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This data package accompanies the report "2016 U.S. Petroleum Fuels Life Cycle Baseline." It contains the openLCA life cycle model that generated the results and an Excel spreadsheet that contains well-to-tank life cycle inventory for all products.
There are numerous applications for microalgae in spaceflight missions and on Earth, such as oxygen production, carbon dioxide removal, nutrition, wastewater processing, and biofuel production. Space Algae-2 aims to test the genetic stability of Arthrospira platensis, commonly known as spirulina, during six-months of continuous culture on the International Space Station. Long-duration exposure to ionizing radiation and microgravity may impact growth, nutrient composition, and genetic stability. The high protein, vitamin, antioxidant content, and radiation resistance make spirulina a promising candidate for bioregenerative life support systems. A concept of operations was developed to grow and harvest algal biomass in space. Preflight testing experiments optimized conditions for an extended growth period in a gas permeable bioreactor bag. Preflight and post-harvest storage methods were developed in addition to a novel cryopreservation method. After sample return, multi-omics analysis will be conducted to determine the mutation rate, gene expression, and the protein and metabolite profile. The concept of operations for Space Algae-2 was tested during a lunar mission simulation within a semi-controlled environment. During a six-day lunar analog mission at the Hawai’i Space Exploration Analog and Simulation (HI-SEAS) A. platensis was successfully grown using flight-like hardware. The cyanobacteria were harvested and used to supplement bread as an example of spirulina biomass utilization. Overall, the data collected from Space Algae-2 will inform potential bioengineering of spirulina for space and terrestrial applications.
To understand the eco-evolutionary significance of plant viruses in nature, we must (i) quantify the effects of infection on plant fitness and (ii) recognize that native plants are increasingly exposed to crop-associated viruses. Studies of perennials are particularly needed: most of our knowledge of plant-virus interactions is from annuals, yet long-lived species dominate landscapes. Here we used aster models for life-history analysis and longitudinal measures of plant virus status to evaluate multi-year consequences of crop virus infection in a native perennial. We used Barley yellow dwarf virus acquired from wheat to inoculate seedlings of Panicum virgatum L. (switchgrass), a North American prairie grass. We grew inoculated and mock-inoculated individuals of two ecotypes for 3 years in the field. We measured plant size, infection status and fitness components. Aster modelling provided integrated multi-year measures of fitness. Crop virus inoculation reduced multi-year native plant fitness by 30% over 2 years despite generally asymptomatic infection and evidence of resistance. This reduction was greater than predicted from individual fitness components or most size measures. Ecotypes differed in response, with the lowland ecotype experiencing higher apparent recovery from infection. Virus treatment in the upland ecotype delayed flowering phenology and reduced seed filling. Synthesis. Our use of field experimentation, surveys of plant infection status and aster modelling demonstrates a rigorous and broadly applicable approach for quantifying the effects of viruses and other microbes on multi-year plant fitness. We found that a crop virus had negative multi-year effects on native plant fitness even after infection was no longer detected. Viruses may have substantial effects on native vegetation with domestication of landscapes and agricultural expansion.
Computer simulations were conducted to analyze the performance and operating cost of a photovoltaic energy source combined with a diesel generator system and battery storage. The simulations were based on the load demand profiles used for the design of an all photovoltaic energy system installed in the remote Papago Indian Village of Schuchuli, Arizona. Twenty year simulations were run using solar insolation data from Phoenix SOLMET tapes. Total energy produced, energy consumed, operation and maintenance costs were calculated. The life cycle and levelized energy costs were determined for a variety of system configurations (i.e., varying amounts of photovoltaic array and battery storage).
The theory behind re-engineering a business process is to remove the non-value added activities thereby lowering the process cost. In order to achieve this, one must be able to identify where the non-value added elements are located which is not a trivial task. This is because the non-value added elements are often hidden in the form of overhead and/or pooled resources. In order to be able to isolate these non-value added processes from among the other processes, one must first decompose the overall top level process into lower layers of sub-processes. In addition, costing data must be assigned to each sub-process along with the value the sub-process adds towards the final product. IDEF0 is a Federal Information Processing Standard (FIPS) process-modeling tool that allows for this functional decomposition through structured analysis. In addition, it illustrates the relationship of the process and the value added to the product or service. The value added portion is further defined in IDEF1X which is an entity relationship diagramming tool. The entity relationship model is the blueprint of the product as it moves along the 'assembly line' and therefore relates all of the parts to each other and the final product. It also relates the parts to the tools that produce the product and all of the paper work that is used in their acquisition. The use of IDEF therefore facilitates the use of Activity Based Costing (ABC). ABC is an essential method in a high variety, product-customizing environment, to facilitate rapid response to externally caused change. This paper describes the work being done in the Mission Operations Division to re-engineer the development and operation life cycle of Mission Operations Centers using these tools.
