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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 775 records · Page 43

All sources NASA NTRS DOE OSTI

DOE OSTI2024

A methodology for selection of solid desiccants in energy recovery ventilators

Controlling indoor humidity levels is essential for maintaining acceptable indoor air quality in buildings. The use of energy recovery ventilators (ERVs) is an energy-efficient way to regulate indoor air humidity. Fixed-bed regenerators and rotary wheels are widely used ERVs because of their high sensible and latent effectiveness. These ERVs are made of desiccant-coated substrates, which enable them to transfer moisture between the supply and exhaust air streams. However, the moisture transfer ability of ERVs depends on the physiochemical and sorption properties of desiccants. Extensive, full-scale experiments are required to determine the best desiccant material for these systems. This paper presents a simplified method of selecting suitable desiccant materials for ERVs. The methodology involves important characterization methods, literature correlations for performance prediction, and cost-effective testing methods prior to full-scale testing, and full-scale test methods are discussed in detail. Furthermore, the performance of a few newly derived materials is evaluated and compared with that of conventional desiccants such as silica gel and molecular sieves. The highest latent effectiveness was obtained for composite of super absorbent polymer (SAP) with potassium formate (SAP-HCO2K-50 %), all-polymer porous solid desiccant (APPSD) and metal organic framework (MOF)–MIL–101 (Cr), followed by activated carbon fibre felt (ACFF) Silica sol-LiCl30, SAP, silica gel, MOF–303, and molecular sieve. Researchers and manufacturers would benefit from the proposed methodology and presented data in developing new desiccant materials for ERV applications.

Energy recovery↗

DOE OSTI2021

Computational Framework for Machine-Learning-Enabled 13 C Fluxomics

13 C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for 13 C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical 13 C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.

13C metabolic flux analysis↗

DOE OSTI2024

Performance Characterization and Provenance of Distributed Task-based Workflows on HPC Platforms

Understanding performance and provenance of task-based workflows poses significant challenges, particularly in distributed configurations where resources are shared by multiple applications. Task-based workflow management systems further complicate performance predictability because of their dynamicity that subtly alters task execution order from run to run. In this paper we propose a layered characterization framework for performance and task provenance for Dask.distributed workflows running on high-performance computing (HPC) platforms. It collects data from jobs, the workflow management system, and the operating system to aid in understanding the performance of these workflows. Our approach encompasses three main contributions: first, an extension of Dask.distributed to capture high-fidelity task provenance using Mochi data services; second, the adaptation of the established HPC I/O characterization tool Darshan to gather high-fidelity I/O data, thereby enhancing the granularity of our analysis; and third, a framework to combine and process the collected data and provide helpful insights into performance characterization and reproducibility, alongside our lessons learned.

Dask↗

NASA NTRS1972

Apollo 15 mission report. Supplement 2: Service propulsion system final flight evaluation

The command and service module 112 service propulsion system performance for the Apollo 15 mission was evaluated and found to be satisfactory. The following items were considered: (1) the steady-state performance as determined from analysis of the third and eight burns; (2) techniques, problems, and assumptions; (3) flight analysis results as compared to the preflight predicted performance; (4) the propellant utilization and gaging system operation; (5) the pressurization system performance; (6) transient data; and (7) revision of estimated propellant consumption.

Smith, R. J.↗

NASA NTRS1979

Single shaft automotive gas turbine engine characterization test

An automotive gas turbine incorporating a single stage centrifugal compressor and a single stage radial inflow turbine is described. Among the engine's features is the use of wide range variable geometry at the inlet guide vanes, the compressor diffuser vanes, and the turbine inlet vanes to achieve improved part load fuel economy. The engine was tested to determine its performance in both the variable geometry and equivalent fixed geometry modes. Testing was conducted without the originally designed recuperator. Test results were compared with the predicted performance of the nonrecuperative engine based on existing component rig test maps. Agreement between test results and the computer model was achieved.

