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Results for “Coefficient of performance”

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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Effects of the cooling system parameters on heat transfer and performance of the PAFC stack during transient operation

An experimental investigation for the effects of transient operation of a phosphoric acid fuel-cell stack on heat transfer and temperature distribution in the electrodes has been conducted. The proposed work utilized the experimental setup with modifications, which was designed and constructed under NASA Contract No. NCC-3-17(5). The experimental results obtained from this investigation and the mathematical model obtained under NASA Contract No. NCC3-17(4) after modifications, were utilized to develop mathematical models for transient heat transfer coefficient and temperature distribution in the electrode and to evaluate the performance of the cooling - system under unsteady state conditions. The empirical formulas developed were then implemented to modifying the developed computer code. Two incompressible coolants were used to study experimentally the effect of the thermophysical properties of the cool-ants on the transient heat transfer coefficient and the thermal contact resistance during start-up and shut-down processes. Coolant mass flow rates were verified from 16 to 88.2 Kg/hr during the transient process when the electrical power supply was gradually increased or decreased in the range (O to 3000 W/sq m). The effect of the thermal contact resistance with a range of stack pressure from O to 3500 KPa was studied.

Ridha, Rabi M. J.↗
NASA NTRS1983

A comparison between backscattering coefficients using Gaussian and non-Gaussian surface statistics

Backscattering coefficients computed using Gaussian and non-Gaussian surface statistics are compared. The computations are performed for a class of surface-height distributions and surface correlation functions. Results indicate that the coherent component of the scattering coefficient is strongly dependent upon the surface-height distribution. The noncoherent component also depends on the surface-height distribution and the surface root-mean-square (rms) slope when the rms surface height is large relative to the incident wavelength. On the other hand, when the surface rms height is moderate or small, the noncoherent component is sensitive mainly to the surface-height correlation function.

Eom, H. J.↗
DOE OSTI2025

Temporal Forecasting of Distributed Temperature Sensing in a Thermal Hydraulic System With Machine Learning and Statistical Models

We benchmark performance of long-short term memory (LSTM) network machine learning model and autoregressive integrated moving average (ARIMA) statistical model in temporal forecasting of distributed temperature sensing (DTS). Data in this study consists of fluid temperature transient measured with two co-located Rayleigh scattering fiber optic sensors (FOS) in a forced convection mixing zone of a thermal tee. We treat each gauge of a FOS as an independent temperature sensor. We first study prediction of DTS time series using Vanilla LSTM and ARIMA models trained on prior history of the same FOS that is used for testing. The results yield maximum absolute percentage error (MaxAPE) and root mean squared percentage error (RMSPE) of 1.58% and 0.06% for ARIMA, and 3.14% and 0.44% for LSTM, respectively. Next, we investigate zero-shot forecasting (ZSF) with LSTM and ARIMA trained on history of the co-located FOS only, which is advantageous when limited training data is available. The ZSF MaxAPE and RMSPE values for ARIMA are comparable to those of the Vanilla use case, while the error values for LSTM increase. We show that in ZSF, performance of LSTM network can be improved by training on most correlated gauges between the two FOS, which are identified by calculating the Pearson correlation coefficient. The improved ZSF MaxAPE and RMSPE for LSTM are 4.4% and 0.33%, respectively. Performance of ZSF LSTM can be further enhanced through transfer learning (TL), where LSTM is re-trained on a subset of the FOS that is the target of forecasting. We show that LSTM pre-trained on correlated dataset and re-trained on 30% of testing target dataset achieves MaxAPE and RMSPE values of 2.32% and 0.28%, respectively.

ARIMA↗
NASA NTRS2002

NASA IKONOS Radiometric Characterization

The objective of this program: Perform radiometric vicarious calibrations of IKQNOS imagery and compare with Space Imaging calibration coefficients The approach taken: utilize multiple well-characterized sites which are widely used by the NASA science community for radiometric characterization of airborne and spaceborne sensors; and to Perform independent characterizations with independent teams. Each team has slightly different measurement techniques and data processing methods.

