<div class="section abstract"><div class="htmlview paragraph">Advancement in modern aircraft with the development of more dynamic and efficient technologies has led to these technologies increasingly operated near or at their operation limits. More comprehensive analysis methods based on high-fidelity models co-simulated in an integrated environment are needed to support the full utilization of these advanced technologies. Furthermore, the additional information provided by these new analyses needs to be correlated with updates to traditional metrics and specifications. One such case is the thermal limit requirement that sets the upper bound on a thermal system temperature. Traditionally, this bound is defined based on steady-state conditions. However, advanced thermal management systems experience dynamic events where the temperature is not static and may violate steady-state requirements for brief periods of time. Due to the large thermal time constants for many components, such transient violations may not represent system failure and an understanding of transient temperature limits is beneficial. To meet this need, this paper introduces the transient thermal limit via control volume representation. Instead of a constant thermal limit, the transient thermal limit approach generates a dynamic temperature profile limit by representing the thermal system with a control volume and scaling the input temperature profile such that the control volume does not exceed the steady-state temperature limit. This simple approach is based on a physical representation that is customizable to each system and can dynamically adjust the limit based on system conditions. Additionally, this control volume transient temperature limit methodology was developed to minimize information sharing between proprietary systems. The details of this transient limit generation methodology will be reviewed in this paper and illustrated through application on an example thermal system.</div></div>
<div class="section abstract"><div class="htmlview paragraph">In this paper, a MATLAB-Simulink based general co-simulation approach is presented which supports multi-resolution simulation of distributed models in an integrated architecture. This approach was applied to simulating aircraft thermal performance in our Vehicle Systems Model Integration (VSMI) framework. A representative advanced aircraft thermal management system consisting of an engine, engine fuel thermal management system, aircraft fuel thermal management system and a power and thermal management system was used to evaluate the advantages and tradeoffs in using a co-simulation approach to system integration modeling. For a system constituting of multiple interacting sub-systems, an integrated model architecture can rapidly, and cost effectively address technology insertions and system evaluations. Utilizing standalone sub-system models with table-based boundary conditions often fails to effectively capture dynamic subsystem interactions that occurs in an integrated system. Additionally, any control adjustments, model changes or technology insertions that are applied to any one of the connecting subsystems requires iterative updates to the boundary conditions. When evaluating a large set of trade studies, the number of boundary condition models and time to generate these models becomes intractable and affects capturing the results accurately. A single interconnected model of all the subsystems may be impractical and using additional external packages may be prohibitive in terms of cost or compatibility. This general approach requires no additional MATLAB toolboxes. Two different data interchange mechanisms are presented. A dynamic vehicle system integrated model was developed to enable customizability and flexibility. The developed co-simulation approach was combined with this flexible architecture to enable system evaluation. Example applications using the vehicle system model integrated architecture with the co-simulation approach are discussed.</div></div>
This paper presents a new method for determining the site for power system stabilizer (PSS) installation. This method tracks the eigenvalues as they go from the interconnected power system to a decoupled system of single machine infinite buses (SMIB). The site for PSS installation is determined by observing to which machine the unstable eigenvalues from the interconnected system track to the decoupled system. This method is compared to the participation factor (PF) placement method for two different power system cases. The results show that the eigenvalue tracking and the PF placement method determined the same site for PSS installment in most cases. In the cases that they did not determine the same location, the eigenvalue tracking method determined the better site for PSS installment in a majority of cases. For completeness both the sites pointed out by the eigenvalue tracking method and the PF placement method should be investigated for PSS installment.I.
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