Full-Scaled Advanced Systems Testbed: Ensuring Success of Adaptive Control Research Through Project Lifecycle Risk Mitigation

The National Aeronautics and Space Administration's Dryden Flight Research Center completed flight testing of adaptive controls research on the Full-Scale Advance Systems Testbed (FAST) in January of 2011. The research addressed technical challenges involved with reducing risk in an increasingly complex and dynamic national airspace. Specific challenges lie with the development of validated, multidisciplinary, integrated aircraft control design tools and techniques to enable safe flight in the presence of adverse conditions such as structural damage, control surface failures, or aerodynamic upsets. The testbed is an F-18 aircraft serving as a full-scale vehicle to test and validate adaptive flight control research and lends a significant confidence to the development, maturation, and acceptance process of incorporating adaptive control laws into follow-on research and the operational environment. The experimental systems integrated into FAST were designed to allow for flexible yet safe flight test evaluation and validation of modern adaptive control technologies and revolve around two major hardware upgrades: the modification of Production Support Flight Control Computers (PSFCC) and integration of two, fourth-generation Airborne Research Test Systems (ARTS). Post-hardware integration verification and validation provided the foundation for safe flight test of Nonlinear Dynamic Inversion and Model Reference Aircraft Control adaptive control law experiments. To ensure success of flight in terms of cost, schedule, and test results, emphasis on risk management was incorporated into early stages of design and flight test planning and continued through the execution of each flight test mission. Specific consideration was made to incorporate safety features within the hardware and software to alleviate user demands as well as into test processes and training to reduce human factor impacts to safe and successful flight test. This paper describes the research configuration, experiment functionality, overall risk mitigation, flight test approach and results, and lessons learned of adaptive controls research of the Full-Scale Advanced Systems Testbed

Flight Test Engineering

Although the scope of flight test engineering efforts may vary among organizations, all point to a common theme: flight test engineering is an interdisciplinary effort to test an asset in its operational flight environment. Upfront planning where design, implementation, and test efforts are clearly aligned with the flight test objective are keys to success. This chapter provides a top level perspective of flight test engineering for the non-expert. Additional research and reading on the topic is encouraged to develop a deeper understanding of specific considerations involved in each phase of flight test engineering

Flight Test Approach to Adaptive Control Research

The National Aeronautics and Space Administration s Dryden Flight Research Center completed flight testing of adaptive controls research on a full-scale F-18 testbed. The validation of adaptive controls has the potential to enhance safety in the presence of adverse conditions such as structural damage or control surface failures. This paper describes the research interface architecture, risk mitigations, flight test approach and lessons learned of adaptive controls research

Drivers of Sub-Seasonal to Interannual Shoreline Change at Sunset State Beach in Monterey Bay, CA

Expectations of future change call for a thorough understanding of short- and long-time scale processes that impact sandy beaches, as well as tests of coastal change models in a variety of coastal settings. However, existing shoreline change models have primarily been developed and tested in open coast environments. Therefore, this study takes place in the northern Monterey Bay where we investigate the effects of headland sheltering and complex inner shelf bathymetry on shoreline change at a sandy dune-backed beach, fronted by a submarine canyon system. Twenty months of half-hourly video imagery were used to build a high-resolution time series of shoreline and sandbar positions at Sunset State Beach from September 2017 to May 2019. Past studies have shown that high magnitudes of winter shoreline erosion in the Monterey Bay occur during El Niño periods, when storm tracks over the northeast Pacific Ocean shift southward. This motivated the assessment of interannual shoreline variability by extending the shoreline time series back to September 2014 with biannual in-situ surveys. According to the video derived observations, the shoreline varied by approximately 60 meters while the sandbar varied by approximately 100 meters in the cross-shore direction. Winter shoreline erosion began when nearshore significant wave heights exceeded the 95th percentile (1.7m), and a greater magnitude of shoreline erosion occurred with higher average winter wave energy. Shoreline accretion appeared to be aided by the sandbar, which acted as a source of sediment in the early summer months of 2018. The influence of wave energy and direction on shoreline change was tested using an equilibrium shoreline change model and an alongshore sediment transport model. Shoreline change at Sunset State Beach depended primarily on wave energy, the root-mean-squared error (RMSE) of the equilibrium model alone was 6.4m. The addition of alongshore sediment transport to overall shoreline change resulted in a modest RMSE reduction to 5.6m, but equilibrium model parameters did not change significantly. According to the biannual time series of shoreline observations, high magnitudes of shoreline erosion can also occur during non- El Niño periods, due to westerly waves that bypass the Santa Cruz headlands and expose the northern Monterey Bay to wave attack. The accuracy of the shoreline change models used in this study was limited by annual variability in the summer shoreline position, motivating future investigations of temporally variable alongshore sediment supply. The results suggest that rather than relying on predictions of an El Niño index to predict shoreline change, predictions of the direction of storm tracks over the northeast Pacific Ocean could more accurately inform shoreline change predictions at the study site and in similar environments

