SEARCH · Engineering Papers

Results for “performance prediction”

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.

SourceResearch connectionFrom yearThrough year

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

At least 739 records · Page 41

All sources NASA NTRS DOE OSTI

NASA NTRS2004

Analysis Tools for CFD Multigrid Solvers

Analysis tools are needed to guide the development and evaluate the performance of multigrid solvers for the fluid flow equations. Classical analysis tools, such as local mode analysis, often fail to accurately predict performance. Two-grid analysis tools, herein referred to as Idealized Coarse Grid and Idealized Relaxation iterations, have been developed and evaluated within a pilot multigrid solver. These new tools are applicable to general systems of equations and/or discretizations and point to problem areas within an existing multigrid solver. Idealized Relaxation and Idealized Coarse Grid are applied in developing textbook-efficient multigrid solvers for incompressible stagnation flow problems.

Mineck, Raymond E.↗

NASA NTRS2004

Analysis Tools for CFD Multigrid Solvers

Mineck, Raymond E.↗

NASA NTRS2012

Autonomous Navigation Performance During The Hartley 2 Comet Flyby

On November 4, 2010, the EPOXI spacecraft performed a 700-km flyby of the comet Hartley 2 as follow-on to the successful 2005 Deep Impact prime mission. EPOXI, an extended mission for the Deep Impact Flyby spacecraft, returned a wealth of visual and infrared data from Hartley 2, marking the fifth time that high-resolution images of a cometary nucleus have been captured by a spacecraft. The highest resolution science return, captured at closest approach to the comet nucleus, was enabled by use of an onboard autonomous navigation system called AutoNav. AutoNav estimates the comet-relative spacecraft trajectory using optical measurements from the Medium Resolution Imager (MRI) and provides this relative position information to the Attitude Determination and Control System (ADCS) for maintaining instrument pointing on the comet. For the EPOXI mission, AutoNav was tasked to enable continuous tracking of a smaller, more active Hartley 2, as compared to Tempel 1, through the full encounter while traveling at a higher velocity. To meet the mission goal of capturing the comet in all MRI science images, position knowledge accuracies of +/- 3.5 km (3-?) cross track and +/- 0.3 seconds (3-?) time of flight were required. A flight-code-in-the-loop Monte Carlo simulation assessed AutoNav's statistical performance under the Hartley 2 flyby dynamics and determined optimal configuration. The AutoNav performance at Hartley 2 was successful, capturing the comet in all of the MRI images. The maximum residual between observed and predicted comet locations was 20 MRI pixels, primarily influenced by the center of brightness offset from the center of mass in the observations and attitude knowledge errors. This paper discusses the Monte Carlo-based analysis that led to the final AutoNav configuration and a comparison of the predicted performance with the flyby performance.

flyby↗

NASA NTRS1993

Precision X-Band Doppler and Ranging Navigation For Current and Future Mars Exploration Missions

This paper describes a navigation error convariance analysis of two scenarios derived from the Mars Observer mission and the planned Mars Environmental Survey (MESUR) Pathfinder mission, respectively. The analysis was performed to establish the potential navigational performance of the X-band tracking system in NASA's Deep Space Network, and to evaluate the sensitivity of the Predicted performance to variations in the quantity of data acquired, the ground system error modeling assumptions, and data reduction schemes.

Precision↗

NASA NTRS2005

Low NO(x) Combustor Development

The goal of these efforts was the development of an ultra-low emissions, lean-burn combustor for the High Speed Civil Transport. The HSCT Mach 2.4 FLADE C1 Cycle was selected as the baseline engine cycle. A preliminary compilation of performance requirements for the HSCT combustor system was developed. The emissions goals of the program, baseline engine cycle, and standard combustor performance requirements were considered in developing the compilation of performance requirements. Seven combustor system designs were developed. The development of these system designs was facilitated by the use of spreadsheet-type models which predicted performance of the combustor systems over the entire flight envelope of the HSCT. A chemical kinetic model was developed for an LPP combustor and employed to study NO(x) formation kinetics, and CO burnout. These predictions helped to define the combustor residence time. Five fuel-air mixer concepts were analyzed for use in the combustor system designs. One of the seven system designs, one using the Swirl-Jet and Cyclone Swirler fuel-air mixers, was selected for a preliminary mechanical design study.

Kastl, J. A.↗

DOE OSTI2023

Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1978

Closed loop models for analyzing the effects of simulator characteristics

The optimal control model of the human operator is used to develop closed loop models for analyzing the effects of (digital) simulator characteristics on predicted performance and/or workload. Two approaches are considered: the first utilizes a continuous approximation to the discrete simulation in conjunction with the standard optimal control model; the second involves a more exact discrete description of the simulator in a closed loop multirate simulation in which the optimal control model simulates the pilot. Both models predict that simulator characteristics can have significant effects on performance and workload.

