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At least 865 records · Page 48

All sources NASA NTRS DOE OSTI

NASA NTRS2015

Soil Moisture Active Passive (SMAP) Mission Level 4 Surface and Root Zone Soil Moisture (L4_SM) Product Specification Document

This is the Product Specification Document (PSD) for Level 4 Surface and Root Zone Soil Moisture (L4_SM) data for the Science Data System (SDS) of the Soil Moisture Active Passive (SMAP) project. The L4_SM data product provides estimates of land surface conditions based on the assimilation of SMAP observations into a customized version of the NASA Goddard Earth Observing System, Version 5 (GEOS-5) land data assimilation system (LDAS). This document applies to any standard L4_SM data product generated by the SMAP Project. The Soil Moisture Active Passive (SMAP) mission will enhance the accuracy and the resolution of space-based measurements of terrestrial soil moisture and freeze-thaw state. SMAP data products will have a noteworthy impact on multiple relevant and current Earth Science endeavors. These include: Understanding of the processes that link the terrestrial water, the energy and the carbon cycles, Estimations of global water and energy fluxes over the land surfaces, Quantification of the net carbon flux in boreal landscapes Forecast skill of both weather and climate, Predictions and monitoring of natural disasters including floods, landslides and droughts, and Predictions of agricultural productivity. To provide these data, the SMAP mission will deploy a satellite observatory in a near polar, sun synchronous orbit. The observatory will house an L-band radiometer that operates at 1.40 GHz and an L-band radar that operates at 1.26 GHz. The instruments will share a rotating reflector antenna with a 6 meter aperture that scans over a 1000 km swath.

L4-SM↗

NASA NTRS2017

Methodologies for Root Locus and Loop Shaping Control Design with Comparisons

This paper describes some basics for the root locus controls design method as well as for loop shaping, and establishes approaches to expedite the application of these two design methodologies to easily obtain control designs that meet requirements with superior performance. The two design approaches are compared for their ability to meet control design specifications and for ease of application using control design examples. These approaches are also compared with traditional Proportional Integral Derivative (PID) control in order to demonstrate the limitations of PID control. Robustness of these designs is covered as it pertains to these control methodologies and for the example problems.

Classical ControlFeedback ControlsRoot LocusLoop S↗

NASA NTRS2018

Soil Moisture Active Passive (SMAP) Mission Level 4 Surface and Root Zone Soil Moisture (L4_SM) Product Specification Document

Soil Moisture↗

NASA NTRS2021

The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis

The Soil Moisture Active Passive (SMAP) Level-4 (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 DA system (i.e., the performance improvement in DA relative to OL) is attributed to eight control factors related to the CLSM as well as τ–ω radiative transfer model (RTM) components of the L4 system. Results show that the Spearman rank correlation (R) for L4 SSM with in situ measurements increases for 77 % of the in situ measurement locations (relative to that of OL), with an average R increase of approximately 14 % (ΔR=0.056). RZSM skill is improved for about 74 % of the in situ measurement locations, but the average R increase for RZSM is only 7 % (ΔR=0.034). Results further show that the SSM DA skill improvement is most strongly related to the difference between the RTM-simulated Tb and the SMAP Tb observation, followed by the error in precipitation forcing data and estimated microwave soil roughness parameter h. For the RZSM DA skill improvement, these three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb simulation error, as the soil roughness strongly affects the ingestion of DA increments and further propagation to the subsurface. For the skill of the L4 and OL estimates themselves, the top two control factors are the precipitation error and the SSM–RZSM coupling strength error, both of which are related to the CLSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM–RZSM coupling strength.

Jianxiu Qiu↗

NASA NTRS2018

High Resolution Computed Tomography Inspection of Components Uncovers Root Causes of Vexing Failure Mysteries

During testing of actuators for fight programs, failures have occurred that required closure with an understanding of the root cause of the failure. For some InSight project actuators, intermittent changes of the winding resistance of the motors during temperature changes was extremely baffling. It was critical to determine the cause of the intermittent characteristic because the actuators were stock from a previous program and the company that provided the motors was no longer in business. Using an X-Ray Computed-Tomography (CT) method, the exact source of the intermittent failure was determined and completely understood – at dimensions of less than 20 microns. In another case, after the completion of an actuator’s life-test-to-failure, inspection was required to determine the source of the failure. The size of the actuator (10 mm diameter) required destructive means to get to the interior of the actuator for inspection, risking the loss of the source of the failure. Using CT imaging, the exact source of the failure was determined.

Johnson, Michael R.↗

NASA NTRS2021

Robust Control Methodology Via Loop Shaping, Root Locus, and Model Reference Control

The paper describes the development of a robust control methodology that can tolerate large uncertainties in plant dynamics in terms of proportional gain variations as well as frequencies and damping. For this to be accomplished, the methodology starts by designing a feedback controller based on a loop shaping technique. The design is then improved by employing a root locus control design approach. Finally, the response of the feedback system is further improved by augmenting the control design using model reference control. The design approach also takes into consideration the speed of the process actuator for a fast response and for high disturbance rejection. Results from two control design examples covered in the paper show that this control approach can tolerate plant proportional gain uncertainty of approximately ± two orders of magnitude with relatively large variations in plant frequencies and damping.

