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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Cosmic heavy ion tracks in mesoscopic biological test objects

Since more than 20 years ago, when the National Academy of Sciences and the National Research Council of the U.S.A. released their report on 'HZE particle effects in manned spaced flight', it has been emphasized how difficult - if not even impossible - it is to assess their radiobiological impact on man from conventional studies where biological test organisms are stochastically exposed to 'large' fluences of heavy ions. An alternative, competing approach had been realized in the BIOSTACK experiments, where the effects of single cosmic as well as accelerator - heavy ions on individual biological test organisms could be investigated. Although presented from the beginning as the preferable approach for terrestrial investigations with accelerator heavy ions too ('The BIOSTACK as an approach to high LET radiation research'), only recently this insight is gaining more widespread recognition. In space flight experiments, additional constraints imposed by the infrastructure of the vehicle or satellite further impede such investigations. Restrictions concern the physical detector systems needed for the registration of the cosmic heavy ions' trajectories as well as the biological systems eligible as test organisms. Such optimized procedures and techniques were developed for the investigations on chromosome aberrations induced by cosmic heavy ions in cells of the stem meristem of lettuce seeds (Lactuca sativa) and for the investigation of the radiobiological response of Wolffia arriza, which is the smallest flowering (water) plant. The biological effects were studied by the coworkers of the Russian Institute of Biomedical Problems (IBMP) which in cooperation with the European Space Agency ESA organized the exposure in the Biosatellites of the Cosmos series. Since biological investigations and physical measurements of particle tracks had to be performed in laboratories widely separated, the preferred fixed contact between biological test objects and the particle detectors until the geometrical correlation between tracks and organisms has been established could not be maintained. This gave rise to half a dozen of coordinate systems for different measurements which finally had to be related to a single stack reference system.

Facius, R.↗
NASA NTRS1997

Sliding Mode Control of a Slewing Flexible Beam

An output feedback sliding mode controller (SMC) is proposed to minimize the effects of vibrations of slewing flexible manipulators. A spline trajectory is used to generate ideal position and velocity commands. Constrained nonlinear optimization techniques are used to both calibrate nonlinear models and determine optimized gains to produce a rest-to-rest, residual vibration-free maneuver. Vibration-free maneuvers are important for current and future NASA space missions. This study required the development of the nonlinear dynamic system equations of motion; robust control law design; numerical implementation; system identification; and verification using the Sandia National Laboratories flexible robot testbed. Results are shown for a slewing flexible beam.

Wilson, David G.↗
NASA NTRS1997

Design and Operational Evaluation of the Traffic Management Advisor at the Ft. Worth Air Route Traffic Control Center

NASA and the FAA have designed and developed and an automation tool known as the Traffic Management Advisor (TMA). The system was operationally evaluated at the Ft. Worth Air Route Traffic Control Center (ARTCC). The TMA is a time-based strategic planning tool that provides Traffic Management Coordinators and En Route Air Traffic Controllers the ability to efficiently optimize the capacity of a demand impacted airport. The TMA consists of trajectory prediction, constraint-based runway scheduling, traffic flow visualization and controllers advisories. The TMA was used and operationally evaluated for forty-one rush traffic periods during a one month period in the Summer of 1996. The evaluations included all shifts of air traffic operations as well as periods of inclement weather. Performance data was collected for engineering and human factor analysis and compared with similar operations without the TMA. The engineering data indicates that the operations with the TMA show a one to two minute per aircraft delay reduction during rush periods. The human factor data indicate a perceived reduction in en route controller workload as well as an increase in job satisfaction. Upon completion of the evaluation, the TMA has become part of the normal operations at the Ft. Worth ARTCC.

Swenson, Harry N.↗
NASA NTRSDate not supplied

Pterodactyl: Guidance and Control of a Symmetric Deployable Entry Vehicle using an Aerodynamic Control System

