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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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An algorithm is developed which effects unmanned docking of two space vehicles. The docking is controlled by an onboard computer and sensor. Control laws are expressed in two phase-plane trajectories such that modest computer load results. Simulation reveals time-fuel trades and effects of orbit, noise, etc.
A general simulation program (GSP) for state estimation of a nonlinear space vehicle flight navigation system is developed and used as a basis for evaluating the performance of a Space Tug navigation system. An explanation of the iterative guidance mode (IGM) guidance law, derivation of the dynamics, coordinate frames and state estimation routines are given in order to clarify the assumptions and approximations made. A number of simulation and analytical studies are used to demonstrate the operation of the Tug system. Included in the simulation studies are (1) initial offset vector parameter study; (2) propagation time vs accuracy; (3) measurement noise parametric study and (4) reduction in computational burden of an on-board implementable scheme. From the results of these studies, conclusions and recommendations concerning future areas of practical and theoretical work are presented.
Data from the ISEE 3 satellite reveal a class of impulsive solar electron events with no measurable increase in flux above 10-20 keV. The flux histories and angular distributions indicate that the electrons propagate through interplanetary space with little or no scattering. The smooth extension of the power-law energy spectra down to the limit of the observations at 2 keV indicates that the electrons are accelerated at altitudes greater than 0.5 solar radii in the solar corona. Although these events occur more frequently than any other type of impulsive solar particle event observed, no correlation is found with flares or flare-related activity. However, there is a close correlation with low-frequency type III radio bursts.
An integrated methodology is described for defining static shape control laws for large flexible structures. The techniques include modeling, identifying and estimating the control laws of distributed systems characterized in terms of infinite dimensional state and parameter spaces. The models are expressed as interconnected elliptic partial differential equations governing a range of static loads, with the capability of analyzing electromagnetic fields around antenna systems. A second-order analysis is carried out for statistical errors, and model parameters are determined by maximizing an appropriate defined likelihood functional which adjusts the model to observational data. The parameter estimates are derived from the conditional mean of the observational data, resulting in a least squares superposition of shape functions obtained from the structural model.
The applications of high-order, compact finite difference methods in shock calculations are discussed. The main concern is to define a local mean which will serve as a reference for introducing a local nonlinear limiting to control spurious numerical oscillations while maintaining the formal accuracy of the scheme. For scalar conservation laws, the resulting schemes can be proven total-variation stable in one space dimension and maximum-norm stable in multiple space dimensions. Numerical examples are shown to verify accuracy and stability of such schemes for problems containing shocks. These ideas can also be applied to other implicit schemes such as the continuous Galerkin finite element methods.
Consideration is given to cryogenic system design and optical technology; cryogenic instruments, sensors, and detectors; space cryogenic dewars and coolers; and cryogenic mechanisms, testing, and structures. Particular attention is given to mission optimization of the Space Infrared Telescope Facility (SIRTF), alternative aperture stop position designs for SIRTF, scaling laws for lightweight optics, evaluation of a far-infrared Ge:Ga multiplexed detector array, cryogenic limb array etalon spectrometer calibration, reliability growth of coolers for advanced optical systems and instruments, flight-qualified solid argon cooler for the BBXRT instrument, precision mechanisms for optical alignments at cryogenic temperatures, versatile cryogenic rotary-positioning systems, and optical alignments of the Cosmic Background Explorer observatory.
A new aerodynamic model has been developed and validated for a modified F/A-18A used for the Active Aeroelastic Wing (AAW) research program. The goal of the program was to demonstrate the advantages of using the inherent flexibility of an aircraft to enhance its performance. The research aircraft was an F/A-18A with wings modified to reduce stiffness and a new control system to increase control authority. There have been two flight phases. Data gathered from the first flight phase were used to create the new aerodynamic model. A maximum-likelihood output-error parameter estimation technique was used to obtain stability and control derivatives. The derivatives were incorporated into the National Aeronautics and Space Administration F-18 simulation, validated, and used to develop new AAW control laws. The second phase of flights was used to evaluate the handling qualities of the AAW aircraft and the control law design process, and to further test the accuracy of the new model. The flight test envelope covered Mach numbers between 0.85 and 1.30 and dynamic pressures from 600 to 1250 pound-force per square foot. The results presented in this report demonstrate that a thorough parameter identification analysis can be used to improve upon models that were developed using other means. This report describes the parameter estimation technique used, details the validation techniques, discusses differences between previously existing F/A-18 models, and presents results from the second phase of research flights.
