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At least 343 records · Page 19

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NASA NTRS2009

Autonomously Calibrating a Quadrupole Mass Spectrometer

A computer program autonomously manages the calibration of a quadrupole ion mass spectrometer intended for use in monitoring concentrations and changes in concentrations of organic chemicals in the cabin air of the International Space Station. The instrument parameters calibrated include the voltage on a channel electron multiplier, a discriminator threshold, and an ionizer current. Calibration is achieved by analyzing the mass spectrum obtained while sweeping the parameter ranges in a heuristic procedure, developed by mass spectrometer experts, that involves detection of changes in signal trends that humans can easily recognize but cannot necessarily be straightforwardly codified in an algorithm. The procedure includes calculation of signal-to-noise ratios, signal-increase rates, and background-noise-increase rates; finding signal peaks; and identifying peak patterns. The software provides for several recovery-from-error scenarios and error-handling schemes. The software detects trace amounts of contaminant gases in the mass spectrometer and notifies associated command- and-data-handling software to schedule a cleaning. Furthermore, the software autonomously analyzes the mass spectrum to determine whether the parameters of a radio-frequency ramp waveform are set properly so that the peaks of the mass spectrum are at expected locations.

Lee, Seungwon↗

NASA NTRS2006

A Convex Guidance Algorithm for Formation Reconfiguration

In this paper, a reconfiguration guidance algorithm for formation flying spacecraft is presented. The formation reconfiguration guidance problem is first formulated as a continuous-time minimum-fuel or minimum-energy optimal control problem with collision avoidance and control constraints. The optimal control problem is then discretized to obtain a finite dimensional parameter optimization problem. In this formulation, the collision avoidance constraints are imposed via separating planes between each pair of spacecraft. A heuristic is introduced to choose these separating planes that leads to the convexification of the collision avoidance constraints. Additionally, convex constraints are imposed to guarantee that no collisions occur between discrete time samples. The resulting finite dimensional optimization problem is a second order cone program, for which standard algorithms can compute the global optimum with deterministic convergence and a prescribed level of accuracy. Consequently, the formation reconfiguration algorithm can be implemented onboard a spacecraft for real-time operations.

formation reconfiguration↗

NASA NTRS1991

Application of a directed search to global steering of single gimballed CMGs

A guided depth-first search that manages null motion about torque-producing trajectories calculated with a singularity-robust (SR) inverse is proposed as a practical feedforward steering law that can globally avoid (or minimize the impact of) singular states in minimally-redundant single gimballed CMG systems. Cost and heuristic functions are defined to guide the search procedure in improving CMG trajectories. On-orbit implementation of the steering law is proposed as an extension to momentum management algorithms. A set of simulation examples is presented, illustrating the search performance for a 4-CMG pyramid-mounted array. Sensitivities of feedforward gimbal trajectories are examined in the presence of unmodeled disturbances, and techniques are proposed for avoiding excessive divergence.

Paradiso, Joseph A.↗

NASA NTRS1992

An architecture for the development of real-time fault diagnosis systems using model-based reasoning

Presented here is an architecture for implementing real-time telemetry based diagnostic systems using model-based reasoning. First, we describe Paragon, a knowledge acquisition tool for offline entry and validation of physical system models. Paragon provides domain experts with a structured editing capability to capture the physical component's structure, behavior, and causal relationships. We next describe the architecture of the run time diagnostic system. The diagnostic system, written entirely in Ada, uses the behavioral model developed offline by Paragon to simulate expected component states as reflected in the telemetry stream. The diagnostic algorithm traces causal relationships contained within the model to isolate system faults. Since the diagnostic process relies exclusively on the behavioral model and is implemented without the use of heuristic rules, it can be used to isolate unpredicted faults in a wide variety of systems. Finally, we discuss the implementation of a prototype system constructed using this technique for diagnosing faults in a science instrument. The prototype demonstrates the use of model-based reasoning to develop maintainable systems with greater diagnostic capabilities at a lower cost.

Hall, Gardiner A.↗

DOE OSTI2020

Quantum approximate optimization of the long-range Ising model with a trapped-ion quantum simulator

Quantum computers and simulators may offer significant advantages over their classical counterparts, providing insights into quantum many-body systems and possibly improving performance for solving exponentially hard problems, such as optimization and satisfiability. Here, we report the implementation of a low-depth Quantum Approximate Optimization Algorithm (QAOA) using an analog quantum simulator. We estimate the ground-state energy of the Transverse Field Ising Model with long-range interactions with tunable range, and we optimize the corresponding combinatorial classical problem by sampling the QAOA output with high-fidelity, single-shot, individual qubit measurements. We execute the algorithm with both an exhaustive search and closed-loop optimization of the variational parameters, approximating the ground-state energy with up to 40 trapped-ion qubits. We benchmark the experiment with bootstrapping heuristic methods scaling polynomially with the system size. We observe, in agreement with numerics, that the QAOA performance does not degrade significantly as we scale up the system size and that the runtime is approximately independent from the number of qubits. We finally give a comprehensive analysis of the errors occurring in our system, a crucial step in the path forward toward the application of the QAOA to more general problem instances.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1999

