Engineering Papers⌕ Search

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.

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

At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Comparison of theoretical and experimental thrust performance of a 1030:1 area ratio rocket nozzle at a chamber pressure of 2413 kN/sq m (350 psia)

The Joint Army, Navy, NASA, Air Force (JANNAF) rocket-engine performance-prediction procedure is based on the use of various reference computer programs. One of the reference programs for nozzle analysis is the Two-Dimensional Kinetics (TDK) Program. The purpose of this report is to calibrate the JANNAF procedure that has been incorporated into the December 1984 version of the TDK program for the high-area-ratio rocket-engine regime. The calibration was accomplished by modeling the performance of a 1030:1 rocket nozzle tested at NASA Lewis. A detailed description of the test conditions and TDK input parameters is given. The reuslts indicate that the computer code predicts delivered vacuum specific impulse to within 0.12 to 1.9 percent of the experimental data. Vacuum thrust coefficient predictions were within + or - 1.3 percent of experimental results. Predictions of wall static pressure were within approximately + or - 5 percent of the measured values.

Smith, Tamara A.↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
NASA NTRS1973

Simplified procedures for correlation of experimentally measured and predicted thrust chamber performance

Thrust chamber performance is evaluated in terms of an analytical model incorporating all the loss processes that occur in a real rocket motor. The important loss processes in the real thrust chamber were identified, and a methodology and recommended procedure for predicting real thrust chamber vacuum specific impulse were developed. Simplified equations for the calculation of vacuum specific impulse are developed to relate the delivered performance (both vacuum specific impulse and characteristic velocity) to the ideal performance as degraded by the losses corresponding to a specified list of loss processes. These simplified equations enable the various performance loss components, and the corresponding efficiencies, to be quantified separately (except that interaction effects are arbitrarily assigned in the process). The loss and efficiency expressions presented can be used to evaluate experimentally measured thrust chamber performance, to direct development effort into the areas most likely to yield improvements in performance, and as a basis to predict performance of related thrust chamber configurations.

Powell, W. B.↗
DOE OSTI2025

Characterization of Inlet Guide Vane Performance for Discharge Compressor Operation near the Dome of an sCO 2 Pumped Heat Energy Storage

Southwest Research Institute® (SwRI®) developed and tested a Variable Inlet Guide Vane (IGV herein) assembly on an integrally-geared sCO 2 compressor (IGC) to demonstrate compressor operation at both the compressor design point and near the dome and to define the operating limits of the compressor by monitoring for two-phase flow, flow turbulence from the IGVs, and compressor choke and surge as the CO 2 inlet temperature is varied. Performance testing was conducted on an existing integrally-geared, two-stage main compressor designed for near-critical-point operation with CO 2 . This testing campaign validated the IGV design and operation, as well as improved the understanding and confidence in operating compressors and predicting performance characteristics near the critical point where fluid properties change rapidly with temperature. In addition to improving the robust operating limits of an sCO 2 compressor, the development of an IGV for the IGC system improved off-design compressor efficiency by 12%.

25 ENERGY STORAGE↗
NASA NTRS2013

Thermal Protection Materials and Systems: Past, Present, and Future

Thermal protection materials and systems (TPS) protect vehicles from the heat generated when entering a planetary atmosphere. NASA has developed many TPS systems over the years for vehicle ranging from planetary probes to crewed vehicles. The goal for all TPS is efficient and reliable performance. Efficient means using the right material for the environment and minimizing the mass of the heat shield without compromising safety. Efficiency is critical if the payload such as science experiments is to be maximized on a particular vehicle. Reliable means that we understand and can predict performance of the material. Although much characterization and testing of materials is performed to qualify and certify them for flight, it is not possible to completely recreate the reentry conditions in test facilities, and flight-testing

Johnson, Sylvia M.↗
DOE OSTI2022

An interpretable machine learning model for advancing terrestrial ecosystem predictions

We apply an interpretable Long Short-Term Memory (iLSTM) network for land-atmosphere carbon flux predictions based on time series observations of seven environmental variables. iLSTM enables interpretability of variable importance and variable-wise temporal importance to the prediction of targets by exploring internal network structures. The application results indicate that iLSTM not only improves prediction performance by capturing different dynamics of individual variables, but also reasonably interprets the different contribution of each variable to the target and its different temporal relevance to the target. This variable and temporal importance interpretation of iLSTM advances terrestrial ecosystem model development as well as our predictive understanding of the system.

Lu, Dan↗
DOE OSTI2025

Ca X ML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca X ML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1975

How big is a windmill - Glauert revisited

The obvious similarities to propellers and helicopter rotors suggest that helicopter technology might be used to improve wind generator performance, perhaps including development of a windmill airfoil. In a back-to-basics approach, this paper reviews the analyses of Glauert to determine basic size-power relations. The energy method of Wheatley developed for helicopter/auto-gyro performance prediction is then incorporated into the basic theory. Equations and charts are presented showing ratios of power output to the ideal power capability as functions of mean blade lift and drag coefficients, solidity and rotor tip speed ratio. It is found there is little possibility for improved performance by using improved airfoils. The basic assumptions of the Glauert theory are reviewed and means are suggested for achieving the basic power capability indicated by momentum theory.

