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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 163 records · Page 9

Matlab implementation of a novel semi-structured kinetic model for methanotroph-photoautotroph cocultures

This paper presents the MatLab implementation details of a novel semi-structured kinetic model for methanotroph-photoautotroph cocultures. This includes the parameterization of the modeling equations, and the initialization of the simulation based on experimental conditions. More importantly, it provides details on how the differential equations governing mass balances in both gas and liquid phases are integrated together to simulate the system dynamics over time. The semi-structured kinetic model for methanotroph-photoautotroph coculture is validated using a wide range of experimental conditions. The model: Accurate predicts both the coculture growth in liquid phase and the gas composition changes in head space over time; Explicitly models the exchange of in situ produced O 2 and CO 2 within the coculture; Considers the self-shading effect on the growth of photoautotroph.

42 ENGINEERING↗

SSME LOX post flow analysis/fluid structure interaction. Volume 1: Flow analysis

The realization of measures to improve the performance of the Space Shuttle is, to a large extent, dependent on an improved understanding of the fluid flow phenomena occurring in the main engine. The overall arrangement of the primary components of the Space Shuttle Main Engine (SSME) are presented. The impingement of the hot gases from the transfer ducts onto the LOX posts causes them to be subjected to severe thermal and gas dynamic loads, which in the past have resulted in the occasional breakage of some elements of the outer row of posts during test firings of the engine, particularly at higher power levels. Large velocities in the gap between the LOX posts may also be a contributing factor in causing dynamic stability problems. The deforming structural response of the posts to the pressure loading may likely affect the gas flowfield by producing a moving flowfield boundary, thereby creating a dynamically coupled unsteady fluid-structure system. The objective was to investigate the three-dimensional, turbulent flow around a simplified SSME LOX post array using an existing Reynolds averaged Navier-Stokes flow solver and a suitable turbulence model to parameterize the turbulent shear stresses. Numerical computations were performed to analyze the effect on the flowfield of varying the spacing between the LOX posts, which were modeled as rigid, three-dimensional circular cylinders. The methodology used in the computations is described. Results are presented.

Burke, Roger W.↗

Modeling the Joint Effects of Vegetation Characteristics and Soil Properties on Ecosystem Dynamics in a Panama Tropical Forest

Abstract In tropical forests, both vegetation characteristics and soil properties are important not only for controlling energy, water, and gas exchanges directly but also determining the competition among species, successional dynamics, forest structure and composition. However, the joint effects of the two factors have received limited attention in Earth system model development. Here we use a vegetation demographic model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM‐FATES, to explore how plant traits and soil properties affect tropical forest growth and composition concurrently. A large ensemble of simulations with perturbed vegetation and soil hydrological parameters is conducted at the Barro Colorado Island, Panama. The simulations are compared against observed carbon, energy, and water fluxes. We find that soil hydrological parameters, particularly the scaling exponent of the soil retention curve ( B sw ), play crucial roles in controlling forest diversity, with higher B sw values (>7) favoring late successional species in competition, and lower B sw values (1 ∼ 7) promoting the coexistence of early and late successional plants. Considering the additional impact of soil properties resolves a systematic bias of FATES in simulating sensible/latent heat partitioning with repercussion on water budget and plant coexistence. A greater fraction of deeper tree roots can help maintain the dry‐season soil moisture and plant gas exchange. As soil properties are as important as vegetation parameters in predicting tropical forest dynamics, more efforts are needed to improve parameterizations of soil functions and belowground processes and their interactions with aboveground vegetation dynamics.

54 ENVIRONMENTAL SCIENCES↗

Tracking Aerosol Convection Interactions Experiment (TRACER) Field Campaign Report

