SEARCH · Search NASA

Results for “Distributed model predictive control”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7

All sources NASA NTRS DOE OSTI

NASA NTRS2003

Advanced Software for Analysis of High-Speed Rolling-Element Bearings

COBRA-AHS is a package of advanced software for analysis of rigid or flexible shaft systems supported by rolling-element bearings operating at high speeds under complex mechanical and thermal loads. These loads can include centrifugal and thermal loads generated by motions of bearing components. COBRA-AHS offers several improvements over prior commercial bearing-analysis programs: It includes innovative probabilistic fatigue-life-estimating software that provides for computation of three-dimensional stress fields and incorporates stress-based (in contradistinction to prior load-based) mathematical models of fatigue life. It interacts automatically with the ANSYS finite-element code to generate finite-element models for estimating distributions of temperature and temperature-induced changes in dimensions in iterative thermal/dimensional analyses: thus, for example, it can be used to predict changes in clearances and thermal lockup. COBRA-AHS provides an improved graphical user interface that facilitates the iterative cycle of analysis and design by providing analysis results quickly in graphical form, enabling the user to control interactive runs without leaving the program environment, and facilitating transfer of plots and printed results for inclusion in design reports. Additional features include roller-edge stress prediction and influence of shaft and housing distortion on bearing performance.

Poplawski, J. V.↗

NASA NTRS1986

Prediction of wing aeroelastic effects on aircraft lift and pitching moment characteristics

The distribution of flight loads on an aircraft structure determine the lift and pitching moment characteristics of the aircraft. When the load distribution changes due to the aeroelastic response of the structure, the lift and pitching moment characteristics also change. An estimate of the effect of aeroelasticity on stability and control characteristics is often required for the development of aircraft simulation models of evaluation of flight characteristics. This presentation outlines a procedure for incorporating calculated linear aeroelastic effects into measured nonlinear lift and pitching moment data from wind tunnel tests. Results are presented which were obtained from applying this procedure to data for an aircraft with a very flexible transport type research wing. The procedure described is generally applicable to all types of aircraft.

Eckstrom, Clinton V.↗

NASA NTRS1987

Prediction of wing aeroelastic effects on aircraft life and pitching moment characteristics

Eckstrom, Clinton V.↗

NASA NTRS2019

Simulated Phase-dependent Spectra of Terrestrial Aquaplanets in M Dwarf Systems

Orbital phase-dependent variations in thermal emission and reflected stellar energy spectra can provide meaningful constraints on the climate states of terrestrial extrasolar planets orbiting M dwarf stars. Spatial distributions of water vapor, clouds, and surface ice are controlled by climate. In turn, water, in each of its thermodynamic phases, imposes significant modulations to thermal and reflected planetary spectra. Here we explore these characteristic spectral signals, based on 3D climate simulations of Earth-sized aquaplanets orbiting M dwarf stars near the habitable zone. By using 3D models, we can self-consistently predict surface temperatures and the location ofwater vapor, clouds, and surface ice in the climate system. Habitable zone planets in M dwarf systems are expected to be in synchronous rotation with their host star and thus present distinct differences in emitted and reflected energy fluxes depending on the observed hemisphere. Here we illustrate that icy, temperate, and incipient runaway greenhouse climate states exhibit phase-dependent spectral signals that enable their characterization.

Wolf, E. T.↗

NASA NTRS1992

World Ocean Circulation Experiment

The oceans are an equal partner with the atmosphere in the global climate system. The World Ocean Circulation Experiment is presently being implemented to improve ocean models that are useful for climate prediction both by encouraging more model development but more importantly by providing quality data sets that can be used to force or to validate such models. WOCE is the first oceanographic experiment that plans to generate and to use multiparameter global ocean data sets. In order for WOCE to succeed, oceanographers must establish and learn to use more effective methods of assembling, quality controlling, manipulating and distributing oceanographic data.

