Robe Development for Electrical Conductivity Analysis in an Electron Gun Produced Helium Plasma

The use of magnetohydrodynamic (MHD) power conversion systems, potentially coupled with a fission power source, is currently being investigated as a driver for an advanced propulsion system, such as a plasma thruster. The efficiency of a MHD generator is strongly dependent on the electrical conductivity of the fluid that passes through the generator; power density increases as fluid conductivity increases. Although traditional MHD flows depend on thermal ionization to enhance the electrical conductivity, ionization due to nuclear interactions may achieve a comparable or improved conductivity enhancement while avoiding many of the limitations inherent to thermal ionization. Calculations suggest that nuclear-enhanced electrical conductivity increases as the neutron flux increases; conductivity of pure He-3 greater than 10 mho/m may be achievable if exposed to a flux greater than 10(exp 12) neutrons/cm2/s.) However, this remains to be demonstrated experimentally. An experimental facility has been constructed at the Propulsion Research Center at the NASA Marshall Space Flight Center, using helium as the test fluid. High energy electrons will be used to simulate the effects of neutron-induced ionization of helium gas to produce a plasma. These experiments will be focused on diagnosis of the plasma in a virtually static system; results will be applied to future tests with a MHD system. Initial experiments will utilize a 50 keV electron gun that can operate at up to a current of 200 micro A. Spreading the electron beam over a four inch diameter window results in an electron flux of 1.5x 10(exp 13) e/sq cm/s. The equivalent neutron flux that would produce the same ionization fraction in helium is 1x10(exp 12) n/sq cm/s. Experiments will simulate the neutron generated plasma modeled by Bitteker, which takes into account the products of thermal neutron absorption in He-3, and includes various ion species in estimating the conductivity of the resulting plasma. Several different probes will be designed and implemented to verify the plasma kinetics model. System parameters and estimated operating ranges are summarized. The predicted ionization fraction, electron density, and conductivity levels are provided in for an equivalent neutron flux of 1x10(exp 12) n/cm2/s. Understanding the complex plasma kinetics throughout a MHD channel is necessary to design an optimal power conversion system for space propulsion applications. The proposed experiments seek to fully characterize the helium plasma and to determine the reliability of each measurement technique, such that they may be applied to more advanced MHD studies. The expected value of each plasma parameter determined from theoretical models will be verified experimentally by several independent techniques to determine the most reliable method of obtaining each parameter. The results of these experiments will be presented in the final paper

Status of the Nuclear-Induced Conductivity Experiment (NICE) Project

Nuclear-based magnetohydrodynamic (MHD) energy conversion has been pursued in various forms since the 1950's. The majority of this work was motivated by the compatibility of MHD generators with the high temperature achievable with a nuclear reactor and the associated potential for very high cycle efficiency. As a result of this perspective, methods for enhancing the electrical conductivity of the MHD flow have primarily focused on traditional thermal ionization processes, especially those utilizing alkali metal seeds. However, electrical conductivity enhancement via thermal interactions imposes significant limitations on the flow expansion through the generator, and hence on the ultimate power density. Furthermore, the introduction of an alkali metal seed into the flow significantly complicates the engineering design and increases the potential for system failures due to plating of the evaporated metal on cold surfaces

Data from: The search for loci under selection: trends, biases and progress

Detecting genetic variants under selection using FST outlier analysis (OA) and environmental association analyses (EAA) are popular approaches that provide insight into the genetic basis of local adaptation. Despite the frequent use of OA and EAA approaches and their increasing attractiveness for detecting signatures of selection, their application to field-based empirical data have not been synthesized. Here, we review 66 empirical studies that use Single Nucleotide Polymorphisms (SNPs) in OA and EAA. We report trends and biases across biological systems, sequencing methods, approaches, parameters, environmental variables and their influence on detecting signatures of selection. We found striking variability in both the use and reporting of environmental data and statistical parameters. For example, linkage disequilibrium among SNPs and numbers of unique SNP associations identified with EAA were rarely reported. The proportion of putatively adaptive SNPs detected varied widely among studies, and decreased with the number of SNPs analyzed. We found that genomic sampling effort had a greater impact than biological sampling effort on the proportion of identified SNPs under selection. OA identified a higher proportion of outliers when more individuals were sampled, but this was not the case for EAA. To facilitate repeatability, interpretation and synthesis of studies detecting selection, we recommend that future studies consistently report geographic coordinates, environmental data, model parameters, linkage disequilibrium, and measures of genetic structure. Identifying standards for how OA and EAA studies are designed and reported will aid future transparency and comparability of SNP-based selection studies and help to progress landscape and evolutionary genomics

Table S1 - Full data set.

Data was collected by reading papers associated with environmental association analyses. Data includes location, species, methods used, genetic parameters of data sets reviewed, and analytical parameters of the analyses

R code for mixed-effects linear models

The R code used to create the figures and estimate regressions of the data set