Mccloud, J. L., III↗
NASA NTRS1977

Solar Stirling power generation - Systems analysis and preliminary tests

The feasibility of an electric power generation system utilizing a sun-tracking parabolic concentrator and a Stirling engine/linear alternator is being evaluated. Performance predictions and cost analysis of a proposed large distributed system are discussed. Design details and preliminary test results are presented for a 9.5 ft diameter parabolic dish at the Jet Propulsion Laboratory (Caltech) Table Mountain Test Facility. Low temperature calorimetric measurements were conducted to evaluate the concentrator performance, and a helium flow system is being used to test the solar receiver at anticipated working fluid temperatures (up to 650 or 1200 C) to evaluate the receiver thermal performance. The receiver body is designed to adapt to a free-piston Stirling engine which powers a linear alternator assembly for direct electric power generation. During the next phase of the program, experiments with an engine and receiver integrated into the concentrator assembly are planned.

Selcuk, M. K.↗
NASA NTRS1987

Results of the integration of a transonic full-potential analysis program with a free-wake lifting-line program for hovering rotors

The hovering performance predictions of the TFAR1 and OPLIN codes and the experimental data are discussed. The TFAR1 program solves the full-potential equation in a rotor-fixed coordinate system by use of the line relaxation method. The OPLIN program calculates the positions of wake vortices and rotor performance using the influence-coefficient-and-lifing-line method. The two programs are combined by adding the induced velocities from the OPLIN code to the near flow-field of the rotor from the TFAR1 code. Results show that the TFAR1 program converges better with the downwash-coupling method than the twist correction method to include the wake downwash.

Chung, Song-Young↗
DOE OSTI2022

Identifying Hydrometeorological Factors Influencing Reservoir Releases Using Machine Learning Methods

Simulation of reservoir releases plays a critical role in social-economic functioning and our nation's security. How-ever, it is challenging to predict the reservoir release accurately because of many influential factors from natural environments and engineering controls such as the reservoir inflow and storage. Moreover, climate change and hydrological intensification causing the extreme precipitation and temperature make the accurate prediction of reservoir releases even more challenging. Machine learning (ML) methods have shown some successful applications in simulating reservoir releases. However, previous studies mainly used inflow and storage data as inputs and only considered their short-term influences (e.g, previous one or two days). In this work, we use long short-term memory (LSTM) networks for reservoir release prediction based on four input variables including inflow, storage, precipitation, and temperature and consider their long-term influences. We apply the LSTM model to 30 reservoirs in Upper Colorado River Basin, United States. We analyze the prediction performance using six statistical metrics. More importantly, we investigate the influence of the input hydrometeorological factors, as well as their temporal effects on reservoir release decisions. Results indicate that inflow and storage are the most influential factors but the inclusion of precipitation and temperature can further improve the prediction of release especially in low flows. Additionally, the inflow and storage have a relatively long-term effect on the release. These findings can help optimize the water resources management in the reservoirs.

Fan, Ming↗
NASA NTRS1983

Energy Efficient Engine Flight Propulsion System Preliminary Analysis and Design Report

The final design and analysis of the flight propulsion system is presented. This system is the conceptual study engine defined to meet the performance, economic and environmental goals established for the Energy Efficient Engine Program. The design effort included a final definition of the engine, major components, internal subsystems, and nacelle. Various analytical representations and results from component technology programs are used to verify aerodynamic and structural design concepts and to predict performance. Specific design goals and specifications, reflecting future commercial aircraft propulsion system requirements for the mid-1980's, are detailed by NASA and used as guidelines during engine definition. Information is also included which details salient results from a separate study to define a turbofan propulsion system, known as the maximum efficiency engine, which reoptimized the advanced fuel saving technologies for improved fuel economy and direct operating costs relative to the flight propulsion system.

Bisset, J. W.↗
NASA NTRS1992

The VRT gas turbine combustor - Phase II

An innovative annular combustor configuration is being developed for aircraft and other gas turbine engines. This design has the potential of permitting higher turbine inlet temperatures by reducing the pattern factor and providing a major reduction in NO(x) emission. The design concept is based on a Variable Residence Time (VRT) technique which allows large fuel particles adequate time to completely burn in the circumferentially mixed primary zone. High durability of the combustor is achieved by dual-function use of the incoming air. In Phase I, the feasibility of the concept was demonstrated by water analogue tests and 3D computer modeling. The flow pattern within the combustor was as predicted. The VRT combustor uses only half the number of fuel nozzles of the conventional configuration. In Phase II, hardware was designed, procured, and tested under conditions simulating typical supersonic civil aircraft cruise conditions to the limits of the rig. The test results confirmed many of the superior performance predictions of the VRT concept. The Hastelloy X liner showed no signs of distress after nearly six hours of tests using JP5 fuel.

