SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
SF-23-067 BoxKit is a library that provides building blocks to parallelize and scale data science, high performance computing, and machine learning applications for block-structured datasets. Spatial data from simulations and experiments can be accessed and managed using tools available in this library when working with more data analysis oriented packages like SciKit (https://github.com/scikit-learn/scikit-learn) and FlowNet (https://github.com/NVIDIA/flownet2-pytorch)
A pulsed metallic-plasma generator is described which utilizes a vacuum arc as the plasma source. The arc is initiated on the surface of a consumable cathode which can be any electrically conductive material. Ignition is accomplished by using a current pulse to vaporize a portion of a conductive film on the surface of an insulator separating the cathode from the ignition electrode. The film is regenerated during the ensuing arc. Over 100 million ignition cycles have been accomplished by using four 0.125-in. diameter zinc cathodes operating in parallel and high-density aluminum-oxide insulators. Among the applications being investigated for the generator are metal deposition, vacuum pumping, electric propulsion, and high-power dc arc interruption.
Transformation of statistics from a dimensional set to another dimensional set involves linear functions of the original set of statistics. Similarly, linear functions will transform statistics within a dimensional set such that the new statistics are relevant to a new set of coordinate axes. A restricted case of the latter is the rotation of axes in a coordinate system involving any two correlated random variables. A special case is the transformation for horizontal wind distributions. Wind statistics are usually provided in terms of wind speed and direction (measured clockwise from north) or in east-west and north-south components. A direct application of this technique allows the determination of appropriate wind statistics parallel and normal to any preselected flight path of a space vehicle. Among the constraints for launching space vehicles are critical values selected from the distribution of the expected winds parallel to and normal to the flight path. These procedures are applied to space vehicle launches at Cape Kennedy, Florida.
The paper derives an Ohm's law for single-particle motion in a current sheet, where the magnetic field reverses in direction across the sheet. The result is considerably different from the resistive Ohm's law often used in MHD studies of the geomagnetic tail. Single-particle analysis is extended to obtain a self-consistency relation for a current sheet which agrees with previous results. The results are applicable to the concept of reconnection in that the electric field parallel to the current is obtained for a one-dimensional current sheet with constant normal magnetic field. Dissipated energy goes directly into accelerating particles within the current sheet.
The large eddy interaction model (LEIM) is a statistical model of turbulence based on the interaction of selected eddies with the mean flow and all of the eddies in a turbulent shear flow. It can be utilized as the starting point for obtaining physical structures in the flow. The possible application of the LEIM to a mixing layer formed between two parallel, incompressible flows with a small temperature difference is developed by invoking a detailed similarity between the spectra of velocity and temperature.
The ultimate goal of this research program is to make Probabilistic Structural Analysis (PSA) computationally efficient and hence practical for the design environment by achieving large scale parallelism. The paper identifies the multiple levels of parallelism in PSA, identifies methodologies for exploiting this parallelism, describes the development of a parallel stochastic finite element code, and presents results of two example applications. It is demonstrated that speeds within five percent of those theoretically possible can be achieved. A special-purpose numerical technique, the stochastic preconditioned conjugate gradient method, is also presented and demonstrated to be extremely efficient for certain classes of PSA problems.
During this reporting period effort continued in the areas: (1) understanding the mechanisms responsible for substorm onset, and (2) application of a fundamental description of field-aligned currents and parallel electric fields to the plasma-sheet boundary layer.
Science Applications International Corporation is currently developing the Electric Propulsion Interactions Code, EPIC, as part of a project sponsored by the Space Environments and Effects Program at NASA Marshall Space Flight Center. Now in its second year of development, EPIC is an interactive computer toolset that allows the construction of a 3-D spacecraft model, and the assessment of a variety of interactions between its subsystems and the plume from an electric thruster. This paper reports on the progress of EPZC including the recently added ability to exchange results the NASA Charging Analyzer Program, Nascap-2k. The capability greatly enhances EPIC's range of applicability. Expansion of the toolset's various physics models proceeds in parallel with the overall development of the software. Also presented are recent upgrades of the elastic scattering algorithm in the electric propulsion Plume Tool. These upgrades are motivated by the need to assess the effects of elastically scattered ions on the SIC for ion beam energies that exceed loo0 eV. Such energy levels are expected in future high-power (>10 kW) ion propulsion systems empowered by nuclear sources.
