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Stability and sensitivity requirements considered simultaneously in control system design, minimizing time domain sensitivity index and incorporating transient response characteristics
Stability and sensitivity requirements considered simultaneously in control system design, minimizing time domain sensitivity index and incorporating transient response characteristics
Radiative heating and cooling provide primary source and ultimate sink of energy driving lower planetary atmospheres. Evaluating the sensitivities of atmospheric dynamics models on these primary atmospheric parameters requires knowing how heating and cooling rates depend on these same parameters. We discuss two approaches that make it possible to directly compute the sensitivities of heating and cooling rates in parallel with evaluation of heating and cooling rates themselves.
The performance of Orion flight software, especially its GNC software, is being analyzed by running Monte Carlo simulations of Orion spacecraft flights. The simulated performance is analyzed for conformance with flight requirements, expressed as performance constraints. Flight requirements include guidance (e.g. touchdown distance from target) and control (e.g., control saturation) as well as performance (e.g., heat load constraints). The Monte Carlo simulations disperse hundreds of simulation input variables, for everything from mass properties to date of launch.We describe in this paper a sensitivity analysis tool (Critical Factors Tool or CFT) developed to find the input variables or pairs of variables which by themselves significantly influence satisfaction of requirements or significantly affect key performance metrics (e.g., touchdown distance from target). Knowing these factors can inform robustness analysis, can inform where engineering resources are most needed, and could even affect operations. The contributions of this paper include the introduction of novel sensitivity measures, such as estimating success probability, and a technique for determining whether pairs of factors are interacting dependently or independently. The tool found that input variables such as moments, mass, thrust dispersions, and date of launch were found to be significant factors for success of various requirements. Examples are shown in this paper as well as a summary and physics discussion of EFT-1 driving factors that the tool found.
Recent advances in artificial intelligence, machine learning, data mining and extraction, and especially in sensor technology have resulted in the availability of a vast amount of digital data and information and the development of advanced automated reasoners. This creates the opportunity for the development of a robust dynamic task manager and decision support tool that is context sensitive and integrates information from a wide array of on-board and off aircraft sourcesa tool that monitors systems and the overall flight situation, anticipates information needs, prioritizes tasks appropriately, keeps pilots well informed, and is nimble and able to adapt to changing circumstances. This is the first of two companion reports exploring issues associated with autonomous, context-sensitive, task management and decision support tools. In the first report, we explore fundamental issues associated with the development of an integrated, dynamic, flight information and automation management system. We discuss human factors issues pertaining to information automation and review the current state of the art of pilot information management and decision support tools. We also explore how effective human-human team behavior and expectations could be extended to teams involving humans and automation or autonomous systems.
Recent advances in artificial intelligence, machine learning, data mining and sensor technology have resulted in the availability of a vast amount of digital data and information and the development of advanced automated reasoners. This creates the opportunity for the development of a robust dynamic task manager and decision support tool that is context sensitive and integrates information from a wide array of on-board and off aircraft sourcesa tool that monitors systems and the overall flight situation, anticipates information needs, prioritizes tasks appropriately, keeps pilots well informed, and is nimble and able to adapt to changing circumstances. This is the second of two companion reports exploring issues associated with autonomous, context-sensitive, task management and decision support tools. In the first report, we explored fundamental issues associated with the development of such a system. In this report, we extend this work to focus on two critical aspects of these systems: 1) the constraints and conditions that drive the dynamic prioritization and presentation of data and information to the pilots, and 2) specific data and information to be accessed, monitored, integrated, and displayed in such a system.
Predicting launch vehicle unsteady aerodynamic loads due to buffet remains a significant challenge. Current practices for modeling buffet environments involve the development of buffet forcing functions using unsteady pressure measurements acquired during wind-tunnel tests. These practices often result in significant uncertainty in buffet environments for coupled loads analyses due to the complex spatio-temporal nature of the unsteady pressure field and challenge of its estimation using discrete sensors. Unsteady pressure sensitive paint, on the other hand, can provide unsteady pressure data at a comparatively high-spatial-density and may overcome the challenge of unsteady pressure field estimation with discrete sensors and lead to improvements in the development of buffet forcing functions. In this paper, comparisons of the fluctuating pressure field are made for the Space Launch System Block 1 cargo launch vehicle measured using unsteady pressure sensitive paint and pressure transducers.
