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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Electromagnetic Modeling of Wind tunnel Magnetic Suspension and Balance Systems

An analytical framework for the open-loop behavior of a permanent magnet element levitated within an applied magnetic field are shown, in the context of application to wind tunnel Magnetic Suspension and Balance Systems (MSBS). Various modes of motion are identified, which correlate with observed behavior of the NASA/ODU 6-inch MSBS. This system is being developed as a technology demonstrator for dynamic stability testing of atmospheric entry capsules, with possible application to a supersonic wind tunnel. The analytic equations are populated with field information from experimental measurements and a finite element model. Finally, a system simulation can provide alternative estimates of system dynamic behavior via model linearization, with preliminary comparisons presented. Taken together, the analytical framework, experimental measurement, finite element analysis, and dynamic simulation provide a complete understanding of the characteristics and behavior of the MSBS.

magnetic suspension↗
DOE OSTI2021

Controlled Pyrolysis: A Robust Scalable Composite Recycling Technology

The reinforced composites industry is facing significant challenges in handling the scrap composite material from automobile manufacturing, the wind turbine industry, and others. The fibers in the material, whether they be carbon, glass, or other materials have commercial value if they can be recovered successfully. Successfully means the fibers are clean with no sizing or other binders and have adequate strength and physical properties that would allow them to be economically reprocessed into valuable product. The composites recycling project was an industry-collaborative effort to develop a composite recycling technology using controlled pyrolysis. Through the recycling of scrap and end-of-life (EOL) cured composite materials, this pilot study was intended to create a business case by realizing a cost-effective means for recycling EOL and production scrap composite materials, ultimately reducing the volume of composite materials destined for landfill. The project was led by the Institute for Advanced Composites Manufacturing Innovation (IACMI), the American Composites Manufacturers Association (ACMA), Oak Ridge National Laboratory (ORNL), Continental Structural Plastics (CSP) a Teijin Group Company, CHZ Technologies, and A. Schulman with support from Owens Corning, John Deere, General Electric (GE), Ashland LLC, and Plastics Europe (CEFIC). The team studied and tested CHZ Technologies’ controlled pyrolysis system, known as the Thermolyzer TM , which operates on a scalable basis to convert organic polymer materials into a clean synthesis gas and char containing the recoverable carbon and glass fiber reinforcement. The recoverable energy contained in the input polymers creates the synthesis gas that can be used to provide heat to the Thermolyzer TM primary reactor in a sustainable manner. That is, once the Thermolyzer TM is started with a small amount of external natural gas, the synthesis gas that is created from the polymers will continue to operate the burners so long as feedstock is supplied. The reinforcing fiber materials remaining in the solid phase char were separated and cleaned for re-use in other polymer systems based on the retained properties of the fibers. The study created reports (attached in the appendix) on the Mass and Energy Balances, syngas analytics, VOC assessment, yield analysis and other analytics necessary for a Techno-Economic Analysis (TEA) to quantify the economic impact of the recovery and sustainable re-use of the carbon and glass fibers. The process consisted of 4 steps: Selection of 4 samples of cured composite waste materials from project partners interested in materials recycling and recovering the reinforcing fibers for best case re-use. The materials included glass fiber (GF) polyester/vinyl ester automotive SMC from CSP, GF epoxy balsa/PVC foam wind blades from GE, carbon fiber (CF) epoxy wind blade laminated spar caps from GE, and GF/CF epoxy hybrid assembly from John Deere. Processing the waste composite samples into 1-2” shreds. Packaging the shredded composites into bulk sacks on international shipping pallets for shipment to KUG in Forst (Lausitz), Germany. Pyrolysis of the shredded composites under controlled conditions designed for each polymer system. Collecting samples of the gas and char for analysis. Shipping the char containing the CF/GF back to the US for the next steps of testing the fibers and developing protocols for sustainable re-use of the fibers in composite applications.

36 MATERIALS SCIENCE↗
NASA NTRS1972

Diffusion freezeout in gas-loaded heat pipes.

Experimental diffusion freezeout rates in a gas loaded heat pipe are presented and compared with predictions of an analysis and computer program published earlier. The experimental approach was to pivot a heat pipe on knife edges and use an analytical mass balance to measure the rate of mass migration within the pipe. Diffusion freezeout rates in a water heat pipe were measured as a function of the condenser sink temperature and the condenser-to-sink thermal conductance. Measured rates varied between 19 and 270 mg/hr and yielded 64 to 99 percent agreement with predicted values. These results indicated that the analysis and computer program for predicting the behavior of gas loaded heat pipes are useful tools for designing such systems.