Convective clouds serve a critical role in the Earth’s energy and water cycles through their transport of heat, moisture, momentum, and chemical species through the troposphere driving the global circulation (e.g., Hartmann et al. 1984, Del Genio et al. 2012, Su et al. 2014). On more local scales, convective clouds impact the atmospheric heating profile through diabatic heating effects, removal of water from the atmospheric column through precipitation, and conditioning of the local environment impacting further development of clouds (e.g., Sullivan and Voigt 2021). These critical roles underscore the importance of realistic representation of convective processes across scales of models from large-eddy simulation (LES), to convection-permitting models (CPM; e.g., Kendon et al. 2020, Marinescu et al. 2021), to numerical weather prediction (NWP) models used for operational weather forecasting, to Earth system models used to predict climate sensitivity (Sanderson et al. 2011, Sherwood et al. 2014, Tomassini et al. 2014, Zhao et al. 2016, Cronin et al. 2017). A key component of improving model representation of convective clouds is better quantification and parameterization of updraft microphysics and dynamics, including their interactions with the surrounding environment and storm organization (Bony et al. 2015, Hagos and Houze 2016, Donner et al. 2016, Morrison et al. 2020). Aerosol is an important environmental factor that could affect convective clouds and precipitation since cloud droplet and ice formation processes are initiated by it. Andrae et al. (2004) hypothesized that aerosols associated with increased biomass burning particles acting as cloud condensation nuclei (CCN) result in smaller and more monodisperse cloud droplets leading to suppression of warm rain formation, ultimately leading to more cloud water being lofted above the freezing level based on observations in the Amazon region. The subsequent increase in latent heat release increases the buoyancy of rising convective parcels invigorating the deep convection. This work was followed by a description of the theoretical basis for this “cold-phase invigoration” by Rosenfeld et al. (2008), who argued that it could have a significant effect for deep convective clouds with warm cloud-bases. Several modeling studies (e.g., Khain et al. 2005, 2009, van den Heever et al. 2006, Fan et al. 2007, 2009, 2012, Lee et al. 2008, Storer et al. 2010, Lebo et al. 2012, Storer and van den Heever 2013, Chen et al. 2020, Dagan et al. 2022) have investigated these aerosol-convection interactions and the environmental factors that influence their relative importance and magnitude. More recently, several studies have indicated that “warm-phase invigoration”, the enhancement of convection through condensational heating, also appears to play a role in enhancing both shallow cumuli (Seiki and Nakajima 2014, Saleeby et al 2015) and deeper tropical convection (Lebo and Seinfeld 2011, Khain et al. 2012, Sheffield et al 2015, Fan et al. 2018, Igel and van den Heever 2021), as well as Houston thunderstorms (Fan et al. 2007, 2020). However, still other studies have provided additional evidence of systematic biases in simulated convective outflow ice size distribution properties, which are consistent with a lack of poorly understood secondary ice production within convective updrafts (e.g., Fridlind et al. 2017). To help address these critical gaps in our understanding of cloud processes, aerosol processes and aerosol-cloud interactions, the Tracking Aerosol Convection Interactions Experiment was designed building upon efforts by the Aerosol, Cloud, Precipitation and Climate (ACPC) Initiative (http://acpcintiative.org/), a joint effort of the International Geosphere-Biosphere Programme (IGBP) and the World Climate Research Program (WCRP) that focused on resolving uncertainties in the interactions between aerosol and clouds towards better understanding the role that these interactions play in the climate system. The TRACER campaign was motivated by recommendations from a number of pilot studies undertaken by ACPC (van den Heever et al. 2017, Fridlind et al. 2019, Hu et al. 2019, Fan et al. 2020, Marinescu et al. 2021, Hernandez-Deckers et al. 2022) that pointed towards the southeastern Texas region as a locale where aerosol-convection interactions could be studied owing to the copious occurrence of isolated convection during the summer months accompanied by diverse and significant sources of aerosols from both anthropogenic and natural sources. The TRACER campaign began on 01 October 2021 and extended through 30 September 2022 with an intensive operational period (IOP) during June-September 2022. Three main sites (Table 1) were managed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility.