The NASA-funded Pterodactyl project seeks to advance the state-of-the-art for varying entry vehicle types by developing unconventional guidance and control technologies for Deployable Entry Vehicles (DEVs) that can be applied to different entry vehicle configurations. Prior work by the authors [1–5] involved developing both traditional and novel integrated guidance and control solutions for a Pterodactyl Baseline Vehicle (PBV), a variant of an asymmetric DEV called the Lifting Nano ADEPT (LNA) [6]. In the prior studies, two different guidance schemes were designed and implemented for the PBV: (i) traditional bank angle guidance developed using the Fully Numerical Predictor-Corrector Entry Guidance (FNPEG) and (ii) novel angle of attack and sideslip (α - β) guidance developed using FNPEG with Uncoupled Range Control [4]. Using Linear Quadratic Regulator (LQR) optimal control methods with state-feedback integral control designs, these guidance trajectories were designed to be tracked using (i) a conventional propulsive entry vehicle control hardware architecture - reaction control systems (RCS) and (ii) novel non-propulsive entry vehicle control systems - aerodynamic flap control system (FCS) and moving mass control system (MMCS) [1]. The novel FCS and MMCS architectures were designed to track α - β guidance commands while the RCS was designed to track bank angle commands. It was discovered that the asymmetric DEV, the PBV, experienced a non-zero induced roll moment due to sideslip that the FCS and MMCS architectures had limited capability to trim out. These two architectures were designed to provide independent angle of attack and sideslip commands with limited consideration for roll moment generation to trim. As a result, for the PBV, the FCS and MMCS configurations as designed, were limited in providing the control authority needed to track an α - β guidance trajectory [1]. These results form the motivation for the work presented in this paper - utilizing an aerodynamic control system to track α - β guidance commands for a symmetric DEV with the expectation that a symmetric entry vehicle will have zero or significantly reduced roll moment due to sideslip that the FCS can handle when tracking an α - β guidance trajectory. To demonstrate the feasibility of a novel guidance and control architecture on a DEV, we utilize a symmetric DEV, the PBV-II, for (i) the novel α - β guidance development using FNPEG with Uncoupled Range Control and (ii) LQR control design using eight aerodynamic control surfaces. This paper demonstrates that the novel uncoupled α - β guidance tracking can be achieved using aerodynamic control surfaces on a symmetric deployable entry vehicle configuration.

Wendy A Okolo↗
NASA NTRSDate not supplied

Markov Decision Process based Trajectory Planning for UAVs under Uncertain Wind Conditions

In this paper we propose a Markov Decision Process (MDP) algorithm for path-planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to path-planning for UAVs are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs needs to account for dynamically changing flying conditions such as weather, obstacle or no-fly zones, degraded vehicle health and off-nominal battery power consumption. Machine learning methods such as Markov Decision Process (MDPs) have the potential to revolutionize how vehicles navigate in such uncertain environments. Previous papers have demonstrated the use of MDPs to optimize UAV path-planning for energy consumption under time-varying wind distribution. In this study, UAV trajectories from a pre-determined waypoint to target cell, will be computed on a 7X7 grid environment by optimizing parameters for mission assurance and safety limits in addition to the energy consumption, and operation time. The UAV navigates the grid by taking actions to move in either of the eight cardinal and intercardinal directions, under constant thrust profile. The next state of the UAV is calculated by considering its action, transition probability, obstacle cells and the wind speed magnitude and direction. Both constant and stochastic wind will be considered in this paper, the parameters being extracted from real wind measurements in proximity to an experimental UAV flight. One of the studies to be demonstrated in this paper is that as the unmanned airspace gets more complex with multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance needs to be made. Further, MDPs are capable of fast computation of UAV trajectories under varying wind, hence making them suitable for in-flight path planners.

decision-making↗
DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2022

Conflict Resolution Strategies for Balloon-Airship Encounters in Upper Class E Air Traffic Management (ETM)

This paper examines strategic conflict resolution strategies for pair-wise high-altitude balloon, airship encounters. By strategic is meant that the conflict is identified (detected) at the time of flight plan submission or flight plan alteration through a service, such as an ETM Service Supplier (ESS). We investigate optimal control solutions for two classes of problems: non-cooperative, where the balloon must alter its ascent trajectory in order to avoid the airship and cooperative, where both vehicles make maneuvers through a negotiation protocol.

airship balloon negotiation optimal control↗
NASA NTRS2022

Conflict Resolution Strategies for Balloon-Airship Encounters in Upper Class E Air Traffic Management (ETM)

This paper examines strategic conflict resolution strategies for pair-wise high-altitude balloon, airship encounters. By strategic is meant that the conflict is identified (detected) at the time of flight plan submission or flight plan alteration through a service, such as an ETM Service Supplier (ESS). We investigate optimal control solutions for two classes of problems: non-cooperative, where the balloon must alter its ascent trajectory in order to avoid the airship and cooperative, where both vehicles make maneuvers through a negotiation protocol.

