Farooq Akram

Improved Technology Impact Modeling Through Technology Synergy Matrices

The management of technology portfolios is an important element of aerospace system design. New technologies are often applied to new product designs to ensure their competitiveness at the time they are introduced to market. A designer may forecast the future capability of these technologies, expecting the portfolio of technologies to mature in parallel with the product design. The long lead times, high development costs, and performance uncertainties make this both a challenging and risky problem. Designers need an effective method to identify and select promising technologies based on the information at hand. This information may come from expert opinion, theory, and/or limited experimentation. Previously proposed methods for technology impact forecasting attempt to map technology selection directly to system performance metrics, also known as k-factors, through technology impact matrices (TIM) and technology compatibility matrices (TCM). The TIM and TCM are formalized methods of capturing expert opinion regarding the impacts and feasibility of new technology combinations. A downside of this approach is that 2 nd order and higher technology interactions are neglected. These methods assume that technologies are either incompatible or linearly independent. In other words, their net impact on the system is additive. This paper proposes the use of technology synergy matrices (TSM) to increase the fidelity of TIM and TCM methods by including second order technology interactions. A strategy for TSM creation is described in detail and the complete process of TIM, TCM, and TSM utilization is demonstrated. Five new methods of TSM implementation are proposed: weighted pair-wise comparison, direct product, averaged, minimum, and maximum. A notional example problem, where seven interacting propulsion and aerodynamic technologies are applied to improve the fuel consumption of a transport aircraft, is presented. By applying each method to the example problem, the limitations and benefits of each method are explored. These results show that the inclusion of 2 nd order technology impacts can significantly change the technology portfolio that is selected as well as the predicted system performance.

An Improved Approach to Technology Portfolio Prioritization Process Under Uncertainty

Accurate technology modeling is a challenge, especially when it comes to revolutionary concepts. Absence of historical trends and experimental data for these concepts make it harder to predict precise effects. This situation makes it imperative to make use of subject area expert elicitation. This knowledge generally comes with subjective opinion about impact of technology on performance and market related metrics. These opinions from multiple subject matter experts may vary depending upon their past experiences and personalized preferences. In order to cater to difference of opinion from experts, uncertainty quantification on these inputs and its propagation to the performance and marketing metrics is very important. In addition to input uncertainties, technology interactions play a vital role when multiple technologies are selected simultaneously. There are some processes already in practice to deal with these interactions. These interactions and incompatibilities are currently modeled through technology impact matrices (TIM) and technology compatibility matrices (TCM). It however requires some further refinement. Generally these processes assume the impact of these technologies to be additive when portfolio of technologies are applied. In reality, these technologies are not additive in nature. This problem is addressed through introduction of Technology Synergy Matrices (TSM). In this paper, an evidence theory based TIM and TSM process is demonstrated within the context of an aircraft engine design problem. A representative set of candidate technologies and impacts are provided as examples. Once a combination of technologies is selected, an uncertainty propagation approach is used to evaluate the range of potential effects of the system. In the end, results are compared with those obtained from deterministic approach. The TSM, when used in conjunction with TIM, offers more accurate quantification of technology interactions and allow for technology nonlinearities. At the same time, uncertainty quantification enables the designer to capture the probabilistic Pareto-frontiers that allow the designer to select robust portfolio of technologies. This eliminates unnecessary assumptions while constructing deterministic TIM. Comparison of results from proposed methodologies with deterministic approach shows the design space previously unexplored due to limitations of existing practices.Copyright

A comparison between Monte Carlo and Evidence Theory Approaches for Technology Portfolio Planning

Uncertainty propagation is an important step in technology forecasting. Uncertainty in technology impacts and other system variables must be propagated into the system performance metrics that will be used to judge competing technology portfolios. Monte Carlo simulation and Evidence Theory are two uncertainty propagation techniques commonly used for this purpose. This paper explores the differences between these two techniques when applied to a representative aircraft engine technology forecasting problem. As an example problem, uncertainties in compressor, turbine, and combustor efficiencies are propagated into seven aircraft engine performance metrics. A surrogate model built from NPSS and WATE++ data provides modeling and simulation of the system. A representative set of expert opinion derived uncertainty intervals drives this process. The MATLAB IPP Toolbox is used to conduct Evidence Theory uncertainty propagation on the example problem. The results presented show the impact of sample size selection (number of cases), method selection (Monte Carlo or Evidence Theory), and mixing rule (Dempster’s rule or weighted mixing) on the resulting CDFs. The increase in runtime with sample size for both methods is shown, as well.