54 ENVIRONMENTAL SCIENCES↗

Data-driven models of nonautonomous systems

Nonautonomous dynamical systems are characterized by time-dependent inputs, which complicates the discovery of predictive models describing the spatiotemporal evolution of the state variables of quantities of interest from their temporal snapshots. When dynamic mode decomposition (DMD) is used to infer a linear model, this difficulty manifests itself in the need to approximate the time-dependent Koopman operators. Our approach is to approximate the original nonautonomous system with a modified system derived via a local parameterization of the time-dependent inputs. The modified system comprises a sequence of local parametric systems, which are subsequently approximated by a parametric surrogate model using the DRIPS (dimension reduction and interpolation in parameter space) framework. The offline step of DRIPS relies on DMD to build a linear surrogate model, endowed with reduced-order bases for the observables mapped from training data. The online step interpolates on suitable manifolds to construct a sequence of iterative parametric surrogate models; the target/test parameter points on these manifolds are specified by a local parameterization of the test time-dependent inputs. Here, we use numerical experimentation to demonstrate the robustness of our method and compare its performance with that of deep neural networks.

97 MATHEMATICS AND COMPUTING↗

X-38 Application of Dynamic Inversion Flight Control

This paper summarizes the application of a nonlinear dynamic inversion (DI) flight control system (FCS) to an autonomous flight test vehicle in NASA's X-38 Project, a predecessor to the International Space Station (ISS) Crew Return Vehicle (CRV). Honeywell's Multi-Application Control-H (MACH) is a parameterized FCS design architecture including both model-based DI rate-compensation and classical P+I command-tracking. MACH was adopted by X-38 in order to shorten the design cycle time for different vehicle shapes and flight envelopes and evolving aerodynamic databases. Specific design issues and analysis results are presented for the application of MACH to the 3rd free flight (FF3) of X-38 Vehicle 132 (V132). This B-52 drop test, occurring on March 30, 2000, represents the first flight test of MACH and one of the first few known applications of DI in the primary FCS of an autonomous flight test vehicle.

Wacker, Roger↗

The photometric properties of brightest cluster galaxies. II - SIT and CCD surface photometry

Surface photometry of the first-ranked galaxy in 108 Abell clusters is presented. Galaxy structure, as parameterized by simple Hubble law models, is found to correlate with galaxy absolute magnitude and cluster structure. All the structure data support the dynamical friction evolution model. Twenty-eight percent of the galaxies have multiple component nuclei; the short lifetimes of such systems provide the best available evidence that ongoing evolution actually occurs. Average magnitude and structure evolution rates are derived from the data.

Hoessel, J. G.↗

New dynamic variables for rotating spacecraft

This paper introduces two new seven-parameter representations for spacecraft attitude dynamics modeling. The seven parameters are the three components of the total system angular momentum in the spacecraft body frame; the three components of the angular momentum in the inertial reference frame; and an angle variable. These obey a single constraint as do parameterizations that include a quaternion; in this case the constraint is the equality of the sum of the squares of the angular momentum components in the two frames. The two representations are nonsingular if the system angular momentum is non-zero and obeys certain orientation constraints. The new parameterizations of the attitude matrix, the equations of motion, and the relation of the solution of these equations to Euler angles for torque-free motion are developed and analyzed. The superiority of the new parameterizations for numerical integration is shown in a specific example.

Markley, F. Landis↗

Data-Driven Learning of Nonautonomous Systems

In this work, we present a numerical framework for recovering unknown nonautonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the nonautonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper model, for example, polynomial regression, in the pieces determined by the time instances. This transforms the original system into a piecewise parametric system that is locally time invariant. We then design a deep neural network structure to learn the local models. Once the network model is constructed, it can be iteratively used over time to conduct global system prediction. We provide theoretical analysis of our algorithm and present a number of numerical examples to demonstrate the effectiveness of the method.

97 MATHEMATICS AND COMPUTING↗

Alternatives for jet engine control

Research centered on basic topics in the modeling and feedback control of nonlinear dynamical systems is reported. Of special interest were the following topics: (1) the role of series descriptions, especially insofar as they relate to questions of scheduling, in the control of gas turbine engines; (2) the use of algebraic tensor theory as a technique for parameterizing such descriptions; (3) the relationship between tensor methodology and other parts of the nonlinear literature; (4) the improvement of interactive methods for parameter selection within a tensor viewpoint; and (5) study of feedback gain representation as a counterpart to these modeling and parameterization ideas.

