The document describes the development of a training curriculum called the Aerospace Optimization Academy (AOA) to teach the application of optimization technologies to aerospace design problems. The AOA includes modules on various optimization topics delivered through hands-on exercises using real-world aerospace applications. The modules cover areas like basic and finite element-driven optimization, optimization of composites and joints, and the use of spreadsheet analysis models. Students work through a series of exercises of increasing complexity over approximately 120 hours to gain optimization skills and experience leading to certification.
Airbus - Topology Optimization Methods for Optimal Aircraft ComponentsAltair ProductDesign
Application of Topology, Sizing and Shape Optimization Methods to Optimal Design of Aircraft Components - a Technical Engineering & Analysis Paper from Altair ProductDesign
Leveraging Geometric Shape Complexity, in Optimal Design for Additive Manufac...Altair
Additive manufacturing (AM) technology enables the possibility of realizing highly efficient, optimized structural components with configurations not achievable using conventional manufacturing methods. The Altair and Solid Thinking toolsets provide advanced capabilities to design structural topologies to minimize weight and maximize other performance criteria. However, conventional manufacturing processes require application of design constraints, such as directional access for machining, in the optimization that limit the structural efficiency of the resulting design. AM can remove many of these constraints to allow for more efficient configurations under the applied loading conditions. Case studies show the potential to reduce weight up to 30% for components with applied bending and torsional loads by allowing increased complexity configurations that could only be manufactured additively.
Composite Plate Optimization with Practical Design ConstraintsAltair
Composite free size optimization has the potential to generate weight savings and performance improvements for many applications of composite structures. Key to realizing such improvements is practical application of design and manufacturing constraints in the optimization model.
Airbus - Topology Optimization Methods for Optimal Aircraft ComponentsAltair ProductDesign
Application of Topology, Sizing and Shape Optimization Methods to Optimal Design of Aircraft Components - a Technical Engineering & Analysis Paper from Altair ProductDesign
Leveraging Geometric Shape Complexity, in Optimal Design for Additive Manufac...Altair
Additive manufacturing (AM) technology enables the possibility of realizing highly efficient, optimized structural components with configurations not achievable using conventional manufacturing methods. The Altair and Solid Thinking toolsets provide advanced capabilities to design structural topologies to minimize weight and maximize other performance criteria. However, conventional manufacturing processes require application of design constraints, such as directional access for machining, in the optimization that limit the structural efficiency of the resulting design. AM can remove many of these constraints to allow for more efficient configurations under the applied loading conditions. Case studies show the potential to reduce weight up to 30% for components with applied bending and torsional loads by allowing increased complexity configurations that could only be manufactured additively.
Composite Plate Optimization with Practical Design ConstraintsAltair
Composite free size optimization has the potential to generate weight savings and performance improvements for many applications of composite structures. Key to realizing such improvements is practical application of design and manufacturing constraints in the optimization model.
Optimization of Bolted Joints for Aircraft Engine Using Genetic AlgorithmsIJMER
Genetic Algorithms mimic the evolving technique of nature to better fit populations to a certain environment. Despite this technique has proved its adequacy in several fields, its application in Aerospace is still limited, mostly because of the high quantity of acceptability criteria that the design
must pass and the amount of design parameters. The presented paper explores required GA architecture’s adaptations to be applied in highly restricted systems such as those commonly found in Aerospace applications. The proposed GA was applied to the design of an Aircraft Engine’s Axial Casing bolted joint following static strength restrictions as per FAR 33 regulations. The set of Elitism,
interdependent geometric restrictions, Crossing, and Reproduction modules proved the applicability of
the presented multi-objective GA architecture under 14 restrictions for normal, limit and ultimate loads.
As it is described, the conversion is quickly achieved due to the shortage of the search space; therefore a
modified Variable Crossing per Scheme is proposed to expand the diversity of the genome to compensate
the relatively low impact of the Mutation module. Finally, the process and solutions found were compared against the traditional design process, showing the feasibility of this technique in complex applications in terms of quality of the solution and developing time.
DESIGN AND OPTIMIZATION OF CRITICAL PART OF A ROTARY TABLE USED IN HORIZONTAL...IAEME Publication
Growing industry demands low manufacturing cost, saving of material, low cost material, ease of transportation etc. This demand leads to use different type of material and various techniques to increase productivity.
A study on DOE of tubular rear axle twist beam using HyperStudyAltair
In terms of the compliance with new legal requirements and reduction of greenhouse gas effect , automotive industry focuses on weight reduction of vehicle components. Furthermore, manufacturers studies on new concept designs, processes and new generation materials without compromising the safety of the vehicle components. The optimization tools take up significant place in the automotive industry to analyze feasibility of parts quickly due to competitive market requirements. Furthermore, Hyperstudy offers a solution for rapid DOE opportunity in the product development cycle, minimizing optimization challenges and also costs.
In this study, DOE methodology is used in order to optimize tubular rear axle twist beam which meets forces from ground to car body and belongs to semi-independent suspension system by using Hyperstudy.
Speakers
Metin Çalli, FEA Responsible, COSKUNOZ A.S. R&D Department
Structural analysis of the Baakenhafen Bridge and the optimisation of chosen ...Altair
The following paper consist of two parts. The aim of the first part is to analyse the problem of thermal loads of steel skew bridges and the displacement caused by them. A 3-spanned steel road bridge named Baakenhafen West is being analysed. In the second part, an optimization of the primary beams of the structure was made. Thus was shown, that there was a possibility to reduce the steel tonnage of the bridge by keeping it safe to use.