An Application of Evidence Theory to Subject Matter Expert based Technology Portfolio Analysis

Technology portfolio management remains a challenging problem, especially in the context of gas turbine component design. The future performance of yet-to-be designed components is inherently uncertain, necessitating statistical methods and subject matter expert knowledge. Estimates of the appropriate parameter settings often must come from disciplinary experts, who may disagree with each other due to varying experience and background. Components interact non-linearly through their aerodynamic, thermal, and mechanical linkages. Because of these linkages, new technologies in one component may induce secondary impacts in other engine components. Thus, technology portfolio analysis for gas turbines is further complicated by the dynamics of component interaction. Various techniques have been proposed to solve the technology portfolio analysis problem for gas turbine engines. Many of these techniques are deterministic or require the user to make many assumptions regarding the probability distributions of future technologies’ performance. This paper explores the application of a newly proposed evidence theory based technique to subject matter expert driven technology portfolio planning. Authors of this paper recently proposed this technique that offsets some of the problems faced by using deterministic or traditional probabilistic approaches in such cases. One focus of this paper is the demonstration of the steps required to setup a modeling and simulation environment, formulate the multiobjective problem, and produce results using current modeling and optimization tools. Another focus of this paper is a description of the subject matter expert elicitation process, an evaluation of the subject matter expert data required, and a sensitivity analysis of the portfolio solution results with respect to subject matter expert error.

An Improved Methodology for Gas Turbine Technology Portfolio Planning, Including Technology Synergy Matrices and Real Options Analysis

The management of technologies remains a challenging and risky constituent of gas turbine component design. The performance impact of a single new technology on a gas turbine component is often uncertain, requiring statistical methods to fully quantify. The inclusion of multiple new technologies further complicates gas turbine component performance prediction because there may be significant non-linear technology interactions. When designing gas turbine components to operate within the context of a fully specified gas turbine engine, each component introduces its own mix of new technologies. Because gas turbine components are linked thermodynamically, a new technology in one component may induce secondary impacts in other engine components. Thus, technology portfolio analysis for gas turbines is further complicated by the dynamics of component interaction. In this paper, a new technology portfolio planning methodology is presented that directly addresses these three inherent complexities of gas turbine component design. This methodology includes Technology Impact Matrices to quantify component level technology impacts as well as Technology Compatibility Matrices and Technology Synergy Matrices to account for non-linear component level technology interactions. Through surrogate modeling, a physicsbased dynamic engine model is formed that quickly translates component technology impacts into total-system thermodynamic performance. Real Options analysis is used to translate engine performance metrics to net engine economic performance. Finally, a probabilistic multi-objective genetic algorithm is used to discover a robust Pareto front of optimal technology combinations with respect to system level thermodynamic and economic performance metrics. This paper first describes the proposed methodology in detail. Next, a benchmark gas turbine technology portfolio problem is outlined. Finally, a baseline technology portfolio planning methodology and the newly proposed methodology are both executed upon the benchmark problem, allowing for the benefits of the new methodology to be quantified.

Uncertainty Propagation in Technology Valuation Based On Expert Elicitation

Technology valuation for technologies under development is a complex process. The impacts of these technologies on system level performance metrics are often unknown. Modeling of these technologies combined with appropriate quantification methods pose further challenges to the process. Absence of any historical data to capture the impacts of these technologies on the system necessitates elicitation of information from subject matter experts (SMEs). The SMEs are asked about their opinion regarding estimated impacts on the system based on their experience and prior knowledge. Due to the inherently uncertain nature of expert elicitation in the technology valuation process, appropriate uncertainty quantification and propagation is very critical. The uncertainty in defining the impact of an input on performance parameters of a system makes it difficult to use traditional probability theory. Often the available information is not enough to assign the appropriate probability distributions to uncertain inputs. This work explores the application of the Dempster-Shafer theory of evidence based technique to SME-driven technology valuation. The proposed technique seeks to offset some of the problems faced by using deterministic or traditional probabilistic approaches for uncertainty propagation. It is observed that an evidence theory based technique provides more insight on the uncertainties arising from incomplete information or lack of knowledge as compared to deterministic or probability theory methods.