We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO2, characterized by an effective sensitivity S . This evidence includes feedback process understanding, the historical climate record, and the paleoclimate record. An S value lower than 2 K is difficult to reconcile with any of the three lines of evidence. The amount of cooling during the Last Glacial Maximum provides strong evidence against values of S greater than 4.5 K. Other lines of evidence in combination also show that this is relatively unlikely. We use a Bayesian approach to produce a probability density (PDF) for S given all the evidence, including tests of robustness to difficult‐to‐quantify uncertainties and different priors. The 66% range is 2.6‐3.9 K for our Baseline calculation, and remains within 2.3‐4.5 K under the robustness tests; corresponding 5‐95% ranges are 2.3‐4.7 K, bounded by 2.0‐5.7 K (although such high‐confidence ranges should be regarded more cautiously). This indicates a stronger constraint on S than reported in past assessments, by lifting the low end of the range. This narrowing occurs because the three lines of evidence agree and are judged to be largely independent, and because of greater confidence in understanding feedback processes and in combining evidence. We identify promising avenues for further narrowing the range in S , in particular using comprehensive models and process understanding to address limitations in the traditional forcing‐feedback paradigm for interpreting past changes.
Predicting launch vehicle unsteady aerodynamic loads due to buffet remains a significant challenge. Current practices for modeling buffet environments involve the development of buffet forcing functions using discrete unsteady pressure measurements acquired during wind-tunnel tests. These practices often result in significant uncertainty in buffet environments for coupled loads analyses due to the complex spatio-temporal nature of the unsteady pressure field and the challenge of its estimation using discrete sensors. Unsteady pressure sensitive paint, on the other hand, can provide unsteady pressure data at a comparatively high spatial density and may overcome the challenge of unsteady pressure field estimation with discrete sensors and lead to improvements in the development of buffet forcing functions. In this paper, comparisons of the fluctuating pressure field are made for the Space Launch System Block 1 cargo launch vehicle measured using unsteady pressure sensitive paint and pressure transducers.
Predicting launch vehicle unsteady aerodynamic loads due to buffet remains a significant challenge. Current practices for modeling buffet environments involve the development of buffet forcing functions using discrete unsteady pressure measurements acquired during wind-tunnel tests. These practices often result in significant uncertainty in buffet environments for coupled loads analyses due to the complex spatio-temporal nature of the unsteady pressure field and the challenge of its estimation using discrete sensors. Unsteady pressure sensitive paint, on the other hand, can provide unsteady pressure data at a comparatively high spatial density and may overcome the challenge of unsteady pressure field estimation with discrete sensors and lead to improvements in the development of buffet forcing functions. In this paper, comparisons of the fluctuating pressure field are made for the Space Launch System Block 1 cargo launch vehicle measured using unsteady pressure sensitive paint and pressure transducers.
The technique of Pressure-Sensitive Paint (PSP) is commonly used in the aerospace industry to measure surface pressures on the model of launch vehicles and airplanes in the wind tunnel test. Recent research has demonstrated that Unsteady Pressure-Sensitive Paint (uPSP) can be an essential tool for the assessment of the unsteady, aerodynamic phenomena. The work described in this paper is a part of NASA’s development of a new state-of-the-art uPSP capability in production wind tunnels. This paper describes the spectral analysis of integrated pressures on patches of the scale model of the Space Launch System (SLS) Block 1 cargo vehicle with the uPSP measurements, which were collected in the Ascent Transient Aerodynamics Test (ATAT) with the Unitary Plan Wind Tunnel 11-by-11-foot Transonic Wind Tunnel in September 2019 at NASA Ames Research Center. The patches are defined with x station values, indicating the position along length of the SLS vehicle, and azimuth angles of the scale model. For each patch, the polygons are determined from the surface cells of the grid of the model, clipped with the edges of the patch, and each of the polygons is divided into triangles. The integrated pressure of the patch is determined as the ratio of the sum of the forces on the triangles over the sum of the areas of the triangles. For each run of the test, the Cross Power Spectral Density (CPSD) and magnitude squared coherence are computed from the time series of the integrated pressures on the patches. The pressure integration is coded in C++ and the spectral analysis is coded in MATLAB. The results were generated with the execution of the compiled C++ and MATLAB codes in parallel on the NASA Pleiades supercomputer. The results of pressure integration and spectral analysis are presented in this paper, and the data consistency of the test is also demonstrated. Funding for this research was provided by the NASA Aerosciences Evaluation and Test Capabilities Project.