Clarke, R. Allyn↗

NASA NTRS1985

Prediction of wing aeroelastic effects on aircraft lift and pitching moment characteristics

The distribution of flight loads on an aircraft structure determines the lift and pitching moment characteristics of the aircraft. When the load distribution changes due to the aeroelastic response of the structure, the lift and pitching moment characteristics also change. Some estimate of the effect of aeroelasticity on stability and control characteristics, particularly lift and pitching moment, is required for use in aircraft simulation models for evaluation of flight characteristics. This presentation outlines a procedure to incorporate aeroelastic effects into lift and pitching moment data from wind tunnel tests. Results are presented which were obtained from applying this procedure to an aircraft with a very flexible transport-type research wing. The procedure described is generally applicable to all types of aircraft.

Eckstrom, C. V.↗

NASA NTRS2017

The Mars Crustal Magnetic Field Control of Plasma Boundary Locations and Atmospheric Loss: MHD Prediction and Comparison with MAVEN

We present results from a global Mars time-dependent MHD simulation under constant solar wind and solar radiation impact considering inherent magnetic field variations due to continuous planetary rotation. We calculate the 3-D shapes and locations of the bow shock (BS) and the induced magnetospheric boundary (IMB) and then examine their dynamic changes with time. We develop a physics-based, empirical algorithm to effectively summarize the multidimensional crustal field distribution. It is found that by organizing the model results using this new approach, the Mars crustal field shows a clear, significant influence on both the IMB and the BS. Specifically, quantitative relationships have been established between the field distribution and the mean boundary distances and the cross-section areas in the terminator plane for both of the boundaries. The model-predicted relationships are further verified by the observations from the NASA Mars Atmosphere and Volatile EvolutioN (MAVEN) mission. Our analysis shows that the boundaries are collectively affected by the global crustal field distribution, which, however, cannot be simply parameterized by a local parameter like the widely used subsolar longitude. Our calculations show that the variability of the intrinsic crustal field distribution in Mars-centered Solar Orbital itself may account for approx.60% of the variation in total atmospheric loss, when external drivers are static. It is found that the crustal field has not only a shielding effect for atmospheric loss but also an escape-fostering effect by positively affecting the transterminator ion flow cross-section area.

Global↗

NASA NTRS1996

Bubble and Slug Flow at Microgravity Conditions: State of Knowledge and Open Questions

Based on the experiments carried out over the past decade at microgravity conditions, an overview of our current knowledge of bubbly and slug flows is presented. The transition from bubble to slug flow, the void fraction and the pressure drop are discussed from the data collected in the literature. The transition from bubble to slug flow may be predicted by introducing a critical void fraction that depends on the fluid properties and the pipe diameter; however, the role of coalescence which controls this transition is not clearly understood. The void fraction may be accurately calculated using a drift-flux model. It is shown from local measurements that the drift of the gas with respect to the mixture is due to non-uniform radial distribution of void fraction. The pressure drop happens to be controlled by the liquid flow for bubbly flow whereas for slug flow the experimental results show that pressure drops is larger than expected. From this study, the guidelines for future research in microgravity are given.

Colin, C.↗

NASA NTRS2015

Construction of Polarimetric Radar-Based Reference Rain Maps for the Iowa Flood Studies Campaign