Improved Submerged Inlet Conceptual Design Process using Data Mining and Surrogate Modeling

Submerged inlets have the potential to increase aircraft performance, but are difficult to design and integrate within an aircraft fuselage. This difficulty comes from the inherent complexity of submerged inlet flowfields. The strong vortices, thick boundary layers, and turbulent behavior of the submerged inlet requires that Navier-Stokes simulation or wind tunnel testing be used to provide design analysis. For the designer who wishes to optimize submerged inlet geometries at the low to medium fidelity “conceptual design” level, few viable optimization strategies are available. RANS CFD-based shape optimization is useful for producing localized optima for detailed design but remains too computationally expensive for large scale conceptual design studies. The execution of a coarse set of wind tunnel experiments at the conceptual level is prohibitively expensive and time consuming. Published wind tunnel studies provide limited sensitivity information, tend to be univariate in nature, and provide data within a limited range of applicability. The ability to interpolate a large mixed database of separate wind tunnel or computational experiments would provide an enhanced multivariate simulation capability for the inlet designer. This paper presents a methodology for the efficient data mining of a large non-homogenous, mixed database of submerged inlet experiments for the purposes of conceptual design. The methodology presented uses the techniques of design of experiments, surrogate modeling, and genetic algorithm optimization to efficiently determine an optimized inlet design. Steps of the proposed methodology are described in detail. Finally, a practical example problem is shown using NACA submerged inlet wind tunnel experiments as a database and ANSYS Fluent® with Sculptor® by Optimal Solutions for final solution verification.

Design Space Exploration of Submerged Inlet Capturing Interaction between Design Parameters

Submerged inlets have been of interest to the aeronautical community for years due to the high on-design (cruise) performance they may potentially provide. Submerged inlets require less weight and experience lower drag than other inlet topologies. Engineers have been slow to adopt the submerged inlet topology for their designs because of the inherent difficulty of submerged inlet design. The complex interaction between vortices and boundary layer growth within the submerged inlet necessitates the use of experiment or Navier-Stokes CFD simulation to adequately predict the performance of a new design. Accurate prediction of the submerged inlet flow field is required to minimize the off-design (non-cruise) performance degradation seen in this inlet configuration. This paper extends and further demonstrates the authors’ two part methodology for submerged inlet design. First, an experiment based data mining and surrogate modeling technique is used for a low-fidelity prediction of the design parameter interactions and optimization. This includes use of 3 rd order response surface effects screening and neural network based sensitivity profiler in SAS JMP® to characterize design variable sensitivities. Next, a CFD based design of experiments is used to sample the design space around the candidate design geometry, produce a high fidelity surrogate model, and further refine a submerged inlet design. This paper demonstrates the application of this methodology to the optimization of pressure recovery for an Unmanned Aerial Vehicle (UAV) submerged inlet operating in the high subsonic flight regime. For the example problem, CFD results are generated by the combination of the ANSYS Fluent® solver and Optimal Solutions Sculptor® mesh deformation tools. The benchmark example problem is described and each step of the methodology is demonstrated. Predictions of design variable sensitivity and design variable interactions are provided during the process. Results from this example problem show that the database driven surrogate model provides a candidate optimum that is very close to the CFD predicted optimum while reducing the required CFD runtime by at least 60% compared to CFD based global optimization approaches.

Surrogate Modeling Based Uncertainty Quantification In Subject Matter Expert Systems

The future performance of yet-to-be designed technologies is inherently uncertain. Absence of any historical data to capture the impacts of these technologies on the system necessitates the elicitation of subject matter expert knowledge, application of statistical methods and selection of suitable techniques for quantification of uncertainties in the process. Estimates of the appropriate parameter settings often come from disciplinary experts, who may disagree with each other because of varying experience and backgrounds. It is observed that in case of such lack of knowledge about the problem, epistemic uncertainty is most suitable representation of the process. Epistemic uncertainty can be quantified by many techniques. In present study it is proposed that interval analysis and DempsterShafer theory of evidence are better suited for quantification of epistemic uncertainty in technology valuation process. One of the problems faced in applying these non-deterministic methods is the heavy computational effort that is required to perform such analyses. Surrogate models can help alleviate this issue. The surrogate models enable fast execution of disciplinary codes and hence act as an enabler for uncertainty quantification techniques that are inherently expensive as far computational efforts are concerned. Proposed technique seeks to offset some of the problems faced by using deterministic or traditional probabilistic approaches for uncertainty propagation. A detailed modeling and simulation environment for gas turbine is setup. Latin hypercube DoE was used to sample the design space of a gas turbine. A surrogate model was created using a neural network. Sensitivity analysis is then carried out. The inputs for the M&S environment are selected based on this sensitivity analysis. The results generated from proposed technique provide a comprehensive understanding on the effects of uncertainty on the problem.