Matching of cycle models to test results for gas turbine engines is fundamentally an exercise in optimal robust parameter estimation. The problem is inherently nondeterministic in nature, so it stands to reason that probabilistic information can be used to secure improved match accuracy if properly employed. This paper proposes an approach to status matching that eliminates the need for parameter weightings, gives an intuitive feel for the results in terms of probabilistic parameters, utilizes all available information (probabilistic and deterministic), and yields high-accuracy matches between model prediction and experiment. The method development begins with a typical sum-squared errors approach and subsequently builds on this. A deviation penalty function formulation is introduced to improve solution conditioning and a multi-objective measure of fit based on the Z-score is introduced. Several measures of model fit are compared including summed-squared errors, minimax, and sum absolute error formulations. This approach is first demonstrated for a simple cantilever beam parameter-matching problem, and is later discussed in the context of turbofan engine matching.
The emergence of the field of mini-to meso-scale unmanned aerial vehicle (UAV) design has generated renewed interest in propeller modeling, analysis and design. This paper presents a procedure for deriving the performance of an UAV-scale propeller from geometric measurements using commercially available airfoil modeling software and the vortex theory of airscrew propellers. Vortex theory formulations using the Prandtl tip loss factor as well as the Goldstein circulation function are presented and results are compared to wind-tunnel tests of UAV propellers. The effects of measurement and modeling uncertainties on the performance of the propeller are quantified and propagated through the algorithm using system sensitivity analysis.
Traditionally, gas turbine power plant preventive maintenance schedules are set with constant intervals based on recommendations from the equipment suppliers. Preventive maintenance is based on fleet-wide experience as a guideline as long as individual unit experience is not available. In reality, the operating conditions for each gas turbine may vary from site to site and from unit to unit. Furthermore, the gas turbine is a repairable deteriorating system, and preventive maintenance usually restores only part of its performance. This suggests a gas turbine needs more frequent inspection and maintenance as it ages. A unit-specific sequential preventive maintenance approach is therefore needed for gas turbine power plant preventive maintenance scheduling. Traditionally, the optimization criteria for preventive maintenance scheduling is usually cost based. However, in the deregulated electric power market, a profit-based optimization approach is expected to be more effective than the cost-based approach. In such an approach, power plant performance, reliability, and the market dynamics are considered in a joint fashion. In this paper, a novel idea that economic factors drive maintenance frequency and expense to more frequent repairs and greater expense as equipment ages is introduced, and a profitbased unit-specific sequential preventive maintenance scheduling methodology is developed. To demonstrate the feasibility of the proposed approach, a conceptual level study is performed using a base load combined cycle power plant with a single gas turbine unit.The deregulation of the electric power market has introduced a strong element of competition. As a result, power plant operators are striving to develop advanced operational strategies to maximize the profitability in the dynamic electric power market.A systematic approach for profit-based outage planning is introduced in Ref. ͓1͔, with consideration given to system performance, the aging and reliability of equipment, maintenance practices, and market dynamics accounting for the price and availability of fuel as well as the generation of revenues in competing markets. A dual time-scale method is developed to project coupled optimal generation scheduling and outage planning for a single operations and maintenance cycle. This paper studies gas turbine power plant maintenance scheduling with consideration given to multiple operations and maintenance cycles. Specifically, the impact of unit aging on maintenance frequency is investigated over the life cycle of power plants.Gas turbine units are widely used for land electric power generation, and maintenance planning has a strong impact on the profitability of a gas turbine power plant. Performance requirements for modern heavy-duty gas turbines necessitate extreme operating conditions for hot gas path components. As a result, these critical components have a limited life span and, more generally, a gas turbine represents an aging system experiencing continuous degradation during its operation. This physical degradation m...
We describe the use of the Cyber-Physical Modeling Language (CyPhyML) to support trade studies and integration activities in system-level vehicle designs. CyPhyML captures parameterized component behavior using acausal models (i.e. hybrid bond graphs and Modelica) to enable automatic composition and synthesis of simulation models for significant vehicle subsystems. Generated simulations allow us to compare performance between different design alternatives. System behavior and evaluation are specified independently from specifications for design-space alternatives. Test bench models in CyPhyML are given in terms of generic assemblies over the entire design space, so performance can be evaluated for any selected design instance once automated design space exploration is complete. Generated Simulink models are also integrated into a mobility model for interactive 3-D simulation.
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