A new aerodynamic model has been developed and validated for a modified F/A-18A airplane used for the Active Aeroelastic Wing (AAW) research program. The goal of the program was to demonstrate the advantages of using the inherent flexibility of an aircraft to enhance its performance. The research airplane was an F/A-18A with wings modified to reduce stiffness and a new control system to increase control authority. There have been two flight phases. Data gathered from the first flight phase were used to create the new aerodynamic model. A maximum-likelihood output-error parameter estimation technique was used to obtain stability and control derivatives. The derivatives were incorporated into the National Aeronautics and Space Administration F-18 simulation, validated, and used to develop new AAW control laws. The second phase of flights was used to evaluate the handling qualities of the AAW airplane and the control law design process, and to further test the accuracy of the new model. The flight test envelope covered Mach numbers between 0.85 and 1.30 and dynamic pressures from 600 to 1250 pound-force per square foot. The results presented in this report demonstrate that a thorough parameter identification analysis can be used to improve upon models that were developed using other means. This report describes the parameter estimation technique used, details the validation techniques, discusses differences between previously existing F/A-18 models, and presents results from the second phase of research flights.
Spectral double power laws are common in solar high-energy phenomena such as flares and interplanetary energetic-electron events. However, the physical mechanism that produces the changes in power-law index within a single spectrum is unclear. We developed a fully analytical method of forming single power-law spectra from sequential acceleration of particles orbiting inside and hopping between simulated large-scale magnetic islands formed by flare reconnection. Here, we extend the analytical method to the formation of double power-law spectra by assuming sequential acceleration in two successive regions with different acceleration and particle-transport rates. The resulting spectral distribution is continuous and smooth, with a flattening at low energies, two power-law regions at mid-energies, and a steep rollover at high energies. The model provides analytical expressions for the spectral indices, all energy breaks, and normalization constants as functions of just three physical parameters of each acceleration region: (1) the energy gain in each accelerator, (2) the percentage of particles transferred between accelerators, and (3) the number of accelerators visited. One of the most salient predictions of our work is that the spectral index at high (low) energies is determined by the parameters of the first "seed" (second) acceleration region. By constructing the spectral distribution through an iterative analytical process, the evolution toward a double power law is easily characterized and explained. Our analytical model provides tools to interpret space- and ground-based observations from RHESSI, FOXSI, NuSTAR, Solar Orbiter/STIX, EOVSA, and future high-energy missions.
Fast neutrons born from fission lose energy through scattering interactions in the process of slowing-down. As neutrons thermalize to the order of $k$ $b$ $T$ (where $k$ $b$ is the Boltzmann constant, and $T$ is the temperature of the medium), their de Broglie wavelength and energy approaches the order of inter-atomic spacing and quantized lattice vibrations, i.e., phonons. At thermal energies, the thermal scattering law (TSL), i.e., $S$($α, β$), captures crystal binding contributions to the total reaction rate, or cross section. This dimensionless material property describes the energy ($β$) and momentum ($α$) exchanges available in a medium. Currently, $S$($α, β$) is evaluated in the Full Law Analysis Scattering System Hub (FLASSH) code for discrete inputs and stored as ENDF/B File 7 for 0-phonon elastic (MT 2) and n-phonon inelastic (MT 4) processes. Further processing recasts $S$($α, β$) into cumulative distribution functions for sampling post-collision scattering kinematics. In practice, interpolation schemes are employed to access data between tabulated values. An improvement to this juncture of the nuclear data pipeline is supplying cross sections on-the-fly (OTF), as has been developed for the un-resolved resonance region to minimize non-physical interpolation errors. This capability may improve simulation accuracy for accident and transient analyses, where rapidly varying changes in temperature and pressure are difficult to predict beforehand. To do so, deep artificial neural networks (ANNs) can be employed which collapse non-linear, complex data into a lightweight dictionary of neural weights and biases. This has been successfully demonstrated for the hydrogen in light water $S$($α, β$) dataset in the form of a Neural Thermal Scattering (NeTS) module. In this work, the NeTS framework is extended to consider the impact of material-dependent dynamical features on optimal neural pre-processing and architecture design decisions, such as number of neurons per hidden layer, residual skip connections and neural depth. New NeTS modules for crystalline graphite and beryllium metal illuminate a novel correlation between dynamical nonlinearity and optimal neural parametrization when deploying $S$($α, β$) on-the-fly.
