4_mcmanuslactur1

4_mcmanuslactur1 - Space Systems Architecture Lecture 3...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Space Systems Architecture Lecture 3 Introduction to Tradespace Exploration Hugh McManus Metis Design Space Systems, Policy, and Architecture Research Consortium A joint venture of MIT, Stanford, Caltech & the Naval War College for the NRO Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 1 Tradespace Exploration • A process for understanding complex solutions to complex problems • Allows informed “upfront” decisions and planning Most relevant to processes in these phases Concept Development System-Level Design Phases of Product Development Space Systems, Policy, and Architecture Research Consortium Detail Design Testing and Refinement Production Ramp-Up From Ulrich & Eppinger, Product Design and Development , 1995 ©2002 Massachusetts Institute of Technology 2 1 Architecture Trade Space Exploration A process for understanding complex solutions to complex problems • Model-based high-level assessment of system capability • Ideally, many architectures assessed • Avoids optimized point solutions that will not support evolution in environment or user needs • Provides a basis to explore technical and policy uncertainties • Provides a way to assess the value of potential capabilities Allows informed “upfront” decisions and planning Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 3 Integrated Concurrent Engineering A process creating preliminary designs very fast • State-of-the-art rapid preliminary design method • Design tools linked both electronically and by co-located humans • Design sessions iterate/converge designs in hours • Requires ready tools, well poised requirements Allows rapid reality check on chosen architectures Aids transition to detailed design Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 4 2 Emerging Capability • Linked method for progressing from vague user needs to conceptual/preliminary design very quickly • MANY architectures, several/many designs considered • Understanding the trades allows selection of robust and adaptable concepts, consideration of policy, risk. MATE Architecture Evaluation User Needs ICE Conceptual Design Robust Adaptable Concepts Months, not Years Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 5 What is an Architecture Trade Space? X-TOS • Small low-altitude science mission Km km DESIGN VARIABLES: The architectural trade parameters • Orbital Parameters – – – • Apogee altitude (km) Perigee altitude (km) Orbit inclination Each point is a specific architecture 150-1100 150-1100 0, 30, 60, 90 Physical Spacecraft Parameters – – – – – Antenna gain communication architecture propulsion type power type delta_v Total Lifecycle Cost ($M2002) Assessment of the utility and cost of a large Number f Architectures Explored: 50488 N of possible system architectures space umberoofArchitectures Explored: 50488 Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 6 3 Developing A Trade Space Attributes Mission Concept • • • • • • Define Design Vector Understand the Mission Create a list of “Attributes” Interview the Customer Create Utility Curves Develop the design vector and system model Evaluate the potential Architectures Develop System Model Calculate Utility Estimate Cost Architecture Trade Space Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 7 XTOS Tradespace Development Concept • Small low-altitude science mission • Known instruments Attributes • • • • • Data Life Span Data Collection Altitude(s) Diversity of Latitude(s) Time Spent at Equator Data Latency Design Vector 1 • Number of Vehicles and Mission Design • Apogee Altitude • Perigee Altitude • Orbit Inclination • Antenna Gain • Communications Architecture • Propulsion Type • Power Type • Manuever Delta-V Capability Define Utility 0.9 0.8 Utility 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 150 350 550 750 950 Data Collection Altitude (km) Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 8 4 Continued • • • System Model Orbit Calculations (STK) Spacecraft Estimations (SMAD) Launch Module Calculate Utility • Estimate Cost Multi-Attribute Utility Theory • SMAD/NASA mode 50488 Architectures Explored Pareto front of “best” architectures Each point is a specific architecture Total Lifecycle Cost ($M2002) Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 9 Understanding What Systems Do • • • • Transmit Information Collect Information Move Mass (inc. People) Others (Space Station…) [Beichman et al, 1999] Space Systems, Policy, and Architecture Research Consortium Martin 2000 © 2002 Massachusetts Institute of Technology 10 5 Understanding who cares Stakeholders • Many interested parties in a complex system • Each “customer” has a set of needs • They are different, and can be contradictory Customer Acquirers End Users Consumers Partners Shareholders Enterprise Employees Suppliers Corporation Society Unions Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 11 Concept Selection: Bounding ATOS: Multi-vehicle Ionosphere Explorer In Situ Topside Sounding Direct Scintillation Sensing GPS GPS Occultation Space Systems, Policy, and Architecture Research Consortium UV Sensing ©2002 Massachusetts Institute of Technology 12 6 Scoping Spacecraft Instrument A-TOS scope Ionosphere Raw, commutated, uncalibrated data Control Center Decommutated, calibrated instrument data Hanscom Model “Scientist” User Set Physics Model Instrument -> Local Ionosphere Ionospheric characteristics Database Global Ionospheric Model Current State Other Data Sources (Various assets) “Space Weather” User Set Global Ionospheric Model Predict Future State User-Specific System Integration “Knowledgeable” User Set Go/No-Go “green light” “Warfighter” User Set Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 13 Attributes • “what the decision makers need to consider” • ( and/or what the user truly cares about) • Examples: Billable minutes = GINA metrics • TPF Pictures = camera performance metrics • Rescue/move satellites = mass moving, grappling capability, timeliness [Beichman et al, 1999] – Could have sub-cartoons for above Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 14 7 XTOS Attributes (1) 1) Data Life Span 2) Data Altitude 3) Maximum Latitude 4) Time Spent at Equator 5) Data Latency (3) (2) DATA 2004 km 2005 (4) Km (5) Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 15 Utilities • “What the attributes are WORTH to the decision makers” • Single Attribute utility maps attribute to utility • Multi-attribute utility maps an architecture (as expressed by its attributes) to utility 0 1 Single Attribute Utility function Attribute Space Systems, Policy, and Architecture Research Consortium Good -> Good -> 1 0 Multi-Attribute Utility analysis Expense ©2002 Massachusetts Institute of Technology 16 8 Single Attribute Utilities 1 0.9 0.8 Utility 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 150 350 550 750 950 Data Collection Altitude (km) Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 17 Multi-Attribute Utility Single Attribute Utility Curve for Data Point Altitude 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 150 350 550 Altitude (km) 750 950 N Weight Factors of each Attribute (k values) KU ( X ) + 1 = ’ (KkiU ( X i ) + 1) i =1 0.45 0.4 0.35 0.3 Utility 0.25 0.2 0.15 0.1 0.05 0 Lifespan Latitude Latency Equator Time Altitude Total Lifecycle Cost ($M 2002) Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 18 9 XTOS Design Vector • “Parameters of the Trade Space” Variable: Orbital Parameters: •Apogee altitude (200 to 2000 km) •Perigee altitude (150 to 350 km) •Orbit inclination (0 to 90 degrees) Physical Spacecraft Parameters: •Antenna gain (low/high) •Comm Architechture (TDRSS/AFSCN) •Propulsion type (Hall / Chemical) •Power type (fuel / solar) •Total DV capability (200 to 1000 m/s) Space Systems, Policy, and Architecture Research Consortium First Order Effect: Lifetime, Altitude Lifetime, Altitude Lifetime, Altitude Latitude Range Time at Equator Latency Latency Lifetime Lifetime Lifetime ©2002 Massachusetts Institute of Technology 19 ATOS design Vector • Geometry of the Multi-vehicle Swarm Swarm Orbit Parameters Number of spacecraft in swarm Geometry of swarm Mothership/ no mothership Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 20 10 Attributes Data Lifespan Sample Altitude Diversity of Latitudes Time at Equator Latency Total Cost Total w/Cost 9 9 9 6 0 0 0 6 9 9 9 0 0 0 