The Global Precipitation Measurement (GPM) Mission Iowa Flood Studies (IFloodS) campaign was conducted in central and northeastern Iowa during the months of April-June, 2013. Specific science objectives for IFloodS included quantification of uncertainties in satellite and ground-based estimates of precipitation, 4-D characterization of precipitation physical processes and associated parameters (e.g., size distributions, water contents, types, structure etc.), assessment of the impact of precipitation estimation uncertainty and physical processes on hydrologic predictive skill, and refinement of field observations and data analysis approaches as they pertain to future GPM integrated hydrologic validation and related field studies. In addition to field campaign archival of raw and processed satellite data (including precipitation products), key ground-based platforms such as the NASA NPOL S-band and D3R Ka/Ku-band dual-polarimetric radars, University of Iowa X-band dual-polarimetric radars, a large network of paired rain gauge platforms, and a large network of 2D Video and Parsivel disdrometers were deployed. In something of a canonical approach, the radar (NPOL in particular), gauge and disdrometer observational assets were deployed to create a consistent high-quality distributed (time and space sampling) radar-based ground "reference" rainfall dataset, with known uncertainties, that could be used for assessing the satellite-based precipitation products at a range of space/time scales. Subsequently, the impact of uncertainties in the satellite products could be evaluated relative to the ground-benchmark in coupled weather, land-surface and distributed hydrologic modeling frameworks as related to flood prediction. Relative to establishing the ground-based "benchmark", numerous avenues were pursued in the making and verification of IFloodS "reference" dual-polarimetric radar-based rain maps, and this study documents the process and results as they pertain specifically to efforts using the NPOL radar dataset. The initial portions of the "process" involved dual-polarimetric quality control procedures which employed standard phase and correlation-based approaches to removal of clutter and non-meteorological echo. Calculation of a scale-adaptive KDP was accomplished using the method of Wang and Chandrasekar (2009; J. Atmos. Oceanic Tech.). A dual-polarimetric blockage algorithm based on Lang et al. (2009; J. Atmos. Oceanic Tech.) was then implemented to correct radar reflectivity and differential reflectivity at low elevation angles. Next, hydrometeor identification algorithms were run to identify liquid and ice hydrometeors. After the quality control and data preparation steps were completed several different dual-polarimetric rain estimation algorithms were employed to estimate rainfall rates using rainfall scans collected approximately every two to three minutes throughout the campaign. These algorithms included a polarimetrically-tuned Z-R algorithm that adjusts for drop oscillations (via Bringi et al., 2004, J. Atmos. Oceanic Tech.), and several different hybrid polarimetric variable approaches, including one that made use of parameters tuned to IFloodS 2D Video Disdrometer measurements. Finally, a hybrid scan algorithm was designed to merge the rain rate estimates from multiple low level elevation angle scans (where blockages could not be appropriately corrected) in order to create individual low-level rain maps. Individual rain maps at each time step were subsequently accumulated over multiple time scales for comparison to gauge network data. The comparison results and overall error character depended strongly on rain event type, polarimetric estimator applied, and range from the radar. We will present the outcome of these comparisons and their impact on constructing composited "reference" rainfall maps at select time and space scales.

Radar↗

NASA NTRS2016

Isotopic Ordering in Atmospheric O2 as a Tracer of Ozone Photochemistry and the Tropical Atmosphere

The distribution of isotopes within O2 molecules can be rapidly altered when they react with atomic oxygen. This mechanism is globally important: while other contributions to the global budget of O2 impart isotopic signatures, the O(3P) + O2 reaction resets all such signatures in the atmosphere on subdecadal timescales. Consequently, the isotopic distribution within O2 is determined by O3 photochemistry and the circulation patterns that control where that photochemistry occurs. The variability of isotopic ordering in O2 has not been established, however. We present new measurements of 18O18O in air (reported as delta36 values) from the surface to 33 km altitude. They confirm the basic features of the clumped-isotope budget of O2: Stratospheric air has higher delta36 values than tropospheric air (i.e., more 18O18O), reflecting colder temperatures and fast photochemical cycling of O3. Lower delta36 values in the troposphere arise from photochemistry at warmer temperatures balanced by the influx of high-delta36 air from the stratosphere. These observations agree with predictions derived from the GEOS-Chem chemical transport model, which provides additional insight. We find a link between tropical circulation patterns and regions where delta36 values are reset in the troposphere. The dynamics of these regions influences lapse rates, vertical and horizontal patterns of O2 reordering, and thus the isotopic distribution toward which O2 is driven in the troposphere. Temporal variations in delta36 values at the surface should therefore reflect changes in tropospheric temperatures, photochemistry, and circulation. Our results suggest that the tropospheric O3 burden has remained within a +/-10 percent range since 1978.

oxygen↗

NASA NTRS2016

Convection from Hemispherical and Conical Model Ice Roughness Elements in Stagnation Region Flows

