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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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Subsonic and transonic forced oscillation tests of a 0.0165-scale model of a modified 089B space shuttle orbiter were made in the Langley 8-foot transonic pressure tunnel. The period and damping of the basic unaugmented vehicle were calculated along the entry trajectory utilizing these measured dynamic data. Some parameter analyses were made with the measured dynamic derivatives.
The effects of operating parameters of a dynamic apparatus used to study oxidation and thermal fatigue of gas turbine materials were studied. IN-100, TD-NiCr, and WI-52 were tested at a maximum temperature of 1,090 deg C. Heating time per cycle was varied from 1/20 hr to 10 hr. Minimum temperatures between heating cycles were room temperature, 430 deg, and 650 deg C. Cooling air velocities were zero, Mach 0.7, and Mach 1. Increasing the number of cycles for a given time at temperature increased weight loss. Thermal fatigue was related to number of cycles more than to time at temperature.
Conceptual two-stage software scheme serves as prototype of multistage scheme for digital estimation of phase, frequency, and rate of change of frequency ("Doppler rate") of possibly phase-modulated received sinusoidal signal in communication system in which transmitter and/or receiver traveling rapidly, accelerating, and/or jerking severely. Each additional stage of multistage scheme provides increasingly refined estimate of frequency and phase of signal. Conceived for use in estimating parameters of signals from spacecraft and high dynamic GPS signal parameters, also applicable, to terrestrial stationary/mobile (e.g., cellular radio) and land-mobile/satellite communication systems.
Here, this paper presents a variational multiscale (VMS) based finite element method where the stabilization parameter is computed dynamically. The current dynamic procedure takes in a general structure/form of the stabilization parameter with unknown coefficients and computes them dynamically in a local fashion resulting in a dynamic VMS-based finite element method. Thus, a static stabilization parameter with pre-defined coefficients is not needed. A variational Germano identity (VGI) based local procedure suitable for unstructured meshes is developed to perform the dynamic computation in a local fashion. The local VGI based procedure is applied for each interior vertex in the mesh and unknown coefficients are first determined locally at each vertex, and subsequently, for each element a maximum value is taken over the vertices of the element. To make the current procedure practical, a coarser secondary solution is constructed from the primary coarse-scale solution, which is done locally over a patch of elements around each interior vertex. Further, averaging steps are employed to make the local dynamic procedure robust. Currently, the new dynamic VMS formulation is applied to steady problems governed by the advection-diffusion and incompressible Navier-Stokes equations in both 1D and 2D to demonstrate its efficacy and effectiveness.
The combination of modern advances in electric propulsion, fly-by-wire controls, autonomy, and increasing demand for short range air taxi operations, is currently producing an outburst of vehicle designs more diverse than ever before. Advanced software tools are needed to support the rapid and safe introduction of any design into the airspace, including the safety of the deployed flight control system and vehicle handling qualities. This paper presents a methodology for building air taxi vehicle models with distributed electric propulsion for use in analyzing flight control system safety at the conceptual design level.The approach builds on existing software tools capable of outputting aeromechanics-based linear perturbation models for Vertical Take-off and Landing vehicles with multiple rotors. Rotor torque inputs are then converted into equivalent voltage control inputs, and the linear state and input dynamics matrices are modified to include electric motor dynamics with common parameters for direct-current electric motors. The linear perturbation dynamics are then stitched across multiple operating points into a quasi-Linear Parameter Varying model that covers the full flight envelope. A Model Predictive Controller is developed for use with the full envelope model, and a tradeoff analysis between handling quality and motor requirements is demonstrated using a six passenger NASA air taxi reference design.
In this manuscript, we develop multiple machine learning (ML) models to accelerate a scheme for parameterizing site-based models of exciton dynamics from all-atom configurations of condensed phase sexithiophene systems. This scheme encodes the details of a system’s specific molecular morphology in the correlated distributions of model parameters through the analysis of many single-molecule excited-state electronic-structure calculations. These calculations yield excitation energies for each molecule in the system and the network of pair-wise intermolecular electronic couplings. In this work, we demonstrate that the excitation energies can be accurately predicted using a kernel ridge regression (KRR) model with Coulomb matrix featurization. We present two ML models for predicting intermolecular couplings. The first one utilizes a deep neural network and bi-molecular featurization to predict the coupling directly, which we find to perform poorly. The second one utilizes a KRR model to predict unimolecular transition densities, which can subsequently be analyzed to compute the coupling. We find that the latter approach performs excellently, indicating that an effective, generalizable strategy for predicting simple bimolecular properties is through the indirect application of ML to predict higher-order unimolecular properties. Such an approach necessitates a much smaller feature space and can incorporate the insight of well-established molecular physics.
