Search NASA⌕ Search

SEARCH · Search NASA

Results for “model predictive building 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.

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

At least 37 records · Page 2

Printing Outside the Box: Additive Manufacturing Processes for Fabrication of Large Aerospace Structures

To achieve NASA's mission of space exploration, innovative manufacturing processes are being applied to the fabrication of propulsion elements. Liquid rocket engines (LREs) are comprised of a thrust chamber and nozzle extension as illustrated in figure 1 for the J2X upper stage engine. Development of the J2X engine, designed for the Ares I launch vehicle, is currently being incorporated on the Space Launch System. A nozzle extension is attached to the combustion chamber to obtain the expansion ratio needed to increase specific impulse. If the nozzle extension could be printed as one piece using free-form additive manufacturing (AM) processes, rather than the current method of forming welded parts, a considerable time savings could be realized. Not only would this provide a more homogenous microstructure than a welded structure, but could also greatly shorten the overall fabrication time. The main objective of this study is to fabricate test specimens using a pulsed arc source and solid wire as shown in figure 2. The mechanical properties of these specimens will be compared with those fabricated using the powder bed, selective laser melting technology at NASA Marshall Space Flight Center. As printed components become larger, maintaining a constant temperature during the build process becomes critical. This predictive capability will require modeling of the moving heat source as illustrated in figure 3. Predictive understanding of the heat profile will allow a constant temperature to be maintained as a function of height from substrate while printing complex shapes. In addition, to avoid slumping, this will also allow better control of the microstructural development and hence the properties. Figure 4 shows a preliminary comparison of the mechanical properties obtained.

Babai, Majid↗

Internal temperature gradient of alloy semiconductor melts from interrupted growth experiments

Interrupted growth experiments on Hg(0.8)Cd(0.2)Se alloys were performed in an attempt to estimate the actual internal temperature gradient in the melt during directional solidification. The results have been analyzed in terms of a theoretical model which assumes an axial composition profile characteristic of diffusion controlled solute redistribution during growth. A comparison of the calculated and measured values suggests a reduction in the applied (empty furnace) temperature gradient by about a factor of three. Values of the interface segregation coefficient (k) determined from the phase diagram, predict values of solute concentration build up in the solid during growth interruptions which are inconsistent with measured results. It appears that the k values used tend to underestimate the actual k values for higher alloy compositions and overestimate k for the lower alloy compositions.

Andrews, R. N.↗

In-Flight Thermal Performance of the Lidar In-Space Technology Experiment

The Lidar In-Space Technology Experiment (LITE) was developed at NASA s Langley Research Center to explore the applications of lidar operated from an orbital platform. As a technology demonstration experiment, LITE was developed to gain experience designing and building future operational orbiting lidar systems. Since LITE was the first lidar system to be flown in space, an important objective was to validate instrument design principles in such areas as thermal control, laser performance, instrument alignment and control, and autonomous operations. Thermal and structural analysis models of the instrument were developed during the design process to predict the behavior of the instrument during its mission. In order to validate those mathematical models, extensive engineering data was recorded during all phases of LITE's mission. This inflight engineering data was compared with preflight predictions and, when required, adjustments to the thermal and structural models were made to more accurately match the instrument s actual behavior. The results of this process for the thermal analysis and design of LITE are presented in this paper.

Roettker, William↗

Development of the Surface Management System Integrated with CTAS Arrival Tools

The Surface Management System (SMS) developed by NASA Ames Research Center in coordination with the Federal Aviation Administration (FAA) is a decision support tool to help tower traffic coordinators and Ground/Local controllers in managing and controlling airport surface traffic in order to increase capacity, efficiency, and flexibility. SMS provides common situation awareness to personnel at various air traffic control facilities such as airport traffic control towers (ATCT s), airline ramp towers, Terminal Radar Approach Control (TRACON), and Air Route Traffic Control Center (ARTCC). SMS also provides a traffic management tool to assist ATCT traffic management coordinators (TMCs) in making decisions such as airport configuration and runway load balancing. The Build 1 of the SMS tool was installed and successfully tested at Memphis International Airport (MEM) and received high acceptance scores from ATCT controllers and coordinators, as well as airline ramp controllers. NASA Ames Research Center continues to develop SMS under NASA s Strategic Airspace Usage (SAU) project in order to improve its prediction accuracy and robustness under various modeling uncertainties. This paper reports the recent development effort performed by the NASA Ames Research Center: 1) integration of Center TRACON Automation System (CTAS) capability with SMS and 2) an alternative approach to obtain airline gate information through a publicly available website. The preliminary analysis results performed on the air/surface traffic data at the DFW airport have shown significant improvement in predicting airport arrival demand and IN time at the gate. This paper concludes with recommendations for future research and development.

