Multi-kw dc power distribution system study program

The first phase of the Multi-kw dc Power Distribution Technology Program is reported and involves the test and evaluation of a technology breadboard in a specifically designed test facility according to design concepts developed in a previous study on space vehicle electrical power processing, distribution, and control. The static and dynamic performance, fault isolation, reliability, electromagnetic interference characterisitics, and operability factors of high distribution systems were studied in order to gain a technology base for the use of high voltage dc systems in future aerospace vehicles. Detailed technical descriptions are presented and include data for the following: (1) dynamic interactions due to operation of solid state and electromechanical switchgear; (2) multiplexed and computer controlled supervision and checkout methods; (3) pulse width modulator design; and (4) cable design factors

Research study on multi-KW-DC distribution system

A detailed definition of the HVDC test facility and the equipment required to implement the test program are provided. The basic elements of the test facility are illustrated, and consist of: the power source, conventional and digital supervision and control equipment, power distribution harness and simulated loads. The regulated dc power supplies provide steady-state power up to 36 KW at 120 VDC. Power for simulated line faults will be obtained from two banks of 90 ampere-hour lead-acid batteries. The relative merits of conventional and multiplexed power control will be demonstrated by the Supervision and Monitor Unit (SMU) and the Automatically Controlled Electrical Systems (ACES) hardware. The distribution harness is supported by a metal duct which is bonded to all component structures and functions as the system ground plane. The load banks contain passive resistance and reactance loads, solid state power controllers and active pulse width modulated loads. The HVDC test facility is designed to simulate a power distribution system for large aerospace vehicles

Research study on multi-KW DC distribution system

Power distribution system noise and transient stress on switchgear in large space vehicle power systems were investigated in terms of the effect of flight designs of long power distribution cables on load interface EMI requirements. A fifty meter cable pair was simulated to study interactions between the cable, load, and power source terminations. Power system noise characteristics were evaluated based on current spacecraft data, interface hardware filter designs, and power cable parameters. Parametric approaches were defined for evaluating switching transients at various distribution voltage levels. It is concluded that the state-of-the-art semiconductor switches represent a viable approach toward the implementation of power system design with distribution voltages of 120 VDC or less. The interface definition and design for the bus control unit was updated to be consistent with the established requirements

Crisis and Change: A University Press Environmental Scan

A transcript of the closing keynote address delivered March 6, 2014 at the inaugural Library Publishing Forum in Kansas City, Missouri

Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas

One of the biggest challenges to achieve the goal of producing fusion energy in tokamak devices is the necessity of avoiding disruptions of the plasma current due to instabilities. The disruption event characterization and forecasting (DECAF) framework has been developed in this purpose, integrating physics models of many causal events that can lead to a disruption. Two different machine learning approaches are proposed to improve the ideal magnetohydrodynamic (MHD) no-wall limit component of the kinetic stability model included in DECAF. First, a random forest regressor (RFR), was adopted to reproduce the DCON computed change in plasma potential energy without wall effects, , for a large database of equilibria from the national spherical torus experiment (NSTX). This tree-based method provides an analysis of the importance of each input feature, giving an insight into the underlying physics phenomena. Secondly, a fully-connected neural network has been trained on sets of calculations with the DCON code, to get an improved closed form equation of the no-wall limit as a function of the relevant plasma parameters indicated by the RFR. The neural network has been guided by physics theory of ideal MHD in its extension outside the domain of the NSTX experimental data. The estimated value of has been incorporated into the DECAF kinetic stability model and tested against a set of experimentally stable and unstable discharges. Moreover, the neural network results were used to simulate a real-time stability assessment using only quantities available in real-time. Finally, the portability of the model was investigated, showing encouraging results by testing the NSTX-trained algorithm on the mega ampere spherical tokamak (MAST)

Predicting resistive wall mode stability in NSTX through balanced random forests and counterfactual explanations

Recent progress in the disruption event characterization and forecasting framework has shown that machine learning guided by physics theory can be easily implemented as a supporting tool for fast computations of ideal stability properties of spherical tokamak plasmas. In order to extend that idea, a customized random forest (RF) classifier that takes into account imbalances in the training data is hereby employed to predict resistive wall mode (RWM) stability for a set of high beta discharges from the NSTX spherical tokamak. More specifically, with this approach each tree in the forest is trained on samples that are balanced via a user-defined over/under-sampler. The proposed approach outperforms classical cost-sensitive methods for the problem at hand, in particular when used in conjunction with a random under-sampler, while also resulting in a threefold reduction in the training time. In order to further understand the model’s decisions, a diverse set of counterfactual explanations based on determinantal point processes (DPP) is generated and evaluated. Via the use of DPP, the underlying RF model infers that the presence of hypothetical magnetohydrodynamic activity would have prevented the RWM from concurrently going unstable, which is a counterfactual that is indeed expected by prior physics knowledge. Given that this result emerges from the data-driven RF classifier and the use of counterfactuals without hand-crafted embedding of prior physics intuition, it motivates the usage of counterfactuals to simulate real-time control by generating the β N levels that would have kept the RWM stable for a set of unstable discharges

Mars Spacecraft Power System Development Final Report

Development of optimum Mariner spacecraft power system for application to future flyby and orbiter mission