Pagnutti, Mary↗
DOE OSTI2022

Modeling of vapor-liquid interactions in condensing ejectors

Ejectors are compact mechanical devices that utilize the expansion of a high-pressure primary fluid to entrain and compress a low-pressure secondary fluid by means of momentum transfer between the two streams of fluid. Condensing ejectors feature both momentum and heat transfer through the interaction between the vapor and liquid streams. The goal of this paper is to develop and validate a one-dimensional slug flow model to simulate the vapor–liquid interactions in two types of condensing ejectors – one with primary liquid and secondary vapor flows (Type I), and the other with primary vapor and secondary liquid flows (Type II). Control volume analysis of the mass, momentum, and energy balance in each phase and across the interface was conducted for the ejectors. The slug flow models for both types of ejectors are validated with published results in the literature. The friction coefficient on the inner wall of the ejectors and the interfacial heat transfer coefficient are identified as controlling parameters for the ejector performance in terms of pressure and temperature distributions along the axis. The detailed parametric study shows that the liquid inlet velocity and mixing tube diameter have a significant impact on the performance of the Type I ejectors, and the performance of the Type II ejectors is mainly controlled by the secondary liquid flow inlet temperature.

42 ENGINEERING↗
DOE OSTI2023

Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.

58 GEOSCIENCES↗
DOE OSTI2025

Steam generator model design parameter sensitivity study for small modular reactor system

Here, this study focuses on design parameter sensitivity studies pertaining to several Once-Through Steam Generator (OTSG) model cases both with and without a riser using python and advanced risk assessment and optimization tool, i.e. Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory (INL), to support a Small Modular Reactor (SMR) system. The presented Steam Generator (SG) python-based model is a mathematical representation of a steam-generating unit for a Pressurized Water Reactor (PWR)-type SMR system, including fluid flow and heat transfer equations, models, and correlations. Design studies involve changing the model’s input design parameters (e.g., temperature, pressure, mass flow rate) to observe the resulting effects on the output of the system, such as the Heat Transfer Coefficient (HTC), Reynolds number, Nusselt number, and heat transfer performance. Sensitivity studies analyze the degree to which system output and/or desired parameters (e.g., HTC or heat transfer performance) are sensitive to changes in the input parameters. By using RAVEN, detailed design parametric sensitivity studies. Six input parameters—namely, the pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid) of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (i.e., ±1%, ±5% and ±10 % relative changes) for 600 samples. The analysis results give valuable insights into SG system performance, and provide justification for further research and development such as optimized sensor placement, design verification, validation, and optimization.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Landsat 8 monitoring of multi-depth suspended sediment concentrations in Lake Erie’s Maumee River using machine learning

Satellite remote sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. However, due to the complexity of sediment-water interactions, it has been difficult to derive linear and non-linear regression equations to reliably predict SSC, especially when trying to estimate depth of integrated sediment. Herein, this study uses Landsat 8 OLI (Operational Land Imager) sensor to map SSC within the Maumee River in Ohio, USA, at multiple depth intervals (15, 61, 91, and 182 cm). Simple linear least squares regression (LLSR), and three common machine learning models: random forest (RF), support vector regression (SVR), and model averaged neural network (MANN) were used to estimate SSC at the depth intervals. All machine learning models significantly outperformed LLSR while RF performed the best. In both RF and MANN, R2 (coefficient of determination) increases with depth with a maximum R2 of 0.89 and 0.83, respectively, at a depth of 0–182 cm. The results show that machine learning models can implement nonlinear relationships that produce better predictions than traditional linear regression methods in estimating depth integrated SSC, especially when samples are limited.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Effects of Photovoltaic Module Materials and Design on Module Deformation Under Load

Quasi-static structural finite-element models of an aluminum-framed crystalline silicon photovoltaic module and a glass-glass thin-film module were constructed and validated against experimental measurements of deflection under uniform pressure loading. Specific practices in the computational representation of module assembly were identified as influential to matching experimental deflection observations. Additionally, parametric analyses using Latin hypercube sampling were performed to propagate input uncertainties related to module materials, dimensions, and tolerances into uncertainties in simulated deflection. Sensitivity analyses were performed on the uncertainty quantification datasets using linear correlation coefficients and variance-based sensitivity indices to elucidate key parameters influencing module deformation. Results identified edge tape and adhesive material properties as being strongly correlated to module deflection, suggesting that optimization of these materials could yield module stiffness gains at par with the conventionally structural parameters, such as glass thickness. This exercise verifies the applicability of finite-element models for accurately predicting mechanical behavior of solar modules and demonstrates a workflow for model-based parametric uncertainty quantification and sensitivity analysis. Finally, applications of this capability include the assessment of field environment loads, derivation of representative loading conditions for reduced-scale testing, and module design optimization, among others.

