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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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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

The Kinematic Navigation and Cartography Knapsack (KNaCK): Demonstrating SlLAM (Simultaneous Localization and Mapping) LiDAR as a Tool for Exploration and Mapping of Lunar Pits and Caves

KNaCK (Kinematic Navigation and Cartography Knapsack) is a backpack-mounted mobile LiDAR (Light Detection and Ranging) system. It can map its surroundings in 3 dimensions and localize itself in space. The project is exploring how LiDAR can advance terrain mapping and navigation at the lunar south pole. KNaCK is lead by Dr. Michael Zanetti of NASA MSFC’s Heliophysics and Planetary Science Branch. - KNaCK serves as - A test article for GPS denied mapping and navigation. - A test bed for SLAM (Simultaneous Localization and Mapping) algorithms. - A test bed for commercial LiDAR units. - A tool for terrestrial science.

LiDAR↗
DOE OSTI2024

Flexible dynamic boundary microgrid operation considering network and load unbalances

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Further, negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2020

Path-Adaptive Guidance Algorithm Trades for a Two-Stage Lunar Descent Vehicle

For the next generation of NASA’s missions, the necessity for path-adaptive guidance algorithms has become clear in order to provide the stability and customizability required for a safe and efficient descent to the lunar surface, while meeting specified program and vehicle constraints. Several descent algorithms have been tested and flown for single-stage landers through the Apollo and Altair programs, but thus far little analysis has been conducted involving the application of these algorithms for a two-stage descent vehicle. Due to the limited payload mass constraints of the existing fleet of launch vehicles, multi-stage descent architectures have become a viable course of action. This paper seeks to compare the performance of guidance configurations for a lunar lander system consisting of two stages, one of which separates partway through descent. Through development of this paper, an optimization suite has been written that is specifically designed for optimizing planetary non-atmospheric two-stage descent trajectories, and is used as a comparison baseline for the guidance algorithms tested. Time-to -go computational methods and ignition logic routines that may be employed in a lunar environment are also discussed. Further work is to be completed on trajectory design trades as well as the effects of modifying guidance targets in simulation based on trajectories that are optimized for different performance indices.

Jason M Everett↗
NASA NTRS1983

Programming substructure computations for elliptic problems on the CHiP system

A number of studies have been conducted with the aim to apply parallel computation to problems associated with solving finite element equations arising in structural mechanics and fluid dynamics. These studies have provided many important results. The present investigation is concerned with a set of experiments designed to test two ideas, including configurability and substructuring. The considered algorithms and tests are intended for implementation on the Configurable, Highly Parallel (CHiP) family of architecture described by Snyder (1982). The ChiP computer is composed of homogeneous processing elements (PEs) placed at regular intervals in a lattice of programmable switches. Two examples of the role of configurability and substructuring for simple iterative algorithms are considered, giving attention to conjugate gradient iterations, and tridiagonal systems of equations.

Gannon, D.↗
NASA NTRS1987

Development of a takeoff performance monitoring system

Discussed are the development and testing of a real-time takeoff performance monitoring algorithm. The algorithm is made up of two segments: a pretakeoff segment and a real-time segment. One-time imputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data for that takeoff. The real-time segment uses the scheduled performance data generated in the pretakeoff segment, runway length data, and measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane and engine performance deficiencies are detected and annunciated. An important feature of this algorithm is the one-time estimation of the runway rolling friction coefficient. The algorithm was tested using a six-degree-of-freedom airplane model in a computer simulation. Results from a series of sensitivity analyses are also included.

Srivatsan, Raghavachari↗
NASA NTRS1994

Echo mapping of active galactic nuclei broad-line regions: Fundamental algorithms

We formulate and test a series of algorithms for echo mapping the emission-line regions near active galactic nuclei from measurements of correlated variability in their line and continuum light curves. The linear regularization method (LRM) employs a direct inversion of evenly spaced light-curve data, with a regularization parameter that can be used to control the trade-off between noise and resolution. Matrix formulas express the formal solution as well as its variance and covariance in terms of uncertainties in the measurements. Unlike the maximum-entropy method (MEM), LRM applies to kernels with both positive and negative values, but the results are somewhat limited by ringing effects. A positivity constraint proves effective in controlling the ringing. MEM combines regularization and positivity in a natural way, but similar results are also found using positivity constraints with nonentropic regularization functions. Direct inversions of unevenly sampled light curves require interpolating the noisy data. In this case better results are found by solving for both the continuum light curve and kernel function in a simultaneous fit to the data. Our conclusion is that while echo mapping currently gives ambiguous results, the algorithms are not the limiting factor. Progress depends on efforts to increase the accuracy and completeness of sampling of the observed light curves.

