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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Semi-Classical Models for Virtual Antiparticle Pairs

Virtual particle-antiparticle pairs of massive elementary particle& are predicted in Quantum Field Theory (QFT) to appear from the vacuum and annihilate each other again within their Heisenberg lifetimes h/4mc(exp 2). In this work, semiclassical models of this process - for the cases of massive leptons, quarks, and the massive weak bosons W and Z - are constructed. It is shown that the dynamical lifetime of the particle- antiparticle system in each case equals the Heisenberg lifetime to good approximation, and obeys appropriate quantization conditions on the field fluctuation action. In other words, the dynamical lifetime of the semiclassical model agrees with QED and QCD to good approximation. But the formula for the dynamical lifetime in each model includes the force strength coupling constant (e in the lepton case, alpha(sup s) (q(exp 2)) in the quark cases), while the Heisenberg lifetime formula does not. Observing the agreement of the Heisenberg and dynamical lifetimes, we may derive the QED and QCD coupling constants in terms of h, c, and numerical factors only.

Batchelor, David↗
DOE OSTI2014

Modeling the Virtual Machine Launching Overhead under Fermicloud

FermiCloud is a private cloud developed by the Fermi National Accelerator Laboratory for scientific workflows. The Cloud Bursting module of the FermiCloud enables the FermiCloud, when more computational resources are needed, to automatically launch virtual machines to available resources such as public clouds. One of the main challenges in developing the cloud bursting module is to decide when and where to launch a VM so that all resources are most effectively and efficiently utilized and the system performance is optimized. However, based on FermiCloud’s system operational data, the VM launching overhead is not a constant. It varies with physical resource (CPU, memory, I/O device) utilization at the time when a VM is launched. Hence, to make judicious decisions as to when and where a VM should be launched, a VM launch overhead reference model is needed. The paper is to develop a VM launch overhead reference model based on operational data we have obtained on FermiCloud and uses the reference model to guide the cloud bursting process.

Garzoglio, Gabriele↗
DOE OSTI2016

A Reference Model for Virtual Machine Launching Overhead

Cloud bursting is one of the key research topics in the cloud computing communities. A well designed cloud bursting module enables private clouds to automatically launch virtual machines (VMs) to public clouds when more resources are needed. One of the main challenges in developing a cloud bursting module is to decide when and where to launch a VM so that all resources are most effectively and efficiently utilized and the system performance is optimized. However, based on system operational data obtained from FermiCloud, a private cloud developed by the Fermi National Accelerator Laboratory for scientific workflows, the VM launching overhead is not a constant. It varies with physical resource utilization, such as CPU and I/O device utilizations, at the time when a VM is launched. Hence, to make judicious decisions as to when and where a VM should be launched, a VM launching overhead reference model is needed. In this paper, we first develop a VM launching overhead reference model based on operational data we have obtained on FermiCloud. Second, we apply the developed reference model on FermiCloud and compare calculated VM launching overhead values based on the model with measured overhead values on FermiCloud. Our empirical results on FermiCloud indicate that the developed reference model is accurate. We believe, with the guidance of the developed reference model, efficient resource allocation algorithms can be developed for cloud bursting process to minimize the operational cost and resource waste.

Wu, Hao↗
NASA NTRS2008

SHINE Virtual Machine Model for In-flight Updates of Critical Mission Software

This software is a new target for the Spacecraft Health Inference Engine (SHINE) knowledge base that compiles a knowledge base to a language called Tiny C - an interpreted version of C that can be embedded on flight processors. This new target allows portions of a running SHINE knowledge base to be updated on a "live" system without needing to halt and restart the containing SHINE application. This enhancement will directly provide this capability without the risk of software validation problems and can also enable complete integration of BEAM and SHINE into a single application. This innovation enables SHINE deployment in domains where autonomy is used during flight-critical applications that require updates. This capability eliminates the need for halting the application and performing potentially serious total system uploads before resuming the application with the loss of system integrity. This software enables additional applications at JPL (microsensors, embedded mission hardware) and increases the marketability of these applications outside of JPL.

Plesea, Lucian↗
DOE OSTI2020

Scheduling and Control of Flexible Building Loads for Grid Services Based on a Virtual Battery Model

This paper presents a framework for modeling, scheduling, and controlling residential thermostatically controlled loads (TCLs) to provide multiple grid services, such as energy shifting, peak load reduction, and ancillary services. A modeling method is proposed to characterize the aggregate flexibility from heterogeneous TCLs using a battery-equivalent model. Based on the flexibility model, a multi-period optimal scheduling formulation is developed to best utilize the flexibility from building loads and maximize total benefits from stacked value streams. An algorithm is proposed to control individual TCLs to follow the desired power consumption in real-time. The proposed methods are illustrated and validated through simulations.

Wu, Di↗