CRESCENDO Full virtuality in design and product development within the extended enterprise Naples,...

CRESCENDOCRESCENDO

Full virtuality in design and product development Full virtuality in design and product development within the extended enterprisewithin the extended enterprise

Naples, 28 Nov. 2007

INASCOINASCOA high-technology privately held industrial SME founded in 1989

Areas of expertise:

A high-technology privately held industrial SME founded in 1989

Areas of expertise:

Company overview:• 20 Top rate researchers/developers• Multidisciplinary expertise: Process Monitoring Sensors, Composites Manufacturing, Materials Science, CAD/CAM, Engine Noise Control• 1,5 m€ per annum in the last 2 years invested in New Research Studies and Technologies development• 2 m€ investment on new manufacturing plant for high-end aerospace components (commencing manufacturing activities in 3rd Q 2009)

Company overview:• 20 Top rate researchers/developers• Multidisciplinary expertise: Process Monitoring Sensors, Composites Manufacturing, Materials Science, CAD/CAM, Engine Noise Control• 1,5 m€ per annum in the last 2 years invested in New Research Studies and Technologies development• 2 m€ investment on new manufacturing plant for high-end aerospace components (commencing manufacturing activities in 3rd Q 2009)

INASCO expertise related to CRESCENDOINASCO expertise related to CRESCENDO

CRESCENDO GOALS INASCO EXPERTISE

Virtual Overall Aircraft Model.Virtual Stochastic Life – Cycle Design (VSLCD) platform (1)

Methodology and tools: Uncertainty Management and Decision Support, Unified Analysis

Joint Probabilistic Decision Making (JPDM) technique (2)

Probabilistic Analysis including mechanical, thermal, aerodynamic, noise, weight and cost.

Prometheus software (3)

Engineering Capabilities: Multidisciplinary Investigation of solution field, Early Multidisciplinary Design

Multidisciplinary Design Optimization tools (4)


VSLCD: A novel multidisciplinary environment for design, in which new techniques in such areas as physics-based analysis, uncertainty modeling, prediction, system synthesis, and decision-making are integrated.

VSLCD: A novel multidisciplinary environment for design, in which new techniques in such areas as physics-based analysis, uncertainty modeling, prediction, system synthesis, and decision-making are integrated.

Challenge of next – generation systems design: Traditional methodologies are becoming ineffective for designing complex systems that meet multiple goals and disciplines.

Manufacturing and Inspection related issues must be considered in concert with product Performance in the presence of Uncertainty.

Descriptor Meaning

Virtual Physics – based system Life – Cycle prediction

Stochastic Time – varying uncertainty is modeled

Life-Cycle Design, Manufacturing, Operational Simulation and Inspection.

Descriptor Meaning

Virtual Physics – based system Life – Cycle prediction

Stochastic Time – varying uncertainty is modeled

Life-Cycle Design, Manufacturing, Operational Simulation and Inspection.

1. Development of Virtual Stochastic Life-Cycle Design (VSLCD) platform1. Development of Virtual Stochastic Life-Cycle Design (VSLCD) platform


NDI

Manufacturing Manufacturing & real – time & real – time monitoringmonitoring

Life Prediction

JPDM

NDI Simulation NDI Simulation PlatformPlatform

Probabilistic Life – Probabilistic Life – Cycle Prediction Cycle Prediction

FrameworkFramework

Virtual Virtual Stochastic Life – Stochastic Life –

Cycle DesignCycle Design

Probabilistic association of Manufacturing

process parameters to Defects generation.

Manufacturing Process

Monitoring Data for Structural

Analysis.

Life Prediction incorporating

Manufacturing and NDI Data / Parameters.

Life – Cycle Design Optimization using Joint Probabilistic Decision Making.

