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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (40,000 pound force thrust) (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a Simulink (R) library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

numerical simulation↗
NASA NTRS2015

A Modular Framework for Modeling Hardware Elements in Distributed Engine Control Systems

Progress toward the implementation of distributed engine control in an aerospace application may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new control architectures and hardware outside of a physical test cell environment. One component required in an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the control law. The control system developed for the Commercial Modular Aero-Propulsion System Simulation 40k (C-MAPSS40k) provides a verifiable baseline for development of a model for simulating a distributed control architecture. This distributed controller model will contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data processing, and a model of the controller network. A multilevel framework is presented that establishes three sets of interfaces in the control platform: communication with the engine (through sensors and actuators), communication between hardware and controller (over a network), and the physical connections within individual pieces of hardware. This introduces modularity at each level of the model, encouraging collaboration in the development and testing of various control schemes or hardware designs. At the hardware level, this modularity is leveraged through the creation of a SimulinkR library containing blocks for constructing smart transducer models complying with the IEEE 1451 specification. These hardware models were incorporated in a distributed version of the baseline C-MAPSS40k controller and simulations were run to compare the performance of the two models. The overall tracking ability differed only due to quantization effects in the feedback measurements in the distributed controller. Additionally, it was also found that the added complexity of the smart transducer models did not prevent real-time operation of the distributed controller model, a requirement of an HIL system.

propulsion simulation↗
NASA NTRS2015

The AgMIP GRIDded Crop Modeling Initiative (AgGRID) and the Global Gridded Crop Model Intercomparison (GGCMI)

Climate change is a significant risk for agricultural production. Even under optimistic scenarios for climate mitigation action, present-day agricultural areas are likely to face significant increases in temperatures in the coming decades, in addition to changes in precipitation, cloud cover, and the frequency and duration of extreme heat, drought, and flood events (IPCC, 2013). These factors will affect the agricultural system at the global scale by impacting cultivation regimes, prices, trade, and food security (Nelson et al., 2014a). Global-scale evaluation of crop productivity is a major challenge for climate impact and adaptation assessment. Rigorous global assessments that are able to inform planning and policy will benefit from consistent use of models, input data, and assumptions across regions and time that use mutually agreed protocols designed by the modeling community. To ensure this consistency, large-scale assessments are typically performed on uniform spatial grids, with spatial resolution of typically 10 to 50 km, over specified time-periods. Many distinct crop models and model types have been applied on the global scale to assess productivity and climate impacts, often with very different results (Rosenzweig et al., 2014). These models are based to a large extent on field-scale crop process or ecosystems models and they typically require resolved data on weather, environmental, and farm management conditions that are lacking in many regions (Bondeau et al., 2007; Drewniak et al., 2013; Elliott et al., 2014b; Gueneau et al., 2012; Jones et al., 2003; Liu et al., 2007; M¨uller and Robertson, 2014; Van den Hoof et al., 2011;Waha et al., 2012; Xiong et al., 2014). Due to data limitations, the requirements of consistency, and the computational and practical limitations of running models on a large scale, a variety of simplifying assumptions must generally be made regarding prevailing management strategies on the grid scale in both the baseline and future periods. Implementation differences in these and other modeling choices contribute to significant variation among global-scale crop model assessments in addition to differences in crop model implementations that also cause large differences in site-specific crop modeling (Asseng et al., 2013; Bassu et al., 2014).

climate↗
NASA NTRS2015

Multimodel Ensembles of Wheat Growth: Many Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop model scan give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 2438 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

model intercomparison↗
NASA NTRS2018

Tuning Process for the Modified Magnussen Combustion Model

In the application of CFD to turbulent reacting flows, one of the main limitations to predictive accuracy is the chemistry model. Using a full or skeletal kinetics model may provide good predictive ability, however, at considerable computational cost. Adding the ability to account for the interaction between turbulence and chemistry improves the overall fidelity of a simulation but adds to this cost. An alternative is the use of simple models, such as the Magnussen model, which has negligible computational overhead, but lacks general predictive ability except for cases that can be tuned to the flow being solved. In this paper, a technique will be described that allows the tuning of the Magnussen model for an arbitrary fuel and flow geometry without the need to have experimental data for a particular case. The tuning is based on comparing the results of the Magnussen model and full finite-rate chemistry when applied to perfectly and partially stirred reactor sim- ulations. In addition, a modification to the Magnussen model is proposed that allows the upper kinetic limit for the reaction rate to be set, giving better physical agreement with full kinetic mechanisms. In order to improve the agreement with flame temperatures, the thermal properties of the product species is adjusted to better match the mixture proper- ties of the full mechanism. The combustion model is then applied to the simulation of a representative scramjet flowpath, and the results compared to experimental data and other kinetic models. This procedure allows a simple reacting model to be used in a predictive manner, and affords significant savings in computational costs for CFD simulations.