Vio, Roberto↗
NASA NTRS1977

An evaluation of the signature extension approach to large area crop inventories utilizing space image data

The author has identified the following significant results. Two examples of haze correction algorithms were tested: CROP-A and XSTAR. The CROP-A was tested in a unitemporal mode on data collected in 1973-74 over ten sample segments in Kansas. Because of the uniformly low level of haze present in these segments, no conclusion could be reached about CROP-A's ability to compensate for haze. It was noted, however, that in some cases CROP-A made serious errors which actually degraded classification performance. The haze correction algorithm XSTAR was tested in a multitemporal mode on 1975-76 LACIE sample segment data over 23 blind sites in Kansas and 18 sample segments in North Dakota, providing wide range of haze levels and other conditions for algorithm evaluation. It was found that this algorithm substantially improved signature extension classification accuracy when a sum-of-likelihoods classifier was used with an alien rejection threshold.

Nalepka, R. F.↗
NASA NTRS1980

Development and test results of a flight management algorithm for fuel conservative descents in a time-based metered traffic environment

A simple flight management descent algorithm designed to improve the accuracy of delivering an airplane in a fuel-conservative manner to a metering fix at a time designated by air traffic control was developed and flight tested. This algorithm provides a three dimensional path with terminal area time constraints (four dimensional) for an airplane to make an idle thrust, clean configured (landing gear up, flaps zero, and speed brakes retracted) descent to arrive at the metering fix at a predetermined time, altitude, and airspeed. The descent path was calculated for a constant Mach/airspeed schedule from linear approximations of airplane performance with considerations given for gross weight, wind, and nonstandard pressure and temperature effects. The flight management descent algorithm is described. The results of the flight tests flown with the Terminal Configured Vehicle airplane are presented.

Knox, C. E.↗
NASA NTRS2011

Development of a Real-Time Pulse Processing Algorithm for TES-Based X-Ray Microcalorimeters

We report here a real-time pulse processing algorithm for superconducting transition-edge sensor (TES) based x-ray microcalorimeters. TES-based. microca1orimeters offer ultra-high energy resolutions, but the small volume of each pixel requires that large arrays of identical microcalorimeter pixe1s be built to achieve sufficient detection efficiency. That in turn requires as much pulse processing as possible must be performed at the front end of readout electronics to avoid transferring large amounts of data to a host computer for post-processing. Therefore, a real-time pulse processing algorithm that not only can be implemented in the readout electronics but also achieve satisfactory energy resolutions is desired. We have developed an algorithm that can be easily implemented. in hardware. We then tested the algorithm offline using several data sets acquired with an 8 x 8 Goddard TES x-ray calorimeter array and 2x16 NIST time-division SQUID multiplexer. We obtained an average energy resolution of close to 3.0 eV at 6 keV for the multiplexed pixels while preserving over 99% of the events in the data sets.

Tan, Hui↗
NASA NTRS2005

Research Supporting Satellite Communications Technology

This report describes the second year of research effort under the grant Research Supporting Satellite Communications Technology. The research program consists of two major projects: Fault Tolerant Link Establishment and the design of an Auto-Configurable Receiver. The Fault Tolerant Link Establishment protocol is being developed to assist the designers of satellite clusters to manage the inter-satellite communications. During this second year, the basic protocol design was validated with an extensive testing program. After this testing was completed, a channel error model was added to the protocol to permit the effects of channel errors to be measured. This error generation was used to test the effects of channel errors on Heartbeat and Token message passing. The C-language source code for the protocol modules was delivered to Goddard Space Flight Center for integration with the GSFC testbed. The need for a receiver autoconfiguration capability arises when a satellite-to-ground transmission is interrupted due to an unexpected event, the satellite transponder may reset to an unknown state and begin transmitting in a new mode. During Year 2, we completed testing of these algorithms when noise-induced bit errors were introduced. We also developed and tested an algorithm for estimating the data rate, assuming an NRZ-formatted signal corrupted with additive white Gaussian noise, and we took initial steps in integrating both algorithms into the SDR test bed at GSFC.

Horan Stephen↗
NASA NTRS2014

Development and Implementation of a Hardware In-the-Loop Test Bed for Unmanned Aerial Vehicle Control Algorithms

Successful prediction and management of battery life using prognostic algorithms through ground and flight tests is important for performance evaluation of electrical systems. This paper details the design of test beds suitable for replicating loading profiles that would be encountered in deployed electrical systems. The test bed data will be used to develop and validate prognostic algorithms for predicting battery discharge time and battery failure time. Online battery prognostic algorithms will enable health management strategies. The platform used for algorithm demonstration is the EDGE 540T electric unmanned aerial vehicle (UAV). The fully designed test beds developed and detailed in this paper can be used to conduct battery life tests by controlling current and recording voltage and temperature to develop a model that makes a prediction of end-of-charge and end-of-life of the system based on rapid state of health (SOH) assessment.