1. Development of Virtual Stochastic Life-Cycle Design (VSLCD) platform1. Development of Virtual Stochastic Life-Cycle Design (VSLCD) platform


ManufacturingManufacturing

Process Process MonitoringMonitoring

InspectionsInspections

Solid MechanicsSolid Mechanics

Collaborative Collaborative MethodsMethods

SubsystemsSubsystems

Virtual Virtual ManufacturingManufacturing

Monitoring SystemMonitoring System

NDI TechniquesNDI Techniques

(Stochastic) FEM(Stochastic) FEM

Life PredictionLife PredictionPrediction ModelsPrediction Models

IntegrationIntegration

MDOMDO

QualityQualityPLCPFPLCPF

Uncertainty Uncertainty PropagationPropagation Probabilistic Probabilistic

MethodsMethods

1. Problem Formulation: Determination of the Design Space Topology• Configurable quantities (variables), bounds, distributions.

• Non – configurable quantities (parameters), distributions.

• Constraints between variables / parameters.

• User – defined Criteria as an implicit or explicit function of variables / parameters

1. Problem Formulation: Determination of the Design Space Topology• Configurable quantities (variables), bounds, distributions.

• Non – configurable quantities (parameters), distributions.

• Constraints between variables / parameters.

• User – defined Criteria as an implicit or explicit function of variables / parameters

1. Development of Virtual Stochastic Life-Cycle Design (VSLCD) methodology1. Development of Virtual Stochastic Life-Cycle Design (VSLCD) methodology














MDOMDO

Probabilistic LPProbabilistic LPPLCPFPLCPF

UncertaintiesUncertaintiesProbabilistic Probabilistic

MethodsMethods

2. Life-Cycle ModelingAnalysis tools (statistical, or physics-based) that are used to assess the contribution of each phase to the integrated framework. More detailed:

• Process monitoring provides useful output (uncertainties, possible defects) to be used for the optimal configuration of the manufacturing parameters (temperature, pressure, sensor topology, etc).

• NDI techniques provide probabilistic information on the defect parameters (type, amount, size, etc) to be used for Life Prediction and correlation with Manufacturing parameters.

• Structural Analysis packages and methods that are able to handle probabilistic input.

• Probabilistic Life Assessment by incorporating Manufacturing, NDI and Life Prediction Models.

• Probabilistic Methods that are employed to evaluate the propagation of uncertainties over time as well as the statistical correlation of different quantities.

• MDO methods make use of the interaction between different disciplines that are created by the breakdown of the structural system into subsystems.

2. Life-Cycle ModelingAnalysis tools (statistical, or physics-based) that are used to assess the contribution of each phase to the integrated framework. More detailed:

• Process monitoring provides useful output (uncertainties, possible defects) to be used for the optimal configuration of the manufacturing parameters (temperature, pressure, sensor topology, etc).

• NDI techniques provide probabilistic information on the defect parameters (type, amount, size, etc) to be used for Life Prediction and correlation with Manufacturing parameters.

• Structural Analysis packages and methods that are able to handle probabilistic input.

• Probabilistic Life Assessment by incorporating Manufacturing, NDI and Life Prediction Models.

• Probabilistic Methods that are employed to evaluate the propagation of uncertainties over time as well as the statistical correlation of different quantities.

• MDO methods make use of the interaction between different disciplines that are created by the breakdown of the structural system into subsystems.















MDOMDO



MethodsMethods

3. Integration: Operates on the knowledge produced by Analysis Models and Problem Formulation to generate design options that will be evaluated at “Decision Making” stage.

3. Integration: Operates on the knowledge produced by Analysis Models and Problem Formulation to generate design options that will be evaluated at “Decision Making” stage.















MDOMDO



MethodsMethods

4. Decision Making: The “core module”

JPDM: Joint Probabilistic Decision Making technique.

A design optimization method that maximizes the Probability of satisfying all design Criteria (manufacturing cost, structural strength, weight, Probability of Defect, etc.) simultaneously.

4. Decision Making: The “core module”

JPDM: Joint Probabilistic Decision Making technique.

A design optimization method that maximizes the Probability of satisfying all design Criteria (manufacturing cost, structural strength, weight, Probability of Defect, etc.) simultaneously.



