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NASA NTRSDate not supplied

COCPIT: Collaborative Activity Planning Software for Mars Perseverance Rover

Since landing on the Martian surface, the Perseverance rover has relied on a distributed team to generate commands for exploring its new environment each sol(Martian day). The team uses a complex suite of software tools to accomplish this challenging task in time for the next window of opportunity to send commands to the rover. A key piece of this software ecosystem is COCPIT (Component-based Campaign Planning, Implementation, and Tactical). COCPIT is part of the next generation of planning and scheduling software tools developed by NASA's Jet Propulsion Laboratory in partnership with NASA's Ames Research Center. COCPIT is a web-based application that allows users to collaboratively view and update the Perseverance rover's activity plans, continuously verify that the plan satisfies constraints, assign targets for directing scientific instruments, document science intent, and model power and data resources. Mars Surface Operations requires diverse expertise from team members within the Engineering, Science, Robotic, and Instrument Operations groups, distributed across North America and Europe. In order to improve efficiency and reduce risk, all teams are able to review and edit their activities simultaneously and see the effects on the plan in its entirety. As part of the Ground Data System (GDS) tool suite, COCPIT is responsible for the activity plan. It provides specialized views that allow operators to understand where there may be room for additional observations, see whether any planning constraints are being violated, and confirm that energy usage and data generation are within the defined limits. It contains details such as which filters a camera will use for a given observation, what the resolution of the images should be, where to store the data onboard, and how long the observation is expected to take. It predicts when specific data will be downlinked from the rover to a passing orbiter, so that the team knows when to expect that data on Earth for evaluation in future planning. Ultimately the information from the COCPIT plan is translated to sequences that will be bundled and radiated to Perseverance for execution. The COCPIT tool is used throughout all planning phases.

activity planning↗

NASA NTRSDate not supplied

COCPIT: Collaborative Activity Planning Software for Mars Perseverance Rover

activity planning↗

NASA NTRS2022

COCPIT: Collaborative Activity Planning Software for Mars Perseverance Rover

Since landing on the Martian surface, the Perseverance rover has relied on a distributed team to generate commands for exploring its new environment each sol (Martian day). The team uses a complex suite of software tools to accomplish this challenging task in time for the next window of opportunity to send commands to the rover. A key piece of this software ecosystem is COCPIT (Component-based Campaign Planning, Implementation, and Tactical). COCPIT is part of the next generation of planning and scheduling software tools developed by NASA's Jet Propulsion Laboratory in partnership with NASA's Ames Research Center. COCPIT is a web-based application that allows users to collaboratively view and update the Perseverance rover's activity plans, continuously verify that the plan satisfies constraints, assign targets for directing scientific instruments, document science intent, and model power and data resources. Mars Surface Operations requires diverse expertise from team members within the Engineering, Science, Robotic, and Instrument Operations groups, distributed across North America and Europe. In order to improve efficiency and reduce risk, all teams are able to review and edit their activities simultaneously and see the effects on the plan in its entirety. As part of the Ground Data System (GDS) tool suite, COCPIT is responsible for the activity plan. It provides specialized views that allow operators to understand where there may be room for additional observations, see whether any planning constraints are being violated, and confirm that energy usage and data generation are within the defined limits. It contains details such as which filters a camera will use for a given observation, what the resolution of the images should be, where to store the data onboard, and how long the observation is expected to take. It predicts when specific data will be downlinked from the rover to a passing orbiter, so that the team knows when to expect that data on Earth for evaluation in future planning. Ultimately the information from the COCPIT plan is translated to sequences that will be bundled and radiated to Perseverance for execution. The COCPIT tool is used throughout all planning phases.

Kanefsky, Bob↗

NASA NTRS2001

Code Sharing and Collaboration: Experiences From the Scientist's Expert Assistant Project and Their Relevance to the Virtual Observatory