Advanced Interactive Display Formats for Terminal Area Traffic Control

This research project deals with an on-line dynamic method for automated viewing parameter management in perspective displays. Perspective images are optimized such that a human observer will perceive relevant spatial geometrical features with minimal errors. In order to compute the errors at which observers reconstruct spatial features from perspective images, a visual spatial-perception model was formulated. The model was employed as the basis of an optimization scheme aimed at seeking the optimal projection parameter setting. These ideas are implemented in the context of an air traffic control (ATC) application. A concept, referred to as an active display system, was developed. This system uses heuristic rules to identify relevant geometrical features of the three-dimensional air traffic situation. Agile, on-line optimization was achieved by a specially developed and custom-tailored genetic algorithm (GA), which was to deal with the multi-modal characteristics of the objective function and exploit its time-evolving nature.

Grunwald, Arthur J.↗

NASA NTRS1999

Advanced Interactive Display Formats for Terminal Area Traffic Control

Grunwald, Arthur J.↗

DOE OSTI2025

Alternative mixed integer linear programming optimization for joint job scheduling and data allocation in grid computing

This paper presents a novel approach to the joint optimization of job scheduling and data allocation in grid computing environments. We formulate this joint optimization problem as a mixed integer quadratically constrained program. To tackle the nonlinearity in the constraint, we alternatively fix a subset of decision variables and optimize the remaining ones via Mixed Integer Linear Programming (MILP). We solve the MILP problem at each iteration via an off-the-shelf MILP solver. Our experimental results show that our method significantly outperforms existing heuristic methods, employing either independent optimization or joint optimization strategies. We have also verified the generalization ability of our method over grid environments with various sizes and its high robustness to the algorithm setting.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Cost function dependent barren plateaus in shallow parametrized quantum circuits

Variational quantum algorithms (VQAs) optimize the parameters θ of a parametrized quantum circuit V(θ) to minimize a cost function C. While VQAs may enable practical applications of noisy quantum computers, they are nevertheless heuristic methods with unproven scaling. Here, we rigorously prove two results, assuming V(θ) is an alternating layered ansatz composed of blocks forming local 2-designs. Our first result states that defining C in terms of global observables leads to exponentially vanishing gradients (i.e., barren plateaus) even when V(θ) is shallow. Hence, several VQAs in the literature must revise their proposed costs. On the other hand, our second result states that defining C with local observables leads to at worst a polynomially vanishing gradient, so long as the depth of V(θ) is Ο(logn). Our results establish a connection between locality and trainability. We illustrate these ideas with large-scale simulations, up to 100 qubits, of a quantum autoencoder implementation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Pressurized Water Reactor Gadolinia Pin Location Optimization

This report presents the results of lattice optimization studies performed to find optimum locations for gadolinia burnable absorber (BA) rods in pressurized water reactor (PWR) lattice fuel designs. Initial excess reactivity suppression allows core designers to further improve operational economics by extending cycle length. Gadolinia BAs are commonly used in boiling water reactor assembly designs for this purpose. In recent years, gadolinia absorbers have been used in PWR designs owing to their longer effectiveness for reactivity suppression compared with common BAs used in PWR assemblies. This report examines the optimum gadolinia pin placement in 17 × 17 PWR lattices at different fuel and gadolinia concentrations for optimized lattice performance, using the SCALE/Polaris lattice physics code. The completed work is continuation of the Light Water Reactor LEU+ Lattice Optimization (ORNL/TM-2021/2366) project. An optimization driver called the metaheuristic optimization tool (MOT) is used to automate domain space exploration and optimization of the lattice designs. Heuristics from previous light-water reactor (LWR) lattice optimization studies were used to construct the objective function and define the domain space for optimization. This work successfully demonstrated that the optimization algorithms of MOT can generate feasible, nonproprietary PWR lattice designs with gadolinia.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Fast Active-Set Thresholding Method for Nonnegative Least Squares

