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Results for “Trajectory Optimization”

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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At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Aircraft trajectories for reduced noise impact

Numerical optimization is used to compute the optimum flight paths, based upon a parametric form that implicitly includes some of the problem restrictions. The other constraints are formulated as penalties in the cost function. Various aircraft on multiple trajectories (landing and takeoff) can be considered. The modular design employed allows for the substitution of alternate models of the population distribution, aircraft noise, flight paths, and annoyance, or for the addition of other features (e.g., fuel consumption) in the cost function. A reduction in the required amount of searching over local minima was achieved through use of the presence of statistical lateral dispersion in the flight paths. Previously announced in STAR as N81-31159

Melton, R. G.↗
DOE OSTI2019

Dual heuristic dynamic programing control of grid-connected synchronverters

A new approach to control a grid-connected synchronverter by using a dual heuristic dynamic programing (DHP) design is presented. The disadvantages of conventional synchronverter controller such as the challenges to cope with nonlinearity, uncertainties, and non-inductive grids are discussed. To deal with the aforementioned challenges a neural network–based adaptive critic design is introduced to optimize the associated cost function. The characteristic of the neural networks facilitates the performance under uncertainties and unknown parameters (e.g. different power angles). The proposed DHP design includes three neural networks: system NN, action NN, and critic NN. The simulation results compare the performance of the proposed DHP with a traditional PI-based design and with a neural network predictive controller. It is shown a well-trained DHP design performs in a trajectory, which is more optimal compared to the other two controllers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2018

Optimization of Second Fault Detection Thresholds to Maximize Mission Probability of Success

In order to support manned spaceflight safety requirements, the Space Launch System (SLS) has defined program-level requirements for key systems to ensure successful operation under single fault conditions. The SLS program has also levied requirements relating to the capability of the Inertial Navigation System to detect a second fault. This detection functionality is required in order to feed abort analysis and ensure crew safety. Increases in navigation state error due to sensor faults in a purely inertial system can drive the vehicle outside of its operational as-designed environmental and performance envelope. As this performance outside of first fault detections is defined and controlled at the vehicle level, it allows for the use of system level margins to increase probability of mission success on the operational edges of the design. A top-down approach is utilized to assess vehicle sensitivity to second sensor faults. A wide range of failure scenarios in terms of both fault magnitude and time is used for assessment. The approach also utilizes a schedule to change fault detection thresholds autonomously. These individual values are optimized along a nominal trajectory in order to maximize probability of mission success in terms of system-level insertion requirements while minimizing the probability of false positives. This paper will describe an approach integrating Genetic Algorithms and Monte Carlo analysis to tune the threshold parameters to maximize vehicle resilience to second fault events over an ascent mission profile. The analysis approach and performance assessment and verification will be presented to demonstrate the applicability of this approach to second fault detection optimization to maximize mission probability of success through taking advantage of existing margin.

Anzalone, Evan J.↗
DOE OSTI2019

Online Optimization as a Feedback Controller: Stability and Tracking

This paper develops and analyzes feedback-based online optimization methods to regulate the output of a linear time-invariant (LTI) dynamical system to the optimal solution of a time-varying convex optimization problem. The design of the algorithm is based on continuous-time primal-dual dynamics, properly modified to incorporate feedback from the LTI dynamical system, applied to a proximal augmented Lagrangian function. The resultant closed-loop algorithm tracks the solution of the time-varying optimization problem without requiring knowledge of (time-varying) disturbances in the dynamical system. The analysis leverages integral quadratic constraints to provide linear matrix inequality (LMI) conditions that guarantee global exponential stability and bounded tracking error. Analytical results show that, under a sufficient time-scale separation between the dynamics of the LTI dynamical system and the algorithm, the LMI conditions can be always satisfied. The paper further proposes a modified algorithm that can track an approximate solution trajectory of the constrained optimization problem under less restrictive assumptions. As an illustrative example, the proposed algorithms are showcased for power transmission systems, to compress the time scales between secondary and tertiary control, and allow to simultaneously power re-balancing and tracking of DC optimal power flow points.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Optimal subhourly electricity resource dispatch under multiple price signals with high renewable generation availability

This article proposes a system-wide optimal resource dispatch strategy that enables a shift from a primarily energy cost-based approach, to a strategy using simultaneous price signals for energy, power and ramping behavior. A formal method to compute the optimal sub-hourly power trajectory is derived for a system when the price of energy and ramping are both significant. Optimal control functions are obtained in both time and frequency domains, and a discrete-time solution suitable for periodic feedback control systems is presented. The method is applied to North America Western Interconnection for the planning year 2024, and it is shown that an optimal dispatch strategy that simultaneously considers both the cost of energy and the cost of ramping leads to significant cost savings in systems with high levels of renewable generation: the savings exceed 25% of the total system operating cost for a 50% renewables scenario.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2016

