Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “different dimensions”

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.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Behavioral study of ultradian activity periods of mice enclosed in experimental cages of different dimensions

Male mice are enclosed in cages of different dimensions (cage A - 23x8x8 cm., cage B - 36x27x17 cm.), in an alternating light/dark regimen, at an ambient temperature of 22 to 23 C. The successions of the behavioral sequences of ultradian activity periods are noticed by direct observation during 11 consecutive hours in light. The experimental situation modifies the mean duration time and the behavioral organization of each activity period. However, the comparison of the overall activity time lengths and the comparison of the overall behavioral frequencies suggest that the energy spent per mouse is constant.

Guillot, A.↗
DOE OSTI2020

Hybrid geological modeling: Combining machine learning and multiple-point statistics

Accurately modeling and constructing a geologically realistic subsurface model remains an outstanding problem as the morphology controls the flow behaviors. Particularly, one of the pattern-based methods, namely cross-correlation based simulation, has been proved to be an effective way to reconstruct a realistic model, at both small and large scales. However, conditioning to point data in the large-scale problems is still a crucial issue in these algorithms, since there is always a trade-off between the quality of the realizations and the degree of point data reproduction. Specifically, it is not practical to build a training image (TI) which includes all the possibilities and variabilities. Therefore, finding a pattern that can represent the point data and, at the same time, preserving the connectivities is difficult. This leads to producing highly-connected realizations with a significant mismatch or poor models with a reasonable degree of point data reproduction. To accurately reproduce the densely distributed hard data, pixel-based methods can also produce some unrealistic artifacts around the hard data. In this paper, to overcome this challenge, however, we use pattern-based methods as they often produce more disconnected geobodies when dealing with dense hard data, and proposed a hybrid algorithm using the pattern-based methods and convolutional neural network (CNN). The trained CNN model is utilized to improve the quality of conditioning to point data for the original realizations generated by the pattern-based algorithm. As such, the mismatch locations are identified, and the same regions are used in the training of CNN to mimic the procedure through which a missing region can be filled. To evaluate the performance of the proposed hybrid algorithm, it is tested on cases with different dimensions and different numbers of facies. Then, the newly improved realizations are compared with the initial realizations generated by the pattern-based algorithm. The comparison is also conducted by the flow simulation test. And it indicates that the proposed hybrid algorithm can better reproduce the point data, while the connectivities are better preserved.

58 GEOSCIENCES↗
NASA NTRS1982

Validation of structural analysis methods using the in-house liner cyclic rigs

Test conditions and variables to be considered in each of the test rigs and test configurations, and also used in the validation of the structural predictive theories and tools, include: thermal and mechanical load histories (simulating an engine mission cycle; different boundary conditions; specimens and components of different dimensions and geometries; different materials; various cooling schemes and cooling hole configurations; several advanced burner liner structural design concepts; and the simulation of hot streaks. Based on these test conditions and test variables, the test matrices for each rig and configurations can be established to verify the predictive tools over as wide a range of test conditions as possible using the simplest possible tests. A flow chart for the thermal/structural analysis of a burner liner and how the analysis relates to the tests is shown schematically. The chart shows that several nonlinear constitutive theories are to be evaluated.

Thompson, R. L.↗
DOE OSTI2023

Advances in multi-dimensional cellulose-based fluorescent carbon dot composites

Recently, fluorescent materials composed of carbon dots (CDs) have attracted increasing attention in diverse fields, including the environment, food, biology, and medicine. Cellulose has emerged as a promising class of materials for carriers due to its low cost, fascinating biodegradability, and various morphologies, especially in available different dimensions. By taking advantage of the respective superiority of fluorescent materials and cellulose carriers, the performance of fluorescence composite materials can be optimized and their application may be extended. Here, in this review, the study emphasizes the synthesis and applications of cellulose-based fluorescent materials with different dimensions. Moreover, some effective strategies and potential challenges for further development of cellulose-based fluorescent materials are discussed. A deeper understanding will provide a guide for the materials with ideal fluorescent performances.

