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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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Utilizing Multi-scale Computed Tomography to Understand Pore Structures of Carbonate Rich Samples for Geologic Carbon Sequestration in the Southeastern, US.
N-port networks interconnection by NASAP-70 transfer function capability for large scale circuit problems, considering Laemmel and Cascade Parameter methods
When there is a fault, the protection system automatically removes one or more transmission lines on a fast time scale of less than one minute. The outaged lines form a pattern in the transmission network. We extract these patterns from utility outage data, determine some key statistics of these patterns, and then show how to generate new patterns consistent with these statistics. The generated patterns provide a new and easily feasible way to model the overall effect of the protection system at the scale of a large transmission system. This new data-driven generative modeling of protection is expected to contribute to simulations of disturbances in large grids so that they can better quantify the risk of blackouts. Analysis of the pattern sizes suggests an index that describes how much outages spread in the transmission network at the fast timescale.
While past research has shown that providing residents with feedback about their electricity usage can reduce demand and its associated environmental burdens, some questions remain regarding what makes such feedback most effective. We followed the electricity usage of 36 residents who each received 14 feedback messages over 2 months. Using approaches borrowed from Natural-Language-Processing, feedbacks were generated automatically, using 10 features in random combinations. Unlike in previous studies, each resident received varying types of messages over time. In 504 observations, the average prompted reduction in electricity usage was 11 ± 3%, compared to a control group of 89 residents who received no messages. Feedback types prompting the largest reductions were self-comparisons with one’s own earlier usage (average reduction 14%) and messages of high variety from one feedback-cycle to the next (average reduction 16%). Comparisons with neighbors did not prompt higher reductions on average. Instead, they prompted reductions only when a resident’s recent usage happened to be higher than the average usage of neighbors, and increases when the reverse was true. Finally, this behavior was exhibited by all residents and is likely explained by a norm-conforming mean reversion of residents to their neighbors’ average usage, rather than an anti-conform “boomerang” behavior previously suggested in similar contexts.
Power is an often-cited reason for the move to advanced architectures on the path to Exascale computing. Here, this is due to practical considerations related to delivering enough power to successfully site and operate these machines, as well as concerns about energy usage while running large simulations. Since obtaining accurate power measurements can be challenging, it may be tempting to use the processor thermal design power (TDP) as a surrogate due to its simplicity and availability. However, TDP is not indicative of typical power usage while running simulations. Using commodity and advanced technology systems at Lawrence Livermore and Sandia National Labs, we performed a series of experiments to measure power and energy usage in running simulation codes. These experiments indicate that large scale Lawrence Livermore simulation codes are significantly more efficient than a simple processor TDP model might suggest.
Power is an often-cited reason for moving to advanced architectures on the path to Exascale computing. This is due to the practical concern of delivering enough power to successfully site and operate these machines, as well as concerns over energy usage while running large simulations. Since accurate power measurements can be difficult to obtain, processor thermal design power (TDP) is a possible surrogate due to its simplicity and availability. However, TDP is not indicative of typical power usage while running simulations. Using commodity and advance technology systems at Lawrence Livermore National Laboratory (LLNL) and Sandia National Laboratory, we performed a series of experiments to measure power and energy usage in running simulation codes. These experiments indicate that large scale LLNL simulation codes are significantly more efficient than a simple processor TDP model might suggest.
The upcoming long baseline neutrino experiments aim to enhance proton beam power to multi-MW scale and utilize large-scale detectors to address the challenge of limited event statistics. The DUNE experiment at LBNF will test the three-neutrino flavor paradigm and directly search for CP violation by studying oscillation signatures in the high intensity (anti-) beam to (anti-) measured over a long baseline. Higher beam power and improved accelerator up-time will enhance neutrino flux for the neutrino program by increasing the number of protons on target. LBNF/DUNE, as well as PIP-II upgrade and Accelerator Complex Evolution (ACE) plan, play a vital role in this effort. The scientific potential of ACE plan extends beyond neutrino physics, encompassing endeavors such as the Muon Collider, Charged Lepton Flavor Violation (CLFV), Dark Sectors, and exploration of neutrinos beyond DUNE.\par In the era of higher-power accelerator operation, research in target materials and beam instrumentation is crucial for optimizing design modifications. This abstract discusses Fermilab ACE, the science opportunities it provides, and how Fermilab is pushing the limits of proton beam power and accelerator infrastructure. By tackling neutrino beam challenges and exploring research and development ideas, we are advancing our understanding of fundamental particles and their interactions.
Usefulness and effectiveness extension of dimensional analysis for modeling and scaling procedures by algorithms
Geologic value of small scale aerial photographs and comparison of high altitude oblique and vertical photographs of California
Force and moment data are presented which were obtained for each vehicle separately at a Mach number of 0.6, and for the mated orbiter/747 configuration at Mach numbers of 0.3, 0.5, 0.6, and 0.7. Orbiter angles of attack from 0 degrees to +12 degrees and 747/Carrier angles of attack from -3 degrees to +7 degrees were investigated at angles of sideslip of 0 degrees and -5 degrees. Model variables include orbiter elevon and rudder deflections, orbiter tail cone-on and off, various orbiter/747 attach structure configurations, 747 stabilizer and rudder deflections, and 747 CAM modification components-on and off. Photographs of test configurations are included.
Data obtained in wind tunnel tests are presented. The objectives of the tests were to: (1) obtain pressure distributions, forces and moments over the vehicle 5 Orbiter in the terminal area energy management (TAEM) and approach phases of flight; (2) obtain elevon and rudder hinge moments in the TAEM and approach phases of flight; (3) obtain body flap and elevon loads for verification of loads balancing with integrated pressure distributions; and (4) obtain pressure distributions near the short OMS pods in the high subsonic, transonic and low supersonic Mach number regimes. Testing was conducted over a Mach number range from 0.6 to 1.4 with Reynolds number variations from 4.57 million to 2.74 million per foot. Model angle-of-attack was varied from -4 to 16 degrees and angles of side slip ranged from -8 to 8 degrees.
Data obtained in wind tunnel test OA148 are presented. The objectives of the test series were to: (1) obtain pressure distributions, forces and moments over the vehicle 5 orbiter in the thermal area energy management (TAEM) and approach phases of flight; (2) obtain elevon and rudder hinge moments in the TAEM and approach phases of flight; (3) obtain body flap and elevon loads for verification of loads balancing with integrated pressure distributions; and (4) obtain pressure distributions near the short OMS pods in the high subsonic, transonic and low supersonic Mach number regimes.
Plots and tabular listings are presented for both force and pressure data for the lower wing.