Speakers
Julia Karasinska, Engineer, BuroHappold Engineering
Optimization of Bolted Joints for Aircraft Engine Using Genetic AlgorithmsIJMER
Genetic Algorithms mimic the evolving technique of nature to better fit populations to a certain environment. Despite this technique has proved its adequacy in several fields, its application in Aerospace is still limited, mostly because of the high quantity of acceptability criteria that the design
must pass and the amount of design parameters. The presented paper explores required GA architecture’s adaptations to be applied in highly restricted systems such as those commonly found in Aerospace applications. The proposed GA was applied to the design of an Aircraft Engine’s Axial Casing bolted joint following static strength restrictions as per FAR 33 regulations. The set of Elitism,
interdependent geometric restrictions, Crossing, and Reproduction modules proved the applicability of
the presented multi-objective GA architecture under 14 restrictions for normal, limit and ultimate loads.
As it is described, the conversion is quickly achieved due to the shortage of the search space; therefore a
modified Variable Crossing per Scheme is proposed to expand the diversity of the genome to compensate
the relatively low impact of the Mutation module. Finally, the process and solutions found were compared against the traditional design process, showing the feasibility of this technique in complex applications in terms of quality of the solution and developing time.
DESIGN AND OPTIMIZATION OF CRITICAL PART OF A ROTARY TABLE USED IN HORIZONTAL...IAEME Publication
Growing industry demands low manufacturing cost, saving of material, low cost material, ease of transportation etc. This demand leads to use different type of material and various techniques to increase productivity.
A study on DOE of tubular rear axle twist beam using HyperStudyAltair
In terms of the compliance with new legal requirements and reduction of greenhouse gas effect , automotive industry focuses on weight reduction of vehicle components. Furthermore, manufacturers studies on new concept designs, processes and new generation materials without compromising the safety of the vehicle components. The optimization tools take up significant place in the automotive industry to analyze feasibility of parts quickly due to competitive market requirements. Furthermore, Hyperstudy offers a solution for rapid DOE opportunity in the product development cycle, minimizing optimization challenges and also costs.
In this study, DOE methodology is used in order to optimize tubular rear axle twist beam which meets forces from ground to car body and belongs to semi-independent suspension system by using Hyperstudy.
Speakers
Metin Çalli, FEA Responsible, COSKUNOZ A.S. R&D Department
Structural analysis of the Baakenhafen Bridge and the optimisation of chosen ...Altair
The following paper consist of two parts. The aim of the first part is to analyse the problem of thermal loads of steel skew bridges and the displacement caused by them. A 3-spanned steel road bridge named Baakenhafen West is being analysed. In the second part, an optimization of the primary beams of the structure was made. Thus was shown, that there was a possibility to reduce the steel tonnage of the bridge by keeping it safe to use.
Speakers
Julia Karasinska, Engineer, BuroHappold Engineering
Evolution of cadcamcae techonology and value to the industry v1.compressedStephen Au
Lecture Note of BIM (1/6)
Objectives
*To understand the evolution of CAX technology in manufacturing industry
*The drivers of adoption of the CAX technology
*The value of using CAX technology in product development
Question
*What if building construction industry can apply the same technology ?
*What are the similarity and what are the difference?
www.mtech.com.hk
Industry engineering process evolves from Hardware based development to Digital based development. This process innovation increases efficiency, reduces development period, and enhances product quality. CAE's responsibility increases and encounter high demand from design engineering, test, styling, and upper management.
How CAE can meet the expectations and can evolve to be the core functionality in development process?
Speakers
Dr. Byungsik Kang, Vice President, Hyundai Kia Motors
Topology Optimization for Additive Manufacturing as an Enabler for Robotic Ar...piyushsingh376
The current research is intended to minimize the mass of T shaped joint by using lattice structure and topological optimization tool.
The stresses, deformation, safety factor of generic and optimized design is evaluated on the basis of these mentioned parameters. The findings have shown that topological optimization method is best as compared to lattice structure method for weight minimization.
Reverse Engineering
Definition
It is described in Wikipedia as:
… the process of extracting knowledge or design information from anything man-made. The process often involves disassembling something (a mechanical device, electronic component, computer program, or biological, chemical, or organic matter) and analyzing its components and workings in detail.
Reverse Engineering
Definition
A process of discovering the technological principles of a human made device, object or system through analysis of its structure, function and operation
Systematic evaluation of a product with the purpose of replication.
Design of a new part
Copy of an existing part
Recovery of a damaged or broken part
An important step in the product development cycle.
Topology Optimization
Topology optimization is concerned with material distribution and how the members within a structure are connected. It treats the “equivalent density” of each element as a design variable.
The solver calculates an equivalent density for each element, where 1 is equivalent to 100% material, while 0 is equivalent to no material in the element. The solver then seeks to assign elements that have a low stress value a lower equivalent density before analyzing the effect on the remaining structure. In this way extraneous elements tend towards a density of 0, with the optimum design tending towards 1. As a designer, you will need to exercise your judgment. For example, you may decide that you will omit material from all (finite) elements whose density is less than 0.3 (or 30%). Using an iso-plot of element densities helps to visualize the “remaining” structure as elements with a density below this threshold can be masked leaving behind the optimum design. Then you will need to take this geometry back to your CAD modeler, smooth it out (that is, use geometrically regular edges or surfaces, etc.) and re-evaluate the design for stresses, displacements, frequencies etc..
CAE FEA Services from ProSIM Bangalore (Updated 22092022).pptxprosim1
Pro Sim offers the best Computer Aided Engineering outsourcing services in Bangalore. We are an engineering and design company a way to outsource the work to companies that specialize in engineering and design.
The operation research book that involves all units including the lpp problems, integer programming problem, queuing theory, simulation Monte Carlo and more is covered in this digital material.
Model-Based User Interface Optimization: Part IV: ADVANCED TOPICS - At SICSA ...Aalto University
Tutorial on Model-Based User Interface Optimization. Part IV: ADVANCED TOPICS.