Marcus, B. D.↗
NASA NTRS1995

Numerical modeling of flame-balls in fuel-air mixtures

At low gravity, when buoyancy effects are small, flame-balls can be generated. These are stationary spherical structures whose existence appears to require a near-limit mixture, a small Lewis number and heat losses from radiation. It is our goal to combine computational modeling with existing experimental and theoretical studies (NASA) of these structures so that an improved understanding of flammability limits and near-limit phenomena will occur. The question of flammability limits is of fundamental importance and has long been examined. It is of great practical importance to predict, from first principles, a limit mixture strength that agrees with experimental values for the configuration at hand. Flame-balls provide an excellent configuration in which convective losses can be eliminated and the resulting stable solutions are produced from a diffusive, reactive and radiative balance. Although analytical modeling provides convincing evidence that the key physical ingredients of flame-balls have been identified, quantitative confirmation can only come from detailed numerical simulations. Our goal is to predict theoretically the mass fractions of the species and the temperature as functions of the independent coordinate r.

Smooke, Mitchell D.↗
DOE OSTI2022

Nuclear Remote System Design: Radiation and Electronics

Idaho National Laboratory has unique opportunities to examine fuels, materials, and experiment inside of the available radiological hot cells. Due to the uninhabitable nature of a hot cell environment, everything done inside of the hot cell is operated remotely. Remote operations are optimized by Nuclear Remote System Design (NRSD). NRSD is the designing, altering, or configuring of structures, items, and systems that will be placed into radiological environments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Preparing Distribution Utilities for the Future - Unlocking Demand-Side Management Potential: A Novel Analytical Framework

The balance of supply and demand in the power systems has traditionally been served solely through generation and network capacity planning and operations. However, with increased requirements for flexibility due to the uptake in variable renewable generation sources such as wind and solar there is a need to increased demand-side flexibility. In addition, there are increased communications and flexibility capabilities emerging on the demand-side from the adoption of advanced metering infrastructures and smart meter deployment and intelligent loads such as smart thermostats and schedulable white goods (e.g. dishwashers and washing machines). Unlocking demand-side flexibility can bring system benefits from peak load reduction bringing about generation capacity and network upgrade deferral, to reducing demand and more efficient utilization of generation and network capacity. Unlocking demand-side flexibility is an evolving process for utilities and solutions must be tailored to each specific customer group. Demand-side management (DSM) is a broad set of tools that can include demand response (both dispatchable and non-dispatchable), energy efficiency and distributed energy resources and demand-side technologies. The National Renewable Energy Laboratory (NREL), in collaboration with BSES Rajdhani Power Ltd. (BRPL) and Deloitte, examined the potential of DSM in BRPL’s service territory, developing detailed information on customer classes and willingness to participate in DSM. The study developed modeling frameworks for load analysis and the analysis tools to assess the potential of time-of-use tariffs in motivating customers to reduce their peak period energy consumption. The study shows that BRPL customers, specifically their domestic customers, are willing to participate in DSM programs and that time-of-use pricing can help BRPL reduce their peak demand and help unlock demand-side flexibility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
NASA NTRS1979

Development of flexible rotor balancing criteria

Several studies in which analytical procedures were used to obtain balancing criteria for flexible rotors are described. General response data for a uniform rotor in damped flexible supports were first obtained for plain cylindrical bearings, tilting pad bearings, axial groove bearings, and partial arc bearings. These data formed the basis for the flexible rotor balance criteria presented. A procedure by which a practical rotor in bearings could be reduced to an equivalent uniform rotor was developed and tested. It was found that the equivalent rotor response always exceeded to practical rotor response by more than sixty percent for the cases tested. The equivalent rotor procedure was then tested against six practical rotor configurations for which data was available. It was found that the equivalent rotor method offered a procedure by which balance criteria could be selected for practical flexible rotors, using the charts given for the uniform rotor.

