Abstract: Presented are methods for determining computational flow for a data-dependent constraint network that are especially useful for modeling and trading off alternative configurations in a single computational environment, during design analysis and optimization of an engineering system. These methods leverage a preexisting constraint management system that uses a bipartite graph of variables and constraints to model an engineering system and employ logic formulae based world sets to determine the applicability of the constraints to different system configurations. These methods ensure that a data-dependent constraint network is in a consistent state, and are the essential foundation for other techniques that rely on a consistent constraint network to produce computational plans for propagating values and uncertainties through the constraint network during tradeoff analyses.

Abstract: Presented are rapid and robust techniques for estimating the uncertainty in product attributes (performance, cost, etc.) during the multi-disciplinary design and analysis phase of the product life-cycle. The techniques leverage the capabilities of a preexisting constraint management system that may be used to calculate performance and cost metrics of an engineering system as a function of the design structure and operational scenarios. The techniques are particularly useful when the constraint management system is used to automate the reverse computation required when the analyst specifies cost, schedule, or performance targets using approaches such as cost as independent variable. Disclosed techniques may also be applied to constraint management systems that include compound-valued variables.

Abstract: Presented are rapid and robust techniques for estimating the uncertainty in product attributes (performance, cost, etc.) during the multi-disciplinary design and analysis phase of the product life-cycle. The techniques leverage the capabilities of a preexisting constraint management system that may be used to calculate performance and cost metrics of an engineering system as a function of the design structure and operational scenarios. The techniques are particularly useful when the constraint management system is used to automate the reverse computation required when the analyst specifies cost, schedule, or performance targets using approaches such as cost as independent variable. Disclosed techniques may also be applied to constraint management systems that include compound-valued variables.

Abstract: Presented are methods for determining computational flow for a data-dependent constraint network that are especially useful for modeling and trading off alternative configurations in a single computational environment, during design analysis and optimization of an engineering system. These methods leverage a preexisting constraint management system that uses a bipartite graph of variables and constraints to model an engineering system and employ logic formulae based world sets to determine the applicability of the constraints to different system configurations. These methods ensure that a data-dependent constraint network is in a consistent state, and are the essential foundation for other techniques that rely on a consistent constraint network to produce computational plans for propagating values and uncertainties through the constraint network during tradeoff analyses.

Abstract: Presented are rapid and robust techniques for estimating the uncertainty in product attributes (performance, cost, etc.) during the multi-disciplinary design and analysis phase of the product life-cycle. The techniques leverage the capabilities of a preexisting constraint management system that may be used to calculate performance and cost metrics of an engineering system as a function of the design structure and operational scenarios. The techniques are particularly useful when the constraint management system is used to automate the reverse computation required when the analyst specifies cost, schedule, or performance targets using approaches such as cost as independent variable. Disclosed techniques may also be applied to constraint management systems that include compound-valued variables.

Abstract: Presented are rapid and robust techniques for estimating the uncertainty in product attributes (performance, cost, etc.) during the multi-disciplinary design and analysis phase of the product life-cycle. The techniques leverage the capabilities of a preexisting constraint management system that may be used to calculate performance and cost metrics of an engineering system as a function of the design structure and operational scenarios. The techniques are particularly useful when the constraint management system is used to automate the reverse computation required when the analyst specifies cost, schedule, or performance targets using approaches such as cost as independent variable. Disclosed techniques may also be applied to constraint management systems that include compound-valued variables.

Abstract: A method of determining a conditional computational plan for a data dependent constraint network represented by a bipartite graph containing input variable nodes, output variable nodes, and relation nodes, may include specifying, using a variable node specifier, at least one output variable node for which a plan is desired. The method may further include determining, using a plan determiner, a plan from the input variable nodes to the output variable node using a backward chaining search of the bipartite graph.

Abstract: A method of determining a conditional computational plan for a data dependent constraint network represented by a bipartite graph containing input variable nodes, output variable nodes, and relation nodes, may include specifying, using a variable node specifier, at least one output variable node for which a plan is desired. The method may further include determining, using a plan determiner, a plan from the input variable nodes to the output variable node using a backward chaining search of the bipartite graph.

Abstract: A pipeline status database may store information about the current status of a plurality of pipeline portions comprising a pipeline, each pipeline portion being adapted to transport a substance. An intelligent pipeline monitoring platform coupled to the pipeline status database may include a mapping module to automatically determine location information associated with each of the plurality of pipeline portions and a graphical user interface module having access to real world map information. A communication port coupled to intelligent pipeline monitoring platform may transmit information to create for a user a visual representation of the plurality of pipeline portions, including information about the current status of at least one pipeline portion, on a graphical user interface map display in accordance with the location information.

Abstract: A method for simplifying a complex well-formed formula (WFF) may include receiving, in a formula processor, an input WFF and converting the input WFF into an initial bit array. The method may further include simplifying the initial bit array into a simplified bit array by removing predicates from the initial bit array not necessary to represent the input WFF. The method may include converting the simplified bit array into a return WFF in either a conjunctive normal form or a disjunctive normal form.

Abstract: A method for simplifying a complex well-formed formula (WFF) may include receiving, in a formula processor, an input WFF and converting the input WFF into an initial bit array. The method may further include simplifying the initial bit array into a simplified bit array by removing predicates from the initial bit array not necessary to represent the input WFF. The method may include converting the simplified bit array into a return WFF in either a conjunctive normal form or a disjunctive normal form.

Abstract: In one embodiment, a method for troubleshooting a fault to determine a root cause of the fault. A Bayesian network model is created based on information obtained from a Fault Isolation Manual (FIM), where the FIM provides tests to be performed in troubleshooting the fault to determine a root cause of the fault. Heuristics are used to determine a structure and conditional probabilities for the Bayesian network. A plurality of test costs inherent in the FIM are imputed by first generating a plurality of constraints between the cost of each test and fault probabilities that hold for all fault scenarios. A linear programming algorithm is used to solve the plurality of constraints, and to construct a tuned Bayesian network model. The tuned Bayesian network model is used to iteratively rank likely faults according to their probabilities given accumulating test evidence, and to rank pending tests according to their value.

Abstract: Multi-Objective Optimization (MOO) is integrated with a Constraint Management System (CMS) so as to rapidly and flexibly search large design spaces and focus on “interesting” designs as determined by user-specified criteria. A method embeds a trade space and its trade-off envelope within the same CMS network used to calculate the values of the points in the trade space itself. The method supports automatic variation of the resulting trade-off analyses in response to variations in upstream parameters, assumptions, and/or requirements. When feasible, the CMS can back-solve for values of upstream parameters so that selected attributes of the trade-off envelope achieve user-specified values. The coupled use of a CMS with multi-objective optimization avoids having to generate a large set of permutations of inputs and factors and then restricting the analysis to those having desired outputs or of having to manually “reformulate” the equation set so that it solves in the desired direction.

Abstract: In accordance with an embodiment, a method for troubleshooting a fault to determine a root cause of the fault. A Bayesian network model is created based on information obtained from a Fault Isolation Manual (FIM), where the FIM provides tests to be performed in troubleshooting the fault to determine a root cause of the fault. Heuristics are used to determine a structure and conditional probabilities for the Bayesian network. A plurality of test costs inherent in the FIM are imputed by first generating a plurality of constraints between the cost of each test and fault probabilities that hold for all fault scenarios. A linear programming algorithm is used to solve the plurality of constraints, and to construct a tuned Bayesian network model. The tuned Bayesian network model is used to iteratively rank likely faults according to their probabilities given accumulating test evidence, and to rank pending tests according to their value.