Presented by Antti Oulasvirta (Aalto University) at SICSA Summer School on Computational Interaction in 2015 in Glasgow. Note: This one-day lecture is divided into multiple parts.
Altair offers a unique set of simulation tools to evaluate product feasibility, optimize the manufacturing process, and run virtual try-outs for many traditional, subtractive, and additive manufacturing processes.
Smart Product Development: Scalable Solutions for Your Entire Product LifecycleAltair
Being connected to your products opens doors to recurring and value-based revenue streams. It not only solves your customer's toughest challenges; it also helps build a sustainable future for your company. Try SmartWorks IoT today, for free trial .
An engineer working for Northrop Grumman Systems Corporation Marine Systems (NGSC-MS) was given a project to improve their teams’ current NASTRAN results post-processing workflow by writing a script to automate the task. They reached out to Altair for collaboration and Altair engineers were able to quickly determine that Altair’s mathematical modeling environment – “Altair Compose” – would be the ideal solution due to its ability to read, manipulate, and write NASTRAN results. Also, the Open Matrix Language is a scripting language that is familiar to the engineering community. Given sample NASTRAN results and requirements Altair engineers provided a “template” script. The NGSC-MS team was able to quickly understand and modify the script to their goals. The custom results were then viewable in HyperView as a contour plot, which saved a considerable amount of time during post-processing and documentation workflows.
Designing for Sustainability: Altair's Customer StoryAltair
Bush Bohlman was required to perform the structural analysis and timber design for the British Columbia Institute of Technology, (BCIT), student plaza, a pedestrian and public transport user gateway for the institute. The structure needed to establish a strong campus identity with a biophilic design and demonstrable support for sustainable building practices while ensuring structural safety according to local design codes. The hybrid mass timber structure consists of a Cross-Laminated Timber (CLT) canopy, CLT columns, and steel columns. By using S-TIMBER, the engineers were able to simulate the complex two-way bending behavior of the cantilevering roof panels and asymmetrical column layout. Having the model in S-TIMBER allowed for changes to be analyzed and re-designed, without the need to manually design individual timber and steel elements. S-TIMBER's design reports presented the design calculations concisely, yet transparently, for faster and easier reviews.
why digital twin adoption rates are skyrocketing.pdfAltair
Even though digital twin technology isn’t necessarily new, its adoption is sweeping regions and industries at astonishing rates. Organizations are rushing to adopt digital twins, learning how they can use it for different applications and purposes, and foresee even more growth in the coming few years. In this infographic, remember the big story about digital twin adoption and find out what companies worldwide have in store for their digital twin futures.
Digital twin technology has the potential to usher in unprecedented sustainability breakthroughs in industries around the world. As the world sprints toward a net zero future, organizations are rushing to adopt solutions that will create a more sustainable planet filled with technology that will enable people to minimize their impact on the people, wildlife, and environments around them. In this infographic, see how companies are flocking to digital twin technology to meet their sustainability objectives and where digital twin can have the greatest impact.
Altair’s industrial design tools allow designers, architects, and digital artists to create, evaluate, and visualize their vision faster than ever before. Focus on ideas instead of being hindered by shortcomings of the software tools and liberate creativity with design software that lets the user model freely, make changes effortlessly, and render beautifully.
Analyze performance and operations of truck fleets in real timeAltair
Altair’s event processing and data visualization tools enable fleet operators to analyze critical data streaming in from sensors and other sources. This real-time visibility into vehicle and driver performance helps reduce operating costs, improve driver safety, and increase fleet productivity. Analysts can display maps showing the current position of all assets, examine route deviations, program alerts on any set of parameters, and compare drivers’ behavior. Analysts can design and modify analytical dashboards as needed without writing a single line of code.
Knowledge Studio text analytics add-on is an industry-first application that combines visual text discovery and sentiment analysis with the power of predictive analytics. It delivers unparalleled voice of the customer insights to support customer experience management.
Altair’s Data Analytics solutions help reduce healthcare IT complexities and add efficiencies in areas like claims/reimbursement processing, revenue cycle management, interoperability, patient adherence and satisfaction analysis, and physician performance analysis.
Altair allows healthcare organizations to access, cleanse, and transform data—helping to break down data application silos and building automated workflows into standardized, shareable assets for optimizing strategic planning, streamlining operations, and maximizing resources.
Altair’s artificial intelligence (AI) and machine learning (ML) software helps materials scientists understand how to best fill gaps in their material databases, even when it’s impossible to test all possible variants. These advanced tools also optimize testing programs, improve efficiency, and reduce the time required to complete materials testing.
Altair High-performance Computing (HPC) and CloudAltair
Altair’s industry-leading HPC tools let you orchestrate, visualize, optimize, and analyze your most demanding workloads, easily migrating to the cloud and eliminating I/O bottlenecks. Top500 systems and small to mid-sized computing environments alike rely on Altair to keep infrastructure running smoothly. With longstanding hardware and cloud provider partnerships, we handle the integrations for you so your team can focus on moving business forward.
No Code Data Transformation for Insurance with Altair MonarchAltair
Altair Monarch is the fastest and easiest way to extract data from dark, semi-structured sources like PDFs, spreadsheets, and text files, as well as from Big Data and other structured sources. Monarch cleans, transforms, blends, and enriches data with an easy-to-use interface free of coding and scripting. For 30 years Monarch has helped insurers worldwide save time and money by enabling people of different skill sets to transform data quickly and precisely for efficient analysis around calculating premiums, identifying fraudulent claims, optimizing customer retention strategies, and more.
Altair Data analytics for Banking, Financial Services and Insurance Altair
Data is a significant asset for any organization. The older the data get, the more valuable it becomes. But the value of data doesn't lie in that you have it but in how you utilize it. Altair provides you the complete Data analytics, AI, and ML solutions across industries like manufacturing, insurance, finance, and government sectors to help you make smarter data decisions.
