Report of particles and fields working group
Magnetic and electric fields, solar particles, galactic cosmic rays, radio propagation, and other atmospheric conditions encountered during lunar explorations
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Magnetic and electric fields, solar particles, galactic cosmic rays, radio propagation, and other atmospheric conditions encountered during lunar explorations
The science of field geology is the investigative process of determining the distribution of rock units and structures on a planet fs surface, and it is the first-order data set that informs all subsequent studies of a planet, such as geochemistry, geochronology, geophysics, or remote sensing. For future missions to the Moon and Mars, the surface systems deployed must support the conduct of field geology if these endeavors are to be scientifically useful. This lecture discussed what field geology is all about.why it is important, how it is done, how conducting field geology informs many other sciences, and how it affects the design of surface systems and the implementation of operations in the future.
The ground observations undertaken over the northern position of Minas Gerais State, and part of Distrito Federal from 7 to 12 December 1982, along the Space Shuttle 2 flying orbit 22 of November 1981 are described. Field data related mostly with lithology, geological structures and forest cover, and specific geomorphological and pedological aspects were collected. Ground data are applied to evaluate the SIR-A Experiment, developed in the Space Shuttle-2 mission for natural resources mapping and prospecting.
The use of analog environments in preparing for future planetary surface exploration is key in ensuring we both understand the processes shaping other planetary surfaces as well as develop the technology, systems, and concepts of operations necessary to operate in these geologic environments. While conducting fieldwork and testing technology in relevant terrestrial field environments is crucial in this development, it is often the case that operational testing requires a time-intensive iterative process that is hampered by the rigorous conditions (e.g. terrain, weather, location, etc.) found in most field environments. Additionally, field deployments can be costly and must be scheduled months in advance, therefore limiting the testing opportunities required to investigate and compare science operational concepts to only once or twice per year.
Interflow, throughflow and subsurface stormflow are interchangeable terms that refer to the lateral subsurface flow above a restricting layer of lower hydraulic conductivity that occurs during and following storm events. Interflow (used here) is a more dominant process in steeper catchments with high infiltration capacity soils overlying a more impermeable soil or geologic layer. Interflow as a runoff process was first recognised in the early 1900s, yet hydrologists still struggle to predict its occurrence, persistence, importance, interaction with other streamflow generation processes, and potential to connect to valleys and streams during and following storms. We review the history of interflow research and address some of the challenges in understanding its role in runoff production. We argue that characterising the controls on interflow initiation and occurrence relies on detailed field observations of subsurface properties, which exist only in limited experimental settings. This data shortcoming contributes to our inability to predict interflow or determine its contribution to streamflow more broadly. There remain many opportunities to advance our understanding of interflow that include both modelling and experimental or observational approaches in hydrology.
The last century has seen marked increases in global temperature.
The overarching goal of this project is to develop a scalable black-box training capability for scientific machine learning (SciML) problems that are non-trainable with existing automatic differentiation (AD)-based algorithms. AD assumes that a loss function can be decomposed into a sequence of elementary operations whose derivatives are known. This assumption is violated when the loss function includes a black-box physical model (e.g., a legacy simulator). The current strategy, converting a black-box simulator to an AD-enabled code via differential programming, is inflexible and time-, labor-consuming. Thus, black-box optimization is a main workhorse for training SciML models, e.g., in scientific reinforcement learning, hyper-parameter fine tuning, designing SciML models with adversarial robustness, etc.
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Over 600 fields in the corn, soybean and wheat growing regions of the Argentine pampa were categorized by crop or cover type and ancillary data including crop calendars, historical crop production statistics and certain cropping practices were also gathered. A summary of the field work undertaken is included along with a country overview, a chronology of field trip planning and field work events, and the field work inventory of selected sample segments. LANDSAT images were annotated and used as the field work base and several hundred ground and aerial photographs were taken. These items along with segment descriptions are presented. Meetings were held with officials of the State Secretariat of Agriculture (SEAG) and the National Commission on Space Investigations (CNIE), and their support to the program are described.
Work carried out by the JPL on several applied field MPD (magnetoplasmasynamic) or hybrid engines, both radiation cooled and liquid cooled, is described. A radiation cooled device was successfully life tested for 554 hrs with ammonia as the propellant. While the electrode erosion was moderate, the boron nitride insulator was badly eroded. Hydrogen and lithium were the other propellants used in these devices. In the case of lithium, a radiation cooled engine had an efficiency of 70 percent at about 5500 sec.