When a neutron born in fission thermalizes to the order of $k$ $B$ $T$, it’s de-Broglie wavelength and energy approach the order of inter-atomic spacing and elementary lattice oscillations, respectively. $S$($a,β,t$) or the scattering law, uuantify these temperature-dependent crystallographic contributions to total cross section (or reaction rate). In a Monte Carlo analysis, cumulative distribution functions (CDFs) of $S$($a,β,t$) are loaded to memory from “A Compact ENDF” (ACE) files for stochastically selecting thermal scattered neutron trajectories. In this work, novel neural thermal scattering (NeTS) modules for $S$($a,β,t$) CDFs are designed, trained, serialized and embedded within SERPENT using Python’s limited C-API for on-the-fly deployment of crystalline graphite $S$($a,β,t$) sampling. Torchscript tracing and Numba just-in-time (JIT) compilation streamline neural inference on NVIDIA GPUs with CUDA libraries. Demonstrations of bare sphere thermalization of fast and thermal sources show excellent agreement between embedded NeTS in SERPENT and MCNP. With an explicit model of the reactor, NeTS can predict on-the-fly changes in TREAT neutron spectra as a function of local temperature, which can serve to improve transient and accident predictions in a multiphysics analysis framework. This framework can be further extended to account on-the-fly for changes in local graphitic microstructure to scattering cross sections, and outlines a novel coupling of modern machine learning with state-of-the-art reactor physics methods.
The use of eigenspace assignment techniques to synthesize flight control systems for flexible aircraft is explored. Eigenspace assignment techniques are used to achieve a specified desired eigenspace, chosen to yield desirable system impulse residue magnitudes for selected system responses. Two of these are investigated. The first directly determines constant measurement feedback gains that will yield a close-loop system eigenspace close to a desired eigenspace. The second technique selects quadratic weighting matrices in a linear quadratic control synthesis that will asymptotically yield the close-loop achievable eigenspace. Finally, the possibility of using either of these techniques with state estimation is explored. Application of the methods to synthesize integrated flight-control and structural-mode-control laws for a large flexible aircraft is demonstrated and results discussed. Eigenspace selection criteria based on design goals are discussed, and for the study case it would appear that a desirable eigenspace can be obtained. In addition, the importance of state-space selection is noted along with problems with reduced-order measurement feedback. Since the full-state control laws may be implemented with dynamic compensation (state estimation), the use of reduced-order measurement feedback is less desirable. This is especially true since no change in the transient response from the pilot's input results if state estimation is used appropriately. The potential is also noted for high actuator bandwidth requirements if the linear quadratic synthesis approach is utilized. Even with the actuator pole location selected, a problem with unmodeled modes is noted due to high bandwidth. Some suggestions for future research include investigating how to choose an eigenspace that will achieve certain desired dynamics and stability robustness, determining how the choice of measurements effects synthesis results, and exploring how the phase relationships between desired eigenvector elements effects the synthesis results.
In this paper, we discuss effects of space-charge fields on imaging performance in a single-shot time-resolved transmission electron microscope. Using a Green’s function perturbation method, we derive analytical estimates for the effects of space-charge nonlinearity on the image formation process and the associated aberration coefficients. The dependence of these coefficients on the initial beam phase space distribution is elucidated. The results are validated by particle tracking simulations and provide fundamental scaling laws for the trade-off between temporal and spatial resolutions in single-shot time-resolved transmission electron microscopy.
The quasi-instantaneous emission from a relativistic surface endowed with a Lorentz factor that decreases away from the outflow symmetry axis can naturally explain the three phases observed by Swift X-Ray Telescope (XRT) in gamma-ray bursts (GRBs) and their afterglows (GRB tail, afterglow plateau, and postplateau) based only on the angular change of the relativistic Doppler boost across the outflow surface. We develop further the analytical formalism of the “larger-angle emission” model for the case of “n-exponential” outflows (where the Lorentz factor Γ dependence of the angular location θ is Γ ~ exp{-(θ/θ c ) n }), and compare its ability to account for the X-ray emission of XRT afterglows relative to that of “power-law” outflows (Γ ∼ θ −g ). Power-law outflows yield longer afterglow plateaus, followed by slower postplateau flux decays than n-exponential outflows, features which may be used in identifying which type of angular structure is at work in a given afterglow. Identifying the Γ(θ) angular structure that accommodates XRT light curves is slightly complicated by the fact that the afterglow X-ray light curve is also determined by how two characteristics of the comoving-frame emission spectrum (peak energy $E'_p$ and peak intensity $i'_p$) change with the angular location or, equivalently, with the Lorentz factor. Here, we assume power-law Γ dependences of those spectral characteristics and find that, unlike power-law outflows, n-exponential outflows cannot account for plateaus with a temporal dynamical range larger than 100 (2 dex in logarithmic space). To capture all the information contained in XRT afterglow measurements (0.3–10 keV unabsorbed flux and effective spectral slope), we calculate 0.3 and 10 keV light curves using a broken-power-law emission spectrum of peak energy and low- and high-energy slopes that are derived from the effective slope measured by XRT. This economical peak energy determination is found to be consistent with the results of more expensive spectral fits. The angular distributions of the Lorentz factor, comoving frame peak energy, and peak intensity (Γ(θ), $E'_p$(θ), $i'_p$(θ)) constrain the (yet-to-be determined) convolution of various features of the production of relativistic jets by solar-mass black holes and of their propagation through the progenitor/circumburst medium, while the $E'_p$(Γ) and $i'_p$(Γ) dependences may constrain the GRB dissipation mechanism and the GRB emission process.