JPDM is an in-house Probabilistic Multi – Criteria Decision Making and Optimisation tool. It maximizes the Probability of Success, POS of a set of Criteria simultaneously by taking account Uncertainties arising from the environment or structure. JPDM can be applied on every Design phase (Conceptual, Preliminary, Detailed) as long as system models and uncertainty information are available in any format.

JPDM is an in-house Probabilistic Multi – Criteria Decision Making and Optimisation tool. It maximizes the Probability of Success, POS of a set of Criteria simultaneously by taking account Uncertainties arising from the environment or structure. JPDM can be applied on every Design phase (Conceptual, Preliminary, Detailed) as long as system models and uncertainty information are available in any format.

Methodology Steps:

Criteria Definition – Weighting

Variables/Parameters &

Distributions definition

Simulation

Joint Probability Distribution

Evaluation

Decision Making

Change of target values

(trade – off procedure)

Methodology Steps:

Criteria Definition – Weighting

Variables/Parameters &

Distributions definition

Simulation

Joint Probability Distribution

Evaluation

Decision Making

Change of target values

(trade – off procedure)

2. Implementation of Joint Probabilistic Decision Making technique2. Implementation of Joint Probabilistic Decision Making technique


Benefits:

Uncertainties are taken into account due to method’s Probabilistic nature.

Multi-criteria information into a single objective (Probability of Success).

Enables requirements trade – off studies.

Benefits:

Uncertainties are taken into account due to method’s Probabilistic nature.

Multi-criteria information into a single objective (Probability of Success).

Enables requirements trade – off studies.

Case study: NACRE Project (New Aircraft Concepts Research)

Evaluation of Cabin – Structural – Aerodynamic concepts.

Engine positioning optimization.

Manufacture-driven wing optimization.

Assessment of the potential for economics and time reduction

in the manufacture and maintenance of wing.

In – side - out approach (cabin – skin – skeleton) is adopted and different cabin concepts are under

investigation and comparison.

2. Implementation of Joint Probabilistic Decision Making technique2. Implementation of Joint Probabilistic Decision Making technique


ADMIRE

Prometheus Software is an in-house Probabilistic Design software tool. Its modules have been successfully applied on various probabilistic structural analysis problems such as: i) fatigue crack growth Reliability and Sensitivity Analysis and ii) ageing prediction of various aircraft components (Ref: ADMIRE, RAMGT, TATEM).

Prometheus Software is an in-house Probabilistic Design software tool. Its modules have been successfully applied on various probabilistic structural analysis problems such as: i) fatigue crack growth Reliability and Sensitivity Analysis and ii) ageing prediction of various aircraft components (Ref: ADMIRE, RAMGT, TATEM).

3. PROMETHEUS Software: Reliability Analysis - Sensitivity3. PROMETHEUS Software: Reliability Analysis - Sensitivity


.

.

Optimal Latin Hypercube and Kriging Surrogate Model comprise “state of the art” tools for MDO (HISAC project)

Implementation of advanced modules applied on Multidisciplinary Design Optimisation (projects: HISAC, MUSCA).

“State of the art” Sampling techniques (Quasi – Random Monte Carlo, Optimal Latin

Hypercube), Surrogate Models (Voronoi Tessellation , Kriging), and robust Evolutionary Optimization algorithms are mainly used for Optimizing complex systems with a large amount of variables using a relatively low amount of high – fidelity information.

Implementation of advanced modules applied on Multidisciplinary Design Optimisation (projects: HISAC, MUSCA).

“State of the art” Sampling techniques (Quasi – Random Monte Carlo, Optimal Latin

Hypercube), Surrogate Models (Voronoi Tessellation , Kriging), and robust Evolutionary Optimization algorithms are mainly used for Optimizing complex systems with a large amount of variables using a relatively low amount of high – fidelity information.

MUSCA

4. Multidisciplinary Design Optimization tools4. Multidisciplinary Design Optimization tools

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