In the Virtual Observatory (VO), software tools will perform the functions that have traditionally been performed by physical observatories and their instruments. These tools will not be adjuncts to VO functionality but will make up the very core of the VO. Consequently, the tradition of observatory and system independent tools serving a small user base is not valid for the VO. For the VO to succeed, we must improve software collaboration and code sharing between projects and groups. A significant goal of the Scientist's Expert Assistant (SEA) project has been promoting effective collaboration and code sharing among groups. During the past three years, the SEA project has been developing prototypes for new observation planning software tools and strategies. Initially funded by the Next Generation Space Telescope, parts of the SEA code have since been adopted by the Space Telescope Science Institute. SEA has also supplied code for the SIRTF (Space Infrared Telescope Facility) planning tools, and the JSky Open Source Java library. The potential benefits of sharing code are clear. The recipient gains functionality for considerably less cost. The provider gains additional developers working with their code. If enough users groups adopt a set of common code and tools, de facto standards can emerge (as demonstrated by the success of the FITS standard). Code sharing also raises a number of challenges related to the management of the code. In this talk, we will review our experiences with SEA--both successes and failures, and offer some lessons learned that might promote further successes in collaboration and re-use.

Korathkar, Anuradha↗

DOE OSTI2026

New approaches to Bayesian uncertainty quantification for Nuclear Science (Final Technical Report)

Inverse problems play a central role in experimentation and theory/data comparisons for many areas of modern Nuclear Physics (NP) and High-Energy Physics (HEP). Bayes’s Theorem is a powerful tool for solving Inverse Problems, providing conceptually transparent and unbiased constraints on theoretical parameters and their uncertainties (“Bayesian Inference”) and enabling the quantification of agreement or tension between models and data. However, analyses based on Bayesian Inference are often challenging for NP and HEP applications, either because of the large number of parameters in the problem, the high computational cost, or both. We propose a multi-institutional collaboration to develop and deploy novel Bayesian analysis tools that advance the scientific scope of a broad range of current and future NP experiments. This project brings together NP domain scientists working on several high-profile NP projects for which new, high-performance Bayesian Uncertainty Quantification (“Bayesian UQ”) methods are essential to carry out the science, and data scientists who are developing state-of-the-art methods applicable to these problems. The NP projects in this proposal comprise measurements of the mass and fundamental nature of the neutrino; study of the Quark-Gluon Plasma that filled the early universe; and mapping of natural and anthropogenic radiation environments. While these NP projects have very different scientific goals, with datasets and analysis approaches that differ significantly, they share common requirements for improving computationally intensive Bayesian analyses using advanced Machine Learning algorithms and will benefit strongly from a coherent effort to develop general solutions. This proposal brings together these projects and forefront ML-based data science algorithms to develop such general solutions. The methods developed in this project will also be more widely applicable, thereby advancing science in the larger Nuclear Physics portfolio.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2016

Mechanical Prototyping and Manufacturing Internship

The internship was located at the Johnson Space Center (JSC) Innovation Design Center (IDC), which is a facility where the JSC workforce can meet and conduct hands-on innovative design, fabrication, evaluation, and testing of ideas and concepts relevant to NASA's mission. The tasks of the internship included mechanical prototyping design and manufacturing projects in service of research and development as well as assisting the users of the IDC in completing their manufacturing projects. The first project was to manufacture hatch mechanisms for a team in the Systems Engineering and Project Advancement Program (SETMAP) hexacopter competition. These mechanisms were intended to improve the performance of the servomotors and offer an access point that would also seal to prevent cross-contamination. I also assisted other teams as they were constructing and modifying their hexacopters. The success of this competition demonstrated a proof of concept for aerial reconnaissance and sample return to be potentially used in future NASA missions. I also worked with Dr. Kumar Krishen to prototype an improved thermos and a novel, portable solar array. Computer-aided design (CAD) software was used to model the parts for both of these projects. Then, 3D printing as well as conventional techniques were used to produce the parts. These prototypes were then subjected to trials to determine the success of the designs. The solar array is intended to work in a cluster that is easy to set up and take down and doesn't require powered servomechanisms. It could be used terrestrially in areas not serviced by power grids. Both projects improve planetary exploration capabilities to future astronauts. Other projects included manufacturing custom rail brackets for EG-2, assisting engineers working on underwater instrument and tool cases for the NEEMO project, and helping to create mock-up parts for Space Center Houston. The use of the IDC enabled efficient completion of these projects at significantly reduced cost. I acquired and improved manufacturing and prototyping skills during my tour including learning about a CAD (Computer-Aided Design) program called Creo (Creo Parametric; design software), gaining valuable conventional machining experience with lathes, CNC (Computer Numerical Control) milling machines and various other tools, and improving my engineering project communication and collaboration skills. The internship also allowed me to better understand operations at NASA. I plan to work in the aerospace industry or do academic research benefitting space science and exploration, and this internship experience will enable me to have insight into manufacturing processes for research and development.

