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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Seizure-induced alterations in fast-spiking basket cell GABA currents modulate frequency and coherence of gamma oscillation in network simulations

Gamma frequency oscillations have been proposed to contribute to memory formation and retrieval. Fast-spiking basket cells (FS-BCs) are known to underlie development of gamma oscillations. Fast, high amplitude GABA synapses and gap junctions have been suggested to contribute to gamma oscillations in FS-BC networks. Recently, we identified that, apart from GABAergic synapses, FS-BCs in the hippocampal dentate gyrus have GABAergic currents mediated by extrasynaptic receptors. Our experimental studies demonstrated two specific changes in FS-BC GABA currents following experimental seizures [Yu et al., J. Neurophysiol. 109, 1746 (2013)]: increase in the magnitude of extrasynaptic (tonic) GABA currents and a depolarizing shift in GABA reversal potential (E{sub GABA}). Here, we use homogeneous networks of a biophysically based model of FS-BCs to examine how the presence of extrasynaptic GABA conductance (g{sub GABA-extra}) and experimentally identified, seizure-induced changes in g{sub GABA-extra} and E{sub GABA} influence network activity. Networks of FS-BCs interconnected by fast GABAergic synapses developed synchronous firing in the dentate gamma frequency range (40–100 Hz). Systematic investigation revealed that the biologically realistic range of 30 to 40 connections between FS-BCs resulted in greater coherence in the gamma frequency range when networks were activated by Poisson-distributed dendritic synaptic inputs rather than by homogeneous somatic current injections, which were balanced for FS-BC firing frequency in unconnected networks. Distance-dependent conduction delay enhanced coherence in networks with 30–40 FS-BC interconnections while inclusion of gap junctional conductance had a modest effect on coherence. In networks activated by somatic current injections resulting in heterogeneous FS-BC firing, increasing g{sub GABA-extra} reduced the frequency and coherence of FS-BC firing when E{sub GABA} was shunting (−74 mV), but failed to alter average FS-BC frequency when E{sub GABA} was depolarizing (−54 mV). When FS-BCs were activated by biologically based dendritic synaptic inputs, enhancing g{sub GABA-extra} reduced the frequency and coherence of FS-BC firing when E{sub GABA} was shunting and increased average FS-BC firing when E{sub GABA} was depolarizing. Shifting E{sub GABA} from shunting to depolarizing potentials consistently increased network frequency to and above high gamma frequencies (>80 Hz). Since gamma oscillations may contribute to learning and memory processing [Fell et al., Nat. Neurosci. 4, 1259 (2001); Jutras et al., J. Neurosci. 29, 12521 (2009); Wang, Physiol. Rev. 90, 1195 (2010)], our demonstration that network oscillations are modulated by extrasynaptic inhibition in FS-BCs suggests that neuroactive compounds that act on extrasynaptic GABA receptors could impact memory formation by modulating hippocampal gamma oscillations. The simulation results indicate that the depolarized FS-BC GABA reversal, observed after experimental seizures, together with enhanced spillover extrasynaptic GABA currents are likely to promote generation of focal high frequency activity associated with epileptic networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Union: An Automatic Workload Manager for Accelerating Network Simulation

With the rapid growth of the machine learning applications, the workloads of future HPC systems are anticipated to be a mix of scientific simulation, big data analytics, and machine learning applications. Simulation is a great research vehicle to understand the performance implications of co-running scientific applications with big data and machine learning workloads on large-scale systems. In this paper, we present Union, a workload manager that provides an automatic framework to facilitate hybrid workload simulation in CODES. Furthermore, we use Union, along with CODES, to investigate various hybrid workloads composed of traditional simulation applications and emerging learning applications on two dragonfly systems. The experiment results show that both message latency and communication time are important performance metrics to evaluate network interference. Network interference on HPC applications is more reflected by the message latency variation, whereas ML application performance depends more on the communication time.