Nonnegative Least Squares (NNLS) is a fundamental constrained optimization problem encountered in many applications such as image deblurring, signal processing, nonnegative matrix factorization, magnetic microscopy, and hyperspectral imaging. Active-set based methods are a common class of algorithms for solving NNLS which identify the optimal variable set of the NNLS solution. They do so by iteratively solving a series of unconstrained least squares problems, identifying which variables violate the nonnegativity constraints, and then swapping variables in/out of consideration until the optimal set of variables is found. Several variations improving upon this method exist in the literature. In this work, we propose an active-set swap heuristic which further improves upon existing active-set based methods for NNLS. Our optimizations are based upon adding multiple variables to the passive set within a threshold of the smallest gradient value and removing variables within a similar threshold of the closest boundary constraint. We leverage these optimizations to yield a Fast Active-Set Thresholding NNLS (FAST-NNLS) algorithm which significantly outperforms the existing state-of-the-art NNLS algorithms for a wide range of problems. Rigorous convergence guarantees are proven for the proposed method. We demonstrate the effectiveness of our proposed method on multiple synthetic datasets and two realworld text analysis applications. In doing so, we present the most comprehensive NNLS solver comparison in the literature to date.

Cobb, Benjamin [Georgia Institute of Technology]↗

DOE OSTI2022

Hierarchical Control of Megawatt-Scale Charging Stations for Electric Trucks with Distributed Energy Resources

Electrifying medium- and heavy-duty trucks is critical to decarbonizing the transportation sector. Energy needs of electric trucks will likely require megawatt-scale charging stations, which could significantly stress the electric distribution grid. Distributed energy resources (DER) can alleviate this stress and reduce charging costs with proper management. To that end, this work develops a hierarchical predictive control algorithm for future multi-port megawatt-scale charging stations that can provide real-time energy management for stations, decide charging rates, dispatch energy storage system (ESS), and provide grid voltage support. We integrate three algorithmic components: (i) an energy management optimization (EMO) that provides supervisory control to DER assets and charging loads at minute scale, (ii) a real-time energy management system (RT-EMS) that heuristically compensates for fast disturbances at sub-second scale, and (iii) a model predictive control (MPC)-based battery management system (BMS) that communicates future charging demands to the EMO, to manage the overall megawatt-scale site. Additionally, validation in a controller hardware-in-the-loop (CHIL) environment shows that the hierarchical controller can reduce the total energy consumption from the grid by approximately 28% compared to an uncontrolled case for the station configuration in this paper, without impacting charging time.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1991

Data analysis using scale-space filtering and Bayesian probabilistic reasoning

This paper describes a program for analysis of output curves from Differential Thermal Analyzer (DTA). The program first extracts probabilistic qualitative features from a DTA curve of a soil sample, and then uses Bayesian probabilistic reasoning to infer the mineral in the soil. The qualifier module employs a simple and efficient extension of scale-space filtering suitable for handling DTA data. We have observed that points can vanish from contours in the scale-space image when filtering operations are not highly accurate. To handle the problem of vanishing points, perceptual organizations heuristics are used to group the points into lines. Next, these lines are grouped into contours by using additional heuristics. Probabilities are associated with these contours using domain-specific correlations. A Bayes tree classifier processes probabilistic features to infer the presence of different minerals in the soil. Experiments show that the algorithm that uses domain-specific correlation to infer qualitative features outperforms a domain-independent algorithm that does not.

Kulkarni, Deepak↗

NASA NTRS1990

Using a multifrontal sparse solver in a high performance, finite element code

We consider the performance of the finite element method on a vector supercomputer. The computationally intensive parts of the finite element method are typically the individual element forms and the solution of the global stiffness matrix both of which are vectorized in high performance codes. To further increase throughput, new algorithms are needed. We compare a multifrontal sparse solver to a traditional skyline solver in a finite element code on a vector supercomputer. The multifrontal solver uses the Multiple-Minimum Degree reordering heuristic to reduce the number of operations required to factor a sparse matrix and full matrix computational kernels (e.g., BLAS3) to enhance vector performance. The net result in an order-of-magnitude reduction in run time for a finite element application on one processor of a Cray X-MP.

King, Scott D.↗

NASA NTRS2017

Heuristic Area Cost Estimation for Observational Coverage Schedulers

This paper presents a comparison of heuris- tics used to estimate the amount of time it would take for a spacecraft to image an area using Boustrophedon decomposition (Choset and Pignon 1998). Machine learning tech- niques are used to characterize algorithmic performance of coverage algorithms. It is shown that an ordinary least-squares linear model is among the most accurate in a set of constant and linear order regression models both in terms of memory consumption and schedule duration. These are demonstrated using the ASPEN planning system (Fukunaga et al. 1997) on the Eagle Eye domain.