42 ENGINEERING↗
NASA NTRS1991

Comparison of different fractal dimension measuring algorithms for RE-TM M-O films

Noise in magneto-optical recording devices is discussed. In general, it appears that either the divider technique or amplitude spectrum technique may be used interchangeably to measure the fractal dimension (D) in the domain wall structure of ideal images. However, some caveats must be observed for best results. The divider technique is attractive for its simplicity and relatively modest computation requirements. However, it is sensitive to noise, in that noise pixels that touch the domain boundary are interpreted as being part of the boundary, skewing the measurement. Also, it is not useful in measuring nucleation-dominated films or domains that have significant amounts of structure within the interior of the domain wall. The amplitude spectrum method is more complex, and less intuitive than the divider method, and somewhat more expensive to implement computationally. However, since the camera noise tends to be white, the noise can be avoided in the measurement of D by avoiding that portion of the curve that is flat (due to the white noise) when the least squares line is fit to the plot. Also, many image processing software packages include a Fast Fourier Transformation (FFT) facility, while the user will most likely have to write his own edge extraction routine for the divider method. The amplitude spectrum method is a true two dimensional technique that probes the interior of the domain wall, and in fact, can measure arbitrary clusters of domains. It can also be used to measure grey-level images, further reducing processing steps needed to threshold the image.

Bernacki, Bruce E.↗
NASA NTRS1998

Mechanical spin bearings

A spin bearing assembly including, a pair of mutually opposing complementary bearing support members having mutually spaced apart bearing support surfaces which may be, for example, bearing races and a set of spin bearings located therebetween. Each spin bearing includes a pair of end faces, a central rotational axis passing through the end faces, a waist region substantially mid-way between the end faces and having a first thickness dimension, and discrete side surface regions located between the waist region and the end faces and having a second thickness dimension different from the first thickness dimension of the waist region and wherein the side surface regions further have respective curvilinear contact surfaces adapted to provide a plurality of bearing contact points on the bearing support members.

Vranish, John M.↗
DOE OSTI2024

Equity and modeling in sustainability science: Examples and opportunities throughout the process

Equity is core to sustainability, but current interventions to enhance sustainability often fall short in adequately addressing this linkage. Models are important tools for informing action, and their development and use present opportunities to center equity in process and outcomes. This Perspective highlights progress in integrating equity into systems modeling in sustainability science, as well as key challenges, tensions, and future directions. We present a conceptual framework for equity in systems modeling, focused on its distributional, procedural, and recognitional dimensions. We discuss examples of how modelers engage with these different dimensions throughout the modeling process and from across a range of modeling approaches and topics, including water resources, energy systems, air quality, and conservation. Synthesizing across these examples, we identify significant advances in enhancing procedural and recognitional equity by reframing models as tools to explore pluralism in worldviews and knowledge systems; enabling models to better represent distributional inequity through new computational techniques and data sources; investigating the dynamics that can drive inequities by linking different modeling approaches; and developing more nuanced metrics for assessing equity outcomes. We also identify important future directions, such as an increased focus on using models to identify pathways to transform underlying conditions that lead to inequities and move toward desired futures. By looking at examples across the diverse fields within sustainability science, we argue that there are valuable opportunities for mutual learning on how to use models more effectively as tools to support sustainable and equitable futures.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Architecture for Adaptive Intelligent Systems

We identify a class of niches to be occupied by 'adaptive intelligent systems (AISs)'. In contrast with niches occupied by typical AI agents, AIS niches present situations that vary dynamically along several key dimensions: different combinations of required tasks, different configurations of available resources, contextual conditions ranging from benign to stressful, and different performance criteria. We present a small class hierarchy of AIS niches that exhibit these dimensions of variability and describe a particular AIS niche, ICU (intensive care unit) patient monitoring, which we use for illustration throughout the paper. We have designed and implemented an agent architecture that supports all of different kinds of adaptation by exploiting a single underlying theoretical concept: An agent dynamically constructs explicit control plans to guide its choices among situation-triggered behaviors. We illustrate the architecture and its support for adaptation with examples from Guardian, an experimental agent for ICU monitoring.

ACTIVE CONTROL↗
NASA NTRS1994

Computation of wind tunnel wall effects for complex models using a low-order panel method

A technique for determining wind tunnel wall effects for complex models using the low-order, three dimensional panel method PMARC (Panel Method Ames Research Center) has been developed. Initial validation of the technique was performed using lift-coefficient data in the linear lift range from tests of a large-scale STOVL fighter model in the National Full-Scale Aerodynamics Complex (NFAC) facility. The data from these tests served as an ideal database for validating the technique because the same model was tested in two wind tunnel test sections with widely different dimensions. The lift-coefficient data obtained for the same model configuration in the two test sections were different, indicating a significant influence of the presence of the tunnel walls and mounting hardware on the lift coefficient in at least one of the two test sections. The wind tunnel wall effects were computed using PMARC and then subtracted from the measured data to yield corrected lift-coefficient versus angle-of-attack curves. The corrected lift-coefficient curves from the two wind tunnel test sections matched very well. Detailed pressure distributions computed by PMARC on the wing lower surface helped identify the source of large strut interference effects in one of the wind tunnel test sections. Extension of the technique to analysis of wind tunnel wall effects on the lift coefficient in the nonlinear lift range and on drag coefficient will require the addition of boundary-layer and separated-flow models to PMARC.