Altair data analytics and artificial intelligence solutionsAltair
Altair enables organisations worldwide to compete more effectively by operationalizing data analytics and AI with secure, governed, and scalable strategies. We deliver world-class, self-service analytics solutions for data preparation, predictive modeling, stream processing, visualization, and more. With a no-code, cloud-ready interface, organisations can harness the full power of analytics and AI throughout their complete data lifecycle, driving next-level business results.
Are You Maximising the Potential of Composite Materials?Altair
This presentation provides a summary of the talks given at Altair's Composite Design ATCx seminar which took place in the UK on 26th June, 2018. The presentation includes input from Gordon Murray Design, McLaren, Simpact and many more, describing how they are using Altair technologies to reduce composite product weight, reduce time to market, improve impact performance and much more.
Lead time reduction in CAE: Automated FEM Description ReportAltair
For each deliverable FE-Model a FEM description report needs to be generated. Since this document contains always the same type of information, it is an ideal candidate to automate the creation of this report. Based on the Hyper Report Tool from Altair, RUAG Space and Altair developed a tool to automatically generate the FEM Description Report. The tool requires the HyperMesh data base and the output files from FEM checks as inputs. Together with the tool template, guidelines are provided on how the data base needs to be set up, such that the report can be created automatically. The main structure of the FEM Description Report is dependent on the assembly structure of the HM data base.
Car makers have to reduce consumption of vehicles and so, are continually looking for solutions to lighten components. For powertrain, components generally mean screwed assembly, contact and fitting interfaces, with different kind of loading to take into account (static and dynamic). Hence, we decided to apply with Altair assistance, a process of topology optimization on an assembly of gearbox housing in order to check its feasibility and efficiency. Several steps had to be solved from exhaustive identification of all mechanical constraints to execution of large models with Optistruct. By the end, the process has been defined and implemented on an existing gearbox and will be soon apply on the next one to design.
Speakers
Philippe Dausse, Modelization Specialist, PSA Peugeot Citroen Automobiles
The Team H2politO: vehicles for low consumption competitions using HyperWorks Altair
The Team H2politO is a group of students of the Politecnico di Torino. The student’s background and profiles are very diverse, everyone comes from a different discipline of engineering and together they compose a complete Team. The disciplines range from Automotive and Mechanical to Electronics, Aerospace, Energy, Mathematics, Computer Science, Mechatronics, Management, Cinema and Media and Industrial Design. The Team mission is to shape a new generation of engineers, leaders in their fields, who represent the educational excellence in regard of each of their competencies.
The results of Team passion and hard work are three low-energy consumption vehicles completely designed and made by the Team: IDRA - hydrogen powered prototype; XAM – bioethanol powered parallel hybrid urban concept; XAM 2.0 –EREV city vehicle.
The main goal is to take part and win in Shell Eco-marathon, a competition that every year involves more than one hundreds of students teams arriving from all over Europe. Especially we would like to spread the Shell Eco-marathon values through ours, combining the sustainable development with a vehicle that uses the least possible amount of energy.
H2politO is a different, innovative and somehow unique project, is not just a Team but something more: it is a new type of conceiving educational, professional and personal growth. Team members aim at being perceived as an experimental laboratory where competences, capabilities and potentialities of future’s engineers are fostered. Students strive to become not only solid and advanced technical experts but, equally important, down-to-earth managers having excellent communication, leadership and teamwork skills.
Practical and hands-on experiences are doubtlessly a complementary and enriching form of educational path where it is very important the use of simulation software like HyperWorks. Team members have a real opportunity to lead their educational path by building and crafting their own thesis. Final papers are indeed part of a cluster of thesis which combines all the technological and organizational areas of development H2politO has envisioned and embraced.
The Team believes in hard work as the basis of future success. Students crave for continuously improving and strive for exceeding expectations by nurturing the team spirit in order to create those synergies able to add value to individual performances and capabilities. As a consequence, passion and team-spirit are really the foundation of H2politO values.