Battery Testbed↗
DOE OSTI2023

Bayesian Cloud Property Retrievals from ARM Active and Passive Measurements

The optimum use of the continuous measurements of thermodynamics, radiation, aerosols, clouds and precipitation from the DOE Atmospheric Radiation Measurement (ARM) program is key to achieve the DOE Atmospheric System Research (ASR)’s objectives. One of the key mission requirements is to retrieve cloud and precipitation properties, as well as vertical motion parameters, along the vertical cross- section defined by the profiling active sensors. Such retrievals are challenging to perform continuously in the entire spectrum of cloud and precipitation conditions due to the large natural microphysical and dynamical variability, the often-limited information content in the measurements, and the lack of proper characterization of measurement quality and uncertainty. Today, the acquisition of new remote and in-situ sensors by the ARM program creates opportunities to address the microphysical retrieval problem by exploiting new, more robust retrieval techniques and integrating various scattered advancements in both sensor techniques and retrieval algorithms. During this project, we constructed a robust Bayesian Markov chain Monte Carlo (MCMC) cloud property retrieval algorithm that includes a state of the art radar forward model. Our MCMC-based retrieval produces both the best estimate of height-resolved cloud and precipitation properties in the radar profile, as well as an estimate of the in-cloud vertical motion and turbulence. In addition, the MCMC algorithm automatically produces robust and flexible estimates of retrieval uncertainty. We tested the algorithm on several synthetic cloud profiles obtained from large eddy simulation (LES) models with bin-resolved microphysics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ARENA: Adversary-Resistant Evolving Neural Architectures

Neural networks are becoming the cornerstone for national security prediction tasks. However, designing them requires significant research and trial/error, as they have many hyperparameters, including their computation graph (“architecture”). Neural architecture search (NAS) employs secondary optimizers to search for architectures maximizing objectives like accuracy. Evolutionary algorithms (EAs) are the most used class of optimizer for NAS. However, existing Python libraries for writing EAs limit the complexity of experiments a user can design. In this project, we built ARENA, a Python framework that encodes complex, hyper-realistic EAs. ARENA collects detailed information as it runs and is flexible enough to encode non-EA search algorithms. We tested ARENA on 4 toy optimization problems by encoding 3 search algorithms for each—random search, an EA, and simulated annealing. We also designed an EA that performs NAS on the MNIST dataset. Our experiments suggest the potential for immediate mission impact through solving lab-wide optimization problems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

Comparison of Nonequilibrium Solution Algorithms Applied to Chemically Stiff Hypersonic Flows

Three solution algorithms, explicit under-relaxation, point implicit, and lower-upper symmetric Gauss-Seidel, are used to compute nonequilibrium flow around the Apollo 4 return capsule at the 62-km altitude point in its descent trajectory. By varying the Mach number, the efficiency and robustness of the solution algorithms were tested for different levels of chemical stiffness.The performance of the solution algorithms degraded as the Mach number and stiffness of the flow increased. At Mach 15 and 30, the lower-upper symmetric Gauss-Seidel method produces an eight order of magnitude drop in the energy residual in one-third to one-half the Cray C-90 computer time as compared to the point implicit and explicit under-relaxation methods. The explicit under-relaxation algorithm experienced convergence difficulties at Mach 30 and above. At Mach 40 the performance of the lower-upper symmetric Gauss-Seidel algorithm deteriorates to the point that it is out performed by the point implicit method. The effects of the viscous terms are investigated. Grid dependency questions are explored.

Palmer, Grant↗
NASA NTRS1998

Efficient and Optimal Attitude Determination Using Recursive Global Positioning System Signal Operations

In this paper, a new and efficient algorithm is developed for attitude determination from Global Positioning System signals. The new algorithm is derived from a generalized nonlinear predictive filter for nonlinear systems. This uses a one time-step ahead approach to propagate a simple kinematics model for attitude determination. The advantages of the new algorithm over previously developed methods include: it provides optimal attitudes even for coplanar baseline configurations; it guarantees convergence even for poor initial conditions; it is a non-iterative algorithm; and it is computationally efficient. These advantages clearly make the new algorithm well suited to on-board applications. The performance of the new algorithm is tested on a dynamic hardware simulator. Results indicate that the new algorithm accurately estimates the attitude of a moving vehicle, and provides robust attitude estimates even when other methods, such as a linearized least-squares approach, fail due to poor initial starting conditions.

Crassidis, John L.↗
NASA NTRS1995

Using Visual Programming to Simulate, Test, and Display a Telemetry Stream

Advantages of using visual programming to create, modify, test and display a telemetry stream are presented. Commercial visual programming software is being used to test new algorithms as part of the ground support for the Galileo spacecraft Test Bed.

visual programming real-time data display text-bas↗
NASA NTRS2024

A Convexification-Based Outer-Approximation Method for Convex and Nonconvex MINLP

The advancement of domain reduction techniques has significantly enhanced the performance of solvers in mathematical programming. This paper delves into the impact of integrating convexification and domain reduction techniques within the Outer-Approximation method. We propose a refined convexification-based Outer-Approximation method alongside a Branch-and-Bound method for both convex and nonconvex Mixed-Integer Nonlinear Programming problems. These methods have been developed and incorporated into the open-source Mixed-Integer Nonlinear Decomposition Toolbox for Pyomo-MindtPy. Comprehensive benchmark tests were conducted, validating the effectiveness and reliability of our proposed algorithms. These tests highlight the improvements achieved by incorporating convexification and domain reduction techniques into the Outer-Approximation and Branch-and-Bound methods.

Optimization↗