Baron, S.↗

NASA NTRS1978

HEAO attitude reference design

The paper deals with the precision onboard attitude reference implemented as part of the attitude control and determination subsystem for the three High Energy Astronomy Observatories (HEAO-A, HEAO-B, and HEAO-C) in the HEAO scientific spacecraft program. The first observatory (HEAO-A, designated HEAO-1 when in orbit) was launched successfully into near-earth orbit on August 12, 1977. The HEAO attitude reference, analysis techniques for performance prediction, and flight results from the HEAO-1 observatory during its first months of operation. The HEAO-B design is specifically described and analyzed in terms of gyro processing, kinematic integration, ground update algorithm, and star tracker update algorithm. Attitude reference performance estimates are also discussed. It is shown that the orbital performance of the attitude reference correlates very well with the developmental predictions, thereby validating the analytical techniques used during the development. This validation provides a firm basis from which to extrapolate to other applications and related design concepts.

Hoffman, D. P.↗

NASA NTRS1978

Closed loop models for analyzing the effects of simulator characteristics

The optimal control model (OCM) of the human operator is used to develop closed-loop models for analyzing the effects of (digital) simulator characteristics on predicted performance and/or workload. Two approaches are considered: the first utilizes a continuous approximation to the discrete simulation in conjunction with the standard optimal control model; the second involves a more exact discrete description of the simulator in a closed-loop multi-rate simulation in which the optimal control model 'simulates' the pilot. Both models predict that simulator characteristics can have significant effects on performance and workload.

Baron, S.↗

DOE OSTI2020

An Exploratory Approach Using Regression and Machine Learning in the Analysis of Mass Absorption Cross Section of Black Carbon Aerosols: Model Development and Evaluation

Mass absorption cross-section of black carbon (MAC BC ) describes the absorptive cross-section per unit mass of black carbon, and is, thus, an essential parameter to estimate the radiative forcing of black carbon. Many studies have sought to estimate MAC BC from a theoretical perspective, but these studies require the knowledge of a set of aerosol properties, which are difficult and/or labor-intensive to measure. We therefore investigate the ability of seven data analytical approaches (including different multivariate regressions, support vector machine, and neural networks) in predicting MAC BC for both ambient and biomass burning measurements. Our model utilizes multi-wavelength light absorption and scattering as well as the aerosol size distributions as input variables to predict MAC BC across different wavelengths. We assessed the applicability of the proposed approaches in estimating MAC BC using different statistical metrics (such as coefficient of determination (R 2 ), mean square error (MSE), fractional error, and fractional bias). Overall, the approaches used in this study can estimate MAC BC appropriately, but the prediction performance varies across approaches and atmospheric environments. Based on an uncertainty evaluation of our models and the empirical and theoretical approaches to predict MAC BC , we preliminarily put forth support vector machine (SVM) as a recommended data analytical technique for use. We provide an operational tool built with the approaches presented in this paper to facilitate this procedure for future users.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1974

Single-stage experimental evaluation of tandem-airfoil rotor and stator blading for compressors, part 8

An experimental investigation was conducted with an 0.8 hub/tip ratio, single-stage, axial flow compressor to determine the potential of tandem-airfoil blading for improving the efficiency and stable operating range of compressor stages. The investigation included testing of a baseline stage with single-airfoil blading and two tandem-blade stages. The overall performance of the baseline stage and the tandem-blade stage with a 20-80% loading split was considerably below the design prediction. The other tandem-blade stage, which had a rotor with a 50-50% loading split, came within 4.5% of the design pressure rise (delta P(bar)/P(bar) sub 1) and matched the design stage efficiency. The baseline stage with single-airfoil blading, which was designed to account for the actual rotor inlet velocity profile and the effects of axial velocity ratio and secondary flow, achieved the design predicted performance. The corresponding tandem-blade stage (50-50% loading split in both blade rows) slightly exceeded the design pressure rise but was 1.5 percentage points low in efficiency. The tandem rotors tested during both phases demonstrated higher pressure rise and efficiency than the corresponding single-airfoil rotor, with identical inlet and exit airfoil angles.

Brent, J. A.↗

NASA NTRS1981

Buffer thermal energy storage for an air Brayton solar engine

The application of latent-heat buffer thermal energy storage to a point-focusing solar receiver equipped with an air Brayton engine was studied. To demonstrate the effect of buffer thermal energy storage on engine operation, a computer program was written which models the recuperator, receiver, and thermal storage device as finite-element thermal masses. Actual operating or predicted performance data are used for all components, including the rotating equipment. Based on insolation input and a specified control scheme, the program predicts the Brayton engine operation, including flows, temperatures, and pressures for the various components, along with the engine output power. An economic parametric study indicates that the economic viability of buffer thermal energy storage is largely a function of the achievable engine life.