heterogeneous workloads↗
DOE OSTI2026

Tensor network simulations of quasi-GPDs in the massive Schwinger model

Generalized parton distribution functions (GPDs) are off-diagonal light-cone matrix elements that encode the internal structure of hadrons in terms of quark and gluon degrees of freedom. In this work, we present the first nonperturbative study of quasi-GPDs in the massive Schwinger model, quantum electrodynamics in 1+1 dimensions (QED 2 ), within the Hamiltonian formulation of lattice field theory. Quasidistributions are spatial correlation functions of boosted states, which approach the relevant light-cone distributions in the luminal limit. Using tensor networks, we prepare the first excited state in the strongly coupled regime and boost it to close to the light-cone on lattices of up to 400 lattice sites. We compute both quasiparton distribution functions and, for the first time, quasi-GPDs, and study their convergence for increasingly boosted states. In addition, we perform analytic calculations of GPDs in the two-particle Fock-space approximation and in the Reggeized limit, providing qualitative benchmarks for the tensor network results. Our analysis establishes computational benchmarks for accessing partonic observables in low-dimensional gauge theories, offering a starting point for future extensions to higher dimensions, non-Abelian theories, and quantum simulations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Heat transport in liquid water from first-principles and deep neural network simulations

In this work, we compute the thermal conductivity of water within linear response theory from equilibrium molecular dynamics simulations, by adopting two different approaches. In one, the potential energy surface (PES) is derived on the fly from the electronic ground state of density functional theory (DFT) and the corresponding analytical expression is used for the energy flux. In the other, the PES is represented by a deep neural network (DNN) trained on DFT data, whereby the PES has an explicit local decomposition and the energy flux takes a particularly simple expression. By virtue of a gauge invariance principle, established by Marcolongo, Umari, and Baroni, the two approaches should be equivalent if the PES were reproduced accurately by the DNN model. We test this hypothesis by calculating the thermal conductivity, at the GGA (PBE) level of theory, using the direct formulation and its DNN proxy, finding that both approaches yield the same conductivity, in excess of the experimental value by approximately 60%. Besides being numerically much more efficient than its direct DFT counterpart, the DNN scheme has the advantage of being easily applicable to more sophisticated DFT approximations, such as meta-GGA and hybrid functionals, for which it would be hard to derive analytically the expression of the energy flux. We find in this way that a DNN model, trained on meta-GGA (SCAN) data, reduces the deviation from experiment of the predicted thermal conductivity by about 50%, leaving the question open as to whether the residual error is due to deficiencies of the functional, to a neglect of nuclear quantum effects in the atomic dynamics, or, likely, to a combination of the two.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Localized reactive flow in carbonate rocks: Core-flood experiments and network simulations

We conducted four core-flood experiments on samples of a micritic, reef limestone from Abu Dhabi under conditions of constant flow rate. The pore fluid was water in equilibrium with CO 2 , which, because of its lowered pH, is chemically reactive with the limestone. Flow rates were between 0.03 and 0.1 mL/min. The difference between up and downstream pore pressures dropped to final values <<1 MPa over periods of 3–18 h. Scanning electron microscope and microtomography imaging of the starting material showed that the limestone is mostly calcite and lacks connected macroporosity and that the prevailing pores are few microns large. During each experiment, a wormhole formed by localized dissolution, an observation consistent with the decreases in pressure head between the up and downstream reservoirs. Moreover, we numerically modeled the changes in permeability during the experiments. We devised a network approach that separated the pore space into competing subnetworks of pipes. Thus, the problem was framed as a competition of flow of the reactive fluid among the adversary subnetworks. The precondition for localization within certain time is that the leading subnetwork rapidly becomes more transmissible than its competitors. This novel model successfully simulated features of the shape of the wormhole as it grew from few to about 100 µm, matched the pressure history patterns, and yielded the correct order of magnitude of the breakthrough time. Finally, we systematically studied the impact of changing the statistical parameters of the subnetworks. Larger mean radius and spatial correlation of the leading subnetwork led to faster localization.

58 GEOSCIENCES↗
DOE OSTI2021

Performance Evaluation of Engineered Geothermal Systems Using Discrete Fracture Network Simulations

Electrical power production from geothermal energy has a solid record of success for permeable reservoirs such as The Geysers in northern California and geothermal systems in Iceland and New Zealand, among other places. Such permeable reservoirs, however, represent only a small fraction of the available heat energy in the earth’s shallow crust. Most of the available energy resides in rocks with insufficient permeability and storage to produce commercial volumes of heated fluids.

15 GEOTHERMAL ENERGY↗
NASA NTRS1988

Electronic Neural-Network Simulator

Experimental circuits faster than simulation programs run on digital computers. Serial shift register routes clock pulses C1 to neurons in sequence. Clock pulses C2 interrogate neurons. Neuron interconnection information stored in simulated synapses. Can be expanded to greater complexity.

Moopenn, Alex W.↗