We have investigated how extracellular matrix (ECM) alters the mechanical properties of the cytoskeleton (CSK). Mechanical stresses were applied to integrin receptors on the apical surfaces of adherent endothelial cells using RGD-coated ferromagnetic microbeads (5.5-microns diameter) in conjunction with a magnetic twisting device. Increasing the number of basal cell-ECM contacts by raising the fibronectin (FN) coating density from 10 to 500 ng/cm2 promoted cell spreading by fivefold and increased CSK stiffness, apparent viscosity, and permanent deformation all by more than twofold, as measured in response to maximal stress (40 dyne/cm2). When the applied stress was increased from 7 to 40 dyne/cm2, the stiffness and apparent viscosity of the CSK increased in parallel, although cell shape, ECM contacts, nor permanent deformation was altered. Application of the same stresses over a lower number ECM contacts using smaller beads (1.4-microns diameter) resulted in decreased CSK stiffness and apparent viscosity, confirming that this technique probes into the depth of the CSK and not just the cortical membrane. When magnetic measurements were carried out using cells whose membranes were disrupted and ATP stores depleted using saponin, CSK stiffness and apparent viscosity were found to rise by approximately 20%, whereas permanent deformation decreased by more than half. Addition of ATP (250 microM) under conditions that promote CSK tension generation in membrane-permeabilized cells resulted in decreases in CSK stiffness and apparent viscosity that could be detected within 2 min after ATP addition, before any measurable change in cell size.(ABSTRACT TRUNCATED AT 250 WORDS).
This application concerns a simple passive circuit that improves current balance in paralleled power MOSFETs that are not precisely matched and that are operated in their active region from a common gate drive. A nonlinear circuit consisting of diodes and resistors generates a differential gate potential required to correct for unbalance while maintaining low losses over a range of current.
Symbolic execution is a well-known program analysis technique which represents values of program inputs with symbolic values instead of concrete (initialized) data and executes the program by manipulating program expressions involving the symbolic values. Symbolic execution has been proposed over three decades ago but recently it has found renewed interest in the research community, due in part to the progress in decision procedures, availability of powerful computers and new algorithmic developments. We provide a survey of some of the new research trends in symbolic execution, with particular emphasis on applications to test generation and program analysis. We first describe an approach that handles complex programming constructs such as input data structures, arrays, as well as multi-threading. We follow with a discussion of abstraction techniques that can be used to limit the (possibly infinite) number of symbolic configurations that need to be analyzed for the symbolic execution of looping programs. Furthermore, we describe recent hybrid techniques that combine concrete and symbolic execution to overcome some of the inherent limitations of symbolic execution, such as handling native code or availability of decision procedures for the application domain. Finally, we give a short survey of interesting new applications, such as predictive testing, invariant inference, program repair, analysis of parallel numerical programs and differential symbolic execution.
A C software module has been developed that creates lightweight processes (LWPs) dynamically to achieve parallel computing performance in a variety of engineering simulation and analysis applications to support NASA and DoD project tasks. The required interface between the module and the application it supports is simple, minimal and almost completely transparent to the user applications, and it can achieve nearly ideal computing speed-up on multi-CPU engineering workstations of all operating system platforms. The module can be integrated into an existing application (C, C++, Fortran and others) either as part of a compiled module or as a dynamically linked library (DLL).
The GNC software onboard ISS utilizes TORS command loads, and a simplistic model of TORS orbital motion to generate onboard TORS state vectors. Each TORS command load contains five "invariant" orbital elements which serve as inputs to the onboard propagation algorithm. These elements include semi-major axis, inclination, time of last ascending node crossing, right ascension of ascending node, and mean motion. Running parallel to the onboard software is the TORS Command Builder Tool application, located in the JSC Mission Control Center. The TORS Command Builder Tool is responsible for building the TORS command loads using a ground TORS state vector, mirroring the onboard propagation algorithm, and assessing the fidelity of current TORS command loads onboard ISS. The tool works by extracting a ground state vector at a given time from a current TORS ephemeris, and then calculating the corresponding "onboard" TORS state vector at the same time using the current onboard TORS command load. The tool then performs a comparison between these two vectors and displays the relative differences in the command builder tool GUI. If the RSS position difference between these two vectors exceeds the tolerable lim its, a new command load is built using the ground state vector and uplinked to ISS. A command load's lifetime is therefore defined as the time from when a command load is built to the time the RSS position difference exceeds the tolerable limit. From the outset of TORS command load operations (STS-98), command load lifetime was limited to approximately one week due to the simplicity of both the onboard propagation algorithm, and the algorithm used by the command builder tool to generate the invariant orbital elements. It was soon desired to extend command load lifetime in order to minimize potential risk due to frequent ISS commanding. Initial studies indicated that command load lifetime was most sensitive to changes in mean motion. Finding a suitable value for mean motion was therefore the key to achieving this goal. This goal was eventually realized through development of an Excel spreadsheet tool called EMMIE (Excel Mean Motion Interactive Estimation). EMMIE utilizes ground ephemeris nodal data to perform a least-squares fit to inferred mean anomaly as a function of time, thus generating an initial estimate for mean motion. This mean motion in turn drives a plot of estimated downtrack position difference versus time. The user can then manually iterate the mean motion, and determine an optimal value that will maximize command load lifetime. Once this optimal value is determined, the mean motion initially calculated by the command builder tool is overwritten with the new optimal value, and the command load is built for uplink to ISS. EMMIE also provides the capability for command load lifetime to be tracked through multiple TORS ephemeris updates. Using EMMIE, TORS command load lifetimes of approximately 30 days have been achieved.