A globally adaptive image compression structure has been developed for use in the tactical environment of a remotely piloted vehicle. The control structure is based on an image compression algorithm RM2 but can be easily extended to standard algorithms. It is shown that this structure provides an operator with the flexibility to dynamically maximize the usefulness of a limited and changing bit rate and should significantly improve overall system performance in tactical environments.
Previous papers have described the modification of the Earth Radiation Budget Satellite (ERBS) Attitude Determination System (ADS) to overcome the impact of on board gyro degradation and failure on attitude ground support of the mission. Two approaches were taken: implementing a Kalman filter in place of the batch-least-squares attitude estimator to account for the propagation error produced by high-noise gyro data, and modeling the ERBS attitude dynamics to restore rate information in the case of gyro failure. Both of these methods had shortcomings. In practice, the filter attitude diverged without complete sensor observability, and accurate dynamics modeling required knowledge of disturbance torque parameters that had to be determined manually. These difficulties have been overcome by improved tuning of the filter and by incorporating dynamics parameter estimation into the ERBS ADS.
Plant functional traits determine vegetation responses to environmental variation, but variation in trait values is large, even within a single site. Likewise, uncertainty in how these traits map to Earth system feedbacks is large. We use a vegetation demographic model (VDM), the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to explore parameter sensitivity of model predictions, and comparison to observations, at a tropical forest site: Barro Colorado Island in Panama. We define a single 12-dimensional distribution of plant trait variation, derived primarily from observations in Panama, and define plant functional types (PFTs) as random draws from this distribution. We compare several model ensembles, where individual ensemble members vary only in the plant traits that define PFTs, and separate ensembles differ from each other based on either model structural assumptions or non-trait, ecosystem-level parameters, which include (a) the number of competing PFTs present in any simulation and (b) parameters that govern disturbance and height-based light competition. While single-PFT simulations are roughly consistent with observations of productivity at Barro Colorado Island, increasing the number of competing PFTs strongly shifts model predictions towards higher productivity and biomass forests. Different ecosystem variables show greater sensitivity than others to the number of competing PFTs, with the predictions that are most dominated by large trees, such as biomass, being the most sensitive. Changing disturbance and height-sorting parameters, i.e., the rules of competitive trait filtering, shifts regimes of dominance or coexistence between early- and late-successional PFTs in the model. Increases to the extent or severity of disturbance, or to the degree of determinism in height-based light competition, all act to shift the community towards early-successional PFTs. In turn, these shifts in competitive outcomes alter predictions of ecosystem states and fluxes, with more early-successional-dominated forests having lower biomass. It is thus crucial to differentiate between plant traits, which are under competitive pressure in VDMs, from those model parameters that are not and to better understand the relationships between these two types of model parameters to quantify sources of uncertainty in VDMs.
Maximum likelihood estimation (MLE) is a method used to calculate the parameters of a dynamic system. It can be applied to a large class of problems and has good statistical properties. The main disadvantage of the MLE method is the amount of computation required. This paper describes how the computation time can be reduced significantly by using an array processor. The estimation of the parameters of a dynamic model of the Space Station is used as an example to evaluate the method.
A method is developed for estimating model parameters, such as nondimensional stability and control derivatives, by fitting transfer function or state-space models to empirical frequency response data using the output-error approach. The frequency response data were computed using Fourier transforms of measured input and output data. The control surfaces were excited with periodic multisine inputs which facilitated time-ecient estimation of multiple-input multiple-output frequency responses. The method was applied to lateral data from a nonlinear flight dynamics simulation of the F-16 aircraft, and to longitudinal data from multiple repeated flight test maneuvers of the NASA T-2 subscale aircraft. Results using simulation data showed the frequency response method compared well to other standard methods for parameter estimation. In addition to including all the available inputs, outputs, and harmonic frequencies in the estimation, relatively small subsets of the measured data could also be used to focus on identifying specific parts of the model. Results from flight test data showed that parameter estimates and uncertainties determined from repeated maneuvers were accurate and in statistical agreement with each other.