Sain, M. K.↗

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↗

T-MATS Toolbox for the Modeling and Analysis of Thermodynamic Systems

The Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) is a MATLABSimulink (The MathWorks Inc.) plug-in for creating and simulating thermodynamic systems and controls. The package contains generic parameterized components that can be combined with a variable input iterative solver and optimization algorithm to create complex system models, such as gas turbines.

system modeling↗

X-ray illuminated stellar winds - Ionization effects in the radiative driving of stellar winds in massive X-ray binary systems

This paper presents calculations of the effect of changing X-ray ionization conditions on the radiative force experienced by the stellar wind material in a massive X-ray binary system. The radiative line force from the radiation field of the primary is parameterized in terms of the Castor et al. (1975) force multiplier. The results show that the line force decreases sharply, but in a nonlinear way, with increasing X-ray ionization. The dynamic consequences of this effect are discussed.

Stevens, Ian R.↗

Development of Renormalization Group Analysis of Turbulence

The renormalization group (RG) procedure for nonlinear, dissipative systems is now quite standard, and its applications to the problem of hydrodynamic turbulence are becoming well known. In summary, the RG method isolates self similar behavior and provides a systematic procedure to describe scale invariant dynamics in terms of large scale variables only. The parameterization of the small scales in a self consistent manner has important implications for sub-grid modeling. This paper develops the homogeneous, isotropic turbulence and addresses the meaning and consequence of epsilon-expansion. The theory is then extended to include a weak mean flow and application of the RG method to a sequence of models is shown to converge to the Navier-Stokes equations.

L M Smith↗

Investigating Uncertainty in Predicting Carbon Dynamics in North American Biomes: Putting Support-Effect Bias in Perspective

A fundamental strategy in NASA's Earth Observing System's (EOS) monitoring of vegetation and its contribution to the global carbon cycle is to rely on deterministic, process-based ecosystem models to make predictions of carbon flux over large regions. These models are parameterized (that is, the input variables are derived) using remotely sensed images such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS), ground measurements and interpolated maps. Since early applications of these models, investigators have noted that results depend partly on the spatial support of the input variables. In general, the larger the support of the input data, the greater the chance that the effects of important components of the ecosystem will be averaged out. A review of previous work shows that using large supports can cause either positive or negative bias in carbon flux predictions. To put the magnitude and direction of these biases in perspective, we must quantify the range of uncertainty on our best measurements of carbon-related variables made on equivalent areas. In other words, support-effect bias should be placed in the context of prediction uncertainty from other sources. If the range of uncertainty at the smallest support is less than the support-effect bias, more research emphasis should probably be placed on support sizes that are intermediate between those of field measurements and MODIS. If the uncertainty range at the smallest support is larger than the support-effect bias, the accuracy of MODIS-based predictions will be difficult to quantify and more emphasis should be placed on field-scale characterization and sampling. This talk will describe methods to address these issues using a field measurement campaign in North America and "upscaling" using geostatistical estimation and simulation.

Dungan, Jennifer L.↗

Data-Driven Learning of Non-Autonomous Systems

We present a numerical framework for recovering unknown non-autonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the non-autonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper model, for example, polynomial regression, in the pieces determined by the time instances. This transforms the original system into a piecewise parametric system that is locally time invariant. We then design a deep neural network structure to learn the local models. Once the network model is constructed, it can be iteratively used over time to conduct global system prediction. We provide theoretical analysis of our algorithm and present a number of numerical examples to demonstrate the effectiveness of the method.

97 MATHEMATICS AND COMPUTING↗

Determination of Quantum Chemistry Based Force Fields for Molecular Dynamics Simulations of Aromatic Polymers

Ab initio quantum chemistry calculations for model molecules can be used to parameterize force fields for molecular dynamics simulations of polymers. Emphasis in our research group is on using quantum chemistry-based force fields for molecular dynamics simulations of organic polymers in the melt and glassy states, but the methodology is applicable to simulations of small molecules, multicomponent systems and solutions. Special attention is paid to deriving reliable descriptions of the non-bonded and electrostatic interactions. Several procedures have been developed for deriving and calibrating these parameters. Our force fields for aromatic polyimide simulations will be described. In this application, the intermolecular interactions are the critical factor in determining many properties of the polymer (including its color).

Jaffe, Richard↗

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

NASA Discipline Regulatory Physiology↗