Knight, Russell↗

NASA NTRS1992

External calibration of polarimetric radar images using distributed targets

A new technique is presented for calibrating polarimetric synthetic aperture radar (SAR) images using only the responses from natural distributed targets. The model for polarimetric radars is assumed to be X = cRST where X is the measured scattering matrix corresponding to the target scattering matrix S distorted by the system matrices T and R (in general T does not equal R(sup t)). To allow for the polarimetric calibration using only distributed targets and corner reflectors, van Zyl assumed a reciprocal polarimetric radar model with T = R(sup t); when applied for JPL SAR data, a heuristic symmetrization procedure is used by POLCAL to compensate the phase difference between the measured HV and VH responses and then take the average of both. This heuristic approach causes some non-removable cross-polarization responses for corner reflectors, which can be avoided by a rigorous symmetrization method based on reciprocity. After the radar is made reciprocal, a new algorithm based on the responses from distributed targets with reflection symmetry is developed to estimate the cross-talk parameters. The new algorithm never experiences problems in convergence and is also found to converge faster than the existing routines implemented for POLCAL. When the new technique is implemented for the JPL polarimetric data, symmetrization and cross-talk removal are performed on a line-by-line (azimuth) basis. After the cross-talks are removed from the entire image, phase and amplitude calibrations are carried out by selecting distributed targets either with azimuthal symmetry along the looking direction or with some well-known volume and surface scattering mechanisms to estimate the relative phases and amplitude responses of the horizontal and vertical channels.

Yueh, Simon H.↗

DOE OSTI2020

Dynamic optimization and economic evaluation of flexible heat integration in a hybrid concentrated solar power plant

Hybridization of concentrated solar power (CSP) plants provides flexibility in operation that can drastically improve the solar-to-electric (STE) efficiency and levelized cost of electricity (LCOE) relative to standalone CSP plants. Flexible heat integration (FHI) is a novel concept where the collection and integration of CSP within a power plant is modified relative to the amount of solar energy available. FHI improves the thermal efficiency of a hybrid solar tower steam Rankine cycle power plant but leads to increased pumping needs due to continuously elevated molten salt flow rates through the collection system, which can negatively impact STE efficiency. The present work is carried out to maximize the STE efficiency of a hybrid CSP plant utilizing FHI by employing a dynamic optimization framework where a genetic algorithm optimizes the operation of the plant over a given solar irradiance profile. The study concerns a plant hypothetically located in Salt Lake City, Utah. Here, the optimization results confirm the accuracy of a predictive heuristic where the preferred operation of the plant can be estimated relative to local peaks in the incident power generated by the heliostat collection field. The optimized FHI operation demonstrates a yearly STE efficiency of 13.8%, whereas the equivalent base-level hybrid and solar-only plants exhibit solar efficiencies of 13.4% and 11.2%, respectively. Economic analysis shows that FHI reduces yearly natural gas costs, leading to a $\$0.5$/MWh reduction in LCOE relative to the base-level hybrid configuration. Overall, the results show that hybrid FHI schemes exhibit economic benefits along with observed thermodynamic improvements.

14 SOLAR ENERGY↗

NASA NTRS2003

Fuzzy Logic Trajectory Design and Guidance for Terminal Area Energy Management

The second generation reusable launch vehicle will leverage many new technologies to make flight to low earth orbit safer and more cost effective. One important capability will be completely autonomous flight during reentry and landing, thus making it unnecessary to man the vehicle for cargo missions with stringent weight constraints. Implementation of sophisticated new guidance and control methods will enable the vehicle to return to earth under less than favorable conditions. The return to earth consists of three phases--Entry, Terminal Area Energy Management (TAEM), and Approach and Landing. The Space Shuttle is programmed to fly all three phases of flight automatically, and under normal circumstances the astronaut-pilot takes manual control only during the Approach and Landing phase. The automatic control algorithms used in the Shuttle for TAEM and Approach and Landing have been developed over the past 30 years. They are computationally efficient, and based on careful study of the spacecraft's flight dynamics, and heuristic reasoning. The gliding return trajectory is planned prior to the mission, and only minor adjustments are made during flight for perturbations in the vehicle energy state. With the advent of the X-33 and X-34 technology demonstration vehicles, several authors investigated implementing advanced control methods to provide autonomous real-time design of gliding return trajectories thus enhancing the ability of the vehicle to adjust to unusual energy states. The bulk of work published to date deals primarily with the approach and landing phase of flight where changes in heading angle are small, and range to the runway is monotonically decreasing. These benign flight conditions allow for model simplification and fairly straightforward optimization. This project focuses on the TAEM phase of flight where mathematically precise methods have produced limited results. Fuzzy Logic methods are used to make onboard autonomous gliding return trajectory design robust to a wider energy envelope, and the possibility of control surface failures, thus increasing the flexibility of unmanned gliding recovery and landing.

Burchett, Bradley↗