Grenfell, Peter↗

NASA NTRSDate not supplied

Fostering Open Science Inclusiveness for Interdisciplinary Users of Earth Observations

The term Open Science is subject to a variety of interpretations because of a key (and useful) ambiguity in the meaning of “Open”. Open in the sense of Transparency enables more trust in science research by making the details of the scientific process visible and accessible to anyone. “Open” in the sense of Inclusiveness enables more scientists from other disciplines to participate in research in a given discipline, thus producing more interdisciplinary research. Data Systems can play a major role in enabling Open (Inclusive) Science by making it easier for users from other disciplines to work with data within a given discipline. This is challenging for Earth Observation datasets, most of which are the product of advanced instrumentation and sophisticated, specialized variable retrieval algorithms and code. Serving the “extra-disciplinary”communities begins with simple things, like accessible, readable data documentation with adequate scaffolding. But just as important is provisioning Analysis-Ready data that does not require expert pre-processing. Disciplines also often have dominant toolsets, such as R in the biomass community or GIS in many applications communities. Ensuring that EO data are easy to use in the tools favored in other communities will enable more interdisciplinary research. Ideally, interdisciplinary research also benefits from scientists with different domain expertise. Platforms and frameworks that facilitate frictionless collaboration with discipline experts, together with capacity building efforts in those external disciplines also improve the inclusiveness aspect of Open Science. In short, Open Science is at root a way of thinking about how users from diverse discipline can best access and use data and services from a particular discipline.

Christopher Lynnes↗

NASA NTRSDate not supplied

Localizing Earth Science: The NASA DEVELOP Model of Community Science & Engagement

NASA’s DEVELOP Program conducts rapid feasibility studies that apply Earth observations to address community priorities and needs and enable informed decision making. Part of NASA’s Applied Sciences’ Capacity Building Program, DEVELOP builds skills in both participants (students, recent graduates, and early/transitioning career professionals) who conduct projects on small teams, and partner organizations (state and local governments, federal agencies, non-profit and for-profit organizations, and international organizations) that work closely with DEVELOP to design the project. While only a subset of DEVELOP partner organizations are local municipalities or community-led non-profits, the DEVELOP model is an effective engagement tool and often used by NASA as an introduction for communities to engage in Earth science research and familiarization with satellite remote sensing. This presentation will highlight the DEVELOP model of co-production and community collaboration, lessons learned in partnering with local communities, and evaluation activities that look at the program’s reach and efforts.

Robert Cecil Byles↗

NASA NTRSDate not supplied

Sherlock Data Warehouse

Technical Interchange Meeting 2 of 2, with two linked events. Overall Purpose of the Technical Interchange Meeting was to facilitate collaboration on NAS data analytics between FAA and NASA teams, leveraging the great tool developed by NASA, Sherlock Data Warehouse. Conducted the two TIM events, next step is for the leadership to meet and discuss potential future collaborations. Purpose for Technical Interchange Meeting #2 (TIM 2o2); NASA presented data science related use-cases and applications where they have leveraged the NASA Sherlock Data Warehouse to address aviation transportation problems, with priority on aviation safety.

Two TIMs↗

DOE OSTI2019

An Expert System to Assist Researchers with Nuclear Materials Experiment Design

The Nuclear Science User Facilities (NSUF) was created in 2007 to open National Laboratory capabilities to university, laboratory, and industry researchers. Originally founded at the Idaho National Laboratory (INL), NSUF leveraged excess capacity in the Advanced Test Reactor (ATR) and the Hot Fuel Examination Facility (HFEF) hot cells. In the eleven years since its inception, the NSUF has expanded to include most of the capabilities at the Idaho National Laboratory as well as twenty external ‘partner’ facilities. The inclusion of the partner facilities makes the NSUF the first distributed user facility, a ‘laboratory without walls.’ This paper will present recent developments in CoMET, including how the databases interconnect to create a powerful tool for NSUF users and how the Wizard is expected to change the workflow for NSUF users, staff, and partner facilities. The paper will conclude with some remarks of how CoMET demonstrates the feasibility of a broader integration of nuclear information and tools, paving the way for further collaboration and innovation following the NuKIT paradigm.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2024

