Simulation of a Lunar Surface Base Power Distribution Network for the Constellation Lunar Surface Systems

The Lunar Surface Power Distribution Network Study team worked to define, breadboard, build and test an electrical power distribution system consistent with NASA's goal of providing electrical power to sustain life and power equipment used to explore the lunar surface. A testbed was set up to simulate the connection of different power sources and loads together to form a mini-grid and gain an understanding of how the power systems would interact. Within the power distribution scheme, each power source contributes to the grid in an independent manner without communication among the power sources and without a master-slave scenario. The grid consisted of four separate power sources and the accompanying power conditioning equipment. Overall system design and testing was performed. The tests were performed to observe the output and interaction of the different power sources as some sources are added and others are removed from the grid connection. The loads on the system were also varied from no load to maximum load to observe the power source interactions

Dynamic Simulation of Radially Oriented Permanent Magnet-Type Electronically Operated Synchronous Machines with Parameters Obtained from Finite Element Field Solutions

A dynamic model for simulation of the transient interaction between radially oriented permanent magnet-type synchronous machines and their corresponding transistorized current source power conditioners is presented. Some key machine parameters used in this dynamic model were obtained from finite element field solutions. This dynamic model was used to obtain the transient interaction between a 15-hp samarium cobalt radially oriented permanent magnet electronically operated synchronous machine and its corresponding power conditioner. This machine was constructed for electric vehicle propulsion. Excellent correlation between various digitally simulated and actual test current and voltage waveforms, in various branches of the machine-conditioner network, has been achieved. These results are given. This modeling approach is applied to machines during the design stage, where the finite element modeling is the only way to obtain the necessary machine parameters for dynamic simulation. It is shown how such a combination of the computer-aided design tools can help in prevention of design mis-judgements that can prove costly to remedy once the hardware is in place. This is done through an actual design example of an additional machine being manufactured for electric propulsion applications

Dynamic Simulation of Radially Oriented Permanent Magnet-Type Electronically Operated Synchronous Machines with Parameters Obtained from Finite Element Field Solutions

A dynamic model for simulation of the transient interaction between radially oriented permanent magnet-type synchronous machines and their corresponding transistorized current source power conditioners is presented. Some key machine parameters used in this dynamic model were obtained from finite element field solutions. This dynamic model was used to obtain the transient interaction between a 15-hp samarium cobalt radially oriented permanent magnet electronically operated synchronous machine and its corresponding power conditioner. This machine was constructed for electric vehicle propulsion. Excellent correlation between various digitally simulated and actual test current and voltage waveforms, in various branches of the machine-conditioner network, has been achieved. These results are given. This modeling approach is applied to machines during the design stage, where the finite element modeling is the only way to obtain the necessary machine parameters for dynamic simulation. It is shown how such a combination of the computer-aided design tools can help in prevention of design mis-judgements that can prove costly to remedy once the hardware is in place. This is done through an actual design example of an additional machine being manufactured for electric propulsion applications

Predicting September Arctic sea ice: a multi-model seasonal skill comparison

This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance

Biological and Environmental Research Exascale Requirements Review

The article of record as published may be found at http://dx.doi.org/10.2172/1375720An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Biological and Environmental Research, March 28-31, 2016, Rockville, MarylandUnderstanding the fundamentals of genomic systems or the processes governing impactful weather patterns are examples of the types of simulation and modeling performed on the most advanced computing resources in America. High-performance computing and computational science together provide a necessary platform for the mission science conducted by the Biological and Environmental Research (BER) office at the U.S. Department of Energy (DOE). This report reviews BER’s computing needs and their importance for solving some of the toughest problems in BER’s portfolio. BER’s impact on science has been transformative. Mapping the human genome, including the U.S.-supported international Human Genome Project that DOE began in 1987, initiated the era of modern biotechnology and genomics-based systems biology. And since the 1950s, BER has been a core contributor to atmospheric, environmental, and climate science research, beginning with atmospheric circulation studies that were the forerunners of modern Earth system models (ESMs) and by pioneering the implementation of climate codes onto high-performance computers. See http://exascaleage.org/ber/ for more information.USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21)USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23