Jung, Yoon C.↗

Chill Down Process of Hydrogen Transport Pipelines

A pseudo-steady model has been developed to predict the chilldown history of pipe wall temperature in the horizontal transport pipeline for cryogenic fluids. A new film boiling heat transfer model is developed by incorporating the stratified flow structure for cryogenic chilldown. A modified nucleate boiling heat transfer correlation for cryogenic chilldown process inside a horizontal pipe is proposed. The efficacy of the correlations is assessed by comparing the model predictions with measured values of wall temperature in several azimuthal positions in a well controlled experiment by Chung et al. (2004). The computed pipe wall temperature histories match well with the measured results. The present model captures important features of thermal interaction between the pipe wall and the cryogenic fluid, provides a simple and robust platform for predicting pipe wall chilldown history in long horizontal pipe at relatively low computational cost, and builds a foundation to incorporate the two-phase hydrodynamic interaction in the chilldown process.

Mei, Renwei↗

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on current and forecast of traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. In this paper, a methodology using supervised learning is developed to build a predictive model for RCM decision-support from large volumes of historical data. Data from two full years (2018 and 2019) related to current and forecast weather, demand/capacity, etc. is collected, analyzed, and fused together. A variety of supervised learning algorithms are tested for predicting runway configuration and hyperparameter tuning is carried out to select the best performing model. The validation process involves two airports of low (Charlotte Douglas International Airport, CLT) and high (Denver International Airport, DEN) complexity of configuration decision-making. The results show significant promise for the two airports with test accuracy of 93% (CLT) and 73% (DEN). The methodology is scalable and generalizable to other airports across the U.S. National Airspace System.

air traffic management↗

Predicting Airport Runway Configuration for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on current and forecast of traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. In this paper, a methodology using supervised learning is developed to build a predictive model for RCM decision-support from large volumes of historical data. Data from two full years (2018 and 2019) related to current and forecast weather, demand/capacity, etc. is collected, analyzed, and fused together. A variety of supervised learning algorithms are tested for predicting runway configuration and hyperparameter tuning is carried out to select the best performing model. The validation process involves two airports of low (Charlotte Douglas International Airport, CLT) and high (Denver International Airport, DEN) complexity of configuration decision-making. The results show significant promise for the two airports with test accuracy of 93% (CLT) and 73% (DEN). The methodology is scalable and generalizable to other airports across the U.S. National Airspace System.

air traffic management↗

Ball bearing vibrations amplitude modeling and test comparisons

Bearings generate disturbances that, when combined with structural gains of a momentum wheel, contribute to induced vibration in the wheel. The frequencies generated by a ball bearing are defined by the bearing's geometry and defects. The amplitudes at these frequencies are dependent upon the actual geometry variations from perfection; therefore, a geometrically perfect bearing will produce no amplitudes at the kinematic frequencies that the design generates. Because perfect geometry can only be approached, emitted vibrations do occur. The most significant vibration is at the spin frequency and can be balanced out in the build process. Other frequencies' amplitudes, however, cannot be balanced out. Momentum wheels are usually the single largest source of vibrations in a spacecraft and can contribute to pointing inaccuracies if emitted vibrations ring the structure or are in the high-gain bandwidth of a sensitive pointing control loop. It is therefore important to be able to provide an a priori knowledge of possible amplitudes that are singular in source or are a result of interacting defects that do not reveal themselves in normal frequency prediction equations. This paper will describe the computer model that provides for the incorporation of bearing geometry errors and then develops an estimation of actual amplitudes and frequencies. Test results were correlated with the model. A momentum wheel was producing an unacceptable 74 Hz amplitude. The model was used to simulate geometry errors and proved successful in identifying a cause that was verified when the parts were inspected.