The present report is intended to provide process flow diagrams (PFDs) and energy and mass balance data sheets for a U.S. industrial sector subset with which nuclear heat and power could be integrated—a subset that includes the oil refining, methanol and pulp and paper industries. Coupling options for integrating nuclear energy into these industries are quantitatively outlined for reference systems, and future work will extend this analysis in greater detail. Opportunities for integrating small modular nuclear reactors (SMNRs) were investigated for each of the industrial process configurations. Aspen HYSYS and Cycle-Tempo models for a high-temperature gas-cooled reactor were developed to evaluate the proposed integration. This introductory evaluation provides a general description and assessment of the operating principles, reactor coolant core outlet temperature, and reactor size to be integrated with industry. The industrial processes of oil refining and the production of methanol, pulp and paper were simulated by using Aspen HYSYS, Aspen Plus, and the PRELIM (Petroleum Refinery Life Cycle Inventory Model) tool to develop process details. Cycle-Tempo models then extend the process modeling results to obtain net energy demands (e.g., heat, steam, and electricity) when accounting for process steam and waste heat recovery. This information is intended to foster the analysis of integrating an SMNR to decarbonize industrial facilities. The SMNR would provide reliable, competitive, and sustainable clean energy while reducing carbon emissions and other environmental impacts, such as water withdrawals, consumption, and contamination. The refining industry, exhibited in Figure ES1, is a leading consumer of fossil -fuel-based heat, power, and hydrogen in the U.S. industrial sector, generating over 164 million metric tons (MMT) of CO 2 emissions in 2023. The overall mass and energy pertaining to a generalized complex refinery in the United States is reflected in Figure ES1, along with energy metrics regarding integration with a nuclear power plant (NPP). Data sheets were developed to indicate the energy requirements for the overall refinery and each refinery process. The data sheet for the overall refinery is shown in Table ES2.
This data set contains QA/QC-ed (Quality Assurance and Quality Control) water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location at the East River Watershed, Colorado, USA. These wells are used to monitor subsurface water and carbon inventories and fluxes, and to determine the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry (see related references) can be used to estimate rates of solute export from the hillslope to the floodplain and river. QA/QC analysis of measured groundwater levels in monitoring wells PLM-1 and PLM-6 included identification and flagging of duplicated values of timestamps, gap filling of missing timestamps and water levels, removal of abnormal/bad and outliers of measured water levels. The QA/QC analysis also tested the application of different QA/QC methods and the development of regular (5-minute, 1-hour, and 1-day) time series datasets, which can serve as a benchmark for testing other QA/QC techniques, and will be applicable for ecohydrological modeling. The package includes a Readme file, one R code file used to perform QA/QC, a series of 8 data csv files (six QA/QC-ed regular time series datasets of varying intervals (5-min, 1-hr, 1-day) and two files with QA/QC flagging of original data), and three files for the reporting format adoption of this dataset (InstallationMethods, file level metadata (flmd), and data dictionary (dd) files).QA/QC-ed data herein were derived from the original/raw data publication available at Williams et al., 2020 (DOI: 10.15485/1818367). For more information about running R code file (10.15485_1866836_QAQC_PLM1_PLM6.R) to reproduce QA/QC output files, see README (QAQC_PLM_readme.docx). This dataset replaces the previously published raw data time series, and is the final groundwater data product for the PLM wells in the East River. Complete metadata information on the PLM1 and PLM6 wells are available in a related dataset on ESS-DIVE: Varadharajan C, et al (2022). https://doi.org/10.15485/1660962. These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales. 2022/09/09 Update: Converted data files using ESS-DIVE’s Hydrological Monitoring Reporting Format. With the adoption of this reporting format, the addition of three new files (v1_20220909_flmd.csv, V1_20220909_dd.csv, and InstallationMethods.csv) were added. The file-level metadata file (v1_20220909_flmd.csv) contains information specific to the files contained within the dataset. The data dictionary file (v1_20220909_dd.csv) contains definitions of column headers and other terms across the dataset. The installation methods file (InstallationMethods.csv) contains a description of methods associated with installation and deployment at PLM1 and PLM6 wells. Additionally, eight data files were re-formatted to follow the reporting format guidance (er_plm1_waterlevel_2016-2020.csv, er_plm1_waterlevel_1-hour_2016-2020.csv, er_plm1_waterlevel_daily_2016-2020.csv, QA_PLM1_Flagging.csv, er_plm6_waterlevel_2016-2020.csv, er_plm6_waterlevel_1-hour_2016-2020.csv, er_plm6_waterlevel_daily_2016-2020.csv, QA_PLM6_Flagging.csv). The major changes to the data files include the addition of header_rows above the data containing metadata about the particular well, units, and sensor description. 