Tailoring Interfacial Interactions via Polymer-Grafted Nanoparticles Improves Performance of Parts Created by 3D Printing
Abstract not provided
Street, Dayton P.↗
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Results for “storage complex”
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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River Dynamics Control Transit Time Distributions and Biogeochemical Reactions in a Dam-Regulated River Corridor
Residence Time Distributions (RTDs) exert an important control on biogeochemical translation in the hydrological systems. Previous tracer studies have revealed that RTDs often followed time-invariant exponential, lognormal, or heavy-tailed RTDs that have power-law behavior for long tails in headwater or low-order streams. However, there is a recognition that RTDs can be more complicated and time-variable in response to dynamic hydrological forcing. Here, we use particle tracking to estimate RTDs along the Hanford Reach of the Columbia River and to quantify the influences of river stage fluctuations. Particle tracking is conducted using the velocity field from high-resolution 3D groundwater flow simulations. The effects of hydrological forcing on the residence time distribution were evaluated by varying river flow boundary conditions and releasing particles in different time windows. Our results revealed that dynamic stage fluctuations created rapidly changing losing-gaining conditions in the river and led to highly transient RTDs, which contributes to multiple modes of RTDs. Dam-induced high-frequency (sub-daily) flow variation contributes to the short-time (sub-daily) component of the RTDs. Deviation of the reactant consumption under the single-mode assumption compared to the multimodal RTDs is relatively small (~5%) and appears when the Damköhler number was close to one.More specifically, high-frequency (daily) dam-induced stage variations have higher impacts on biogeochemical reactions with faster reaction rate, and can potentially increase the reactant consumption by 26.68%. Based on these findings, we suggest that current river basin models could be improved by including bank storage and more complex RTDs influenced by both short and long term river stage fluctuations.
54 ENVIRONMENTAL SCIENCES↗
Efficient Streaming Dynamic Mode Decomposition
We propose a reformulation of the streaming dynamic mode decomposition method that requires maintaining a single orthonormal basis, thereby reducing computational redundancy. The proposed efficient streaming dynamic mode decomposition method results in a constant-factor reduction in computational complexity and memory storage requirements. Numerical experiments on representative canonical dynamical systems show that the enhanced computational efficiency does not compromise the accuracy of the proposed method.
97 MATHEMATICS AND COMPUTING↗
Multilevel Techniques for Compression and Reduction of Scientific Data---The Unstructured Case
Previous work on multilevel techniques for compression and reduction of scientific data is extended to the case of data given on unstructured meshes in two and three dimensions. The centerpiece of the work is a decomposition algorithm which is shown to be optimal, in terms of both storage and operational complexity, applicable to unstructured grids in both two and three dimensions, and which implicitly gives a Riesz basis that can be exploited to reduce the data while maintaining rigorous bounds on the loss incurred. The flexibility of the approach is illustrated by applications to potential flow around an airfoil and the effect of compression on quantities of interest relevant to airfoil design; compression of computational simulation of a nonlinear reaction-diffusion system with special attention given to the problem of time series reduction; and, data from a simulation of magnetically confined plasma in a fusion reactor reduced so as to preserve the electric field computed from the data.