42 ENGINEERING↗
DOE OSTI2025

Throughput Measurements and Profile Analysis of Cloud Networks

Cloud networks utilize virtual connections to connect virtual machines distributed across cloud sites. They are increasingly deployed due to flexible provisioning using software and cost-effectiveness in not requiring to build physical network infrastructure. However, their extensive virtualization makes it unclear how well the established practices of conventional networks translate to them. Here, we study throughput measurements over a Google Cloud network using a matching hardware emulated conventional network, which provide production and exploratory conditions, respectively. The measurements span connections representing local, cross-continental and around the Earth distances. We study the effects of parallel flows, congestion control algorithms and retransmissions on the network throughput profile expressed as a function of RTT. We compare the throughput profile of Google Cloud network with those of emulated network under various loss conditions, including those too disruptive or expensive in the former. Our analysis based on the concave-convex shape and utilization-concavity coefficients of throughput profiles indicates an overall agreement of performance between the two networks, thereby justifying the use of conventional network emulations to analyze cloud networks. In terms of practical use, our study establishes that BBR and BBRv2 alpha TCP achieve higher throughput compared to loss-based congestion control algorithms under most network configurations, especially, under losses at large RTT.

Phanekham, Derek [Southern Methodist Univ., Dallas↗
DOE OSTI2021

Electronic, Ionic, and Mixed Conduction in Polymeric Systems

Polymers that simultaneously transport electrons and ions are paramount to drive the technological advances necessary for next-generation electrochemical devices, including energy storage devices and bioelectronics. However, efforts to describe the motion of ions or electrons separately within polymeric systems become inaccurate when both species are present. In this paper, we highlight the basic transport equations necessary to rationalize mixed transport and the multiscale materials properties that influence their transport coefficients. Potential figures of merit that enable a suitable performance benchmark in mixed conducting systems independent of end application are discussed. Practical design and implementation of mixed conducting polymers require an understanding of the evolving nature of structure and transport with ionic and electronic carrier density to capture the dynamic disorder inherent in polymeric materials.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Hierarchical Hybrid Multifunctional Materials through Interface Engineering

This project focuses on the development of stimuli-responsive hybrid multifunctional materials. We place emphasis on the design, synthesis, structural characterization, evaluation of functional properties (electronic, thermal and optical) of several (1-x)Cu 2 Se/(x)WBGS hierarchical bulk composites between Cu 2 Se, a narrow band gap semiconductor (NBGS), with a range of wider band gap semiconductors (WBGS) such as CuMSe 2 (M = Al, Ga, In, Fe, Cr) and Cu 4 TiSe 4 . Cu2Se is a well-studied NBGS with excellent thermoelectric properties (high electrical conductivity, large thermopower, etc.) while CuMSe 2 and Cu 4 TiSe 4 are high performance solar absorber materials (large band gap, large absorption coefficient, etc.). Our primary objectives are (i) to demonstrate the ability to integrate dissimilar functional properties such as large optical absorption coefficient and high electronic conductivity, within (1-x)Cu 2 Se/(x)WBGS composite; and (ii) to establish the correlation between the hierarchical structural entanglement of Cu 2 Se with WBGS (CuMSe 2 or Cu 4 TiSe 4 ) phase, the interactions between native electronic defects within the coexisting phases in the resulting (1-x)Cu 2 Se/(x)WBGS bulk composites , and the impacts on their electronic conductivity, thermal transport and optical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Rapid Design of Cost-Effective Refractory High Entropy Alloys Strengthened by Precipitation

Funded by the ARPA-E ULTIMATE program, this project aims to develop cost-effective refractory high entropy alloys (RHEAs) for gas turbine blades applications at 1300 degree Celsius and above. The alloy design focuses on carbides precipitation strengthening in a strong and ductile solid solution BCC matrix. The goal is to achieve balanced mechanical properties including room temperature ductility & fracture toughness and high temperature strength and creep resistance, while maintaining comparable oxidation resistance and densities to Ni-base superalloys. To accelerate alloy design, multiscale modeling including CALPHAD and first-principles density functional theory (DFT) calculations and machine learning are performed. Specifically, high throughput phase diagram calculations and screening are carried out using CALPHAD; DFT calculations are performed to predict intrinsic ductility, grain boundary strengthening potential, coefficient of thermal expansion (CTE) and temperature-dependent elastic constants. Temperature dependent yield stress, creep strain and CTE are also predicted using machine learning. Down-selected alloys are synthesized in small buttons of about 250g using arc melting for rapid evaluation on microstructure and mechanical properties before employing plasma arc melting and additive manufacturing for producing large ingots. Preliminary computational and experimental results of this project will be presented and future direction in RHEA development will be discussed.

high temperature materials↗
NASA NTRS1971

Performance of the Boeing LRV wheels in a lunar soil simulant. Report 2: Effects of speed, Wheel load, and soil

