Fueled by increasing processor speeds and high speed interconnection networks, advances in high performance computer architectures have allowed the development of increasingly complex large scale parallel systems. For computational scientists, programming these systems efficiently is a challenging task. Understanding the performance of their parallel applications is equally daunting. To observe and comprehend the performance of parallel applications that run on these systems, we need performance evaluation tools that can map the performance abstractions to the user's mental models of application execution. For instance, most parallel scientific applications are iterative in nature. In the case of CFD applications, they may also dynamically adapt to changes in the simulation model. A performance measurement and analysis system that can differentiate the phases of each iteration and characterize performance changes as the application adapts will enable developers to better relate performance to their application behavior. In this paper, we present new performance measurement techniques to meet these needs. In section 2, we describe our parallel performance system, TAU. Section 3 discusses how new TAU profiling techniques can be applied to CFD applications with iterative and adaptive characteristics. In section 4, we present a case study featuring the Uintah computational framework and explain how adaptive computational fluid dynamics simulations are observed using TAU. Finally, we conclude with a discussion of how the TAU performance system can be broadly applied to other CFD frameworks and present a few examples of its usage in this field.