97 MATHEMATICS AND COMPUTING↗
Methods for Representing Flexible, Energy-Constrained Technologies in Utility Planning Tools
Capacity expansion models are widely used by power system researchers, planners, and policy analysts to evaluate alternative power system investment scenarios. With the increasing deployment of wind and solar in the US, there has been much focus on improving the representations of variable generation (VG) technologies within capacity expansion models. Models that capture the variable net-load profiles and larger reserve requirements associated with high penetration VG systems represent an improvement to classic capacity expansion models, but fall short of capturing the complexities associated with storage technologies, such as battery energy storage (BES) and concentrating solar power with thermal energy storage (CSP with TES). While difficult to model in a capacity expansion setting, these technologies are potentially competitive sources of flexibility with the intriguing characteristics of being able to absorb VG that would otherwise be curtailed and directly contribute to renewable energy goals, respectively. In this paper we present methods for accurately representing these technologies in a large-scale capacity expansion model with high electrical and geospatial resolution. VG modeling techniques, including novel methods for capturing curtailment due to unit commitment and other hourly dispatch phenomena, are also reviewed. Modeling for a region in the southwestern United States demonstrates the economic relevance of being able to explicitly trade off the costs and capabilities of energy-constrained technologies, especially BES, against other resources in the near-term, in time to make plans for the coming decade
24 POWER TRANSMISSION AND DISTRIBUTION↗
Extending Parsimonious Bayesian Inference
Parsimonious Bayesian inference is a theoretical framework for efficient data assimilation that seeks to balance increased consistency between predictions and training data against corresponding increases in model complexity. Within this framework, over-training is understood as optimization that encodes excessive information within model parameters while only achieving small improvements between predictions and training data. This project aims to develop practical methods of limiting excess model information during optimization. One key observation is that practical heuristics for parsimonious learning in high-dimensions must balance expressivity, i.e. the ability of the model to capture diverse predictions with only a few non-zero parameters, against discoverability, i.e. the ability to train the model with gradient-based optimization and drive parameters to low information states. As such, we developed logical activation functions that are able to adaptively approximate arbitrary truth tables that define Boolean logic operations within a probabilistic framework. These functions have demonstrated the ability to learn exclusive disjunction (XOR) and conditioned disjunction (if [condition] then [result_if_true] else [result_if_false]) within a single layer of a neural network. To efficiently exploit these activation functions to drive parsimonious learning required several other advances within the domain of variational inference. The most efficient form of complexity suppression is structured sparsification, driving most model parameters to zero while achieving the structural coherence among nonzeros needed for bandwidth reduction. Such models are not only far more efficient at suppressing information-theoretic complexity, they also reduce the other forms of complexity (computations, communication, storage, and the number of dependencies needed to evaluate predictions). Aiming to support enhanced sparsification, this project examined new approaches to high-dimensional variational inference that allow us to calibrate and control parameter uncertainty during optimization. By identifying which parameters can sustain sparsifying perturbations with little impact on prediction quality, we can develop better pruning strategies by framing them as approximate Bayesian inference. These advances also open paths to mitigate concerns with deploying advanced learning methods in resource-constrained environments, such as running models on power-limited or communication-limited devices.
97 MATHEMATICS AND COMPUTING↗
Implicit approximate-factorization schemes for the efficient solution of steady transonic flow problems
Implicit approximate-factorization algorithms (AF) are developed for the solution of steady-state transonic flow problems. The performance of the AF solution method is evaluated relative to that of the standard solution method for transonic flow problems, successive line over-relaxation (SLOR). Both methods are applied to the solution of the nonlinear, two-dimensional transonic small-disturbance equation. Results indicate that the AF method requires substantially less computer time than SLOR to solve the nonlinear finite-difference matrix equation for a transonic flow field. This increase in computational efficiency is achieved with no appreciable increase in computer storage or coding complexity.