Hightower, Richard A., III↗

Parametric Studies of Flow Separation using Air Injection

Boundary Layer separation causes the airfoil to stall and therefore imposes dramatic performance degradation on the airfoil. In recent years, flow separation control has been one of the active research areas in the field of aerodynamics due to its promising performance improvements on the lifting device. These active flow separation control techniques include steady and unsteady air injection as well as suction on the airfoil surface etc. This paper will be focusing on the steady and unsteady air injection on the airfoil. Although wind tunnel experiments revealed that the performance improvements on the airfoil using injection techniques, the details of how the key variables such as air injection slot geometry and air injection angle etc impact the effectiveness of flow separation control via air injection has not been studied. A parametric study of both steady and unsteady air injection active flow control will be the main objective for this summer. For steady injection, the key variables include the slot geometry, orientation, spacing, air injection velocity as well as the injection angle. For unsteady injection, the injection frequency will also be investigated. Key metrics such as lift coefficient, drag coefficient, total pressure loss and total injection mass will be used to measure the effectiveness of the control technique. A design of experiments using the Box-Behnken Design is set up in order to determine how each of the variables affects each of the key metrics. Design of experiment is used so that the number of experimental runs will be at minimum and still be able to predict which variables are the key contributors to the responses. The experiments will then be conducted in the 1ft by 1ft wind tunnel according to the design of experiment settings. The data obtained from the experiments will be imported into JMP, statistical software, to generate sets of response surface equations which represent the statistical empirical model for each of the metrics as a function of the key variables. Next, the variables such as the slot geometry can be optimized using the build-in optimizer within JMP. Finally, a wind tunnel testing will be conducted using the optimized slot geometry and other key variables to verify the empirical statistical model. The long term goal for this effort is to assess the impacts of active flow control using air injection at system level as one of the task plan included in the NASAs URETI program with Georgia Institute of Technology.

Zhang, Wei↗

Mathematical models for space shuttle ground systems

Math models are a series of algorithms, comprised of algebraic equations and Boolean Logic. At Kennedy Space Center, math models for the Space Shuttle Systems are performed utilizing the Honeywell 66/80 digital computers, Modcomp II/45 Minicomputers and special purpose hardware simulators (MicroComputers). The Shuttle Ground Operations Simulator operating system provides the language formats, subroutines, queueing schemes, execution modes and support software to write, maintain and execute the models. The ground systems presented consist primarily of the Liquid Oxygen and Liquid Hydrogen Cryogenic Propellant Systems, as well as liquid oxygen External Tank Gaseous Oxygen Vent Hood/Arm and the Vehicle Assembly Building (VAB) High Bay Cells. The purpose of math modeling is to simulate the ground hardware systems and to provide an environment for testing in a benign mode. This capability allows the engineers to check out application software for loading and launching the vehicle, and to verify the Checkout, Control, & Monitor Subsystem within the Launch Processing System. It is also used to train operators and to predict system response and status in various configurations (normal operations, emergency and contingent operations), including untried configurations or those too dangerous to try under real conditions, i.e., failure modes.

Tory, E. G.↗

A Simulation Model of Carbon Cycling and Methane Emissions in Amazon Wetlands

An integrative carbon study is investigating the hypothesis that measured fluxes of methane from wetlands in the Amazon region can be predicted accurately using a combination of process modeling of ecosystem carbon cycles and remote sensing of regional floodplain dynamics. A new simulation model has been build using the NASA- CASA concept for predicting methane production and emission fluxes in Amazon river and floodplain ecosystems. Numerous innovations area being made to model Amazon wetland ecosystems, including: (1) prediction of wetland net primary production (NPP) as the source for plant litter decomposition and accumulation of sediment organic matter in two major vegetation classes - flooded forests (varzea or igapo) and floating macrophytes, (2) representation of controls on carbon processing and methane evasion at the diffusive boundary layer, through the lake water column, and in wetland sediments as a function of changes in floodplain water level, (3) inclusion of surface emissions controls on wetland methane fluxes, including variations in daily surface temperature and of hydrostatic pressure linked to water level fluctuations. A model design overview and early simulation results are presented.