Johnson, R. A.↗

NASA NTRS2011

Reduced Pressure Cabin Testing of the Orion Atmosphere Revitalization Technology

An amine-based carbon dioxide (CO2) and water vapor sorbent in pressure-swing regenerable beds has been developed by Hamilton Sundstrand and baselined for the Atmosphere Revitalization System for moderate duration missions of the Orion Multipurpose Crew Vehicle. In previous years at this conference, reports were presented on extensive Johnson Space Center testing of this technology in a sea-level pressure environment with simulated and actual human metabolic loads in both open and closed-loop configurations. In 2011, the technology was tested in an open cabin-loop configuration at ambient and two sub-ambient pressures to compare the performance of the system to the results of previous tests at ambient pressure. The testing used a human metabolic simulator with a different type of water vapor generation than previously used, which added some unique challenges in the data analysis. This paper summarizes the results of: baseline and some matrix testing at all three cabin pressures, increased vacuum regeneration line pressure with a high metabolic load, a set of tests studying CO2 and water vapor co-adsorption effects relative to model-predicted performance, and validation tests of flight program computer model predictions with specific operating conditions.

Button, Amy↗

NASA NTRS2013

Reduced Pressure Cabin Testing of the Orion Atmosphere Revitalization Technology

Button, Amy↗

NASA NTRS2013

Reduced Pressure Cabin Testing of the Orion Atmosphere Revitalization Technology

An amine-based carbon dioxide (CO2) and water vapor sorbent in pressure-swing regenerable beds has been developed by United Technologies Corp. Aerospace Systems (UTAS, formerly Hamilton Sundstrand) and baselined for the Atmosphere Revitalization System for moderate duration missions of the Orion Multipurpose Crew Vehicle (MPCV). In previous years at this conference, reports were presented on extensive Johnson Space Center testing of this technology in a sea-level pressure environment with simulated and actual human metabolic loads in both open and closed-loop configurations. In 2011, the technology was tested in an open cabin-loop configuration at ambient and two sub-ambient pressures to compare the performance of the system to the results of previous tests at ambient pressure. The testing used a human metabolic simulator with a different type of water vapor generation than previously used, which added some unique challenges in the data analysis. This paper summarizes the results of: baseline and some matrix testing at all three cabin pressures, increased vacuum regeneration line pressure testing with a high metabolic load, a set of tests studying CO2 and water vapor co-adsorption effects relative to model-predicted performance, and validation tests of flight project computer model predictions with specific operating conditions.

Button, Amy B.↗

DOE OSTI2024

Spectral Data Fusion From Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and X-ray Fluorescence (XRF) Analyzers for Improved Detection of Cerium in a Simulated Dispersal Accident

Here, this work implements a mid-level data fusion methodology on spectral data from handheld X-ray fluorescence and laser-induced breakdown spectroscopy analyzers to quantify plutonium surrogate (CeO 2 ) contamination in soil samples for the first time. Spectral data from each analyzer were used independently to train supervised machine learning regressions to predict Ce concentration. Fused features from both data sets were then used to train the same models, comparing prediction performance by evaluating model precision and sensitivity. Fusing principal component scores from the two sensors yielded an order of magnitude improvement in precision and sensitivity of predictions made with an artificial neural network, compared to predictions made by models trained on independent sensor data. As a result, a boosted ensemble trained on the fused spectral features yielded an ideal predictor with root-mean-squared error on the order of 10 –6 and calculated limit of detection order 10 –5 wt %.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1991

CFD code validation for nozzle flowfields

Renewed interest in hypersonic airbreathing flight has resulted in heavy reliance on computational fluid dynamics for all aspects of aerodynamic and scramjet component modeling. Credible numerical performance predictions require calibration to unit type flowfields. This paper presents results of one such calibration to nonreacting simulated scramjet nozzle flowfields. The experimental test program was performed on a static thrust stand. Computed results are in excellent agreement with measured wall pressure, pitot pressure profiles, and thrust. Agreement with thrust is shown to be within the reported accuracy of the experimental measurements.