Speakers
Prof. Massimiliana Carello, Politecnico di Milano
Improving of Assessment Quality of Fatigue Analysis Using: MS, FEMFAT and FEM...Altair
Better correlation of measurement data using Motion Solve and FEFMAT LAB virtual iteration Matching of locally measured data calculating excitations (input) based on MBS process (MotinSolve) to reach local measured data Using this process and the output of MotionSolve for a hybrid MBS- fatigue process
Speakers
Axel Werkhausen, Manager Sales & Support, MAGNA / Engineering Center Steyr GmbH & Co KG
2. Project Objective
• Develop training curriculum to
transfer knowledge in application of
optimization technologies in design
of aerospace structures
• Move beyond typical training module
prescriptive approach to using OptiStruct
with simple parts
• Leverage knowledge base in optimization
application
• Teach application of optimization
technologies to typical aerospace design
scenarios
• Aerospace Optimization Academy
3. Approach
• Develop optimization knowledge,
skills, and experience
• Practical, real-world applications
• Hands-on exercises
• Self-paced
• Modular
• Accessible online
• Assignments leading to certification
• Approximately 120 hours in 2 hour
sections
• Students can be assigned an Altair
mentor for questions, guidance, and
general assistance
• Certification for Course Completion
4. Typical Student Profile
• The Aerospace Optimization Academy student
will have
• 2-3 years aerospace structural analysis
experience
• Basic finite element analysis skills & tool familiarity
• Limited optimization knowledge, experience
• Prerequisites
• Intro CAE
• HyperMesh & HyperView
• OptiStruct
• Intro Aero Concepts
• Aero Structural Analysis
• Materials
• Intro Composites
• Intro to Structural Optimization
7. AOA Module 1—Optimization Overview
• Objective: Develop familiarity with
• Optimization methods, tools, and processes used in
aerospace applications
• What is optimization—goals, why and when to use
optimization
• Overview of topology, shape, size, and combined
optimization
• Elements of optimization problem formulation
• Design variables, objective function, constraints, and
other terminology
• Basic theoretical concepts of optimization algorithms
• Unconstrained minimization
• Constrained optimization
• Optimality criteria and dual methods
• Approximation techniques
• Sensitivity analysis
• Case studies illustrating tools, methods, and
applications
• No Exercises
8. AOA Module 2—Project Management
• Objective: Develop
familiarity with optimization
process, including tasks,
models, data flow, and
potential issues
• Optimization Project
Checklist
• Data Development
• Baseline Assessment
• Concept Design
• Design Refinement
• Deliverables
• No Exercises
Item Description Milestone Meetings ECD Actual Delivery
1
Create project schedule and process flow chart, deliver to owner of
structure
2 Gather project data: □ CAD □ FEM □ Loads □ Materials □ Design Space
3
Project kick off meeting. Agenda: Project schedule, Review project data
set, solicit baseline displacements/stress, equilibrium forces, preferred
modeling assumptions, bcs, loading, find out if stress is allowed to follow
plastic stress/strain for Ftu for usage of Neuber. □
4
Design Space Given? □ yes □ no, If no, submit design space proposal using
Design_Space_Documentation_Template.ppt
5 Design Space review □
6 Constraints Document □
6 Baseline assessment:
7 If baseline is available:
8 Convert baseline FEM (if available) to OptiStruct
9 Run Analysis
10
Compare displacements/stress/buckling results to requirements or
baseline strength check notes
11 Review FEM using "basic FEM checklist"
12
Is the OptiStruct displacement/stress similar to the previous analysis
work? (if available)
13 If baseline is not available:
14
Before meshing CAD, decide whether to partition geometry for topology
efficiency (nondesign/design).
15
If shape opti is planned, the mesh should be built such that it
suitable for shape perturbations
16 Build OptiStruct FEM from CAD
17 Run Analysis
18 Review FEM using "basic FEM checklist"
19 Communicate baseline assessment □
20
Use free body loads for optimization □ yes □ no. Free body loads are
sometimes appropriate for optimization work in cases where loads to the
structure aren't expected to change, where a large portion of non-design
structure can be removed from the FE model to reduce iteration time, etc.
21 If yes, data in free body model and non-free body must be identical.
22 Verify free body contains expected applied and reacted loads
23 □ yes □ no
24 Verify that stresses are identical to the original model.
25 □ yes □ no
26 Write .spcf file, verify spc forces are negligible.
27 □ yes □ no
28
Non-linear gaps solutions only: Use non-linear gap status (by way of
GAPPRM,HMGAPST,YES) and GAP-to-MPC macro for optimization □ yes □ no. This
can reduce optimization cpu time by representing the non-linear gap status
by means of MPC equations in a linear analysis. An initial non-linear
analysis must be run to generate the status file. Expected percentage
reduction in cpu time is 75%.
29
If yes, Verify, using the Free Body Forces tool, that reaction forces
at the gap locations are identical in the non-linear gap model and the
MPC'd linear model.
30 □ yes □ no
31 Optimize structural layout using conceptual design tools
32 □ topology □ free size □ topography
33 □ shells □ hexas □ 1st order tets
34
Run analysis with 100% material fraction (topology) or thickness (free
size) to see if any responses violate constraints and cannot be
rectified.
35 Run optimization jobs
36 Review FEM using "basic FEM checklist"
37 Design interpretation of conceptual design results
38
Design interpretation of topology results can be enhanced by running
topology optimization on individual load cases. These results can
then be compared to the combined topology run. Structural features
should become more meaningful. Load path analysis should become
clearer.
39
□ Consider producibility in the interpretation, minimum gauge, common
radii, cutting tools, undercuts, etc.
40
Secondary conceptual design (if appropriate). Sometimes running a secondary
topology using the interpreted design can help refine the design quicker
than going directly to shape optimization. The main goal of this secondary
conceptual work is to pinpoint where inefficient structure exists.