Rapid Preliminary Design of Interplanetary Trajectories Using the Evolutionary Mission Trajectory Generator

This set of tutorial slides is an introduction to the Evolutionary Mission Trajectory Generator (EMTG), NASA Goddard Space Flight Center's autonomous tool for preliminary design of interplanetary missions. This slide set covers the basics of creating and post-processing simple interplanetary missions in EMTG using both high-thrust chemical and low-thrust electric propulsion along with a variety of operational constraints.

optimization↗
DOE OSTI2025

Quantum simulations of nuclear resonances with variational methods

Background: The many-body nature of nuclear physics problems poses significant computational challenges. These challenges become even more pronounced when studying the resonance states of nuclear systems, which are governed by the non-Hermitian Hamiltonian. Quantum computing, particularly for quantum many-body systems, offers a promising alternative, especially within the constraints of current noisy intermediate-scale quantum (NISQ) devices. Purpose: This work aims to simulate nuclear resonances using quantum algorithms by developing a variational framework compatible with non-Hermitian Hamiltonians and implementing it fully on a quantum simulator. Methods: We employ the complex scaling technique to extract resonance positions classically and adapt it for quantum simulations using a two-step algorithm. First, we transform the non-Hermitian Hamiltonian into a Hermitian form by using the energy variance as a cost function within a variational framework. Second, we perform 𝜃-trajectory calculations to determine optimal resonance positions in the complex energy plane. To address resource constraints on NISQ devices, we utilize Gray code (GC) encoding to reduce qubit requirements. Results: We first validate our approach using a schematic potential model that mimics a nuclear potential, successfully reproducing known resonance energies with high fidelity. We then extend the method to a more realistic 𝛼−𝛼 nuclear potential and compute the 𝐷- and 𝐺-wave resonance energies with a basis size of 𝑁=16, using only four qubits. The quantum simulation results closely match the classical values, demonstrating the feasibility of our approach. Conclusions: This study demonstrates, for the first time, that the complete 𝜃-trajectory method can be implemented on a quantum computer without relying on any classical input beyond the Hamiltonian. The results establish a scalable and efficient quantum framework for simulating resonance phenomena in nuclear systems. This work represents a significant step toward quantum simulations of open quantum systems and lays the foundation for future investigations into resonance structures in nuclear, atomic, and molecular physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1972

Rapid optimization of multiple-burn rocket flights.

Different formulations of the fuel optimization problem for multiple burn trajectories are considered. It is shown that certain customary idealizing assumptions lead to an ill-posed optimization problem for which no solution exists. Several ways are discussed for avoiding such difficulties by more realistic problem statements. An iterative solution of the boundary value problem is presented together with efficient coast arc computations, the right end conditions for various orbital missions, and some test results.

Brown, K. R.↗
NASA NTRS2005

Evolutionary Computing for Low-thrust Navigation

The development of new mission concepts requires efficient methodologies to analyze, design and simulate the concepts before implementation. New mission concepts are increasingly considering the use of ion thrusters for fuel-efficient navigation in deep space. This paper presents parallel, evolutionary computing methods to design trajectories of spacecraft propelled by ion thrusters and to assess the trade-off between delivered payload mass and required flight time. The developed methods utilize a distributed computing environment in order to speed up computation, and use evolutionary algorithms to find globally Pareto-optimal solutions. The methods are coupled with two main traditional trajectory design approaches, which are called direct and indirect. In the direct approach, thrust control is discretized in either arc time or arc length, and the resulting discrete thrust vectors are optimized. In the indirect approach, a thrust control problem is transformed into a costate control problem, and the initial values of the costate vector are optimized. The developed methods are applied to two problems: 1) an orbit transfer around the Earth and 2) a transfer between two distance retrograde orbits around Europa, the closest to Jupiter of the icy Galilean moons. The optimal solutions found with the present methods are comparable to other state-of-the-art trajectory optimizers and to analytical approximations for optimal transfers, while the required computational time is several orders of magnitude shorter than other optimizers thanks to an intelligent design of control vector discretization, advanced algorithmic parameterization, and parallel computing.

optimization↗
NASA NTRSDate not supplied

Onboard Navigation Error Analysis for Aerocapture at Uranus

Capturing into an orbit around Uranus using aerocapture allows one to design a mission with faster interplanetary trajectories and less propellant requirements. Such an aerocapture mission would rely on the onboard Guidance, Navigation, and Control (GNC) subsystems to successfully capture into an orbit around Uranus. Uncertainty in the state information and the noise in the sensor measurements induce navigation errors in the guidance and control subsystems, which can affect the overall performance of the aerocapture mission at Uranus. Understanding the effect of these navigation errors on mission performance is essential. To this end, this work considers different sensors with varying quality to understand their impact on the overall mission performance. In addition, this paper studies the impact of the uncertainty in the initial states used to initialize the onboard navigation filter and understands their effect on mission performance. This paper also shows the onboard navigation errors obtained from the Linear Covariance (LinCov) analysis and uses them for verification and validation (V&V) of the results from Program to Optimize and Simulate Trajectories-II (POST2).

