in the vicinity of heat sources are reported. Results have shown that there is a decrease of maximum temperature on heat source modules with increase in free-stream velocity of the fluid. Results show that the change of location and thickness of heat source modules has a significant effect on thermal flow field characteristics. The maximum temperature and average Nusselt number on heat source modules are reported at different parameters. The reported results agreed well with experimental results available in the literature.

Mixed convection flow over heat source modules mounted on a horizontal plate has been studied numerically. The present analysis is valid when the buoyancy force effects are small compared to forced convection effects. The mixed convection flow problem is formulated by two-dimensional incompressible flow with the buoyancy term represented by the Boussinesq approximation. The governing equations are solved in the stream function and vorticity formulation using high accuracy finite difference schemes. Results are reported for single and two heat source modules mounted on a horizontal plate with and without thickness. The effects of induced velocity, heat input, thickness, and location of heat source modules on heat transfer rate and thermal field in the vicinity of heat sources are reported. Results have shown that there is a decrease of maximum temperature on heat source modules with increase in free-stream velocity of the fluid. Results show that the change of location and thickness of heat source modules has a significant effect on thermal flow field characteristics. The maximum temperature and average Nusselt number on heat source modules are reported at different parameters. The reported results agreed well with experimental results available in the literature.

Mixed convection flow over heat source modules mounted on a horizontal plate has been studied numerically. The present analysis is valid when the buoyancy force effects are small compared to forced convection effects. The mixed convection flow problem is formulated by two-dimensional incompressible flow with the buoyancy term represented by the Boussinesq approximation. The governing equations are solved in the stream function and vorticity formulation using high accuracy finite difference schemes. Results are reported for single and two heat source modules mounted on a horizontal plate with and without thickness. The effects of induced velocity, heat input, thickness, and location of heat source modules on heat transfer rate and thermal field in the vicinity of heat sources are reported. Results have shown that there is a decrease of maximum temperature on heat source modules with increase in free-stream velocity of the fluid. Results show that the change of location and thickness of heat source modules has a significant effect on thermal flow field characteristics. The maximum temperature and average Nusselt number on heat source modules are reported at different parameters. The reported results agreed well with experimental results available in the literature.

Mixed convection flow over heat source modules mounted on a horizontal plate has been studied numerically. The present analysis is valid when the buoyancy force effects are small compared to forced convection effects. The mixed convection flow problem is formulated by two-dimensional incompressible flow with the buoyancy term represented by the Boussinesq approximation. The governing equations are solved in the stream function and vorticity formulation using high accuracy finite difference schemes. Results are reported for single and two heat source modules mounted on a horizontal plate with and without thickness. The effects of induced velocity, heat input, thickness, and location of heat source modules on heat transfer rate and thermal field in the vicinity of heat sources are reported. Results have shown that there is a decrease of maximum temperature on heat source modules with increase in free-stream velocity of the fluid. Results show that the change of location and thickness of heat source modules has a significant effect on thermal flow field characteristics. The maximum temperature and average Nusselt number on heat source modules are reported at different parameters. The reported results agreed well with experimental results available in the literature.

It is notoriously hard to develop dependable distributed systems. This is partly due to the difficulties in foreseeing various corner cases and failure scenarios while implementing a system that will be deployed over an asynchronous network. In contrast, reasoning about the desired distributed system behavior and the corresponding invariants is easier than reasoning about the code itself. Further, the invariants can be used for testing, theorem proving, and runtime enforcement. In this paper, we propose an approach to observe the system behavior and automatically infer invariants which reveal implementation bugs. Using our tool, Avenger, we automatically generate a large number of potentially relevant properties, check them within the time and spatial domains using traces of system executions, and filter out all but a few properties before reporting them to the developer. Our key insight in filtering is that a good candidate for an invariant is the one that holds in all but a few cases, i.e., an “almost-invariant”. Our experimental results with the XORP BGP implementation demonstrate Avenger’s ability to identify the almost-invariants that lead the developer to programming errors., QC 20140704

It is notoriously hard to develop dependable distributed systems. This is partly due to the difficulties in foreseeing various corner cases and failure scenarios while implementing a system that will be deployed over an asynchronous network. In contrast, reasoning about the desired distributed system behavior and the corresponding invariants is easier than reasoning about the code itself. Further, the invariants can be used for testing, theorem proving, and runtime enforcement. In this paper, we propose an approach to observe the system behavior and automatically infer invariants which reveal implementation bugs. Using our tool, Avenger, we automatically generate a large number of potentially relevant properties, check them within the time and spatial domains using traces of system executions, and filter out all but a few properties before reporting them to the developer. Our key insight in filtering is that a good candidate for an invariant is the one that holds in all but a few cases, i.e., an “almost-invariant”. Our experimental results with the XORP BGP implementation demonstrate Avenger’s ability to identify the almost-invariants that lead the developer to programming errors., QC 20140704

It is notoriously hard to develop dependable distributed systems. This is partly due to the difficulties in foreseeing various corner cases and failure scenarios while implementing a system that will be deployed over an asynchronous network. In contrast, reasoning about the desired distributed system behavior and the corresponding invariants is easier than reasoning about the code itself. Further, the invariants can be used for testing, theorem proving, and runtime enforcement. In this paper, we propose an approach to observe the system behavior and automatically infer invariants which reveal implementation bugs. Using our tool, Avenger, we automatically generate a large number of potentially relevant properties, check them within the time and spatial domains using traces of system executions, and filter out all but a few properties before reporting them to the developer. Our key insight in filtering is that a good candidate for an invariant is the one that holds in all but a few cases, i.e., an “almost-invariant”. Our experimental results with the XORP BGP implementation demonstrate Avenger’s ability to identify the almost-invariants that lead the developer to programming errors., QC 20140704

It is notoriously hard to develop dependable distributed systems. This is partly due to the difficulties in foreseeing various corner cases and failure scenarios while implementing a system that will be deployed over an asynchronous network. In contrast, reasoning about the desired distributed system behavior and the corresponding invariants is easier than reasoning about the code itself. Further, the invariants can be used for testing, theorem proving, and runtime enforcement. In this paper, we propose an approach to observe the system behavior and automatically infer invariants which reveal implementation bugs. Using our tool, Avenger, we automatically generate a large number of potentially relevant properties, check them within the time and spatial domains using traces of system executions, and filter out all but a few properties before reporting them to the developer. Our key insight in filtering is that a good candidate for an invariant is the one that holds in all but a few cases, i.e., an “almost-invariant”. Our experimental results with the XORP BGP implementation demonstrate Avenger’s ability to identify the almost-invariants that lead the developer to programming errors., QC 20140704

It is notoriously hard to develop dependable distributed systems. This is partly due to the difficulties in foreseeing various corner cases and failure scenarios while implementing a system that will be deployed over an asynchronous network. In contrast, reasoning about the desired distributed system behavior and the corresponding invariants is easier than reasoning about the code itself. Further, the invariants can be used for testing, theorem proving, and runtime enforcement. In this paper, we propose an approach to observe the system behavior and automatically infer invariants which reveal implementation bugs. Using our tool, Avenger, we automatically generate a large number of potentially relevant properties, check them within the time and spatial domains using traces of system executions, and filter out all but a few properties before reporting them to the developer. Our key insight in filtering is that a good candidate for an invariant is the one that holds in all but a few cases, i.e., an “almost-invariant”. Our experimental results with the XORP BGP implementation demonstrate Avenger’s ability to identify the almost-invariants that lead the developer to programming errors., QC 20140704