Ballhaus, W. F.↗
High-Throughput, Adaptive FFT Architecture for FPGA-Based Spaceborne Data Processors
Exponential growth in microelectronics technology such as field-programmable gate arrays (FPGAs) has enabled high-performance spaceborne instruments with increasing onboard data processing capabilities. As a commonly used digital signal processing (DSP) building block, fast Fourier transform (FFT) has been of great interest in onboard data processing applications, which needs to strike a reasonable balance between high-performance (throughput, block size, etc.) and low resource usage (power, silicon footprint, etc.). It is also desirable to be designed so that a single design can be reused and adapted into instruments with different requirements. The Multi-Pass Wide Kernel FFT (MPWK-FFT) architecture was developed, in which the high-throughput benefits of the parallel FFT structure and the low resource usage of Singleton s single butterfly method is exploited. The result is a wide-kernel, multipass, adaptive FFT architecture. The 32K-point MPWK-FFT architecture includes 32 radix-2 butterflies, 64 FIFOs to store the real inputs, 64 FIFOs to store the imaginary inputs, complex twiddle factor storage, and FIFO logic to route the outputs to the correct FIFO. The inputs are stored in sequential fashion into the FIFOs, and the outputs of each butterfly are sequentially written first into the even FIFO, then the odd FIFO. Because of the order of the outputs written into the FIFOs, the depth of the even FIFOs, which are 768 each, are 1.5 times larger than the odd FIFOs, which are 512 each. The total memory needed for data storage, assuming that each sample is 36 bits, is 2.95 Mbits. The twiddle factors are stored in internal ROM inside the FPGA for fast access time. The total memory size to store the twiddle factors is 589.9Kbits. This FFT structure combines the benefits of high throughput from the parallel FFT kernels and low resource usage from the multi-pass FFT kernels with desired adaptability. Space instrument missions that need onboard FFT capabilities such as the proposed DESDynl, SWOT (Surface Water Ocean Topography), and Europa sounding radar missions would greatly benefit from this technology with significant reductions in non-recurring cost and risk.
NguyenKobayashi, Kayla↗
Validation of Heat Transfer Correlations in Line Chill-Down Tests of Cryogenic Fluid in SINDA/FLUINT
Line chill-down is an important process in cryogenic tank propellant management, storage, and usage Complex flow dynamics during these processes: boiling heat transfer (film, transition, and nucleate) Understanding boiling phenomena can lead to efficient line chill-down systems that use less propellant, propellant stored, reducing cost for space missions Line Chill-down heat transfer was modelled using SINDA/FLUINT version 5.8 (SF) Multiple chill-down tests were modelled using: heat transfer correlations readily available in SF using HTN/HTC TIES heat transfer empiricisms developed by the University of Florida (UF) based on a series of liquid nitrogen chill-down tests using SF HTU TIES Chill-down tests modelled: liquid nitrogen tests conducted by the University of Florida horizontal flow, upward flow, and downward flow (Reynolds Numbers ranging 850-231,000)liquid hydrogen tests conducted by NASA Glenn Research Center vertical upward flow (Reynolds Number range of 18,400 - 433,000)The flow rate was measured far downstream of the test section, near the system exit. Where to set the flow rate? SF was highly sensitive, and sometime unstable, setting the test flow rate downstream (the outlet) of the test section model and setting the test pressure upstream (the inlet) of the test section model higher flow rate oscillations at the entrance of the model's test section SF was more stable setting the test flow rate upstream (than the downstream flow rate set case)test pressure was used as an inlet (SF plenum) to set the thermodynamic state (temperature and quality) coming into the system setting the appropriate downstream pressure was the unknown. The pressure drops predicted by SF for the downstream set flow rate boundary condition were much smaller than test section measured pressure drops. The multiphase pressure drop correlations used internally in SF may need to be adjusted. Models with an upstream flow rate set assumed a pressure drop that was small
cryogenic modeling↗
'The surface management system' (SuMS) database: a surface-based database to aid cortical surface reconstruction, visualization and analysis
Surface reconstructions of the cerebral cortex are increasingly widely used in the analysis and visualization of cortical structure, function and connectivity. From a neuroinformatics perspective, dealing with surface-related data poses a number of challenges. These include the multiplicity of configurations in which surfaces are routinely viewed (e.g. inflated maps, spheres and flat maps), plus the diversity of experimental data that can be represented on any given surface. To address these challenges, we have developed a surface management system (SuMS) that allows automated storage and retrieval of complex surface-related datasets. SuMS provides a systematic framework for the classification, storage and retrieval of many types of surface-related data and associated volume data. Within this classification framework, it serves as a version-control system capable of handling large numbers of surface and volume datasets. With built-in database management system support, SuMS provides rapid search and retrieval capabilities across all the datasets, while also incorporating multiple security levels to regulate access. SuMS is implemented in Java and can be accessed via a Web interface (WebSuMS) or using downloaded client software. Thus, SuMS is well positioned to act as a multiplatform, multi-user 'surface request broker' for the neuroscience community.