airship balloon negotiation optimal control↗
DOE OSTI2021

Formation and reconfiguration of tight multi-lane platoons

Advances in vehicular communication technologies are expected to facilitate cooperative driving in the future. Connected and Automated Vehicles (CAVs) are able to collaboratively plan and execute driving maneuvers by sharing their perceptual knowledge and future plans. In this paper, an architecture for autonomous navigation of tight multi-lane platoons traveling on public roads is presented. Using the proposed approach, CAVs are able to form single or multi-lane platoons of various geometrical configurations. They are able to reshape and adjust their configurations according to changes in the environment. The proposed architecture consists of two main components: an offline motion planner system and an online hierarchical control system. The motion planner uses an optimization-based approach for cooperative formation and reconfiguration in tight spaces. A constrained optimization scheme is used to plan smooth, dynamically feasible and collision-free trajectories for all the vehicles within the platoon. The paper addresses online computation limitations by employing a family of maneuvers precomputed offline and stored on a look-up table on the vehicles. The online hierarchical control system is composed of three levels: a traffic operation system (TOS), a decision-maker, and a path-follower. The TOS determines the desired platoon reconfiguration. The decision-maker checks the feasibility of the reconfiguration plan based on real-time information about the surrounding traffic. The reconfiguration maneuver is executed by a low-level path-following feedback controller in real-time. The effectiveness of the approach is demonstrated through simulations of three case studies: (1) formation reconfiguration (2) obstacle avoidance, and (3) benchmarking against behavior-based planning in which the desired formation is achieved using a sequence of motion primitives.

42 ENGINEERING↗
NASA NTRS2017

Application of an Optimal Control Allocation Scheme with Structural Load and Aero Heating Feedback for a Morphing Inflatable Aerodynamic Decelerator

Precision landing of large payloads on Mars presents a challenge to the Entry, Descent, and Landing (EDL) community. Previous studies indicated that by incorporating the capability for a Hypersonic Inflatable Aerodynamic Decelerator (HIAD) to morph during reentry would result in a more accurate landing footprint by allowing modulation of the lift- to-drag (L/D) vector directly instead of through bank angle control. However, morphing the HIAD shape for trajectory control may expose the HIAD to potential structural loads or aero heating concerns. In this study, the application of an optimal control allocation (OCA) technique was investigated that would to enable the morphing HIAD to maximize trajectory control capabilities while simultaneously keeping the structural loads and aero heating below some thresholds. This concept was demonstrated in a 3 degree-of-freedom (DOF) EDL simulation and provides basis for future research.

Bassett, George Z.↗
DOE OSTI2026

Active space selection with self-healing diffusion Monte Carlo algorithms for periodic solids

Multideterminant Diffusion Monte Carlo (DMC) displays improved accuracy over single determinant DMC. Self-Healing Diffusion Monte Carlo (SHDMC) is a DMC based method that iteratively improves a multideterminant trial wavefunction. Although configuration interaction or complete active space (CAS) methods are very accurate and computationally feasible for many systems, they are not optimal for application to solids. SHDMC is accurate and designed for application to solids, so developing SHDMC based active space selection algorithms is a worthy endeavor. Here, we present and compare active space selection algorithms that are designed for use in conjunction with SHDMC, without relying on external approaches. For benchmarking, we calculated the ground state energy of a small unit cell of graphene and compared the results with a complete basis set extrapolated selected CI and a reference SHDMC trajectory. We found that systematically expanding the active space using an “auto-branching” algorithm optimally balances accuracy with computational practicality. To the best of our knowledge, this is the first work that demonstrates completely self-contained DMC-based active space selection algorithms that do not depend on external methods for determinant selection.

Spanedda, Nicole [ORNL]↗
NASA NTRS2004

Tetrahedron Formation Control

This paper considers the preliminary development of a general optimization procedure for tetrahedron formation control. The maneuvers are assumed to be impulsive and a multi-stage optimization method is employed. The stages include (1) targeting to a fixed tetrahedron location and orientation, and (2) rotating and translating the tetrahedron. The number of impulsive maneuvers can also be varied. As the impulse locations and times change, new arcs are computed using a differential corrections scheme that varies the impulse magnitudes and directions. The result is a continuous trajectory with velocity discontinuities. The velocity discontinuities are then used to formulate the cost function. Direct optimization techniques are employed. The procedure is applied to the NASA Goddard Magnetospheric Multi-Scale (MMS) mission to compute preliminary formation control fuel requirements.