Two nearly identical Boeing-GM wire-mesh Lunar Roving Vehicle (LRV) wheels were laboratory tested in a lunar soil simulant to determine the influence of wheel speed and acceleration, wheel load, presence of a fender, travel direction, and soil strength on the wheel performance. Constant-slip and three types of programmed-slip tests were conducted with a single-wheel dynamometer system. Test results indicated that performance of single LRV wheels in terms of pull coefficient, power number, and efficiency were not influenced by wheel speed and acceleration, travel direction, the presence of a fender, or wheel load. Of these variables, only load influenced sinkage, which increased with increasing load. For a given slip, the pull coefficient and power number increased with increasing soil strength. However, for a given pull coefficient or slope, slip was less in firmer soil; thus, the power number decreased and efficiency increased with increasing soil strength.

Melzer, K.↗
NASA NTRS1973

Effects of flaps on buffet characteristics and wind-rock onset of an F-8C airplane at subsonic and transonic speeds

Wind-up-turn maneuvers were performed to establish the values of airplane normal force coefficient for buffet onset, wing-rock onset, and buffet loads with various combinations of leading- and trailing-edge flap deflections. Data were gathered at both subsonic and transonic speeds covering a range from Mach 0.64 to Mach 0.92. Buffet onset and buffet loads were obtained from wingtip acceleration and wing-root bending-moment data, and wing-rock onset was obtained from airplane roll rate data. Buffet onset, wing-rock onset, and buffet loads were similarly affected by the various combinations of leading- and training-edge flaps. Subsonically, the 12 deg leading-edge-flap and trailing-edge-flap combination was most effective in delaying buffet onset, wing-rock onset, and equivalent values of buffet loads to a higher value of airplane normal force coefficient. This was the maximum flap deflection investigated. Transonically, however, the optimum leading-edge flap position was generally less than 12 deg.

Monaghan, R. C.↗
NASA NTRS1973

Thermal shielding by subliming volume reflectors in convective and intense radiative environments.

The behavior of dielectric materials having densely packed internal scattering centers subject to extreme convective and radiative environments is analyzed. Experiments have shown that these materials act as volume reflectors of incident radiation even when the exposed surface is being eroded by thermochemical ablation. The analysis was applied to interpret experiments of subliming Teflon models exposed to combined radiative and convective fluxes up to 1.7 kW/sq cm for several seconds. Results show that, although the exposed surface receded at an apparently steady rate, the internal temperature climbed continually, due to internal absorption of radiation and would have caused failure internally if the test duration were extended a few seconds. Thus, performance is time-limited by the internal absorption coefficient. Results were obtained for larger configurations and other materials. Typically, Teflon shells may withstand radiant fluxes up to 20 kW/sq cm for about 5 sec and fritted quartz up to 50 kW/sq cm for about 8 sec (corresponding to the Jupiter entry).

Howe, J. T.↗
NASA NTRS1979

Performance evaluation of Space Shuttle SRB parachutes from air drop and scaled model wind tunnel tests

The aerodynamic performance characteristics have been determined for the Space Shuttle Solid Rocket Booster drogue, main, and pilot parachutes. The performance evaluation on the 20-degree conical ribbon parachutes is based primarily on air drop tests of full scale prototype parachutes. In addition, parametric wind tunnel tests were performed and used in parachute configuration development and preliminary performance assessments. The wind tunnel test data are compared to the drop test results and both sets of data are used to determine the predicted performance of the Solid Rocket Booster flight parachutes. Data from other drop tests of large ribbon parachutes are also compared with the Solid Rocket Booster parachute performance characteristics. Parameters assessed include full open terminal drag coefficients, reefed drag area, opening characteristics, clustering effects, and forebody interference.

Moog, R. D.↗
NASA NTRS1983

Acoustic measurements of a full-scale coaxial helicopter

Acoustic data were obtained during a full-scale test of the XH-59A Advancing Blade Concept (ABC) Technology Demonstrator in the NASA Ames 40- by 80-Foot Wind Tunnel. The XH-59A is a research helicopter with two coaxial rotors and hingeless blades. Performance, vibration, noise at various forward speeds, rotor lift coefficients, and rotor shaft angles of attack were investigated. In general, the noise level is shown to increase with rotor lift coefficient except under certain operating conditions where it is increased by significant impulsive blade/vortex interactions. The impulsivity appears to depend upon how the lift is distributed between the two rotors. The noise levels measured are shown to be slightly higher than on a modern conventional rotor tested in the same facility.

Mosher, M.↗