NASA Discipline Neuroscience↗
Deep Learning-based Parameterization of Complex 3D CO2 Saturation Data in Large-scale Geological Carbon Storage
In deep learning (DL), dimension reduction plays a pivotal role in improving training efficiency and minimizing overfitting, especially when working with complex datasets like three-dimensional (3D) saturation data. In the context of geological carbon storage (GCS), 3D saturation data introduces unique challenges due to its sparse nature and sharp transitions at plume boundaries, known as shock fronts. To tackle these challenges, we developed a novel DL framework that combines dimension reduction with advanced 3D reconstruction techniques. Our approach utilizes latent variables derived from 2D average saturation fields to efficiently capture the essential features of high-dimensional data while reducing the number of variables. This enhances both the robustness and accuracy of DL models, making the framework more practical for real-world applications. By offering a tailored solution for modeling complex 3D saturation dynamics, this framework holds significant potential for environmental monitoring, energy storage, and other geological applications.
Wang, Hongsheng [University of Texas at Austin]↗
A comprehensive analysis of transient pressure and rate data from CO 2 storage projects in a depleted pinnacle reef oil field complex, Michigan, USA
Pressure and rate data are commonly recorded as part of a basic monitoring program in CCS projects. This paper discusses the application of multiple analytical techniques to interpret pressure and rate transient data from CO 2 injection and storage operations. The techniques of interest, i.e., injection-falloff analysis, injectivity/productivity index analysis and pressure pulse arrival time analysis, are commonly used in the oil and gas industry to assess reservoir properties, but not well known in the CCS literature (especially the last two). Injection-falloff analysis involves log-log pressure derivative plotting for the falloff data and history-matching of the entire injection-falloff sequence to determine permeability. In the injectivity/productivity index analysis, rate-normalized pressure buildup is plotted against material balance time or ratio of cumulative injection to injection rate to determine the injectivity index (ratio of injection rate to stabilized pressure buildup) which can be related to the permeability-thickness product. The arrival time analysis identifies the arrival of a pressure disturbance (~0.1 psi change from ambient) to determine the hydraulic diffusivity from which permeability can be estimated. The applicability of these techniques is demonstrated via illustrative examples from multiple wells in different pinnacle carbonate reefs undergoing CO 2 -EOR in Northern Michigan. The paper ends with a discussion of the relative merits of each interpretive technique, as well as recommendations that could be useful for other field projects.
42 ENGINEERING↗
Programming Abstractions for Managing Workflows on Tiered Storage Systems
Scientific workflows in High Performance Computing (HPC) environments are processing large amounts of data. The storage hierarchy on HPC systems is getting deeper, driven by new technologies (NVRAMs, SSDs, etc.) There is a need for new programming abstractions that allow users to seamlessly manage data at the workflow level on multi-tiered storage systems, and provide optimal workflow performance and use of storage resources. In previous work, we introduced a software architecture Managing Data on Tiered Storage for Scientific Workflows (MaDaTS) that used a Virtual Data Space (VDS) abstraction to hide the complexities of the underlying storage system while allowing users to control data management strategies. In this article, we detail the data-centric programming abstractions that allow users to manage a workflow around its data on the storage layer. The programming abstractions simplify data management for scientific workflows on multi-tiered storage systems, without affecting workflow performance or storage capacity. We measure the overheads and effectiveness introduced by the programming abstractions of MaDaTS. Our results show that these abstractions can optimally use the storage capacity in lesser capacity storage tiers, and simplify data management without adding any performance overheads.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Pad B Liquid Hydrogen Storage Tank
Kennedy Space Center is home to two liquid hydrogen storage tanks, one at each launch pad of Launch Complex 39. The liquid hydrogen storage tank at Launch Pad B has a significantly higher boil off rate that the liquid hydrogen storage tank at Launch Pad A. This research looks at various calculations concerning the at Launch Pad B in an attempt to develop a solution to the excess boil off rate. We will look at Perlite levels inside the tank, Boil off rates, conductive heat transfer, and radiant heat transfer through the tank. As a conclusion to the research, we will model the effects of placing an external insulation to the tank in order to reduce the boil off rate and increase the economic efficiency of the liquid hydrogen storage tanks.