Here we propose a generalized space-time domain decomposition approach for the physics-informed neural networks (PINNs) to solve nonlinear partial differential equations (PDEs) on arbitrary complex-geometry domains. The proposed framework, named eXtended PINNs ( X P I N N s ), further pushes the boundaries of both PINNs as well as conservative PINNs (cPINNs), which is a recently proposed domain decomposition approach in the PINN framework tailored to conservation laws. Compared to PINN, the XPINN method has large representation and parallelization capacity due to the inherent property of deployment of multiple neural networks in the smaller subdomains. Unlike cPINN, XPINN can be extended to any type of PDEs. Moreover, the domain can be decomposed in any arbitrary way (in space and time), which is not possible in cPINN. Thus, XPINN offers both space and time parallelization, thereby reducing the training cost more effectively. In each subdomain, a separate neural network is employed with optimally selected hyperparameters, e.g., depth/width of the network, number and location of residual points, activation function, optimization method, etc. A deep network can be employed in a subdomain with complex solution, whereas a shallow neural network can be used in a subdomain with relatively simple and smooth solutions. We demonstrate the versatility of XPINN by solving both forward and inverse PDE problems, ranging from one-dimensional to three-dimensional problems, from time-dependent to time-independent problems, and from continuous to discontinuous problems, which clearly shows that the XPINN method is promising in many practical problems. The proposed XPINN method is the generalization of PINN and cPINN methods, both in terms of applicability as well as domain decomposition approach, which efficiently lends itself to parallelized computation. The XPINN code is available on h t t p s : / / g i t h u b . c o m / A m e y a J a g t a p / X P I N N s .
The growing gap between sustained and peak performance for scientific applications has become a well-known problem in high performance computing. The recent development of parallel vector systems offers the potential to bridge this gap for a significant number of computational science codes and deliver a substantial increase in computing capabilities. This paper examines the intranode performance of the NEC SX6 vector processor and the cache-based IBM Power3/4 superscalar architectures across a number of key scientific computing areas. First, we present the performance of a microbenchmark suite that examines a full spectrum of low-level machine characteristics. Next, we study the behavior of the NAS Parallel Benchmarks using some simple optimizations. Finally, we evaluate the perfor- mance of several numerical codes from key scientific computing domains. Overall results demonstrate that the SX6 achieves high performance on a large fraction of our application suite and in many cases significantly outperforms the RISC-based architectures. However, certain classes of applications are not easily amenable to vectorization and would likely require extensive reengineering of both algorithm and implementation to utilize the SX6 effectively.
Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).
Massively Parallel Processor algorithms were developed for the interactive manipulation of flat shaded digital terrain models defined over grids. The emphasis is on real time manipulation of stereo images. Standard graphics transformations are applied to a 128 x 128 grid of elevations followed by shading and a perspective projection to produce the right eye image. The surface is then rendered using a simple painter's algorithm for hidden surface removal. The left eye image is produced by rotating the surface 6 degs about the viewer's y axis followed by a perspective projection and rendering of the image as described above. The left and right eye images are then presented on a graphics device using standard stereo technology. Performance evaluations and comparisons are presented.
A new methodology to increase the utility of the Massively Parallel Processor (MPP) was developed, and is presented as an addition to the current methods of using the MPP. This methodology provides for the development of an MPP side abstraction layer that is callable from any host side high level language. Routines in the abstraction layer have the option of using a powerful software tool for accessing the stager as virtual memory. An additional abstraction layer that allows for remote access to the MPP via DECnet is discussed. This integrated approach to programming the MPP is a valuable tool for the implementation of interactive user driver systems that require the computational capabilities of the MPP as well as a controlled user view. It is expected that this methodology will be used to integrate the MPP into many such systems, and thus promote greater use of the MPP by scientific researchers who are accustomed to user friendly environments.