Pardha Sai Chadalavada↗
NASA NTRSDate not supplied

Onboard Navigation Error Analysis for Aerocapture at Uranus

Capturing into an orbit around Uranus using aerocapture allows one to design a mission with faster interplanetary trajectories and less propellant requirements. Such an aerocapture mission would rely on the onboard Guidance, Navigation, and Control (GNC) subsystems to successfully capture into an orbit around Uranus. Uncertainty in the state information and the noise in the sensor measurements induce navigation errors in the guidance and control subsystems, which can affect the overall performance of the aerocapture mission at Uranus. Understanding the effect of these navigation errors on mission performance is essential. To this end, this work considers different sensors with varying quality to understand their impact on the overall mission performance. In addition, this paper studies the impact of the uncertainty in the initial states used to initialize the onboard navigation filter and understands their effect on mission performance. This paper also shows the onboard navigation errors obtained from the Linear Covariance (LinCov) analysis and uses them for verification and validation (V\&V) of the results from Program to Optimize and Simulate Trajectories-II (POST2).

Aerocapture↗
NASA NTRS2013

Orbital Transfer Techniques for Round-Trip Mars Missions

The human exploration of Phobos and Deimos or the retrieval of a surface sample launched to low-Mars orbit presents a highly constrained orbital transfer problem. In general, the plane of the target orbit will not be accessible from the arrival or departure interplanetary trajectories with an (energetically optimal) tangential burn at periapsis. The orbital design is further complicated by the addition of a high-energy parking orbit for the relatively massive Deep Space Vehicle to reduce propellant expenditure, while the crew transfers to and from the target orbit in a smaller Space Exploration Vehicle. The proposed strategy shifts the arrival and departure maneuvers away from periapsis so that the apsidal line of the parking orbit lies in the plane of the target orbit, permitting highly efficient plane change maneuvers at apoapsis of the elliptical parking orbit. An apsidal shift during the arrival or departure maneuver is approximately five times as efficient as maneuvering while in Mars orbit, thus significantly reducing the propellant necessary to transfer between the arrival, target, and departure orbits.

human missions↗
NASA NTRS1975

Deterministic optimal maneuver strategy for multi-target missions

This paper presents an optimal strategy for making impulsive correction to a multi-target trajectory by a single maneuver. The concept of an optimal maneuver time is introduced. The choice of suitable weighting functions is explored to enable one to properly translate the subjective desire of mission success into an objective cost function whose minimization yields the optimal strategy. It is shown that a number of strategies previously formulated are derivable from one general expression. A number of other interesting properties of the optimal strategy are described. Numerical results are presented for a typical two-target mission. It is shown that the strategy formulated is optimal. For some perturbations, there exists an optimal maneuver time different from the time of initiation of the perturbation. That is, the physical properties of the trajectory can be exploited to select the optimal time of making a corrective maneuver.

Dwivedi, N. P.↗
DOE OSTI2017

Estimating traffic volumes for signalized intersections using connected vehicle data

Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model in this paper vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Finally, considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Direct observation of coherence transfer and rotational-to-vibrational energy exchange in optically centrifuged CO2 super-rotors

Abstract Optical centrifuges are laser-based molecular traps that can rotationally accelerate molecules to energies rivalling or exceeding molecular bond energies. Here we report time and frequency-resolved ultrafast coherent Raman measurements of optically centrifuged CO 2 at 380 Torr spun to energies beyond its bond dissociation energy of 5.5 eV ( J max = 364, E rot = 6.14 eV, E rot / k B = 71, 200 K). The entire rotational ladder from J = 24 to J = 364 was resolved simultaneously which enabled a more accurate measurement of the centrifugal distortion constants for CO 2 . Remarkably, coherence transfer was directly observed, and time-resolved, during the field-free relaxation of the trap as rotational energy flowed into bending-mode vibrational excitation. Vibrationally excited CO 2 ( ν 2 > 3) was observed in the time-resolved spectra to populate after 3 mean collision times as a result of rotational-to-vibrational (R-V) energy transfer. Trajectory simulations show an optimal range of J for R-V energy transfer. Dephasing rates for molecules rotating up to 5.5 times during one collision were quantified. Very slow decays of the vibrational hot band rotational coherences suggest that they are sustained by coherence transfer and line mixing.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Deep kernel methods learn better: from cards to process optimization

Abstract The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards dataset and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The Jupyter Notebooks that encapsulate the complete analysis accompany the article.

97 MATHEMATICS AND COMPUTING↗