Seasonal water storage in high-elevation alpine catchments are critical sources of water for mountainous regions like the western U.S. The spatial distribution of snow in these topographically complex catchments is primarily governed by orography, solar radiation, and wind redistribution. While the effect of solar shading is relatively consistent from year-to-year, the redistribution of snow due to wind is more variable–capable of producing snowpacks that have varying degrees of uniformity across these hydrologically-important catchments. A reasonable hypothesis is that a warmer climate will cause snowfall to become more dense (i.e. wetter and heavier), possibly leading to less wind redistribution and thus produce a more uniformly distributed snowpack across the landscape. In this study, we investigate the role of increasingly uniform spatial snowpack distributions on streamflow generation in the Green Lakes Valley Niwot Ridge Long Term Ecological Research station, within the headwaters of the Boulder Creek watershed in Colorado. A set of idealized hydrologic simulation experiments driven by reconstructed snowpacks spanning 2001–2014 show that more a more uniform spatial snowpack distribution leads to an earlier melt-out of31 days on average and tends to produce less total streamflow, with maximum decreases as large as 7.5%. Isolating the role of snowpack heterogeneity from melt-season precipitation, we find that snowpack uniformity reduces total streamflow by as much as 13.2%. Reductions in streamflow are largely explained by greater expo-sure to solar radiation in the uniformly distributed case relative to a more heterogeneous snowpack, with this exposure driving shifts towards earlier snowmelt and changes in soil water storage. Overall, we find that the runoff efficiency from shallower snowpacks is more sensitive to the effects of uniformity than deeper snowpacks, which has potential implications for a warming climate where shallower snowpacks and enhanced sensitivities may be present.
Pressure transducers have been the instrumentation of choice for measuring unsteady flow phenomena. With recent advances in high-speed cameras, high-powered LEDs, and fast-response, pressure-sensitive paint, the unsteady pressure-sensitive paint (uPSP) technique has become a valuable alternative for production wind tunnel facilities, enabling time-resolved measurements of unsteady pressure fluctuations over a dense spatial grid on a wind tunnel model. Launch vehicle ground tests have proven to be a particularly well-matched application for uPSP due to the high signal level relative to tunnel background acoustics, relatively simple camera optical access, and rigidity of the model in wind-on test conditions. This presentation will highlight recent advances in data reduction of uPSP measurement data from recent launch vehicle wind tunnel tests at the NASA Ames Unitary Plan Wind Tunnel Complex (UPWT). The system can provide both localized surface pressure spectra as well as regional or zonal estimates of turbulence correlation model parameters. In addition, integrated vehicle-scale loads can be provided for buffet analysis. Data is reduced at the on-premise NASA Advanced Supercomputer (NAS) Division for just-in-time delivery of results during an ongoing wind tunnel test.
Unsteady pressure-sensitive paint (uPSP) is a significant new technology for high spatial resolution measurement of complex dynamic surface pressure loading on a wind tunnel model, to better define flight vehicle buffet and aeroacoustic loads. Compared with the very sparse surface pressure measurements of traditional sensors, the pressure sensitivity of uPSP light reflectivity provides a visual measure of surface pressure fluctuations over a wide area illuminated by high intensity light, captured by a high-speed digital camera. Panda et al (AIAA Journal, May 2019) have shown good data is recoverable up to 10 kHz. In cases where available uPSP luminescence is low for a painted wind tunnel test model, significant noise levels have been observed primarily due to the discrete photon arrival variability of low intensity light (termed “shot noise”). In this case, uPSP measurements can exhibit unusually low signal to noise ratio (SNR of 0.1 or less). This paper will report on latest work to recover calibrated uPSP measurements which are noise free over a useful dynamic range of 10-15 dB. It will be shown how the distinct spatial correlations of both the shot noise and the underlying aeroacoustic surface pressure play an important role in obtaining a reliable measurement.