41 □ topology □ free size □ topography
42 □ shells □ hexas □ 1st order tets
43 Concept design analysis
44 □ Weight reduction compared to baseline
45 □ Allowable violations (if any)
46 Communicate concept design. Agenda: Producibility, assembly. □
DATADEVELOPMENT
BASELINECONCEPTDESIGN
9. AOA Module 3—Basic Optimization
• Objective: Develop familiarity with basic
model construction for typical
optimization methods used in aerospace
applications
• Optimization using spreadsheet analysis
model
• Preprocessing in Hypermesh
• Finite element cards used in optimization
• Exercises
1. Analytic model optimization
(HyperStudy/Excel)
2. Basic size optimization
3. Basic shape optimization
4. Basic topology optimization
• Short review for candidates that have
completed OptiStruct training
10. AOA Module 5—Finite Element-driven
Optimization
• Objective: Develop familiarity with
optimization of metallic structural
components for stiffness, strength,
and stability using OptiStruct size,
shape, and topology optimization
• Model development
• Loads
• Design interpretation
• Exercises
• Compact Fitting—Door Hinge
(Exercise 1)
• Stiffened Metallic Skin (Exercises 2-4)
11. AOA Module 5—Exercise 1 Compact
Fitting—Door Hinge
• Objective: Design a door hinge fitting
within a given package space that includes
cutouts for stay-out zones
• Optimization tools: topology, size
• Criteria: Strength, stiffness
• Loading: Point loads
• Model details: contact (gap elements), z-
offsets, 2D and 3D elements
• In this exercise the student will
• Setup topology optimization for the body region
of the hinge tetrahedral element model
• Execute the topology optimization and post-
process the results
• Define a shell element size optimization model
based on the topology optimization results
• Execute the size optimization and post-process
the results
14. AOA Module 5—Exercises 2-4 Stiffened
Metallic Skin
• Objective: Design a multi-bay aft fuselage panel
with a circular cutout
• Optimization tools: size
• Criteria: Strength, stability
• Loading: up bending, down bending, and torsion
conditions combined with pressure
• Model details: 1D and 2D elements
• Optimize design using three methods
• Exercise 2—Global model
• Exercise 3—Carve-out—balanced freebody loads
• Exercise 4—Static Condensation—DMIG reduced
model
• In these exercises, the student will
• Define a size optimization model comprised of shell and
bar elements in a region of a global model
• Execute the size optimization and post-process the
results
17. AOA Module 6—Optimization with
Composites
• Objective: Develop familiarity with optimization of
composite structural components using for stiffness,
strength, and stability using OptiStruct composites tools
• Tools, processes, and methods unique to composites
• Failure criteria
• SMEAR and SMCORE idealizations
• Composite free-size optimization to shape plies
• Ply-based analysis and design variables
• Two approaches for bolted joint bearing/bypass strength—
simplified curve fit equation and externally calculated margin of
safety (e.g. BJSFM)
• Manufacturing rules—ramp rate, ply percentage limits, stacking
sequence, zone continuity
• Core
18. Module 6—Exercises
• Exercise 1—Preliminary Sizing Optimization
• 2 spar, 3 rib wing optimization
• Triangular pressure distribution
• Up Bending, down bending, twist load conditions
• 27 laminate designs—PCOMP with SMEAR, [0,90,45,-45] laminate
family
• Max strain failure criterion
• Exercise 2—Detail Panel Optimization
• Skin panel with cutout
• Carveout model with balanced freebody loads
• Ply thickness optimization of constant thickness laminate
• Strength, stiffness, stability, manufacturing constraints
• Exercise 3—Detail Panel Optimization (Future)
• Skin panel with cutout
• Carveout model with balanced freebody loads
• Ply shape optimization of variable thickness laminate
• Strength, stiffness, stability, manufacturing constraints
• Exercise 4—Detail Panel Optimization with Bearing/Bypass
Constraint (Future)
• Apply bearing/bypass constraints at bolted joints using two methods:
simplified analysis and external tool (e.g. BJSFM)
• Repeat optimization of both constant thickness and variable thickness
laminates
19. Module 6—Exercise 1—Wing Skin
Preliminary Sizing
• Problem Statement
• Objective: minimize mass
• Constraints: Maximum strain,
buckling eigenvalue
• Design variables:
• Composite shell element
thicknesses—skin panels
• Metallic shell element thicknesses—
spars, ribs
• Beam element dimensions—spar
caps
• In this exercise, the student will:
• Define a size optimization model
comprised of 2D shell and 1D beam
elements, including 1D offsets
• Execute the size optimization and
post-process the results
22. Module 6—Exercise 2—Detail Panel
Optimization
• Problem Statement
• Objective: minimize mass
• Constraints: Maximum strain, buckling eigenvalue
• Design variables:
• Composite ply shapes
• Composite ply thicknesses
• In this exercise, you will:
• Create a carve-out model from a global model
• Generate balanced free-body loads
• Refine mesh and create cutout on detail panel
• Define and execute composite optimization models
comprised of composite shell elements
• Free size optimization to determine ply shapes
• Size optimization to determine ply thicknesses
• Shuffle optimization to determine ply stacking sequence
• Post-process the results
24. AOA Module 7—Optimization with Joints
• Objective: Develop familiarity with optimization
of joint placement, joint number and joint size
• Joints loads based on elasticity
• Exercise 1—Overview of Joint Analysis and
Optimization
• Joint modeling—hole detail, fastener elements,
fastener end conditions, fastener length, plate
offsets
• Data analysis—bearing loads, bypass loads, plate
stresses
• Plate gauge and fastener optimization
• Joint fatigue details: reference stress, bearing
stress, bypass stress, bearing load, fastener
diameter, plate thickness and t/D ratio
• Exercise 2—Joint Optimization with Bearing
Bypass Stress Calculation Using an External
Function
• Repeat exercise 1 using calculations from DRESP3
external function defined in HyperMath
28. AOA Module 4—Spreadsheet-driven
Optimization
• Objective: Develop familiarity with optimization of metallic
structural components for stiffness, strength, and stability using
spreadsheet-driven classical analysis methods
• Link HyperStudy with existing spreadsheet analysis methods
• Define design variables inside spreadsheet
• Exercise
• Optimize multi-section beam using classical analysis from spreadsheet
Variables: Equations:
faxial = axial stress along beam = (P/A)
fsweb = shear stress in the web = (V/A) (where Rb is either Rt or Rc )
fscord = shear stress in cords = (V/A)
fbtxx = tensile stress at top of I-beam (from bending about x-axis)
fctxx = compressive stress at bottom of I-beam (from bending about x-axis) Procedure:
fbty y = tensile stress from bending about y-axis 1. calculate all stress values based on geometry and load inputs.
fcty y = compressive stress from bending about y-axis 2. combine stress values to obtain total stress at each of the four corners of the I-beam.
