SEARCH · Engineering Papers
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.
Multiloop feedback in active distributed RC networks for low parameter sensitivity with low amplifier gain compared to single loop circuits
Distributed RC networks combined with lumped passive and active elements to produce distributed-lumped-active /DLA/ networks for filter requirements
Digital computer program for distributed-lumped- active network analysis
Tapered distributed RC low pass network configuration with voltage-controlled sources for sensitivity reduction low and high Q factors
A new distributed-active circuit is described that has a lower sensitivity and utilizes its capacitor area more effectively than the circuit presented in an earlier correspondence by Howe (1969). The described circuit is approximately 2.8 times as effective in its use of capacitance, a fact which is directly translatable into savings of space and weight.
Complex optimization and its application to distributed-lumped-active networks
Constrained complex optimization method for synthesizing distributed-lumped-active networks
Topology and component values in computerized design of distributed lumped active networks
ANLYZ digital computer program subroutine for complex analysis of distributed lumped active networks
Distributed-lumped-active network to produce transfer function with pair of high Q zero real part sensitivity poles
The use of digital computational techniques in the analysis and synthesis of DLA (distributed lumped active) networks is considered. This class of networks consists of three distinct types of elements, namely, distributed elements (modeled by partial differential equations), lumped elements (modeled by algebraic relations and ordinary differential equations), and active elements (modeled by algebraic relations). Such a characterization is applicable to a broad class of circuits, especially including those usually referred to as linear integrated circuits, since the fabrication techniques for such circuits readily produce elements which may be modeled as distributed, as well as the more conventional lumped and active ones.
Complex optimization techniques applied to various sensitivity coefficient determination for distributed-lumped-active networks
Automated generation of distributed-lumped-active network design charts by digital computer root- locus technique
Digital computer analysis and synthesis of distributed-lumped-active networks
Digital computer program for analysis and synthesis of distributed lumped activity networks
Analysis and synthesis of distributed, lumped, and active networks by digital computers
Here this letter proposes a novel data-driven probabilistic physics-informed graph convolutional network (GCN) for active distribution system voltage prediction with PVs and EVs. It leverages both measurements and network topology to accurately and efficiently predict node voltages without the need for an accurate distribution system power flow model. The dropout-enabled Bayesian inference is developed to achieve uncertainty quantification of the voltage prediction. Thanks to the network model embedding, it also has robustness against topology changes, a key difference with existing machine learning-based approaches. Comparison results with other state-of-the-art machine learning methods on a realistic 759-node distribution system demonstrate that the proposed method can achieve better accuracy and robustness under different scenarios.
In extending Howe's (1969) work on design equations for active distributed RC networks, expressions relating the nondominant pole locations of a distributed-active low-pass network to the dominant pole locations are derived, along with expressions relating the Q of the nondominant poles to the Q of the dominant poles. It is shown that, in general, the effect of the nondominant poles is very small.