Strumpf, H. J.↗

NASA NTRS1997

By Hand or Not By-Hand: A Case Study of Alternative Approaches to Parallelize CFD Applications

While parallel processing promises to speed up applications by several orders of magnitude, the performance achieved still depends upon several factors, including the multiprocessor architecture, system software, data distribution and alignment, as well as the methods used for partitioning the application and mapping its components onto the architecture. The existence of the Gorden Bell Prize given out at Supercomputing every year suggests that while good performance can be attained for real applications on general purpose multiprocessors, the large investment in man-power and time still has to be repeated for each application-machine combination. As applications and machine architectures become more complex, the cost and time-delays for obtaining performance by hand will become prohibitive. Computer users today can turn to three possible avenues for help: parallel libraries, parallel languages and compilers, interactive parallelization tools. The success of these methodologies, in turn, depends on proper application of data dependency analysis, program structure recognition and transformation, performance prediction as well as exploitation of user supplied knowledge. NASA has been developing multidisciplinary applications on highly parallel architectures under the High Performance Computing and Communications Program. Over the past six years, the transition of underlying hardware and system software have forced the scientists to spend a large effort to migrate and recede their applications. Various attempts to exploit software tools to automate the parallelization process have not produced favorable results. In this paper, we report our most recent experience with CAPTOOL, a package developed at Greenwich University. We have chosen CAPTOOL for three reasons: 1. CAPTOOL accepts a FORTRAN 77 program as input. This suggests its potential applicability to a large collection of legacy codes currently in use. 2. CAPTOOL employs domain decomposition to obtain parallelism. Although the fact that not all kinds of parallelism are handled may seem unappealing, many NASA applications in computational aerosciences as well as earth and space sciences are amenable to domain decomposition. 3. CAPTOOL generates code for a large variety of environments employed across NASA centers: MPI/PVM on network of workstations to the IBS/SP2 and CRAY/T3D.

Yan, Jerry C.↗

NASA NTRS2003

Design and Analysis of Turbines for Space Applications

In order to mitigate the risk of rocket propulsion development, efficient, accurate, detailed fluid dynamics analysis of the turbomachinery is necessary. This analysis is used for component development, design parametrics, performance prediction, and environment definition. To support this requirement, a task was developed at NASAh4arshall Space Flight Center (MSFC) to improve turbine aerodynamic performance through the application of advanced design and analysis tools. There are four major objectives of this task: 1) to develop, enhance, and integrate advanced turbine aerodynamic design and analysis tools; 2) to develop the methodology for application of the analytical techniques; 3) to demonstrate the benefits of the advanced turbine design procedure through its application to a relevant turbine design point; and 4) to verify the optimized design and analysis with testing. The turbine chosen on which to demonstrate the procedure was a supersonic design suitable for a reusable launch vehicle (RLV). The hot gas path and blading were redesigned to obtain an increased efficiency. The redesign of the turbine was conducted with a consideration of system requirements, realizing that a highly efficient turbine that, for example, significantly increases engine weight, is of limited benefit. Both preliminary and detailed designs were considered. To generate an improved design, one-dimensional (1D) design and analysis tools, computational fluid dynamics (CFD), response surface methodology (RSM), and neural nets (NN) were used.

Griffin, Lisa W.↗

NASA NTRS2000

Interactive Software System Developed to Study How Icing Affects Airfoil Performance (Phase 1 Results)

SmaggIce (Surface Modeling and Grid Generation for Iced Airfoils), which is being developed at the NASA Glenn Research Center at Lewis Field, is an interactive software system for data probing, boundary smoothing, domain decomposition, and structured grid generation and refinement. All these steps are required for aerodynamic performance prediction using structured, grid-based computational fluid dynamics (CFD), as illustrated in the following figure. SmaggIce provides the underlying computations to perform these functions, as well as a graphical user interface to control and interact with them, and graphics to display the results.

Choo, Yung K.↗

DOE OSTI2023

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1979

Design and evaluation of high performance rocket engine injectors for use with hydrocarbon fuels

An experimental program to determine the feasibility of using a heavy hydrocarbon fuel as a rocket propellant is reported herein. A method of predicting performance of a heavy hydrocarbon in terms of vaporization effectiveness is described and compared to other fuels and to experimental test results. The work was done at a chamber pressure of 4137 KN/sq M (600 psia) with RP-1, JP-10, and liquefied natural gas as fuels, and liquid oxygen as the oxidizer. Combustion length effects were explored over a range of 21.6 cm (8 1/2 in.) to 55.9 cm (22 in.). Four injector types were tested, each over a range of mixture ratios. Further configuration modifications were obtained by 'reaming' each injector several times to provide test data over a range of injector pressure drop.

Pavli, A. J.↗

DOE OSTI2020

Machine learning for international freight transportation management: A comprehensive review

Machine learning (ML) offers a promising avenue for international freight transportation management (IFTM) given its capability to harness the power of data that have become increasingly available to freight transportation researchers and practitioners. This paper conducts a comprehensive investigation of the state-of-the-art in developing ML models for applications to different aspects of IFTM. We start by giving an overview of various fundamental ML methods. Then, how different ML methods have been employed, adapted, and applied to a multitude of subject areas in IFTM are discussed, including demand forecast, operation and asset maintenance, and vehicle trajectory and on-time performance prediction. The potential data sources that may be used to develop ML models are further examined. Subsequently, a synthesis of the exiting work is performed to identify the specific topics addressed in the existing research, ML methods used, the trends of research, and opportunities for further explorations. Four directions for future research are proposed in the end.

Data sources↗