Control Systems↗

NASA NTRSDate not supplied

Root Growth Response of Arabidopsis Thaliana Seeds Exposed to Simulated Solar Particle Event Radiation and Microgravity

As NASA manned missions are planned to go past earth’s orbit, the effects of microgravity and radiation on plants will be essential to know as plants will be a part of the crew’s diet. APPROACH: Imbibed Arabidopsis thaliana seeds were exposed to simulated solar particle event (SPE) radiation, experiencing up to 80 cGy of radiation. After the radiation exposure, they were grown under simulated microgravity using a Random Positioning Machine. Images of the plants were taken after six to seven days of growth and the root length, top angle, tip angle were analyzed.

Karen Perkins↗

NASA NTRSDate not supplied

Root Cause Analysis of the Data Refinement Process – Medical Conditions Capability Resource Tables

The medical system for spaceflight thus far has been designed to support missions in low earth orbit (LEO). Crew capabilities are limited and heavily dependent on the team of medical support staff at Mission Control Center (MCC) to guide diagnosis and management. However, missions to the Moon and Mars will suffer from several constraints that will make this ground support focused approach to care ineffective. In order to update and modify medical system design, NASA has relied on Probabilistic Risk Assessment (PRA) modeling to mitigate medical risk through trade space analysis. Specifically, capability resource tables (CRT’s) were developed to create a dataset of resources required to manage a list of accepted medical conditions significant in exploration spaceflight. With 120 conditions, this dataset contained hundreds of capabilities and thousands of resources with tens of thousands of cells of data. Initially these tables were built in excel for high throughput during development, but ultimately had to be transferred, managed, and modified into the Evidence Library database for modeling purposes. The process of collating and reviewing the Evidence Library revealed numerous errors in the dataset that had to be corrected through iterative changes. Several error types emerged during this process and can be broken into specific classifications defined as “input”, “transcription”, “structural”, “branching”, and “information”. In reviewing these error types through the root cause analysis (RCA) approach, we were able to identify the contributors to these errors which included single data review points, changing product end goals, limited software selection, time constraints and several others. By reviewing and evaluating the underlying causes we can provide possible system improvements that can be implemented for current and future data management in PRA model inputs.

A. Anderson↗

NASA NTRSDate not supplied

Root Cause Analysis of Data Refinement Process – Medical Conditions Capability Resource Tables

- Objective - Background - Capability Resource Tables (CRT) - Root Cause Analysis(RCA) - Error types - RCA - Fishbone; 5 whys - Lessons Learned

Arian Anderson↗

DOE OSTI2020

ROOT I/O compression improvements for HEP analysis

We overview recent changes in the ROOT I/O system, increasing performance and enhancing it and improving its interaction with other data analysis ecosystems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly to improve experiment's software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically increased during the LHC era. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly: there are significant trade-offs between the increased CPU cost for reading and writing files and the reduce storage space.

Shadura, Oksana↗

DOE OSTI2020

Methods and devices for non-invasive root phenotyping

The present disclosure provides for an electronic sensor for detecting a root of a plant in soil, the electronic sensor that includes a first conductor plate configured to be disposed in soil, a switch, a power supply, and signal extractor. The switch is electrically coupled to the first conductor plate and is configured to switch between a first mode and a second mode. The power supply is electrically coupled to the switch and is configured to provide an electrical charge to the first conductor plate in the first mode of the switch. The signal extractor is electrically coupled to the switch and is configured to extract a signal response at the first conductor plate in the second mode of the switch. The present disclosure further provides a second conductor plate configured to be disposed in soil adjacent to and substantially parallel to the first conductor plate. The second conductor plate is electrically coupled to ground.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Methods and devices for non-invasive root phenotyping

Benfey, Philip N.↗

DOE OSTI2023

Methods and devices for non-invasive root phenotyping

The present disclosure provides for an electronic sensor for detecting a root of a plant in soil, the electronic sensor that includes a first conductor plate configured to be disposed in soil, a switch, a power supply, and a signal extractor. The switch is electrically coupled to the first conductor plate and is configured to switch between a first mode and a second mode. The power supply is electrically coupled to the switch and is configured to provide an electrical charge to the first conductor plate in the first mode of the switch. The signal extractor is electrically coupled to the switch and is configured to extract a signal response at the first conductor plate in the second mode of the switch. The present disclosure further provides a second conductor plate configured to be disposed in soil adjacent to and substantially parallel to the first conductor plate. The second conductor plate is electrically coupled to ground.

Benfey, Philip↗

DOE OSTI2020