Potter, Christopher↗

Dynamic Modeling, Controls, and Testing for Electrified Aircraft

Electrified aircraft have the potential to provide significant benefits for efficiency and emissions reductions. To assess these potential benefits, modeling tools are needed to provide rapid evaluation of diverse concepts and to ensure safe operability and peak performance over the mission. The modeling challenge for these vehicles is the ability to show significant benefits over the current highly refined aircraft systems. The STARC-ABL (single-aisle turbo-electric aircraft with an aft boundary layer propulsor) is a new test proposal that builds upon previous N3-X team hybrid designs. This presentation describes the STARC-ABL concept, the NASA Electric Aircraft Testbed (NEAT) which will allow testing of the STARC-ABL powertrain, and the related modeling and simulation efforts to date. Modeling and simulation includes a turbofan simulation, Numeric Propulsion System Simulation (NPSS), which has been integrated with NEAT; and a power systems and control model for predicting testbed performance and evaluating control schemes. Model predictions provide good comparisons with testbed data for an NPSS-integrated test of the single-string configuration of NEAT.

Connolly, Joseph↗

The South Residual CO2 Cap on Mars: Investigations with a Mars Global Climate Model

The CO2 cycle is one of the three controlling climate cycles on Mars. One aspect of the CO2 cycle that is not yet fully understood is the existence of a residual CO2 ice cap that is offset from the south pole. Previous investigations suggest that the atmosphere could control the placement of the south residual cap (e.g., Colaprete et al., 2005). These investigations show that topographically forced stationary eddies in the south during southern hemisphere winter produce colder atmospheric temperatures and increased CO2 snowfall over the hemisphere where the residual cap resides. Since precipitated CO2 ice produces higher surface albedos than directly deposited CO2 ice, it is plausible that CO2 snowfall resulting from the zonally asymmetric atmospheric circulation produces surface ice albedos high enough to maintain a residual cap only in one hemisphere. Our current work builds on these initial investigations with a version of the NASA Ames Mars Global Climate Model (GCM) that includes a sophisticated CO2 cloud microphysical scheme. Processes of cloud nucleation, growth, sedimentation, and radiative effects are accounted for. Simulated results thus far agree well with the Colaprete et al. study—the zonally asymmetric nature of the atmospheric circulation produces enhanced snowfall over the residual cap hemisphere throughout much of the winter season. However, the predicted snowfall patterns vary significantly with season throughout the cap growth and recession phases. We will present a detailed analysis of the seasonal evolution of the predicted atmospheric circulation and snowfall patterns to more fully evaluate the hypothesis that the atmosphere controls the placement of the south residual cap.

climate cycles on Mars↗

A Koopman-based residual modeling approach for the control of a soft robot arm

Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

Daniel Bruder↗

Advancement of Entry System Modeling to Support Exploration of Giant Planets

This paper describes NASA’s efforts to advance entry system modeling and simulation capabilities to support future exploration of Giant planets. The Giant planets are key destinations of interest to the planetary science community for their potential to provide insight into the formation and evolution of our Solar System, as well as extrasolar planetary systems. To date, the Galileo atmospheric probe is the only purpose-built entry probe to a Giant planet. Post-flight analysis of Galileo’s performance showed that there was significant recession of the thermal protection system (TPS), well beyond what was anticipated on the flank, and this was due in part to insufficiently accurate capability for estimating the flight environment and TPS response. While Galileo ultimately survived its flight, the example serves to highlight the great challenge of designing successful missions for environments that are poorly understood or where models have not yet been validated. An important means to reduce mission risks is the incorporation of physics-based modeling with well-quantified uncertainties. The emphasis on physics-based modeling – in contrast to empirically-driven models – is motivated by the fact that it is impossible to completely replicate entry environments through ground tests and, therefore, extrapolation to the flight environment is required. Basing analysis in fundamental physics removes the bias of ground test limitations, though one must then be careful to properly characterize model inputs, simplifying assumptions, and the limits wherein the model is valid. NASA’s Entry Systems Modeling (ESM) Project is tasked with investigating such considerations for planetary science missions across the Solar System, and in recent years has begun to do so for Giant planets. The most distinctive features of the Giant planets, from an entry system perspective, are the atmospheres composed primarily of hydrogen and helium. The entry velocities of proposed missions are generally very large and can therefore be expected to result in significant convective and radiative heating generated by the vehicle’s shock layer. Yet thermochemical behavior of the hydrogen-helium system is not well understood under such conditions. The ESM project is leading efforts to develop accurate thermochemical databases based on state-of-the-art measurements in the Electric Arc Shock Tube and detailed computational chemistry. The large heat fluxes anticipated by missions has driven interest in new TPS materials, in particular woven materials, which may be enabling but have never been flown before. Consequently, multiscale models are in development to describe properties and performance of the materials from micro- to system-scale. The goal is to not only provide accurate thermal response but also to inform thermostructural reliability predictions for extreme entries. Additionally, new computational models have been developed to evaluate performance of non-destructive evaluation techniques which are vital to establishing acceptance of systems to be free of manufacturing faults like material cracking, voids, and debonding. Finally, in the area of guidance and control, aerocapture has been shown conceptually to provide a number of mission benefits, including reducing transit time and increasing payload fraction. The ESM project is building a launch-to-landing trajectory simulation capability to enable detailed studies of aerocapture maneuvers in the context of Giant planets missions. The final presentation and paper will describe each of these topics in detail, including discussion of specific gaps and the technical approach to solving them. In addition, the final paper will briefly discuss ongoing coordination between ESM project work and an ESA-funded technology development activity comprised of validation testing in the Oxford T6, IRS PWK and IST ESTHER tunnels, as well as state-to-state modeling of the shock layer to better represent non-Boltzmann energy distributions leading to non-equilibrium radiation.

