1 Technology Readiness Level assessment and Capability Route Mapping for Special Engineered Structures for a Far Infrared Space Telescope Alison J. McMillan Glyndwr University 15-17 December 2015 FISICA Workshop
2 Motivation for this presentation? FISICA project is coming to an end: - How do we build on it? Assuming that one output of FISICA is the network: - Do we want to maintain the network? - Diverse partnership, so how do we remain cohesive? Diversity and the opportunities that aren t so obvious: - There isn t just one funding mechanism, and we could maximise total funding by working individually, but supporting each other through unified goals Demands integration of the needs and interests of the science goals, the technology tools and the infrastructural capacity building.
3 So who am I? Not just an engineer Prof Aerospace Technology, Glyndwr University (since 2012) Previously: Engineering Specialist, Roll-Royce plc (~15 years) Mixed tertiary education: - Maths & Physics BSc UCL - Applied Mechanics MSc Cranfield - Impact & composites PhD, Physics, Staffs University Mixed Post-doc positions: - Engineering structure optimisation for semi-passive vibration control (Engineering Science, Oxford) - Non-linear systems dynamics - vibration control (Maths, Keele) - Signal processing at Nyquist limit (Communication & Neuroscience, Keele) Actual research interests: - Computational mechanics; heterogeneous materials, stress wave propagation, mechanics and optimisation algorithm development
Integration of our mutual interests: 4 Data from other sources Science goals Data interpretation Engineering Infrastructure FIRI Programme Measurement hardware Engineering Expertise
Previous slide but in words 5 From discussions over the past two days, I sense that the primary focus of many delegates is on the measurement hardware, and on how the data that would be produced by the hardware would be interpreted. The interpretation of that data would lead to the science goals. Although several presenters list and discuss science goals, this is almost just to justify the activity: the science goals need to be clearer and to be presented with more enthusiasm. Competing capabilities provide other data sources, and although these have been listed, and the differentiation of capability noted, this does need to be much clearer. Besides, there should be greater enthusiasm shown for the benefit of complimentary data from multiple sources, and the intellectual effort of those who use the data to generate conclusions to the science goal questions. Actually, this is important: it is the real science bit. Then there is a notion of there being a group of people called engineers who have some useful expertise, and at some point down the line, you can ask them to do something on the infrastructure, and once they sign it off, it is job done. And the infrastructure isn t too hard, as similar things have been done before. Oh yes, and perhaps they might also be useful on some aspects of hardware design Wrong: Engineers aren t people who fix washing machines, we are highly educated scientists, and those in academia have research goals potentially additional science goals. Furthermore, engineers who have worked in industry have programme development and management expertise to an engineer, engineering means integrating the multitude of customer requirements, the feasibilities of design, and the programme timescales and costs, with the basic applied physics. Engineers have a central role to play, and need to be engaged in the programme development right at the front.
6 Why should Engineering be treated as an equal? The engineering community has a track record of building and programme managing large and complex infrastructural projects it is what engineers DO. Cost - To achieve true INTEGRATION put Engineering design and decision-making in from the start. Sales Revenue or Committed Spend Science Outcomes Actual Spend Time
7 Technology Readiness Levels? Quick summary: TRLs are numbered 1 to 9, where 1 is basic concept and 9 is technology deployed. TRL6 is a major issue, since it marks the successful demonstration in a real environment. Is the concept of TRL helpful? Obviously, yes, but - It provides no measure of the cost or time required to pass from one level to the next: Over simplification - To develop a new technology inevitably requires the development of multiple TRLs: Confusion - The TRL is not measured in terms of the value of the final capability: Not normalised
The innovation S curve cycle? 8 General idea: progress is made by major a innovation step followed by incremental improvements. Benefits Status Quo Eventually the rules change Initially difficult to make new technology competitive Time
9 Interpretation of S curve? Why is it initially difficult to make new technology competitive? - Design, analysis, validation, testing and manufacture for new technologies requires New Skills - New technology substitution into old-style concept implies Compromise - Lack of in-service experience implies conservative Over-design - Existing subtier suppliers offer existing technology at Lower prices Why eventually do the rules change? - Another technology improvement in another part of the product makes the current technology Redundant - The customer requirement changes and the value of technology is Compromised - Operational legislation or the political will changes, potentially Invalidating the technology application - New technology Overtakes the old