Ashby, Dale L.↗
DOE OSTI2020

Patterning of magneto-optical nanomaterials

Patterning of colloidal particles in precisely organized architectures has attracted intense research interest for decades. This is due to their potential applications in flexible electronics, magnetic and optical devices, sensors, biotechnology, communications, etc. However, creation of mesoscale assemblies at commercial scale received less attention. The mesoscale systems reside between the micro- and macroscopic scales, with length dimensions from ≈ 100 um to 5mm. One way to create mesoscale materials is to leverage decades of experimental and theoretical research in nanomaterials field that allows us to precisely create and control the placement of nanoscale materials. We developed a versatile and automatic mesoscale patterning technology that provide precise and consistent control and special arrangement of functional nanomaterials. The versatility of the strategy is demonstrated by patterning nanoparticles with different dimensions, shapes and compositions, tethered with various functionalities and subjected to different external stimuli. Nanomaterials were created via the paten t pending automated flow-throughput domain process (AFTDP), aka nano-additive manufacturing approach, recently developed by us, based on a unique small-scale fluidics concept, that enables a uniform reaction environment for production of high-quality materials in large quantities.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Patterning of magneto-optical nanomaterials

Patterning of colloidal particles in precisely organized architectures has attracted intense research interest for decades. This is due to their potential applications in flexible electronics, magnetic and optical devices, sensors, biotechnology, communications, etc. However, creation of mesoscale assemblies at commercial scales have received less attention. The mesoscale systems reside between the micro- and macroscopic scales, with length dimensions from ≈ 100 µm to 5 mm. By leveraging decades of experimental and theoretical research in nanomaterial fields, we were able to precisely create and control the placement of nanoscale materials, allowing us to create mesoscale materials. We developed a versatile and automatic mesoscale patterning technology (via SEM-FIB and 3D printing) that provides precise and consistent control and special arrangement of functional nanomaterials. The versatility of the strategy is demonstrated by patterning nanoparticles with different dimensions, shapes and compositions, tethered with various functionalities and subjected to different external stimuli.

42 ENGINEERING↗
DOE OSTI2024

Device Feasibility Analysis of Multi-level FeFETs for Neuromorphic Computing

As an emerging non-volatile memory device technology, Ferroelectric Field-Effect Transistors (FeFETs) can enable low-power, adaptive intelligent system design. However, device dimension and operating voltage dependent reliability issues of scaled FeFETs can ultimately lead to degraded performance in solving machine learning tasks. In this article, detailed experimental characterization of FeFET devices of different dimensions have been carried out to explicitly evaluate the non-ideal behavior in device conductance programming properties like number of programming states, cycle-to-cycle (C2C) variations, device-to-device (D2D) variations, and state retention. A hardware-aware software simulation approach has been adopted to capture the adversarial effects of the non-idealities on recognition accuracy through algorithm-level performance assessment by including them in NeuroSim, a popular neural network hardware simulator, to execute a neural network model considering all other hardware constraints. With the added non-idealities, significant accuracy degradation has been observed compared to the ideal scenarios where D2D variations play the most critical role. Thereafter, feasibility of a variation-aware training method has been evaluated to tackle the accuracy drop.

42 ENGINEERING↗
DOE OSTI2021

Two-dimensional phase cartography for high-harmonic spectroscopy

Extremely nonlinear spectroscopy based on high-order-harmonic generation has become a powerful investigation method for attosecond dynamics in gas and solid targets. In particular, the phase of harmonic emission was shown to carry profound insight into atomic and molecular structure and dynamics. However, current techniques offer phase measurements only along specific directions, thus providing partial characterization. Here we report on a new approach combining optical and quantum interferometers measuring along two dimensions the intensity and phase of harmonic emission from aligned molecules in the exact same experimental conditions. This two-dimensional cartography technique measures the phase with no arbitrary offset and no uncertainty on its sign. Measurements along different dimensions can be combined in two ways: either a single mapping or a redundant mapping allowing high-precision phase recovery using a Shack–Hartmann-like algorithm. We demonstrate both methods in a nitrogen test case, which allows disentangling structural and dynamical effects. Two-dimensional phase cartography paves the way to high-resolution high-harmonic spectroscopy for applications such as quantum orbital tomography and attosecond charge migration in molecules.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A comprehensive and fair comparison of two neural operators (with practical extensions) based on $\mathrm{FAIR}$ data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020, and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. Here we consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.