0 0 0 9 0 0 0 0 9 0 0 0 9 0 6 0 0 9 0 0 0 9 3 3 0 0 3 9 9 6 3 21 27 9 6 21 9 9 12 39 9 9 3 6 6 3 6 6 9 30 36 12 12 27 12 15 18 48 Total Impact Power system Mission Scenario Ant. Gain Inclination Comm System Propulsion Apogee Delta-V Perigee Design Vars Scoping-QFDs Identify key interactions for modeling 48 27 18 24 36 Sums identify attributes and Design Variables that are likely to be (or not be) distinguishers Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 21 Scoping-Iteration/evolution TABLE II. EVOLUTION OF DESIGN VECTOR First Cut 10/20/00 Swarm type # sats/swarm # swarms Swarm orbit Intra-swarm orbit Instrument type # instruments/sat TT&C scheme Ground station Mission lifetime Processing scheme Position control scheme Latitude of interest Space Systems, Policy, and Architecture Research Consortium After GINA exercise 10/31/00 Concept type # sats/swarm # swarms per plane # orbital planes Swarm altitude Swarm orientation Swarm geometry Separation within swarm Mothership (yes/no) After utility characterization and module progress 1/15/01 Swarm perigee altitude Swarm apogee altitude # sats/swarm # subplanes/swarm # suborbits/subplane Yaw angle of subplanes Max sat separation Mothership (yes/no) Schedule Crunch 1/21/01 Swarm perigee altitude Swarm apogee altitude # sats/swarm # subplanes/swarm # suborbits/subplane Yaw angle of subplanes Max sat separation ©2002 Massachusetts Institute of Technology 22 11 Mapping Design Vector to Attributes and Utilities - Simulation Models XTOS Simulation Software Flow Chart Orbits All variations on design vector Spacecraft Mission Scenario Satellite database Launch Cost (lifecycle) Output Mission scenarios with acceptable satellites Utility Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 23 Techsat Models Inputs (Design Vector) # S/C per Cluster MATLAB Models …. Constellation Radar # Clusters Key Outputs …. Constellation Altitude S/C Bus Payload Launch & Operations Lifecycle Cost Availability Probability of Detection Revisit Rate Resolution & MDV System Analysis Aperture Diameter …. Antenna Power 100 90 80 70 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 24 12 Exploring the Tradespace Cadillac Many good architectures at c. $0.5M/Image Each point is an evaluated architecture $2M/Image 2200 $0.55M/Image 1350 2000 1800 $0.5M/Image 1300 1600 $0.5M/Image 1400 1200 True Optimal Solution 1250 $0.45M/Image 1200 1000 800 0 Zoom in of the TPF System Trade Space 1400 $1M/Image Lifecycle Cost ($millions) Lifecycle Cost ($millions) TPF System Trade Space 2400 $0.25M/Image 500 1000 1500 2000 2500 3000 3500 4000 Performance (total # of images) Taguchi Solution 1150 2300 2400 $0.4M/Image 2500 2600 2700 2800 2900 Performance (total # of images) 3000 TPF: a science imaging system Chevy Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 25 The Pareto Front • Set of “best” solutions • “Dominated” solutions are more expensive or less capable ) M $ ( 2200 Utility (images) t s 2000 o C 1800 TPF System Trade Space Pareto-Optimal Front Dominated Solutions Non-Dominated Solutions $1M/Image $2M/Image e l 1600 c y c 1400 e f i 1200 L $0.5M/Image 1000 $0.25M/Image 800 0 500 1000 1500 2000 2500 3000 3500 4000 Performance (total # of images) Cost ($M) Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 26 13 Optimization • Can look for the Pareto front using advanced optimization techniques ) s n o i l l i m 1600 $ ( 1500 t s 1400 o C 1300 Pareto Front Multi-Objective SA Exploration of the TPF Trade Space Current Solution Path Pareto-Optimal Set $1M/Image $2M/Image e l 1200 c y 1100 c e 1000 f i L 900 $0.5M/Image 800 $0.25M/Image 700 0 500 1000 1500 2000 Performance (total # images 2500 3000 Space Systems, Policy, and Architecture Research Consortium ©2002 Massachusetts Institute of Technology 27 Using the Trade Space to Evaluate Point Designs TPF • Terrestrial Planet Finder - a large astronomy system • Design space: Apertures separated or connected, 2-D/3D, sizes, orbits • Images vs. cost Designs from traditional process From Jilla, 2002 Space Systems, Policy, and Architecture Research Consortium [Beichman et al, 1999] ©2002 Massachusetts Institute of Technology 28 14 ...
View Full Document

Ask a homework question - tutors are online