Tools for Water Ingress Testing

The Safety Storage and Engineering Team, as part of the Production Support Services division (PSS-2), is tasked with ensuring the safety of containers used for handling and storage of nuclear materials. As part of this work, water ingress tests are conducted to evaluate the water-tightness of containers intended for in-glovebox use. In collaboration, the statistics group of the Computer and Computational Sciences Division (CCS-6) provided support in developing a statistically defensible approach for determining appropriate sample sizes for water ingress testing. Water ingress testing involves multiple measurements on multiple containers. Our approach uses a simple random effects model to analyze a pilot data set, implementing prediction limits to evaluate the efficacy of collecting additional data. Although this study capitalizes on available data, our approach can be used with estimates of the ratio of between and within variability and average values, often available from past testing or expert knowledge. An interactive Shiny tool was developed as a final user-friendly product for future testing. The Shiny interface is an open-source package providing a framework for building web applications. Raw data exploration and prediction interval-based sample size assessments can quickly be conducted by the engineering team without needing to interact with the underlying code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Life Cycle Analysis and Life Cycle Costing for Municipal Solid Waste (MSW) Management and Materials Redeployment to Support ARPA-E’s Potential Program on Waste to Energy and Materials

Municipal solid waste infrastructure in the United States contains complex supply chains to transport, sort, and sequester waste. This project developed models to estimate the environmental and economic costs of waste infrastructure in the U.S. using data from several municipalities. Life cycle costing (LCC) models were developed for waste management relevant supply chain steps, including waste collection, sorting, landfilling, waste-to-energy, ash processing, and recycling. A waste-to-materials and energy (WTM&E) life cycle analysis (LCA) and LCC dashboard was developed in collaboration with Argonne National Laboratory (ANL) and an LCC tool was developed for additional analysis.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2004

Earth Science Education Plan: Inspire the Next Generation of Earth Explorers

The Education Enterprise Strategy, the expanding knowledge of how people learn, and the community-wide interest in revolutionizing Earth and space science education have guided us in developing this plan for Earth science education. This document builds on the success of the first plan for Earth science education published in 1996; it aligns with the new framework set forth in the NASA Education Enterprise Strategy; it recognizes the new educational opportunities resulting from research programs and flight missions; and it builds on the accomplishments th'at the Earth Science Enterprise has made over the last decade in studying Earth as a system. This document embodies comprehensive, practicable plans for inspiring our children; providing educators with the tools they need to teach science, technology, engineering, and mathematics (STEM); and improving our citizens' scientific literacy. This plan describes an approach to systematically sharing knowledge; developing the most effective mechanisms to achieve tangible, lasting results; and working collaboratively to catalyze action at a scale great enough to ensure impact nationally and internationally. This document will evolve and be periodically reviewed in partnership with the Earth science education community.

Source record↗

DOE OSTI2026

NGEE Arctic Authorship Guidelines

Authorship Guidelines were developed to help facilitate trust among team members as we span multiple institutions, scientific disciplines, and career stages. NGEE Arctic was built on a foundation of open science, data sharing, and collaboration. In Phase 4 of the project, it was particularly important to keep this foundation in mind as we develop new collaborations across the Arctic. Included in this package is one *.pdf. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

Iversen, Colleen [ORNL] (ORCID:0000000182933450)↗

DOE OSTI2020

The Quantum Nature and Mass of the Neutrino. FY20 Annual Laboratory Continuation Progress Report