Petruzzo, Charles↗
NASA NTRS2003

Tetrahedron Formation Control

Spacecraft flying in tetrahedron formations are excellent instrument platforms for electromagnetic and plasma studies. A minimum of four spacecraft - to establish a volume - is required to study some of the key regions of a planetary magnetic field. The usefulness of the measurements recorded is strongly affected by the tetrahedron orbital evolution. This paper considers the preliminary development of a general optimization procedure for tetrahedron formation control. The maneuvers are assumed to be impulsive and a multi-stage optimization method is employed. The stages include targeting to a fixed tetrahedron orientation, rotating and translating the tetrahedron and/or varying the initial and final times. The number of impulsive maneuvers citn also be varied. As the impulse locations and times change, new arcs are computed using a differential corrections scheme that varies the impulse magnitudes and directions. The result is a continuous trajectory with velocity discontinuities. The velocity discontinuities are then used to formulate the cost function. Direct optimization techniques are employed. The procedure is applied to the Magnetospheric Multiscale Mission (MMS) to compute preliminary formation control fuel requirements.

Guzman, Jose J.↗
NASA NTRSDate not supplied

Energy-Optimized Path Planning for Uas in Varying Winds Via Reinforcement Learning

In this paper we propose a reinforcement learning (RL) algorithm for path planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to UAV path planning problems are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs need to account for dynamically changing flying conditions such as weather, obstacle or no-fly zones, degraded vehicle health, and off-nominal battery power consumption. Machine learning methods such as reinforcement learning (RL) have the potential to revolutionize how vehicles navigate in such uncertain environments. In this study, we compute UAV trajectories from a pre-determined starting position to a target cell within a 7X7 grid environment by optimizing parameters for mission assurance and safety limits in addition to the energy consumption and operation time. The UAV navigates the grid by taking actions to move in any of the eight cardinal and inter-cardinal directions, under constant thrust profile. The resultant UAV state is sampled from a probability distribution which accounts for the UAV’s action, local wind velocity, and the presence of obstacles or boundaries. As the unmanned airspace gets more complex due to multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance need to be made. Our Markov Decision Process (MDP) environment model can capture any combination of these in the optimization objective, making it novel compared to other work in the field.

trajectory planning↗
NASA NTRS1973

Sensitivity of optimal control systems with bang-bang control.

The effects of small parameter variations on the performance index of optimal control systems with initial and final target manifolds, free end time, and bang-bang control are analyzed in this paper. A new approach to the sensitivity equation is presented. This approach takes into account the pulse-shaped variation produced by the parameter change on the bang-bang control. An expression, that relates the variations of the performance index, the trajectory, the final time, and the parameter, is derived. This expression extends to the class of optimal systems with bang-bang control, a result previously obtained by Courtin and Rootenberg (1971).

Rootenberg, J.↗
NASA NTRS1991

An optimal control strategy for collision avoidance of mobile robots in non-stationary environments

An optimal control formulation of the problem of collision avoidance of mobile robots in environments containing moving obstacles is presented. Collision avoidance is guaranteed if the minimum distance between the robot and the objects is nonzero. A nominal trajectory is assumed to be known from off-line planning. The main idea is to change the velocity along the nominal trajectory so that collisions are avoided. Furthermore, time consistency with the nominal plan is desirable. A numerical solution of the optimization problem is obtained. Simulation results verify the value of the proposed strategy.

Kyriakopoulos, K. J.↗
DOE OSTI2020

MorphOT: transport-based interpolation between EM maps with UCSF ChimeraX

Cryogenic electron microscopy (cryo-EM) offers the unique potential to capture conformational heterogeneity, by solving multiple three-dimensional classes that co-exist within a single cryo-EM image dataset. To investigate the extent and implications of such heterogeneity, we propose to use an optimal-transport-based metric to interpolate barycenters between EM maps and produce morphing trajectories.

47 OTHER INSTRUMENTATION↗

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