Jentink, Thomas N.↗

NASA NTRS2010

A Historical Systems Study of Liquid Rocket Engine Throttling Capabilities

This is a comprehensive systems study to examine and evaluate throttling capabilities of liquid rocket engines. The focus of this study is on engine components, and how the interactions of these components are considered for throttling applications. First, an assessment of space mission requirements is performed to determine what applications require engine throttling. A background on liquid rocket engine throttling is provided, along with the basic equations that are used to predict performance. Three engines are discussed that have successfully demonstrated throttling. Next, the engine system is broken down into components to discuss special considerations that need to be made for engine throttling. This study focuses on liquid rocket engines that have demonstrated operational capability on American space launch vehicles, starting with the Apollo vehicle engines and ending with current technology demonstrations. Both deep throttling and shallow throttling engines are discussed. Boost and sustainer engines have demonstrated throttling from 17% to 100% thrust, while upper stage and lunar lander engines have demonstrated throttling in excess of 10% to 100% thrust. The key difficulty in throttling liquid rocket engines is maintaining an adequate pressure drop across the injector, which is necessary to provide propellant atomization and mixing. For the combustion chamber, cooling can be an issue at low thrust levels. For turbomachinery, the primary considerations are to avoid cavitation, stall, surge, and to consider bearing leakage flows, rotordynamics, and structural dynamics. For valves, it is necessary to design valves and actuators that can achieve accurate flow control at all thrust levels. It is also important to assess the amount of nozzle flow separation that can be tolerated at low thrust levels for ground testing.

Betts, Erin M.↗

DOE OSTI2021

BETO 2021 Peer Review - Process Monitoring and Predictions of Biorefinery Performance

Online process monitoring coupled with rapid predictive tools will be essential to refineries of the future to provide real-time feedback and process control on new, renewable feeds and their accompanying processes and products. This project will provide refinery operators with tools to predict product component concentrations in minutes from online, slip stream mass spectra, allowing for rapid detection of off-specification product. We will arrive at a template for predictive tool generation through the development of a specific tool as a starting point -- co-processing of pyrolysis oils and vacuum gas oil over fluid catalytic cracking (FCC) catalysts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Hybrid electric buses fuel consumption prediction based on real-world driving data

Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. Here, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2% for microscopic models and 5–8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Assessment of Machine Learning for Ultrasonic Nondestructive Evaluation of Alkali–Silica Reaction in Concrete

Alkali–silica reaction (ASR) is a type of material degradation in concrete structures that leads to concrete cracking and rebar corrosion, thereby reducing the material’s structural integrity and the overall structure’s lifetime and raising safety concerns. Ultrasonic nondestructive evaluation (NDE) has been proven to be a valuable technique for assessing concrete properties and monitoring ASR progression in concrete. However, the deployment and analysis of ultrasonic NDE and its data requires specialized expertise, often relying on the engineer’s subjective interpretation. With the surge in computational power, artificial intelligence (AI) and machine learning (ML) algorithms have become popular in automating NDE data analysis. Various industrial sectors are increasingly adopting ML algorithms for NDE data analysis with a growing emphasis on AI–assisted automation. Regulatory agencies are also preparing for this technological shift, anticipating corresponding revisions in standards. Thus, there is an urgent need to identify the capabilities and limitations of current ML technologies for the evaluation of concrete material properties and damage status. Furthermore, the effects of various factors on ML model performance must be thoroughly investigated. The study summarized herein evaluated the effectiveness of two ML models (i.e., support vector regression (SVR) and deep neural network (DNN)) in predicting concrete material damage induced by ASR based on the long-term ultrasonic monitoring data. Four distinct concrete specimens were cast with artificially induced ASR, and over a period exceeding 500 days, ultrasonic signals and expansion data were continuously collected. For the SVR model, wave velocity and 12 other wave features were extracted from the ultrasonic signals, with 6 out of 13 features selected as input for the model. Different combinations of training and testing datasets were designed to explore factors influencing prediction performance, including the range of data within training and testing sets, in addition to various signal preprocessing methodologies. These findings suggest the importance of using a training dataset with a broader data range compared with testing datasets for improved model performance alongside consistent signal preprocessing across datasets.

36 MATERIALS SCIENCE↗

NASA NTRS1976

Guidance logic for spiral approaches

A spiral approach concept is proposed as a standard procedure for independent, commercial VTOL operations conducted in close proximity to CTOL operations. A guidance logic is developed for this VTOL application, although the results may be applicable to curved approaches by STOL or CTOL aircraft as well. The guidance concept attempts to maintain constant airspeed along a fixed radius nominal spiral. The presence of wind requires a continuous variation in bank angle and heading rate to remain on the desired path. Linear perturbation analysis is used to select satisfactory feedback gains for commanded bank angle, longitudinal acceleration and vertical speed. A wind estimator detects differences between the predicted and observed wind and uses the result to modify the nominal control. For 4-D guidance the nominal time must be computed as a function of turn angle, which requires the solution of an elliptic integral. Time-varying longitudinal accelerations are necessary for 4-D guidance when the observed wind differs from the predicted wind, or when wind shear is present. The logic and the linear feedback gains have been tested in a nonlinear simulation. Results have generally verified the performance predicted by linear analysis.