Norris, A. T.↗
NASA NTRSDate not supplied

Liquefaction of Cryogenic Fluids for Production and Storage of Commodities on Extra-Terrestrial Surfaces

The Cryogenic Fluid In-situ Liquefaction for Landers (CryoFILL) activity has been investigating concepts for the liquefaction of cryogenic fluids produced on the surface of the Moon and Mars. CryoFILL consists of four activities: liquid nitrogen liquefaction testing that was completed in 2019, liquid oxygen liquefaction testing planned to begin in early 2022, fiber-optic sensor temperature measurement system development, and lightweight vacuum jacketed systems designed for the atmosphere of Mars. Additional activities include the development and validation of numerical modeling systems at three stages: thermodynamic models of the liquefaction process for basic energy/mass estimates, nodal models to predict liquefaction tank system level performance, and computational fluid dynamics to assess fluid phenomena occurring within the tank, specifically condensation and stratification within the ullage. These developments form a combined liquefaction and storage solution to support Lunar and Martian exploration. Key testing results from liquid nitrogen testing as well as plans for liquid oxygen testing will be discussed including key sensitivities from analytical evaluation of completed test results. Progress on the development and improvements on fiber optic sensor testing and developments will also be provided. Finally, work on the progress of the lightweight vacuum jacketed systems being co-developed by NASA and industry will be summarized.

Liquefaction↗
NASA NTRS1993

Comparing the results of an analytical model of the no-vent fill process with no-vent fill test results for a 4.96 cubic meters (175 cubic feet) tank

The NASA Lewis Research Center (NASA/LeRC) have been investigating a no-vent fill method for refilling cryogenic storage tanks in low gravity. Analytical modeling based on analyzing the heat transfer of a droplet has successfully represented the process in 0.034 m and 0.142 cubic m commercial dewars using liquid nitrogen and hydrogen. Recently a large tank (4.96 cubic m) was tested with hydrogen. This lightweight tank is representative of spacecraft construction. This paper presents efforts to model the large tank test data. The droplet heat transfer model is found to over predict the tank pressure level when compared to the large tank data. A new model based on equilibrium thermodynamics has been formulated. This new model is compared to the published large scale tank's test results as well as some additional test runs with the same equipment. The results are shown to match the test results within the measurement uncertainty of the test data except for the initial transient wall cooldown where it is conservative (i.e., overpredicts the initial pressure spike found in this time frame).

Taylor, William J.↗
NASA NTRS1984

Predictions of spray combustion interactions

Mean and fluctuating phase velocities; mean particle mass flux; particle size; and mean gas-phase Reynolds stress, composition and temperature were measured in stationary, turbulent, axisymmetric, and flows which conform to the boundary layer approximations while having well-defined initial and boundary conditions in dilute particle-laden jets, nonevaporating sprays, and evaporating sprays injected into a still air environment. Three models of the processes, typical of current practice, were evaluated. The local homogeneous flow and deterministic separated flow models did not provide very satisfactory predictions over the present data base. In contrast, the stochastic separated flow model generally provided good predictions and appears to be an attractive approach for treating nonlinear interphase transport processes in turbulent flows containing particles (drops).

Shuen, J. S.↗
NASA NTRS1998

Comparison of Computational, Model and Experimental, Example Trained Neural Networks for Processing Speckled Fringe Patterns

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model- generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

Decker, A. J.↗
NASA NTRS1992

Issues in knowledge representation to support maintainability: A case study in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and runtime estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks. Because the scientific data processing modules (called fittings) evolve to match scientists' needs, issues regarding maintainability are of prime importance in PIPE. This paper describes the PIPE system and describes how issues in maintainability affected the knowledge representation used in PIPE to capture knowledge about the behavior of fittings.

Chien, Steve↗
NASA NTRS2019

How Well Does NASA GEOS Model Perform in Simulating Dust Deposition into the Tropical Atlantic Ocean?