Walter, W. W.↗
NASA NTRS2024

Evaluation, Analysis, and Application of Internal Strain-Gage Balance Data

Experimental processes, analytical methods, and numerical algorithms are described that may be used to predict the forces and moments of an internal strain–gage balance during a wind tunnel test. First, the control volume model of a strain–gage balance and the concepts of load state, load space, and output space are introduced. These important abstractions provide a better understanding of fundamental characteristics of different balance load prediction approaches. Then, the description of strain–gage balance data and the definition of the primary bridge sensitivity are discussed. Afterwards, basic elements of the calibration of a typical six–component balance are reviewed. Two fundamentally different balance load prediction methods, the processing of check loads, and related topics are also discussed. Three real–world balance data examples are reviewed in great detail to illustrate typical analysis results for a variety of strain–gage balance designs. Finally, important observations are summarized and recommendations are provided. – Additional information and detailed mathematical derivations can be found in the appendices of the document. They include the following topics: balance terminology, definitions of important statistical metrics, balance axis system conventions, balance load transformations, the combined load diagram, electrical output format options, bi–directional output characteristics, determination of the natural zeros, derivation of two balance load prediction methods, description of two tare load iteration algorithms, modeling of balance temperature effects, basics of three–component moment balances, definition of the percent contribution, detection of linear and near–linear dependencies in balance calibration data, a regression model search algorithm, balance interactions, and other related information.

strain-gage balance↗
NASA NTRS2021

Evaluation, Analysis, and Application of Internal Strain-Gage Balance Data

Experimental processes, analytical methods, and numerical algorithms are described that may be used to predict the forces and moments of an internal strain-gage balance during a wind tunnel test. First, the control volume model of a strain-gage balance and the concepts of load state, load space, and output space are introduced. These important abstractions provide a better understanding of fundamental characteristics of different balance load prediction approaches. Then, the description of strain-gage balance data and the definition of the primary gage sensitivity is discussed. Afterwards, basic elements of the calibration of a typical six-component balance are reviewed. Two fundamentally different balance load prediction methods, the processing of check loads, and related topics are also discussed. Three real-world balance data examples are reviewed in great detail to illustrate typical analysis results for a variety of strain-gage balance designs. Finally, important observations are summarized and recommendations are provided. Additional information and detailed mathematical derivations can be found in the appendices of the document. They include the following topics: balance terminology, definitions of important statistical metrics, balance axis system conventions, balance load transformations, the combined load diagram, electrical output format options, bi-directional gage output characteristics, determination of the natural zeros, derivation of two balance load prediction methods, description of two tare load iteration algorithms, modeling of balance temperature effects, basics of three-component moment balances, definition of the percent contribution, detection of linear and near-linear dependencies in balance calibration data, a regression model term selection algorithm, and other related topics.

wind tunnel test↗
NASA NTRS1985

Development and application of a unified balancing approach with multiple constraints

The development of a general analytic approach to constrained balancing that is consistent with past influence coefficient methods is described. The approach uses Lagrange multipliers to impose orbit and/or weight constraints; these constraints are combined with the least squares minimization process to provide a set of coupled equations that result in a single solution form for determining correction weights. Proper selection of constraints results in the capability to: (1) balance higher speeds without disturbing previously balanced modes, thru the use of modal trial weight sets; (2) balance off-critical speeds; and (3) balance decoupled modes by use of a single balance plane. If no constraints are imposed, this solution form reduces to the general weighted least squares influence coefficient method. A test facility used to examine the use of the general constrained balancing procedure and application of modal trial weight ratios is also described.

Zorzi, E. S.↗
DOE OSTI2022

A coupled ground heat flux–surface energy balance model of evaporation using thermal remote sensing observations

Abstract. One of the major undetermined problems in evaporation (ET) retrieval using thermal infrared remote sensing is the lack of a physically based ground heat flux (G) model and its integration within the surface energy balance (SEB) equation. Here, we present a novel approach based on coupling a thermal inertia (TI)-based mechanistic G model with an analytical surface energy balance model, Surface Temperature Initiated Closure (STIC, version STIC1.2). The coupled model is named STIC-TI. The model is driven by noon–night (13:30 and 01:30 local time) land surface temperature, surface albedo, and a vegetation index from MODIS Aqua in conjunction with a clear-sky net radiation sub-model and ancillary meteorological information. SEB flux estimates from STIC-TI were evaluated with respect to the in situ fluxes from eddy covariance measurements in diverse ecosystems of contrasting aridity in both the Northern Hemisphere and Southern Hemisphere. Sensitivity analysis revealed substantial sensitivity of STIC-TI-derived fluxes due to the land surface temperature uncertainty. An evaluation of noontime G (Gi) estimates showed 12 %–21 % error across six flux tower sites, and a comparison between STIC-TI versus empirical G models also revealed the substantially better performance of the former. While the instantaneous noontime net radiation (RNi) and latent heat flux (LEi) were overestimated (15 % and 25 %), sensible heat flux (Hi) was underestimated (22 %). Overestimation (underestimation) of LEi (Hi) was associated with the overestimation of net available energy (RNi−Gi) and use of unclosed surface energy balance flux measurements in LEi (Hi) validation. The mean percent deviations in Gi and Hi estimates were found to be strongly correlated with satellite day–night view angle difference in parabolic and linear pattern, and a relatively weak correlation was found between day–night view angle difference versus LEi deviation. Findings from this parameter-sparse coupled G–ET model can make a valuable contribution to mapping and monitoring the spatiotemporal variability of ecosystem water stress and evaporation using noon–night thermal infrared observations from future Earth observation satellite missions such as TRISHNA, LSTM, and SBG.