Entry systems↗

Comparison of Predictive Modeling Methods of Aircraft Landing Speed

Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

Diallo, Ousmane H.↗

Development of a Modeling Framework to Support Control Investigations of Sailcraft Missions A First Cut: ABLE Sailcraft Dynamics Model

Solar Sailcraft, the stuff of dreams of the H.G. Wells generation, is now a rapidly maturing reality. The promise of unlimited propulsive power by harnessing stellar radiation is close to realization. Currently, efforts are underway to build, prototype and test two configurations. These sails are designed to meet a 20m sail requirement, under guidance of the In-Space Propulsion (ISP) technology program office at MSFC. While these sails will not fly , they are the first steps in improving our understanding of the processes and phenomena at work. As part of the New Millennium Program (NMP) the ST9 technology validation mission hopes to launch and fly a solar sail by 2010 or sooner. Though the Solar Sail community has been studying and validating various concepts over two decades, it was not until recent breakthroughs in structural and material technology, has made possible to build sails that could be launched. With real sails that can be tested (albeit under earth conditions), the real task of engineering a viable spacecraft has finally commenced. Since it is not possible to accurately or practically recreate the actual operating conditions of the sailcraft (zero-G, vacuum and extremely low temperatures), much of the work has focused on developing accurate models that can be used to predict behavior in space, and for sails that are 6-10 times the size of currently existing sails. Since these models can be validated only with real test data under "earth" conditions, the process of modeling and the identification of uncertainty due to model assumptions and scope need to be closely considered. Sailcraft models that exist currently, are primarily focused on detailed physical representations at the component level, these are intended to support prototyping efforts. System level models that cut across different sail configurations and control concepts while maintaining a consistent approach are non-existent. Much effort has been focused on the areas of thrust performance, solar radiation prediction, and sail membrane behavior vis-a-vis their reflective geometry, such as wrinkling/folding/furling as it pertains to thrust prediction. A parallel effort has been conducted on developing usable models for developing attitude control systems (ACS), for different sail configurations in different regimes. There has been very little by way of a system wide exploration of the impact of the various control schemes, thrust prediction models for different sail configurations being considered.

Sarathy, Sriprakash↗

On the mechanics of filament winding. I - A generalized model

In this paper the filament winding process was analyzed to relate process variables to structural parameters. An analytical model has been developed for a filament wound preform by determining the necessary geometric and kinematic conditions. An equation of motion of winding was developed to predict layer by layer geometry using the mandrel shape as the initial boundary condition. This model provides the laws of motion of the traverse stroke relative to the mandrel to ensure that the filament laydown is of the predetermined geodesic path over the mandrel, that the winding density is distributed over the mandrel in the prearranged manner, and that the winding build is being formed to reproduce the preassigned shape or contour of the preform. From this analysis, the winding process parameters can be controlled accordingly to produce any given shape. The analytical model was used to produce computer simulation of the winding process. The dynamic simulation of the winding process visualizes and inspects the layer by layer path of the tow and provides process variables required to produce preforms of optimum structural parameters.

Hamouda, Hechmi↗