202 research outputs found
Hierarchical multilevel optimization for reliability target allocation in probabilistic design of decomposed systems
Analytical target cascading (ATC) is a methodology for translating system-level design targets to subsystem and component design specifications in hierarchically decomposed optimal system design problems. In previous work we extended the ATC formulation to account for uncertainties, where the bounds of the probabilistic design constraints were chosen arbitrarily and held fixed during the ATC process. In this work, we extend the probabilistic ATC formulation to include reliability targets in the vector of cascaded quantities. In this manner, we quantify the optimality-reliability tradeoffs for each element of the decomposed system and compute the probabilistic constraint bounds required to satisfy the overall system reliability target. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/61433/1/2060079_ftp.pd
Reliability Allocation in Probabilistic Design Optimization of Decomposed Systems Using Analytical Target Cascading
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76808/1/AIAA-2008-6040-688.pd
Collaborative Product–Service Approach to Aviation Maintenance, Repair, and Overhaul. Part II: Numerical Investigations
This two-part paper proposes a new collaborative approach to airframe maintenance, repair, and overhaul (MRO). A quantitative model is introduced in Part I to represent the business relationships between original equipment manufacturers (OEMs) and MRO enterprises. In Part II, the presented model is used to assess potential financial benefits obtained by each of these stakeholders as a result of the collaboration.
The quantitative model is built to capture the main dependencies between an independent MRO operating in South America and its interactions with three major airframe OEMs. Interviews were conducted with MRO and OEM professionals to identify the most impactful operational resources on MRO activities. Stakeholders with different characteristics in terms of production capacity, annual revenue, fleet size, and age are considered in the numerical studies to quantify the viability of the proposed collaborative business model in different scenarios.
The obtained results show that optimal investment levels must be determined for each stakeholder to ensure the viability of the proposed collaborative business model, confirming the need for a quantitative method to aid service designers making decisions.
This collaborative model contributes to the relatively scarce literature on the topic and promotes effective and structured collaboration between OEMs and MRO enterprises aiming at delivering higher added value to customers (operators)
Optimal Design of Commercial Vehicle Systems Using Analytical Target Cascading
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97129/1/AIAA2012-5524.pd
Impact of Uncertainty Quantification on Design Decisions for a Hydraulic-Hybrid Powertrain Engine
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76707/1/AIAA-2006-2001-814.pd
Exploring the Potential of Digital Twin-Driven Design of Aero-Engine Structures
As the diversity of customer needs increases within the aerospace industry, so does the need for improved design practices to reduce quality issues downstream. When designing new products, design engineers struggle with applying tolerances to features, which often leads to expensive late design iterations. To mitigate this, one aerospace company is looking to reuse tolerance deviation data yielded during manufacturing in design. In the long term these data could provide the basis for a Digital Twin that can be used for improved product development. This article explores how data from production are used today, what issues prevents such data from being exploited in the design phase, and how they potentially could be used for design purposes in the future. To understand the current situation and identify the untapped potential of production data in design, an interview study was conducted in conjunction with a literature review. In this paper the current situation and primary barriers are presented and a possible path for further research and development is suggested
Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates
This work introduces the StoMADS-PB algorithm for constrained stochastic
blackbox optimization, which is an extension of the mesh adaptive direct-search
(MADS) method originally developed for deterministic blackbox optimization
under general constraints. The values of the objective and constraint functions
are provided by a noisy blackbox, i.e., they can only be computed with random
noise whose distribution is unknown. As in MADS, constraint violations are
aggregated into a single constraint violation function. Since all functions
values are numerically unavailable, StoMADS-PB uses estimates and introduces
so-called probabilistic bounds for the violation. Such estimates and bounds
obtained from stochastic observations are required to be accurate and reliable
with high but fixed probabilities. The proposed method, which allows
intermediate infeasible iterates, accepts new points using sufficient decrease
conditions and imposing a threshold on the probabilistic bounds. Using Clarke
nonsmooth calculus and martingale theory, Clarke stationarity convergence
results for the objective and the violation function are derived with
probability one
Sequential stochastic blackbox optimization with zeroth-order gradient estimators
This work considers stochastic optimization problems in which the objective
function values can only be computed by a blackbox corrupted by some random
noise following an unknown distribution. The proposed method is based on
sequential stochastic optimization (SSO): the original problem is decomposed
into a sequence of subproblems. Each of these subproblems is solved using a
zeroth order version of a sign stochastic gradient descent with momentum
algorithm (ZO-Signum) and with an increasingly fine precision. This
decomposition allows a good exploration of the space while maintaining the
efficiency of the algorithm once it gets close to the solution. Under Lipschitz
continuity assumption on the blackbox, a convergence rate in expectation is
derived for the ZO-signum algorithm. Moreover, if the blackbox is smooth and
convex or locally convex around its minima, a convergence rate to an
-optimal point of the problem may be obtained for the SSO algorithm.
Numerical experiments are conducted to compare the SSO algorithm with other
state-of-the-art algorithms and to demonstrate its competitiveness
Dynamic lifecycle cost modeling for adaptable design optimization of additively remanufactured aeroengine components
Additive manufacturing (AM) is being used increasingly for repair and remanufacturing of aeroengine components. This enables the consideration of a design margin approach to satisfy changing requirements, in which component lifespan can be optimized for different lifecycle scenarios. This paradigm requires lifecycle cost (LCC) modeling; however, the LCC models available in the literature consider mostly the manufacturing of a component, not its repair or remanufacturing. There is thus a need for an LCC model that can consider AM for repair/remanufacturing to quantify corresponding costs and benefits. This paper presents a dynamic LCC model that estimates cumulative costs over the in-service phase and a nested design optimization problem formulation that determines the optimal component lifespan range to minimize overall cost while maximizing performance. The developed methodology is demonstrated by means of an aeroengine turbine rear structure
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