Hall, Felicia↗
A proof-of-concept implementation of persistence in a hierarchical storage system
The concept of providing transparent access to a collection of files in a mass storage system is a familiar one. The goal of this project was to investigate the feasibility of providing similar access to a collection of persistent, complex objects. We describe an architecture for interfacing a persistent store of complex objects to a hierarchical storage system. Persistent object stores support the uniform creation, storage, and access of complex objects, regardless of their lifetimes. In other words, a mechanism is provided so that persistent objects outlive the processes which create them and can be accessed in a uniform manner by other processes. We validated this architecture by implementing a proof-of-concept system and testing the system on two stores of data. These tests indicate that this architecture supports the creation, storage, and access of very large persistent object stores.
Grossman, Robert↗
Redox Flow Battery Having Metal-Ligand Complex
Researchers at Los Alamos National Laboratory have developed new non-aqueous electrolyte materials for redox flow batteries (RFB). These batteries are a viable technology for scalable energy storage beyond lithium ion batteries. Cost is currently limiting deployment, with poor energy density being the primary culprit. One way to improve energy density is by increasing the cell potential beyond the current limit in aqueous media. The higher cell potential and additional electrons stored per molecule will increase the energy density of the RFB medium, overcoming a critical limitation to the current RFBs. Like lithium ion batteries, RFB's can span from the kW to multi MW power storage systems. The nickel complexes described herein have demonstrated redox potentials >3 V which is at least twice the aqueous limit. We are interested in partnering with battery developers to further advance this technology to the product prototype stage.
25 ENERGY STORAGE↗
Study to establish cost predictions for the production of Redox chemicals
The chromium and iron chloride chemicals are significant first costs for NASA Redox energy storage systems. This study was performed to determine the lowest cost at which chromium and iron chlorides could be obtained for a complex of redox energy storage systems. In addition, since the solutions gradually become intermixed during the course of operation of Redox units, it was an objective to evaluate schemes for regeneration of the operating solutions. Three processes were evaluated for the production of chromium and iron chlorides. As a basis for the preliminary plant design and economic evaluation, it was assumed that the plant would produce about 25,000 tons of contained chromium as CrCl3 and an equivalent molar quantity of FeCl2. Preliminary plant designs, including materials and energy balances and sizing of major equipment, were prepared, and capital and operating costs were estimated.
Ammann, P. R.↗
Environmental projects. Volume 13: Underground storage tanks, removal and replacement. Goldstone Deep Space Communications Complex
The Goldstone Deep Space Communications Complex (GDSCC), located in the Mojave Desert about 40 miles north of Barstow, California, and about 160 miles northeast of Pasadena, is part of the National Aeronautics and Space Administration's (NASA's) Deep Space Network, one of the world's largest and most sensitive scientific telecommunications and radio navigation networks. Activities at the GDSCC are carried out in support of six large parabolic dish antennas. As a large-scale facility located in a remote, isolated desert region, the GDSCC operations require numerous on-site storage facilities for gasoline, diesel oil, hydraulic oil, and waste oil. These fluids are stored in underground storage tanks (USTs). This present volume describes what happened to the 26 USTs that remained at the GDSCC. Twenty-four of these USTs were constructed of carbon steel without any coating for corrosion protection, and without secondary containment or leak detection. Two remaining USTs were constructed of fiberglass-coated carbon steel but without secondary containment or leak protection. Of the 26 USTs that remained at the GDSCC, 23 were cleaned, removed from the ground, cut up, and hauled away from the GDSCC for environmentally acceptable disposal. Three USTs were permanently closed (abandoned in place).
Bengelsdorf, Irv↗