fb1,2,3,4 = total applied stress at point 1,2,3,or4 (fbtxx + fctxx) 3. calculate the stress ratios for bending and shear
Ftu = ultimate tensile stress (for bending, consider both tensile & compressive stress as well as ultimate and yield for allowable limits)
Fcy = compressive yield stress 4. calculate M.S. using stress ratios
Fsu = ultimate shear stress
Rt = (applied tensile stress) / (allowable tensile stress) References:
Rc = (applied compressive stress) / (allowable compressive stress)
Rs = (applied shear stress) / (allowable shear stress) 18.028
Ra = (applied axial stress) / (allowable compressive stress) ( stress ratio, margin of safety definition, interaction equations: see page C1.7-C1.8, and C3.11 )
1,2 3,4 1,3 2,4
TOP BOTTOM FWD AFT
kips kips in kips kips in kips psi psi psi psi psi psi psi psi psi psi psi psi psi psi
Section LGB Station A A_web A_cords h_total y_bar h - y_bar w_total x_bar w-x_bar Ixx Iyy Paxial Vy Mxx Vx Myy faxial fs web fs cord fbtxx fbcxx fbty y fbcy y fb1 fb2 fb3 fb4 Ftu Fcy Fsu Rt Rc Rs Section
AA 24.000 6.657 2.688 4.185 12.250 6.125 6.125 6.000 3.000 3.000 177.830 12.564 AA
BB 72.000 11.466 2.768 9.196 12.750 6.375 6.375 6.000 3.000 3.000 357.089 27.596 BB
CC 120.000 17.725 3.278 15.319 13.250 6.625 6.625 6.000 3.000 3.000 576.476 45.968 CC
DD 168.000 25.994 3.458 23.967 13.750 6.875 6.875 6.000 3.000 3.000 854.329 71.910 DD
EE 216.000 36.878 3.726 35.362 14.250 7.125 7.125 6.000 3.000 3.000 1166.462 106.094 EE
FF 264.000 47.842 4.282 46.603 14.750 7.375 7.375 6.000 3.000 3.000 1441.337 139.816 FF
1,2 3,4 1,3 2,4
SUBCASELoad Case Independent Margins TOP BOTTOM FWD AFT
Section LGB Station A A_web A_cords h_total y_bar h - y_bar w_total x_bar w-x_bar Ixx Iyy Paxial Vy Mxx Vx Myy faxial fs web fs cord fbtxx fbcxx fbty y fbcy y fb1 fb2 fb3 fb4 Ftu Fcy Fsu Raxial Rt Rc Rs Section
1 AA 24.000 6.657 2.688 4.185 12.250 6.125 6.125 6.000 3.000 3.000 177.830 12.564 -10.000 -15 -360 0 0 -1502 5580 0 12399 -12399 0 0 12399 12399 -12399 -12399 130000 126000 80000 0.01 0.10 0.10 0.07 6.66 AA
2 AA 24.000 6.657 2.688 4.185 12.250 6.125 6.125 6.000 3.000 3.000 177.830 12.564 10.000 -15 -360 -10 -240 1502 5580 2390 12399 -12399 57308 -57308 69708 -44909 44909 -69708 130000 126000 80000 0.01 0.54 0.55 0.07 0.76 AA
3 AA 24.000 6.657 2.688 4.185 12.250 6.125 6.125 6.000 3.000 3.000 177.830 12.564 -10.000 15 360 -10 -240 -1502 5580 2390 -12399 12399 57308 -57308 44909 -69708 69708 -44909 130000 126000 80000 0.01 0.54 0.55 0.07 0.76 AA
4 AA 24.000 6.657 2.688 4.185 12.250 6.125 6.125 6.000 3.000 3.000 177.830 12.564 0.000 -15 -360 0 0 0 5580 0 12399 -12399 0 0 12399 12399 -12399 -12399 130000 126000 80000 0.00 0.10 0.10 0.07 7.29 AA
5 AA 24.000 6.657 2.688 4.185 12.250 6.125 6.125 6.000 3.000 3.000 177.830 12.564 10.000 30 720 -10 -240 1502 11161 2390 -24799 24799 57308 -57308 32509 -82107 82107 -32509 130000 126000 80000 0.01 0.63 0.65 0.14 0.47 AA
6 AA 24.000 6.657 2.688 4.185 12.250 6.125 6.125 6.000 3.000 3.000 177.830 12.564 10.000 30 720 10 240 1502 11161 2390 -24799 24799 -57308 57308 -82107 32509 -32509 82107 130000 126000 80000 0.01 0.63 0.65 0.14 0.47 AA
7 AA 24.000 6.657 2.688 4.185 12.250 6.125 6.125 6.000 3.000 3.000 177.830 12.564 0.000 30 720 10 240 0 11161 2390 -24799 24799 -57308 57308 -82107 32509 -32509 82107 130000 126000 80000 0.00 0.63 0.65 0.14 0.50 AA
8 AA 24.000 6.657 2.688 4.185 12.250 6.125 6.125 6.000 3.000 3.000 177.830 12.564 -10.000 -10 -240 10 240 -1502 3720 2390 8266 -8266 -57308 57308 -49042 65575 -65575 49042 130000 126000 80000 0.01 0.50 0.52 0.05 0.87 AA
9 AA 24.000 6.657 2.688 4.185 12.250 6.125 6.125 6.000 3.000 3.000 177.830 12.564 -10.000 -30 -720 -10 -240 -1502 11161 2390 24799 -24799 57308 -57308 82107 -32509 32509 -82107 130000 126000 80000 0.01 0.63 0.65 0.14 0.47 AA
10 AA 24.000 6.657 2.688 4.185 12.250 6.125 6.125 6.000 3.000 3.000 177.830 12.564 0.000 30 720 0 0 0 11161 0 -24799 24799 0 0 -24799 -24799 24799 24799 130000 126000 80000 0.00 0.19 0.20 0.14 3.15 AA
Lowest Margin for Section is 0.47
MS
Bending
Bruhn, E.F., Analysis & Design of Flight Vehicle
Structures
Rbending
Rbending
stress
ratios
1
1
..