Hollister, W. M.↗

NASA NTRS1989

The active flexible wing aeroservoelastic wind-tunnel test program

For a specific application of aeroservoelastic technology, Rockwell International Corporation developed a concept known as the Active Flexible Wing (AFW). The concept incorporates multiple active leading-and trailing-edge control surfaces with a very flexible wing such that wing shape is varied in an optimum manner resulting in improved performance and reduced weight. As a result of a cooperative program between the AFWAL's Flight Dynamics Laboratory, Rockwell, and NASA LaRC, a scaled aeroelastic wind-tunnel model of an advanced fighter was designed, fabricated, and tested in the NASA LaRC Transonic Dynamics Tunnel (TDT) to validate the AFW concept. Besides conducting the wind-tunnel tests NASA provided a design of an Active Roll Control (ARC) System that was implemented and evaluated during the tests. The ARC system used a concept referred to as Control Law Parameterization which involves maintaining constant performance, robustness, and stability while using different combinations of multiple control surface displacements. Since the ARC system used measured control surface stability derivatives during the design, the predicted performance and stability results correlated very well with test measurements.

Noll, Thomas↗

NASA NTRS1989

New free-wake analysis of rotorcraft hover performance using influence coefficients

Free-wake analyses of helicopter rotor wakes in hover using time stepping have been shown to encounter instabilities which preclude convergence to valid free-vortex solutions for rotor-wake geometries. Previous work has demonstrated that these convergence difficulties can be overcome by implementing a new free-wake analysis method based on the use of influence coefficients. The present paper reviews this approach and documents its incorporation into a hover performance analysis called Evaluation of Hover Performance using Influence Coefficients (EHPIC). The technical principles underlying the EHPIC code are described with emphasis on steps taken to develop the single-filament wake models used in previous work into a multifilament wake valid for realistic hover performance predictions. The coupling of the wake model to a lifting surface loads analysis is described, and sample problems are solved that illustrate the robustness of the method. Performance calculations are also undertaken for hover to illustrate the utility of EHPIC in the analysis of rotorcraft performance.

Quackenbush, Todd R.↗

NASA NTRS2015

Design of an Object-Oriented Turbomachinery Analysis Code: Initial Results

Performance prediction of turbomachines is a significant part of aircraft propulsion design. In the conceptual design stage, there is an important need to quantify compressor and turbine aerodynamic performance and develop initial geometry parameters at the 2-D level prior to more extensive Computational Fluid Dynamics (CFD) analyses. The Object-oriented Turbomachinery Analysis Code (OTAC) is being developed to perform 2-D meridional flowthrough analysis of turbomachines using an implicit formulation of the governing equations to solve for the conditions at the exit of each blade row. OTAC is designed to perform meanline or streamline calculations; for streamline analyses simple radial equilibrium is used as a governing equation to solve for spanwise property variations. While the goal for OTAC is to allow simulation of physical effects and architectural features unavailable in other existing codes, it must first prove capable of performing calculations for conventional turbomachines.OTAC is being developed using the interpreted language features available in the Numerical Propulsion System Simulation (NPSS) code described by Claus et al (1991). Using the NPSS framework came with several distinct advantages, including access to the pre-existing NPSS thermodynamic property packages and the NPSS Newton-Raphson solver. The remaining objects necessary for OTAC were written in the NPSS framework interpreted language. These new objects form the core of OTAC and are the BladeRow, BladeSegment, TransitionSection, Expander, Reducer, and OTACstart Elements. The BladeRow and BladeSegment consumed the initial bulk of the development effort and required determining the equations applicable to flow through turbomachinery blade rows given specific assumptions about the nature of that flow. Once these objects were completed, OTAC was tested and found to agree with existing solutions from other codes; these tests included various meanline and streamline comparisons of axial compressors and turbines at design and off-design conditions.

compressors↗