10 The Five WHYS? This is one of many concepts that have come from the Japanese manufacturing industry, where the focus is on achieving higher productivity, reduced costs, and higher quality. Why? is used as a prompt to understand the root cause of a problem. Applied to the design of a science programme, e.g. - Why are we here? to find answers to the science goals - Why Far Infrared? only observations of this wavelength will answer these science goals - Why these science goals and not others? big question - Why L2? to be outside Earth atmosphere, to avoid the Sun, to be somewhere semi-accessible to Earth, to reduce propellant needs not convinced about these answers - Why Interferometry? another big question - Why a rigid platform? tether option proposed? - Why Fourier? another big question
11 Capability Gaps: how this helps with funding bids? Survey the technology area of interest, and identify: - Integers of required capability (small enough to be realistic) - Assess the current TRL - Estimate the cost & time required to raise the TRL to target level - Identify key risks, & their nature time/cost/impossibility - Is the capability gap critical or optional? Ensure the alternatives are listed too. - Identify the primary science goals the gap would address - Are there secondary goals/customers? - Identify potential funding sources (think broad) What does this give: - The basis for a fully costed & timed programme plan (large scale) - A realistic indication of programme risk - A definitive reference for anyone seeking funding towards the goal - Legitimacy and credibility
12 Intellectual Property and Export Control? Does patenting pay? - Relatively low cost to file a patent and early years - More significant to maintain after first 7 years - Almost impossible for a small entity to protect a patent in a legal battle costs in millions or $ - Patenting only worthwhile if monopoly can be profitable OR - If the licensing or sale of the patent is used by another - Space technology small numbers off, is monopoly useful or should we really be thinking of Technology Transfer? Export control system designed to create a wedge between USA and rest of the world? - Particularly an issue for technologies subject to ITAR regulation
13 Technology Transfer? Spin-out? - Plan for this at the outset of the project: go back to the Capability Gaps register, and review each for alternative use - Present the work of the project (and the gaps) to as many different audiences as possible it is the best advertising Spin-in? - Again, using the Capability Gaps register, target the big technology exhibition events, and find people who might be able to help - Use the internet and the telephone Project Management - These aren t a trivial tasks they need to be planned, and allocated to particular personnel
14 Selling the project: words and body language? well, it s just - Modesty is easily mistaken for disinterest Start by using the word I and make a positive statement - There must be at least one positive statement you can make - Saying something positive demonstrates enthusiasm - It also manifests itself in your body language Presentation of arguments - Don t make assumptions about the audience - Start by defining the goals clearly - Show comparisons clearly in table form - Don t use negative language (e.g. competitors ) about what ought to be complimentary technologies - End with a positive statement to reinforce enthusiasm
15 Food for thought ENGINEERING SCIENCE GOALS
16 Engineering Material properties are easy? Demanding environments are usually: - Between -50 C and near melting point - ~Earth atmospheric pressure - Conditions for manufacturing processes - Corrosive environments, Impact, Vibration and High Cycle Fatigue So, a lack of good quality data for space regime - Coefficient of thermal expansion & Young s modulus at very low temperatures - Failure modes and stress levels for failure at low temperatures - Impact & Fatigue performance at low temperatures - Jury is still out on fracture mechanics as an established science Materials are not continuous and perfect they are made of atoms and are full of flaws. Engineering components are not perfectly sized and perfectly smooth.
17 Finite Element always gives the right answer? FEA is a mature capability: many off-the-shelf vendors: - For many runner-repeater analyses, it is true that FEA can be delegated to non-experts but this work is carefully framed - There is still an art to determining what analysis to do, and how to build an appropriate model - Validation of modelling is necessary FEA still has some inherent problems - Representation of material heterogeneity - Representation of uncertain geometry - Representation of uncertain boundary conditions - Representation of surface roughness Non-linearity tunnel vision thinking - Geometric: large deflection - Material property non-linearity - Non-linear dynamics
Material properties and Geometry and FEA: Axi-symmetric test piece, with Rough geometry, and then loaded to nominal 90% of yield 18
Rough region now comprised of five simple defects of size 16 mm and subjected to fatigue cycling between nominal 90% of yield, in tension and compression 1 st five reversals. 19 1 2 3 4 5
Rough region comprised of complex roughness 20 and viewed at the site of size a large irregular defect, subjected to fatigue cycling 1 st and 5 th reversals shown. This boundary seems to remain constant 1 5