22
sba RRR
SM
Variables:
W width of region
h height of region
A area of region
In moment of inertia of region about it's own centroid
Ixx moment of inertia of entire beam about x-axis
Iy y moment of inertia of entire beam about y-axis
Aweb area of web (reg2+reg3+reg4+part of reg1 +part of reg5)
Acords area of cords (reg1+reg5)
Ybar y-distance from bottom of I-beam to centroid of I-beam
Xbar x-distance from left edge of I-beam to centroid of I-beam
Equations (Flabel, J.C., Practical Stress Analysis for Design Engineers , p.658)
Region W (in) h (in) A (in2
) x (in) Ax (in3
) Ax2
(in4
) y (in) Ay (in3
) Ay2
(in4
) Ix_loc (in4
) Iy _loc (in4
) Aweb: 2.687944 in2
1 6.000 0.349 2.092 3.000 6.277 18.830 12.076 25.265 305.096 0.021 6.277 Acoords: 4.185 in2
2.922925
2 0.308 0.527 0.163 3.000 0.488 1.463 11.638 1.892 22.013 0.004 0.001 Ybar: 6.125 in
3 0.205 10.498 2.148 3.000 6.444 19.331 6.125 13.156 80.578 19.727 0.007 Xbar: 3 in
4 0.308 0.527 0.163 3.000 0.488 1.463 0.612 0.100 0.061 0.004 0.001 Ixx 177.830 in4
5 6.000 0.349 2.092 3.000 6.277 18.830 0.174 0.365 0.064 0.021 6.277 Iyy 12.564 in4
totals: 6 12.250 6.657 19.972 40.777 407.812 19.777 12.564
SECTIONAA
h_total
h
W
Region1
s
Reg.2
Reg.3
Reg.4
Region5
Y
X
12
3
_
bh
I locx
A
Ay
Ybar
AyYAyII barlocxxx
2
_
12
3
_
hb
I locy
AxXAxII barlocyyy
2
_
A
Ax
X bar
Variables
Fcr critical buckling stress (compression)
Fscr critical buckling stress (shear)
fs applied shear stress
fc applied compressive stress (bending about x-axis)
Rb } stress ratios
Rs }
a panel width
b panel height
t panel thickness
Kc compression buckling coefficient
Ks shear buckling coefficient
v 0.33 poissons ratio
E 1.65E+07 Young's modulus
Es = 0.7E 1.16E+07 (secant modulus)
ET=0.85E 1.40E+07 refer to Ramsberg/Osgood equation, Bruhn, pp B1.8
Equations
(compression due to bending)
(shear)
Reference
Bruhn, E.F , pp C5.1-C5.10
pp B1.8
Sect a b t a / b Kc Ks ƞb ƞs Fcr (Bruhn) Fscr X Ybar Ybar_aux1 Ixx Mxx Vy
in in in ksi ksi in in in in^4 in kips kips
AA 15.00 10.50 0.20 1.43 36.00 11.25 0.58 0.56 121.66 36.60 0.88 6.13 5.25 177.83
BB 15.00 10.12 0.19 1.48 36.00 11.00 0.58 0.56 111.81 32.89 1.32 6.38 5.06 357.0886
CC 15.00 9.55 0.21 1.57 36.00 10.75 0.58 0.56 156.19 44.90 1.85 6.63 4.78 576.4758
DD 15.00 8.57 0.19 1.75 36.00 10.50 0.58 0.56 152.50 42.82 2.59 6.88 4.28 854.3288
EE 15.00 7.12 0.15 2.11 36.00 10.00 0.58 0.56 137.96 36.90 3.56 7.13 3.56 1166.462
FF 15.00 5.71 0.13 2.63 36.00 9.75 0.58 0.56 165.10 43.05 4.52 7.38 2.85 1441.337
Bruhn, Bruhn,
fig. C5.15 fig. C5.11
pp C5.7 pp C5.7
a
x
b
Y_bar
Y_bar_aux1
t
a
b
b
2
2
2
112
b
tEK
F c
bcr
2
2
2
112
b
tEK
F s
sscr
2
2
5.0
1
13
15.01
2
s
Ts
b
E
E
E
E
2
2
1
1
E
ES
S
29. AOA Module 8—Optimization with DaDT
constraints
• Objective: Develop familiarity with optimization of metallic
structural components with DaDT stress allowable constraints
• Two approaches for DaDT stress allowable calculation: simplified
curve fit equation and externally calculated margin of safety
• DaDT analysis considerations: loading spectrum, control points, stress
concentrations, design rules
• Exercise
• Optimize DaDT critical structural component for service life stress
constraints
30. AOA Module 9—Structural Concept
Comparison
• Objective: Develop familiarity with methods and tools for truss and
shear web optimization
• Topology optimization setup and parameters for truss or stiffener configuration
• Effect of topology parameter settings
• Free size optimization for shear web thickness configuration
• Comparison of designs and methods
• Exercises
• Topology optimization of structural component
• Free-size optimization of structural component
31. AOA Module 10—Loads
• Objective: Develop familiarity with internal loads model
concepts and how they affect structural optimization
• Critical load surveys
• Carveout model development
• Global-local modeling issues
• Freebody loads
• Analysis issues (e.g. buckling)
• Modeling details
• Load inconsistencies
• Exercise
• Load survey and carveout exercise
33. AOA Module 12—Advanced Concepts
• Objective: Develop familiarity with advanced optimization
concepts
• Possible topics include
• Mesh refinement
• Cross section checks through DRESP3
• Failsafe using MPC
• Multi-objective
• Multidisciplinary
• Robust
• Nonlinear
34. Conclusion
• Aerospace Optimization Academy
• Teach application of optimization technologies to typical aerospace
design scenarios—
• Develop broader application knowledge, skills, and experience
• Practical, real-world applications
• Hands-on exercises
• Self-paced
• Modular
• Initial modules available for training
• Future modules developed based on demand