42 ENGINEERING↗
NASA NTRS1985

Hertzian contact in two and three dimensions

The basic solution to the problem of mechanical contact between elastically deforming solids was proposed by Hertz over a century ago and has been used by tribologists and others ever since in a steadily increasing number of applications. While the theoretical development is not conceptually difficult and treatments exist to suit all tastes, it is nonetheless interesting to trace the relationships among the solutions in different dimensions. Such an approach is used herein to shed light on the curious and sometimes perplexing behavior of line contacts. A number of the more frequently used contact expressions together as a convenient reference and for comparative purposes.

Tripp, J. H.↗
DOE OSTI2024

Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification

In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low-dimensional (latent) descriptors that efficiently parameterize the response of the high-dimensional computational model. To this end, we employ Principal Geodesic Analysis on the Grassmann manifold of the response to identify a set of disjoint principal geodesic submanifolds, of possibly different dimension, that captures the variation in the data. Since operations on the Grassmann require the data to be concentrated, we propose an adaptive algorithm based on Riemannian K-means and the minimization of the sample Fréchet variance on the Grassmann manifold to identify “local” principal geodesic submanifolds that represent different system behavior across the parameter space. Polynomial chaos expansion is then used to construct a mapping between the random input parameters and the projection of the response on these local principal geodesic submanifolds. Here, the method is demonstrated on four test cases, a toy-example that involves points on a hypersphere, a Lotka-Volterra dynamical system, a continuous-flow stirred-tank chemical reactor system, and a two-dimensional Rayleigh-Bénard convection problem.

42 ENGINEERING↗
NASA NTRS2006

Modern Design of Resonant Edge-Slot Array Antennas

Resonant edge-slot (slotted-waveguide) array antennas can now be designed very accurately following a modern computational approach like that followed for some other microwave components. This modern approach makes it possible to design superior antennas at lower cost than was previously possible. Heretofore, the physical and engineering knowledge of resonant edge-slot array antennas had remained immature since they were introduced during World War II. This is because despite their mechanical simplicity, high reliability, and potential for operation with high efficiency, the electromagnetic behavior of resonant edge-slot antennas is very complex. Because engineering design formulas and curves for such antennas are not available in the open literature, designers have been forced to implement iterative processes of fabricating and testing multiple prototypes to derive design databases, each unique for a specific combination of operating frequency and set of waveguide tube dimensions. The expensive, time-consuming nature of these processes has inhibited the use of resonant edge-slot antennas. The present modern approach reduces costs by making it unnecessary to build and test multiple prototypes. As an additional benefit, this approach affords a capability to design an array of slots having different dimensions to taper the antenna illumination to reduce the amplitudes of unwanted side lobes. The heart of the modern approach is the use of the latest commercially available microwave-design software, which implements finite-element models of electromagnetic fields in and around waveguides, antenna elements, and similar components. Instead of building and testing prototypes, one builds a database and constructs design curves from the results of computational simulations for sets of design parameters. The figure shows a resonant edge-slot antenna designed following this approach. Intended for use as part of a radiometer operating at a frequency of 10.7 GHz, this antenna was fabricated from dimensions defined exclusively by results of computational simulations. The final design was found to be well optimized and to yield performance exceeding that initially required.

Gosselin, R. B.↗
NASA NTRS2003

Long-term Isothermal Aging Effects on Weight Loss, Compression Properties, and Dimensions of T650-35 Fabric-reinforced PMR-15 Composites-data

A cooperative program was conducted with the General Electric Aircraft Engines plant in Evendale, Ohio, to study the effects of long-term isothermal aging at elevated temperatures on compression and thermal durability properties of T650 35 fabric-reinforced PMR 15 composites. This degradation study was conducted over an approximate time period of 3 1/2 yr. The aging temperatures were 204, 260, 288, 316, and 343 C. Specimens of different dimensions were evaluated. Specimens with ratios of the cut edge to total surface area of 0.03 to 0.89 were fabricated and aged. The aged and unaged specimens were tested in compression as specified in Test Method for Compressive Properties of Rigid Plastics (ASTM D695M). Thickness changes, degraded surface layer growth, weight loss, and failure modes were monitored and recorded. All property changes were thickness dependent.

Bowles, Kenneth J.↗