The DOE-FIU Science and Technology Workforce Development Program has been designed to build upon the existing DOE/FIU relationship by creating a “pipeline” of minority engineers specifically trained and mentored to enter the Department of Energy workforce in technical areas of need. The main objective of the program is to provide interested students with a unique opportunity to integrate course work, DOE field work, and research work at FIU into a well-structured academic program that leads to entry into DOE EM’s Pathways Program. Students selected as DOE EM Fellows perform research at FIU and at DOE sites, national laboratories, and DOE contractors. Graduation and completion of this fellowship leads to employment opportunities with DOE EM, DOE contractors, DOE national laboratories, other federal agencies, and private industry as well as the pursuit of post-master or post-doctoral positions at DOE national labs.
Recent studies conducted in the Bonanza Test Site, Colorado, area indicated that: (1) more geologic structural information is available from remote sensing data than from conventional techniques; (2) greater accuracy results from using remote sensing data; (3) all major structural features were detected; (4) of all structural interpretations, about 75% were correct; and (5) interpretation of remote sensing data will not supplant field work, but it enables field work to be done much more efficiently.
Processing and interpretation of Thematic Mapper (TM) data, extensive field work, and processing of SPOT data were continued. Results of these analyses led to the testing and rejecting of several of the geologic/tectonic hypotheses concerning the continuation of the Garlock Fault Zone (GFZ). It was determined that the Death Valley Fault Zone (DVFZ) is the major through-going feature, extending at least 60 km SW of the Avawatz Mountains. Two 5 km wide fault zones were identified and characterized in the Soda and Bristol Mountains, forming a continuous zone of NW trending faulting. Geophysical measurements indicate a buried connection between the Avawatz and the Soda Mountains Fault Zone. Future work will involve continued field work and mapping at key locations, further analyses of TM data, and conclusion of the project.
Fifty years ago Apollo astronauts walked on the Moon to explore the geology and collect samples for Earth return. Several authors have discussed the strategic planning and training that enabled the Apollo successes, and assembled recommendations regarding today’s lunar science objectives and astronaut training required to achieve those science goals. Since the 1980s, geoscience training for astronauts focused on observing the Earth from orbit. Today, we are building a geoscience training program to support informed Earth observations as well as the exploration culture for future human missions to the Moon and Mars. Our team partnered with JSC’s crew training and astronaut offices to develop our 4-week geoscience program for the 2017 astronaut class. Because the astronauts have a variety of professional backgrounds, we provide a broad introduction to Earth and planetary sciences. But our prime focus is 2 weeks of intensive field work, a methodology introduced with the 2013 astronaut class. We completed the first half of the training – a field trip to observe hurricane deposits along Galveston Bay; keynotes by Apollo colleagues highlighting Apollo experiences; a tightly-integrated week of introductory geology in the classroom followed by a week of fieldwork in the Rio Grande del Norte National Monument. The classroom included interactive map exercises that allowed the students to progressively build a base map of the field area that they used as a starting point for their week-long mapping exercise. We divided the class into small mapping groups to conduct their observations, mapping and interpretation of the geology. In addition to learning geological field work, our field training provided the platform for practicing expeditionary leadership, a key skill set valued by NASA for astronaut crews. Next summer the capstone fieldwork for the 2017 astronauts will include both mapping and rock sampling. Throughout the mapping, the class will collect additional data to help inform field and sampling decisions using diagnostic field instruments that are being tested in analog settings for their operational efficacy for future planetary exploration.
The purpose of this tool is to estimate key parameters that may be missing in public wastewater composition datasets. The tool can be applied to develop complete treatment and critical mineral extraction profiles for leachate, produced water and other aqueous waste streams. The tool applies machine learning algorithms to replace missing data in a user’s water data set that are adjusted based on user preferences for options including algorithm type, number of features, and classification variables. The tool can use the user’s data alone or combine user data with the NEWTS USGS Produced Water Database for more robust training. This research was funded by the U.S. Department of Energy’s Office Fossil Energy and Carbon Management (FECM) through National Energy Technology Laboratory’s ongoing research under the Water Management for Power System Field Work Proposal, DE-FECM 1022428 and Critical Minerals Field Work Proposal, DE-FECM 1022420.
Determination of the nature and extent of contamination is a critical task in any Resource Conservation and Recovery Act of 1976 (RCRA) Facility Investigation. Nature and extent are typically defined through field investigation results. In some cases, however, physical and/or programmatic limitations on field work can impede efforts to define nature and extent based on field work alone. Both physical and programmatic restrictions affected efforts to characterize soil contamination at Waste Management Area C, on the U.S. Department of Energy's Hanford Site. As a result, fate and transport modeling was undertaken to enhance the definition of the nature and extent of soil contamination at Waste Management Area C. (authors)