To improve ice accretion prediction codes, more data regarding ice roughness and its effects on convective heat transfer are required. The Vertical Icing Studies Tunnel (VIST) at NASA Glenn Research was used to model realistic ice roughness in the stagnation region of a NACA 0012 airfoil. In the VIST, a test plate representing the leading 2% chord of the airfoil was subjected to flows of 7.62 m/s (25 ft/s), 12.19 m/s (40 ft/s), and 16.76 m/s (55 ft/s). The test plate was fitted with multiple surfaces or sets of roughness panels, each with a different representation of ice roughness. The sets of roughness panels were constructed using two element distribution patterns that were created based on a laser scan of an iced airfoil acquired in the Icing Research Tunnel at NASA Glenn. For both roughness patterns, surfaces were constructed using plastic hemispherical elements, plastic conical elements, and aluminum conical elements. Infrared surface thermometry data from tests run in the VIST were used to calculate area averaged heat transfer coefficient values. The values from the roughness surfaces were compared to the smooth control surface, showing convective enhancement as high as 400% in some cases. The data gathered during this study will ultimately be used to improve the physical modeling in LEWICE or other ice accretion codes and produce predictions of in-flight ice accretion on aircraft surfaces with greater confidence.

Icing Physics↗

NASA NTRS2019

Formation of the Potential Jump Over the Geomagnetically Quiet Sunlit Polar Cap Region

We consider the formation of a potential drop over the Earth's polar cap duringgeomagnetically quiet daytime. The observed potential drop is primarily defined by the hydrogen,photoelectron, and polar rain fluxes ratios and depends strongly on the energy distribution of thephotoelectron flux. Polar rain is an essential component of the model required for plasma quasineutrality.The potential distribution along the magnetic field line has two regions, with a small, gradual, potentialdrop of 3â€“4 V and a potential jump. The value of the potential jump depends on the hydrogen ion tophotoelectron flux ratio and is also controlled by polar rain electrons. With quasineutrality required at itsupper boundary, the jump only occurs in the presence of polar rain and its location depends on the polarrain flux. Model predictions compare well with FAST observations presented by Kitamura et al.(2012, https://doi.org/10.1029/2011JA017459).

Khazanov, George V.↗

NASA NTRS2024

Quantification of Uncertainty and Risk Sensitivity for Safety of Emerging Operations

The growing need to develop and deploy small unmanned aerial vehicles (sUAVs) for various applications in the airspace necessitates reliable tools to accurately predict the flight trajectories of the sUAVs. The knowledge of the predicted trajectories help decision makers anticipate potential conflict, assess the risk, and take appropriate risk mitigation actions. In addition, uncertainties in vehicle models, weather, and controller action further highlights the need for reliable prediction tools. In this project, the application of mixed sparse grid-based quadrature and generalized polynomial chaos(gPC) expansion method for uncertainty quantification and collision assessment in air traffic consisting of fixed-wing small unmanned aerial vehicles (sUAV) was studied. From the results obtained, it can be concluded that this provides a reliable framework to carry out quantitative conflict assessment in an unmanned air traffic, which when employed, can improve the functionalities of the unmanned traffic management system. It was observed that the results from the gPC expansion framework developed in the project can be utilized to conduct rapid probabilistic collision assessment for near real-time unmanned traffic management in the airspace. From the vehicle models, position updates, and wind-field data, a priori gPC based 3-σcon-fidence ellipses can provide estimates of potential conflict at some future instants. The computational costs scaled linearly when the uncertain inputs were fewer. Further, the largest allowable distribution of para-metric uncertainties that leads to the smallest risk of collision in traffic of small unmanned aerial vehicles could be calculated. The time of closest approach between two sUAVs can be established paving way for development of proactive mitigation strategies. The separation between the sUAVs was found to be most significantly affected by uncertainties in the maximum available thrusts, zero-lift drag coefficients, and wing planform areas of the sUAVs. The study of uncertain wind-fields indicated that a heterogeneous traffic mix resulted in an increased probability of conflict. Increased measurement update rate reduced the uncertain-ties in the trajectories of the vehicles, further reducing the probability of conflict but rapid updates of all vehicles in the airspace poses a stringent communication limitation. The gPC framework also provided the means to analyze vehicle impact (crash region) due to loss of control resulting from actuator failure in sUAS traffic, essentially to predict impact and crash zones for representative vehicles. The predicted regions when compared with non-participant density, provides a means to develop an early mitigation strategy, should the sUAV detect an imminent actuator failure.