Pressure transducers have been the instrumentation of choice for measuring unsteady flow phenomena. With recent advances in high-speed cameras, high-powered LEDs, and fast-response, pressure-sensitive paint, the unsteady pressure-sensitive paint (uPSP) technique has become a valuable alternative for production wind tunnel facilities, enabling time-resolved measurements of unsteady pressure fluctuations over a dense spatial grid on a wind tunnel model. Launch vehicle ground tests have proven to be a particularly well-matched application for uPSP due to the high signal level relative to tunnel background acoustics, relatively simple camera optical access, and rigidity of the model in wind-on test conditions. This presentation will highlight recent advances in data reduction of uPSP measurement data from recent launch vehicle wind tunnel tests at the NASA Ames Unitary Plan Wind Tunnel Complex (UPWT). The system can provide both localized surface pressure spectra as well as regional or zonal estimates of turbulence correlation model parameters. In addition, integrated vehicle-scale loads can be provided for buffet analysis. Data is reduced at the on-premise NASA Advanced Supercomputer (NAS) Division for just-in-time delivery of results during an ongoing wind tunnel test.
Highly-refined finite element models of three-dimension (3D) woven composite systems currently require excessive computational demands that limit their use in sensitivity analysis, uncertainty quantification, and optimization. An alternative analysis methodology was developed using the NASA Multiscale Analysis Tool (NASMAT) where multiscale models of a 3D woven composite (including inter-tow matrix voids and constituent failure) can be completed on a single central processing unit(CPU)on the order of ~30 s. To develop inputs and validation data for the NASMAT model, coupon and acid-digesting testing and x-ray computed tomography were performed. The NASMAT inputs were parameterized using a set of 25 input variables and distributions. These inputs were randomly sampled to generate a total of 100,000 NASMAT analyses that could be used to understand the influence of different material and geometric properties on the warp and weft-direction stiffness and strength. These analyses (including pre/post-processing) were performed in less than eight hours on a 120 CPU cluster. The computational efficiency of the NASMAT model enabled a sensitivity analysis to be performed, and dominant input variables were able to be identified. Key results were consistent with theoretical and experimental observations for the specific 3D woven system studied in this work.
Highly-refined finite element models of three-dimension (3D) woven composite systems currently require excessive computational demands that limit their use in sensitivity analysis, uncertainty quantification, and optimization. An alternative analysis methodology was developed using the NASA Multiscale Analysis Tool (NASMAT) where multiscale models of a 3D woven composite (including inter-tow matrix voids and constituent failure) can be completed on a single central processing unit (CPU) on the order of ~30s. To develop inputs and validation data for the NASMAT model, coupon and acid-digesting testing and x-ray computed tomography were performed. The NASMAT inputs were parameterized using a set of 25 input variables and distributions. These inputs were randomly sampled to generate a total of 100,000 NASMAT analyses that could be used to understand the influence of different material and geometric properties on the warp and weft-direction stiffness and strength. These analyses (including pre/post-processing) were performed in less than eight hours on a 120 CPU cluster. The computational efficiency of the NASMAT model enabled a sensitivity analysis to be performed, and dominant input variables were able to be identified. Key results were consistent with theoretical and experimental observations for the specific 3D woven system studied in this work.
The lifetime and intensity techniques are two distinct methods used to measure the surface pressure of wind tunnel models with pressure-sensitive paint (PSP). When exposed to 405 nm light for extended periods, both the luminescence and the lifetime ratio were observed to decrease, affecting perceived pressure values. To help further the implementation of the unsteady pressure-sensitive paint (uPSP), this paper describes the efforts to characterize how photo degradation affects the intensity of the paint signal and its lifetime ratio, as well as the intensity calibration's response and how these changes vary with different pressure environments. It was found that the paint photo degrades at different rates depending on the partial pressure of oxygen during excitation. It was also found that the calibration curves exhibit greater changes when exposed to excitation light in higher pressure environments, both indicating photo degradation due to photo decomposition and photo-oxidation processes. These effects are observed on models in a subsonic 14” × 14” wind tunnel as well as a large-scale 11’ x 11’ transonic wind tunnel. The work provides analysis into the photo degradation behavior of PSP and uPSP, paving the way to reduce the error in pressure measurements obtained through these techniques.