Deep operator network (DeepONet) has shown significant promise as surrogate models for systems governed by partial differential equations (PDEs), enabling accurate mappings between infinite-dimensional function spaces. However, when applied to systems with high-dimensional input-output mappings arising from large numbers of spatial and temporal collocation points, these models often require heavily overparameterized networks, leading to long training times. Latent DeepONet addresses some of these challenges by introducing a two-step approach: first learning a reduced latent space using a separate model, followed by operator learning within this latent space. While efficient, this method is inherently data-driven and lacks mechanisms for incorporating physical laws, limiting its robustness and generalizability in data-scarce settings. Here, in this work, we propose PI-Latent-NO, a physics-informed latent neural operator framework that integrates governing physics directly into the learning process. Our architecture features two coupled DeepONets trained end-to-end: a Latent-DeepONet that learns a low-dimensional representation of the solution, and a Reconstruction-DeepONet that maps this latent representation back to the physical space. By embedding PDE constraints into the training via automatic differentiation, our method eliminates the need for labeled training data and ensures physics-consistent predictions. The proposed framework is both memory and compute-efficient, exhibiting near-constant scaling with problem size and demonstrating significant speedups over traditional physics-informed operator models. We validate our approach on a range of parametric PDEs, showcasing its accuracy, scalability, and suitability for real-time prediction in complex physical systems.
Crystals grown from liquid solutions have important industrial applications. Zeolites, for instance, a class of crystalline aluminosilicate materials, form the backbone of the chemical process industry worldwide, as they are used as adsorbents and catalysts. Many of the phenomena associated with crystal growth processes are not well understood due to complex microscopic and macroscopic interactions. Microgravity could help elucidate these phenomena and allow the control of defect locations, concentration, as well as size of crystals. Microgravity in an orbiting spacecraft could help isolate the possible effects of natural convection (which affects defect formation) and minimize sedimentation. In addition, crystals will stay essentially suspended in the nutrient pool under a diffusion-limited growth condition. This is expected to promote larger crystals by allowing a longer residence time in a high-concentration nutrient field. Among other factors, the crystal size distribution depends on the nucleation rate and crystallization. These two are also related to the "gel" polymerization/depolymerization rate. Macroscopic bulk mass and flow transport and especially gravity, force the crystals down to the bottom of the reactor, thus forming a sedimentation layer. In this layer, the growth rate of the crystals slows down as crystals compete for a limited amount of nutrients. The macroscopic transport phenomena under certain conditions can, however, enhance the nutrient supply and therefore, accelerate crystal growth. Several zeolite experiments have been performed in space with mixed results. The results from our laboratory have indicated an enhancement in size of 30 to 70 percent compared to the best ground based controls, and a reduction of lattice defects in many of the space grown crystals. Such experiments are difficult to interpret, and cannot be easily used to derive empirical or other laws since many physical parameters are simultaneously involved in the process. At the same time, however, there is increased urgency to develop such an understanding in order to more accurately quantify the process. In order to better understand the results obtained from our prior space experiments, and design future experiments, a detailed fluid dynamic model simulating the crystal growth mechanism is required. This will not only add to the fundamental knowledge on the crystallization of zeolites, but also be useful in predicting the limits of size and growth of these important industrial materials. Our objective is to develop macro/microscopic theoretical and computational models to study the effect of transport phenomena in the growth of crystals grown in solutions. Our effort has concentrated so far in the development of separate macroscopic and microscopic models. The major highlights of our accomplishments are described.
Analysis and correlation of thermodynamic cycles for spacecraft power systems using maximum cycle irreversibility concept and second law of thermodynamics
Multivariable control theory is applied to the design of a hierarchial attitude control system for the CARD space vehicle. The system selected uses reaction control jets (RCJ) and control moment gyros (CMG). The RCJ system uses linear signal mixing and a no-fire region similar to that used on the Skylab program; the y-axis and z-axis systems which are coupled use a sum and difference feedback scheme. The CMG system uses the optimum steering law and the same feedback signals as the RCJ system. When both systems are active the design is such that the torques from each system are never in opposition. A state-space analysis was made of the CMG system to determine the general structure of the input matrices (steering law) and feedback matrices that will decouple the axes. It is shown that the optimum steering law and proportional-plus-rate feedback are special cases. A derivation of the disturbing torques on the space vehicle due to the motion of the on-board television camera is presented. A procedure for computing an upper bound on these torques (given the system parameters) is included.