Getting to the heart of leading as a Cognitive Coach

While the expectations and challenges facing K–12 educational leaders are considerable and significantly increasing, the support they receive in training, leadership development, and ongoing support has been limited in range and relevance. Although Cognitive Coaching is not a leadership development program, per se, Cognitive Coaching is a model of coaching that has been highly regarded and frequently requested by administrators, teachers, consultants, and literacy coaches in K–12 education, and the components of this coaching model can serve as a basis for leadership development. Previous research about Cognitive Coaching has highlighted the benefits of Cognitive Coaching for students, teachers, administrators, and people in fields outside of teaching. This qualitative study, in the tradition of interpretative phenomenological analysis, focuses on the experiences and perceptions of five educational leaders both in being coached and in using Cognitive Coaching as part of their leadership practice. For these leaders, the story behind mastering the skill and not the concept includes engaging in what is simple but not easy, daring to be vulnerable, embracing service and authenticity, and leaning into the value practices of leading with a learning mindset, confident humility, self-awareness, mindfulness, and intentionality to align their behavior with their values. The findings indicate that Cognitive Coaching is about more than cognition; it is rooted in affect and values. Embracing vulnerability is foundational to Cognitive Coaching and leading as a Cognitive Coach. To get to daring vulnerability, Cognitive Coaching leaders need to be authentic and have service at the heart. Keywords: educational leader, Cognitive Coaching, leadership development, professional development, vulnerability, authenticity, service, affect, emotion, confident humilit

Drivers of sub-seasonal to interannual shoreline change at Sunset State Beach in Monterey Bay, CA

"A thesis presented to the faculty of Moss Landing Marine Laboratories." ABSTRACT: Expectations of future change call for a thorough understanding of short- and long-time scale processes that impact sandy beaches, as well as tests of coastal change models in a variety of coastal settings. However, existing shoreline change models have primarily been developed and tested in open coast environments. Therefore, this study takes place in the northern Monterey Bay where we investigate the effects of headland sheltering and complex inner shelf bathymetry on shoreline change at a sandy dune-backed beach, fronted by a submarine canyon system. Twenty months of half-hourly video imagery were used to build a high-resolution time series of shoreline and sandbar positions at Sunset State Beach from September 2017 to May 2019. Past studies have shown that high magnitudes of winter shoreline erosion in the Monterey Bay occur during El Niño periods, when storm tracks over the northeast Pacific Ocean shift southward. This motivated the assessment of interannual shoreline variability by extending the shoreline time series back to September 2014 with biannual in-situ surveys.According to the video derived observations, the shoreline varied by approximately 60 meters while the sandbar varied by approximately 100 meters in the cross-shore direction. Winter shoreline erosion began when nearshore significant wave heights exceeded the 95th percentile (1.7m), and a greater magnitude of shoreline erosion occurred with higher average winter wave energy. Shoreline accretion appeared to be aided by the sandbar, which acted as a source of sediment in the early summer months of 2018. The influence of wave energy and direction on shoreline change was tested using an equilibrium shoreline change model and an alongshore sediment transport model. Shoreline change at Sunset State Beach depended primarily on wave energy, the root-mean-squared error (RMSE) of the equilibrium model alone was 6.4m. The addition of alongshore sediment transport to overall shoreline change resulted in a modest RMSE reduction to 5.6m, but equilibrium model parameters did not change significantly. According to the biannual time series of shoreline observations, high magnitudes of shoreline erosion can also occur during non- El Niño periods, due to westerly waves that bypass the Santa Cruz headlands and expose the northern Monterey Bay to wave attack. The accuracy of the shoreline change models used in this study was limited by annual variability in the summer shoreline position, motivating future investigations of temporally variable alongshore sediment supply. The results suggest that rather than relying on predictions of an El Niño index to predict shoreline change, predictions of the direction of storm tracks over the northeast Pacific Ocean could more accurately inform shoreline change predictions at the study site and in similar environments. RELATED TITLE: https://csu-mlml.primo.exlibrisgroup.com/permalink/01CALS_MLM/136dcpr/alma991000286389702913"A thesis presented to the faculty of Moss Landing Marine Laboratories.

Ensuring Success of Adaptive Control Research Through Project Lifecycle Risk Mitigation

Lessons Learne: 1. Design-out unnecessary risk to prevent excessive mitigation management during flight. 2. Consider iterative checkouts to confirm or improve human factor characteristics. 3. Consider the total flight test profile to uncover unanticipated human-algorithm interactions. 4. Consider test card cadence as a metric to assess test readiness. 5. Full-scale flight test is critical to development, maturation, and acceptance of adaptive control laws for operational use

Discover Engineering

A presentation defining engineering, the difference and link between science and flight, and NASA flight test at DFRC