Massive dust emitted from North Africa can transport long distances across the tropical Atlantic Ocean, reaching the Americas. Dust deposition along the transit adds microorganisms and essential nutrients to marine ecosystem, which has important implications for biogeochemical cycle and climate. However, assessing the dust-ecosystemclimate interactions has been hindered in part by the paucity of dust deposition measurements and large uncertainties associated with oversimplified representations of dust processes in current models. We have recently produced a unique dataset of seasonal dust deposition flux and dust loss frequency into the tropical Atlantic Ocean at a nominal resolution of 200 km x 500 km by using the decade-long (2007-2016) record of aerosol three-dimensional distribution from four satellite sensors, namely CALIOP, MODIS, MISR, and IASI. On the basis of the ten-year average, the yearly dust deposition into the tropical Atlantic Ocean is estimated at 98-153 Tg. The dust deposition shows large spatial and temporal (on seasonal and interannual scale) variability. The satellite observations also yield an estimate of annual mean dust loss frequency of 0.052 ~ 0.078 d-1, a useful diagnostic that makes it possible to disentangle the dust transport and removal processes from the dust emissions when identifying the major factors contributing to the uncertainties and biases in the model simulated dust deposition. In this study, we use the dataset along with in situ and remote sensing observations to assess how well NASA GEOS model performs in simulating trans-Atlantic dust transport and deposition. We found that the GEOS modeling of dust deposition falls within the range of satellite-based estimates. However, this reasonable agreement in dust deposition is a compensation of the model's underestimate of dust emissions and overestimate of dust removal efficiency. Further, the overestimate of dust removal efficiency results largely from the model's overestimate of rainfall rate. Our results provide insights into the model's deficiencies at process level, which could better guide model improvements.

Yu, Hongbin↗
NASA NTRS2001

Modeling Modern Methane Emissions from Natural Wetlands: Interannual Variations 1982-1993 - 2

A global run of a process-based methane model [Walter et al., this issue] is performed using high-frequency atmospheric forcing fields from ECMWF reanalyses of the period from 1982 to 1993. We calculate global annual methane emissions to be 260 Tg/ yr. 25% of methane emissions originate from wetlands north of 30 deg. N. Only 60% of the produced methane is emitted, while the rest is re-oxidized. A comparison of zonal integrals of simulated global wetland emissions and results obtained by an inverse modeling approach shows good agreement. In a test with data from two wetlands, the seasonality of simulated and observed methane emissions agrees well. The effects of sub-grid scale variations in model parameters and input data are examined. Modeled methane emissions show high regional, seasonal and interannual variability. Seasonal cycles of methane emissions are dominated by temperature in high latitude wetlands, and by changes in the water table in tropical wetlands. Sensitivity tests show that +/- 1 C changes in temperature lead to +/- 20 % changes in methane emissions from wetlands. Uniform changes of +/- 20% in precipitation alter methane emissions by about +/- 18%. Limitations in the model are analyzed. Simulated interannual variations in methane emissions from wetlands are compared to observed atmospheric growth rate anomalies. Our model simulation results suggest that contributions from other sources than wetlands and/or the sinks are more important in the tropics than north-of 30 deg. N. In higher northern latitudes, it seems that a large part, of the observed interannual variations can be explained by variations in wetland emissions. Our results also suggest that reduced wetland emissions played an important role in the observed negative methane growth rate anomaly in 1992.

Walter, Bernadette P.↗
NASA NTRS1987

On the next generation of reliability analysis tools

The current generation of reliability analysis tools concentrates on improving the efficiency of the description and solution of the fault-handling processes and providing a solution algorithm for the full system model. The tools have improved user efficiency in these areas to the extent that the problem of constructing the fault-occurrence model is now the major analysis bottleneck. For the next generation of reliability tools, it is proposed that techniques be developed to improve the efficiency of the fault-occurrence model generation and input. Further, the goal is to provide an environment permitting a user to provide a top-down design description of the system from which a Markov reliability model is automatically constructed. Thus, the user is relieved of the tedious and error-prone process of model construction, permitting an efficient exploration of the design space, and an independent validation of the system's operation is obtained. An additional benefit of automating the model construction process is the opportunity to reduce the specialized knowledge required. Hence, the user need only be an expert in the system he is analyzing; the expertise in reliability analysis techniques is supplied.

Babcock, Philip S., IV↗
NASA NTRS1972

Accretion process of the moon

Recent geochemical and geophysical data suggest that the initial temperature of the moon was strongly peaked toward the lunar surface. To explain such an initial temperature distribution, a simple model of accretion process of the moon is presented. The model assumes that the moon was formed from the accumulation of the solid particles or gases in the isolated, closed cloud. Two equations are derived to calculate the accretion rate and surface temperature of the accreting moon. Numerical calculations are made for a wide range of the parameters particle concentration and particle velocity in the cloud. A limited set of the parameters gives the initial temperature profiles as required by geochemical and geophysical data. These models of the proto-moon cloud indicate that the lunar outermost shell, about 400 km thick, was partially or completely molten just after the accretion of the moon and that the moon should have been formed in a period shorter than 1000 years. If the moon formed at a position nearer to the earth than its present one, the moon might have been formed in a period of less than one year.

Mizutani, H.↗
NASA NTRS2011

Microphysics in the Multi-Scale Modeling Systems with Unified Physics

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (l) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, the microphysics developments of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the heavy precipitation processes will be presented.

Tao, Wei-Kuo↗