Rajnish Bhusal↗

NASA NTRS2013

Modeling Weather Impact on Airport Arrival Miles-in-Trail Restrictions

When the demand for either a region of airspace or an airport approaches or exceeds the available capacity, miles-in-trail (MIT) restrictions are the most frequently issued traffic management initiatives (TMIs) that are used to mitigate these imbalances. Miles-intrail operations require aircraft in a traffic stream to meet a specific inter-aircraft separation in exchange for maintaining a safe and orderly flow within the stream. This stream of aircraft can be departing an airport, over a common fix, through a sector, on a specific route or arriving at an airport. This study begins by providing a high-level overview of the distribution and causes of arrival MIT restrictions for the top ten airports in the United States. This is followed by an in-depth analysis of the frequency, duration and cause of MIT restrictions impacting the Hartsfield-Jackson Atlanta International Airport (ATL) from 2009 through 2011. Then, machine-learning methods for predicting (1) situations in which MIT restrictions for ATL arrivals are implemented under low demand scenarios, and (2) days in which a large number of MIT restrictions are required to properly manage and control ATL arrivals are presented. More specifically, these predictions were accomplished by using an ensemble of decision trees with Bootstrap aggregation (BDT) and supervised machine learning was used to train the BDT binary classification models. The models were subsequently validated using data cross validation methods. When predicting the occurrence of arrival MIT restrictions under low demand situations, the model was able to achieve over all accuracy rates ranging from 84% to 90%, with false alarm ratios ranging from 10% to 15%. In the second set of studies designed to predict days on which a high number of MIT restrictions were required, overall accuracy rates of 80% were achieved with false alarm ratios of 20%. Overall, the predictions proposed by the model give better MIT usage information than what has been currently provided under current day operations. Traffic flow managers can use these predictions to identify potential MIT restrictions to eliminate (e.g., those occurring during low arrival demand periods), and to determine the days in which a significant number of restrictions may be required

Operation↗

NASA NTRS1984

Comparison of analytical and experimental steadyand unsteady-pressure distributions at Mach number 0.78 for a high-aspect-ratio supercritical wing model with oscillating control surfaces

The unsteady aerodynamic lifting surface theory, the Doublet Lattice method, with experimental steady and unsteady pressure measurements of a high aspect ratio supercritical wing model at a Mach number of 0.78 were compared. The steady pressure data comparisons were made for incremental changes in angle of attack and control surface deflection. The unsteady pressure data comparisons were made at set angle of attack positions with oscillating control surface deflections. Significant viscous and transonic effects in the experimental aerodynamics which cannot be predicted by the Doublet Lattice method are shown. This study should assist development of empirical correction methods that may be applied to improve Doublet Lattice calculations of lifting surface aerodynamics.

Mccain, W. E.↗

NASA NTRS1989

The figure of Phobos

The figure of Phobos is presently modeled by a sixth-degree-and-order spherical harmonic expansion that fits the observed surface to an accuracy of a few hundred m, as validated through the use of control points, limbs, terminators, map projections, and predicted rotational properties. Only the region around the large crater, Stickney, exhibits lower accuracy. The forced libration amplitude obtained closely matches the observed value; this supports the view of Phobos as possessing a uniform density distribution.

Duxbury, Thomas C.↗

NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗

NASA NTRS2019

Formation of the Potential Jump over the Geomagnetically Quiet Sunlit Polar Cap Region

We consider the formation of a potential drop over the Earth's polar cap during geomagnetically quiet daytime. The observed potential drop is primarily defined by the hydrogen, photoelectron, and polar rain fluxes ratios and depends strongly on the energy distribution of the photoelectron flux. Polar rain is an essential component of the model required for plasma quasineutrality. The potential distribution along the magnetic field line has two regions, with a small, gradual, potential drop of 3 to 4 volts and a potential jump. The value of the potential jump depends on the hydrogen ion to photoelectron flux ratio and is also controlled by polar rain electrons. With quasineutrality required at its upper boundary, the jump only occurs in the presence of polar rain and its location depends on the polar rain flux. Model predictions compare well with FAST (Fast Auroral SnapshoT) observations presented by Kitamura et al.(2012, https://doi.org/10.1029/2011JA017459).

Khazanov, G. V.↗