Over the last 100 years we have learned a much about the neutrino, but there remain interesting questions about this weakly interacting particle. In particular, at LLNL we are developing experiments to measure the mass and the quantum nature of the neutrino. The 2015 long range plan recommended tonne-scale neutrinoless double beta decay experiments as the top new project priority and this experiment will target the quantum nature of the neutrino. The LLNL team is motivated and focused on developing the best technology for this experiment which is a liquid xenon time project chamber called nEXO. On a longer time scale, LLNL is also developing technology to measure the neutrino mass with tritium decay experiments. Work on nEXO at LLNL started in 2015 with an LDRD and program development funds to develop the nEXO concept, publish sensitivity predictions, explore alternatives and risks, and to build a credible project team and this effort is on going. In addition, we received research funding from DOE-SC NP and LLNL manages pre-conceptual R&D for the nEXO collaboration a portion of which is executed at LLNL. The Project 8 effort is solely funded by LLNL LDRD, and ends this year. A second LDRD effort is focused on repurposing cyclotron radiation emission spectroscopy for eV-scale X-ray detection. This second effort, while separated from Project 8, still has scientific and technical commonalities with Project 8. The funding for this X-ray detection effort will continue for one more year. LLNL has a long history of building large projects, science projects and time projection chambers, but nEXO is in fact the first project of this scale that LLNL is prepared to lead for DOE-SC NP.We have the tools, skills, people and motivation to make nEXO a reality. The current program development dollars are used to prepare the nEXO concept for down select and CD1. These internal LLNL dollars pay for a number of things including project staff to organize the nEXO collaboration for the various reviews that lie ahead, to develop the draft plans and structure to manage the project as well as setting up an external advisory committee. The current LDRD is exploring alternatives that effect the risk profile as well as possible performance improvements. The DOE-SC NP base funding is targeted at simulation and analysis of the nEXO concept in preparation for the down select and refinement of the understanding of the detector as well as scientific effort on the large xenon test stand (LXTS) that will burn down one of the largest risks to the nEXO project. Lastly, LLNL is leading the pre-conceptual R&D effort and managing the funds for the nEXO collaboration. The results of this effort are reported elsewhere, but the scope of this executed at LLNL will be reported here and is primarily the engineering and construction of the LXTS experiment. The primary technical contribution of LLNL to the Project 8 concept is the formation and study of atomic tritium. Atomic tritium is necessary for Project 8 to reach the ultimate sensitivity. LLNL has considerable experience with tritium and is well suited for this crucial work. Although the focus of this work is strictly fundamental science, there has already been one significant spinoff to develop a new means to measure radio xenon in the air

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

Towards an Open (Data) Science Analytics-Hub for Reproducible Multi-Model Climate Analysis at Scale

Open Science is key to future scientific research and promotes a deep transformation in the whole scientific research process encouraging the adoption of transparent and collaborative scientific approaches aimed at knowledge sharing. Open Science is increasingly gaining attention in the current and future research agenda worldwide. To effectively address Open Science goals, besides Open Access to results and data, it is also paramount to provide tools or environments to support the whole research process, in particular the design, execution and sharing of transparent and reproducible experiments, including data provenance (or lineage) tracking. This work introduces the Climate Analytics-Hub, a new component on top of the Earth System Grid Federation (ESGF), which joins big data approaches and parallel computing paradigms to provide an Open Science environment for reproducible multi-model climate change data analytics experiments at scale. An operational implementation has been set up at the SuperComputing Centre of the Euro- Mediterranean Center on Climate Change, with the main goal of becoming a reference Open Science hub in the climate community regarding the multi-model analysis based on the Coupled Model Intercomparison Project (CMIP).

analytics-hub↗

DOE OSTI2022

Computational Modeling and Simulation for Nonproliferation: The History (and Present) of Monte Carlo and the MCNP(R) Code at Los Alamos [Slides]

The emergence of the Monte Carlo method as a research tool springs from work done at Los Alamos in the 1940s.Monte Carlo and the MCNP Code have been and continue to be developed at Los Alamos for many decades. From basic science in support of understanding nuclear interaction physics to global security applications, application uses of the code are extensive. Recent R&D projects and code modernization efforts make the MCNP code a great tool for nuclear nonproliferation applications. In collaboration with nuclear safeguards experts, new training has just recently been developed to help new practitioners learn how to use the code for nuclear safeguards applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Consortium for Algal Biofuel Commercialization (CAB-COMM) Final Report

The Consortium for Algal Biofuel Commercialization (CAB-Comm) was established in 2010 to conduct research to enable commercial viability of alternative liquid fuels produced from algal biomass. The main objective of CAB-Comm was to dramatically improve the viability of algae as a source of liquid fuels to meet US energy needs, by addressing several significant barriers to economic viability. To achieve this goal, CAB-Comm took a diverse set of approaches on three key aspects of the algal biofuels value chain: crop protection; nutrient utilization and recycling; and the development of genetic tools. These projects have been undertaken as collaboration between six academic institutions and two industrial partners: University of California, San Diego; Scripps Institution of Oceanography; University of Nebraska, Lincoln; Rutgers University; University of California, Davis; Johns Hopkins University; Sapphire Energy; and Life Technologies.

09 BIOMASS FUELS↗