Melconian, Jerry O.↗
NASA NTRS2002

Monitoring the Microgravity Environment Quality On-board the International Space Station Using Soft Computing Techniques: Preliminary System Performance Results - Part 2

This paper presents the preliminary performance results of the artificial intelligence monitoring system in full operational mode using near real time acceleration data downlinked from the International Space Station. Preliminary microgravity environment characterization analysis result for the International Space Station (Increment-2), using the monitoring system is presented. Also, comparison between the system predicted performance based on ground test data for the US laboratory "Destiny" module and actual on-orbit performance, using measured acceleration data from the U.S. laboratory module of the International Space Station is presented. Finally, preliminary on-orbit disturbance magnitude levels are presented for the Experiment of Physics of Colloids in Space, which are compared with on ground test data. The ground test data for the Experiment of Physics of Colloids in Space were acquired from the Microgravity Emission Laboratory, located at the NASA Glenn Research Center, Cleveland, Ohio. The artificial intelligence was developed by the NASA Glenn Principal Investigator Microgravity Services Project to help the principal investigator teams identify the primary vibratory disturbance sources that are active, at any moment of time, on-board the International Space Station, which might impact the microgravity environment their experiments are exposed to. From the Principal Investigator Microgravity Services' web site, the principal investigator teams can monitor via a dynamic graphical display, implemented in Java, in near real time, which event(s) is/are on, such as crew activities, pumps, fans, centrifuges, compressor, crew exercise, structural modes, etc., and decide whether or not to run their experiments, whenever that is an option, based on the acceleration magnitude and frequency sensitivity associated with that experiment. This monitoring system detects primarily the vibratory disturbance sources. The system has built-in capability to detect both known and unknown vibratory disturbance sources. Several soft computing techniques such as Kohonen's Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.

Jules, Kenol↗
DOE OSTI2021

EnZymClass: Substrate specificity prediction tool of plant acyl-ACP thioesterases based on ensemble learning

Characterizing the functional properties of plant acyl-ACP thioesterases (TEs), a key enzyme class used in the production of renewable oleochemicals in microbial hosts, experimentally, can be an expensive and time consuming process since it requires manual screening of thousands of candidates in a database. Using amino acid sequence to computationally predict an enzyme’s function might accelerate this process; however obtaining the necessary amount of information on previously characterized enzymes and their respective sequences required by standard Machine Learning (ML) based approaches to accurately infer sequence-function relationships can be prohibitive, especially with a low-throughput testing cycle. Experimental noise, unbalanced dataset where high sequence similarity does not always imply identical functional properties will further prevent robust prediction performance. Herein we present a ML method, Ensemble method for enZyme Classification (EnZymClass), that is specifically designed to address these issues. We used EnZymClass to classify TEs into short, long and mixed free fatty acid substrate specificity categories. While general guidelines for inferring substrate specificity have been proposed before, prediction of chain-length preference from primary sequence has remained elusive for plant acyl-ACP TEs. By applying EnZymClass to a subset of TEs in the ThYme database, we identified two medium chain TEs, ClFatB3 and CwFatB2, with previously uncharacterized activity in E. coli fatty acid production hosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deep graph representations embed network information for robust disease marker identification

We report that the accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

deeprob/ThioesteraseEnzymeSpecificity: EnZymClass-first-release

Characterizing the functional properties of plant acyl-ACP thioesterases (TEs), a key enzyme class used in the production of renewable oleochemicals in microbial hosts, experimentally, can be an expensive and time consuming process since it requires manual screening of thousands of candidates in a database. Using amino acid sequence to computationally predict an enzyme’s function might accelerate this process; however obtaining the necessary amount of information on previously characterized enzymes and their respective sequences required by standard Machine Learning (ML) based approaches to accurately infer sequence-function relationships can be prohibitive, especially with a low-throughput testing cycle. Experimental noise, unbalanced dataset where high sequence similarity does not always imply identical functional properties will further prevent robust prediction performance. Herein we present a ML method, Ensemble method for enZyme Classification (EnZymClass), that is specifically designed to address these issues. We used EnZymClass to classify TEs into short, long and mixed free fatty acid substrate specificity categories. While general guidelines for inferring substrate specificity have been proposed before, prediction of chain-length preference from primary sequence has remained elusive for plant acyl-ACP TEs. By applying EnZymClass to a subset of TEs in the ThYme database, we identified two medium chain TEs, ClFatB3 and CwFatB2, with previously uncharacterized activity in E. coli fatty acid production hosts.

Banerjee, Deepro↗
NASA NTRS2019

2018 Mars Insight Trajectory Reconstruction and Performance from Launch Through Landing

The InSight mission successfully launched to Mars on an Atlas V 401 launch vehicle from the Western Test Range (WTR) at Vandenberg Air Force Base (VAFB) at 04:05:00 PDT on May 5th, 2018 and landed in the Elysium Planitia Region on November 26th, 2018. Data confirming nominal touchdown was received at 11:52:59 AM PST. This paper summarizes in detail the actual vs. predicted performance of the InSight spacecraft and all associated assets in terms of launch vehicle events, injection performance, DSN performance, cruise performance, and Entry, Descent, and Landing events.

Abilleira, Fernando↗