A large class of scientific computational problems can be characterized as a sequence of steps where a significant amount of computation occurs each step, but the work performed at each step is not necessarily identical. Two good examples of this type of computation are: (1) regridding methods which change the problem discretization during the course of the computation, and (2) methods for solving sparse triangular systems of linear equations. Recent work has investigated a means of mapping such computations onto parallel processors; the method defines a family of static mappings with differing degrees of importance placed on the conflicting goals of good load balance and low communication/synchronization overhead. The performance tradeoffs are controllable by adjusting the parameters of the mapping method. To achieve good performance it may be necessary to dynamically change these parameters at run-time, but such changes can impose additional costs. If the computation's behavior can be determined prior to its execution, it can be possible to construct an optimal parameter schedule using a low-order-polynomial-time dynamic programming algorithm. Since the latter can be expensive, the performance is studied of the effect of a linear-time scheduling heuristic on one of the model problems, and it is shown to be effective and nearly optimal.
Temperature is a complicated thermodynamic parameter to measure in dynamic compression experiments. Optical pyrometry is a general-purpose “work-horse” technique for measuring temperature from a radiant surface on these experimental platforms. The optical pyrometry channels are commonly held to the visible or Near-Infrared spectrum, which provides high fidelity temperature measurement for shock temperature above ∼1200–1500 K. However, low temperature (T < 1200 K) dynamic material experiments, including low pressure or quasi-isentropic studies, as well as experiments with complex thermodynamic paths, require Mid-Infrared (Mid-IR) for high fidelity measurements. This article outlines the design, testing, and characterization of a novel Mid-IR pyrometer system that can be configured between 2.5 and 5.0 µm, suitable for lower temperature measurements and for increasing the fidelity and precision of higher temperature measurements. Experimental validation was done on two separate gas gun platforms, with two separate impact velocities, achieving temperatures between 450 and 1100 K.
Dynamic system absolute stability and sensitivity to parameter variations in linear part of system calculated by graphical and analytical techniques
Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calculations than ab initio methods but with compromised accuracy. By replacing these static parameters with machine-learned dynamic values inferred from the local environment, we greatly improve the accuracy of the SEQM methods. Trained on molecular energies and atomic forces, these dynamically generated Hamiltonian parameters show a strong correlation with atomic hybridization and bonding. Trained with only about 60,000 small organic molecular conformers, the resulting model retains interpretability, extensibility, and transferability when testing on much larger chemical systems and predicting various molecular properties. Overall, this work demonstrates the virtues of incorporating physics-based descriptions with ML to develop models that are simultaneously accurate, transferable, and interpretable.
Gas phase effects on slosh dynamics were quantified using computational fluid dynamics (CFD) simulation for a range of propellant and ullage gas combinations. Historical slosh modeling using potential flow solutions typically neglects gas phase effects. Regardless, the results have been shown to compare well with slosh ground tests typically performed with water and air at standard temperature and pressure. CFD analysis reveals that as the liquid-to-gas density ratio decreases, slosh dynamics change due to the relative increase in gas inertia and thus influence on liquid motion. The result is a profound impact on slosh dynamics over certain parameter spaces particularly for liquid hydrogen. Gas phase effects on slosh dynamics should be considered in slosh models especially for liquid hydrogen propellant tanks.
Gas phase effects on slosh dynamics were quantified using computational fluid dynamics (CFD) simulation for a range of propellant and gas combinations. Historical slosh modeling using potential flow solutions typically neglects gas phase effects. Regardless, the results have been shown to compare well with slosh ground tests typically performed with water and air at standard temperature and pressure. CFD analysis reveals that as the liquid-to-gas density ratio decreases, slosh dynamics change due to the relative increase in gas inertia and thus influence on liquid motion. The result is a profound impact on slosh dynamics over certain parameter spaces particularly for liquid hydrogen. Gas phase effects on slosh dynamics should be considered in slosh models especially for liquid hydrogen propellant tanks.
Gas phase effects on slosh dynamics were quantified using computational fluid dynamics (CFD) simulation for a range of propellant and gas combinations. Historical slosh modeling using potential flow solutions typically neglects gas phase effects. Regardless, the results have been shown to compare well with slosh ground tests typically performed with water and air at standard temperature and pressure. CFD analysis reveals that as the liquid-to-gas density ratio decreases, slosh dynamics change due to the relative increase in gas inertia and thus influence on liquid motion. The result is a profound impact on slosh dynamics over certain parameter spaces particularly for liquid hydrogen. Gas phase effects on slosh dynamics should be considered in slosh models especially for liquid hydrogen propellant tanks.