Bhattacharya, Bimal K.↗
NASA NTRS1974

Balancing of high speed interconnect shafting for operation above multiple bending critical speeds

The present work first reviews the key elements of rotating system dynamics which are crucial to the control of lateral vibration levels for supercritical shafting of the type which is considered for use in advanced aircraft applications. Then the results of an analytical investigation of procedures for balancing flexible shafts designed for critical operation are discussed. Some of the conclusions made were: (1) even in undamped configuration, reasonably low vibration levels can be achieved through a sequential multiplane-multispeed balancing process; and (2) combination of analytical response and balancing prediction procedures yields a powerful tool for the dynamic analysis and design of advanced shaft-bearing systems.

Badgley, R. H.↗
NASA NTRS1992

An approach for increasing aeroelastic divergence dynamic pressure of wind-tunnel models

An approach for increasing the aeroelastic divergence dynamic pressure of wind-tunnel model support systems is presented. A study has been conducted to investigate the effect of increased flexibility of the forward portion of balances on the divergence pressure of wind-tunnel models. The study utilized prior divergence analyses completed for various models to be tested in NASA Langley Research Center's National Transonic Facility, the Engineering Analysis Language finite-element analysis code, and an in-house computer program which solves for divergence dynamic pressure utilizing a transfer-matrix method based on fourth-order Runge-Kutta integration. Analytical results for balance flexibility changes versus changes in divergence pressure were obtained and demonstrate that a significant increase in divergence pressure can be obtained from the increased flexibility of the balance's forward portion for many models and their support systems.

Edwards, R. W.↗
DOE OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
NASA NTRS1996

Theoretical and Numerical Investigation of Radiative Extinction of Diffusion Flames

The influence of soot radiation on diffusion flames was investigated using both analytical and numerical techniques. Soot generated in diffusion flames dominate the flame radiation over gaseous combustion products and can significantly lower the temperature of the flame. In low gravity situations there can be significant accumulation of soot and combustion products in the vicinity of the primary reaction zone owing to the absence of any convective buoyant flow. Such situations may result in substantial suppression of chemical activities in a flame, and the possibility of a radiative extinction may also be anticipated. The purpose of this work was to not only investigate the possibility of radiative extinction of a diffusion flame but also to qualitatively and quantitatively analyze the influence of soot radiation on a diffusion flame. In this study, first a hypothetical radiative loss profile of the form of a sech(sup 2) was assumed to influence a pure diffusion flame. It was observed that the reaction zone can, under certain circumstances, move through the radiative loss zone and locate itself on the fuel side of the loss zone contrary to our initial postulate. On increasing the intensity and/or width of the loss zone it was possible to extinguish the flame, and extinction plots were generated. In the presence of a convective flow, however, the movement of the temperature and reaction rate peaks indicated that the flame behavior is more complicated compared to a pure diffusional flame. A comprehensive model of soot formation, oxidation and radiation was used in a more involved analysis. The soot model of Syed, Stewart and Moss was used for soot nucleation and growth and the model of Nagle and Strickland-Constable was used for soot oxidation. The soot radiation was considered in the optically thin limit. An analysis of the flame structure revealed that the radiative loss term is countered both by the reaction term and the diffusion term. The essential balance for the soot volume fraction was found to be between the processes of soot convection and soot growth. Such a balance yielded to analytical treatment and the soot volume fraction could be expressed in the form of an integral. The integral was evaluated using two approximate methods and the results agreed very well with the numerical solutions for all cases examined.

Ray, Anjan↗
DOE OSTI2026

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