2023/01/18 Update: Dataset updated to include additional QA/QC-ed water level data up until 2022-10-12 for ER-PLM1 and 2022-10-13 for ER-PLM6. Reporting format specific files (v2_20230118_flmd.csv, v2_20230118_dd.csv, v2_20230118_InstallationMethods.csv) were updated to reflect the additional data. R code file (QAQC_PLM1_PLM6.R) was added to replace the previously uploaded HTML files to enable execution of the associated code. R code file (QAQC_PLM1_PLM6.R) and ReadMe file (QAQC_PLM_readme.docx) were revised to clarify where original data was retrieved from and to remove local file paths.
In a Nuclear Power Plant, one of the most important components is the concrete nuclear reactor cavity, which serves both a structural and protective function as the biological radiation shield. Given that creep has been identified as a major knowledge gap in the assessment of nuclear structures (NUREG/CR-7153), this work helps to further the understanding of creep behavior of massive concrete containment structures for decades to enable safe and long-term operation of these facilities. This project has developed a robust, experimentally validated model to predict creep in nuclear concrete structures for up 60 years using short-term creep data thereby enabling a longer service life of critical facilities and early detection of structural failure. The work presented in this report is a pairing of computational and experimental methods. For the first time, the time temperature superposition (TTS) principle was successfully used to generate a uniaxial creep compliance master curve to predict mortar creep response for up to 22,500 days (nearly 60 years) at a reference temperature of 20°C. These data were used as input into finite element analysis (FEA) codes that use highly realistic random, 3D concrete microstructures from reconstructed coarse limestone aggregates. Finite element analysis performed provides the ability to quickly upscale mortar viscoelastic behavior to long-term concrete creep/relaxation data. A master creep compliance curve, constructed from the TTS principle, spanning 27 years, was used to validate two and a half decades of simulated concrete creep. Concurrently, three different simulated wall specimens were designed to mimic the behavior of post-tensioned concrete nuclear containment facility vessel walls over time as a result of concrete creep. The specimens were designed with different thicknesses, transverse and longitudinal reinforcement ratios, and level of post-tensioning stress. Each specimen contained various instrumentation to measure internal concrete temperature, concrete strain, and post-tensioning strain hourly for over 3 years. The concrete creep model developed in this project, based on the FEA concrete simulations, was applied to simulate the structural-scale experiments of prestressed concrete walls conducted in this project using the Grizzly code. These models can represent the effects of reinforcing and prestressing. Although there are some discrepancies with the experimental data, the model can predict the overall trends of the creep response in these experiments. One of these experimental models was also applied to an extended time to demonstrate how the findings from this study can be used to predict the behavior of actual structures of interest that have been in service for extended periods of time.
The objective of this study is to complete a life cycle assessment (LCA) of a novel technology that separates the crystalline silicon (c-Si) photovoltaic (PV) module front glass from the backsheet using hot knife technology. This is known to be the most challenging step in module recycling, where the choice of delamination approach can determine the process selection for the next steps of the recycling process, the economic value of the recovered materials, and environmental performance. The life cycle inventory (LCI) reported here is based on primary data from the technology manufacturer. Different LCIs are established following the cut-off approach and the end-of-life (EOL) approach. The environmental impacts of the hot knife delamination process are analysed based on six indicators, and the main contributors to delamination efforts are identified. For the EOL approach, the potential environmental benefits from the recovered materials are compared to the environmental impacts caused by the delamination process. The functional unit of this analysis is the delamination of 1 kg of used framed c-Si PV modules at the place of installation.
Research supported wholly or in part by NASA is summarized, Topics covered include the molecular analysis of ultraviolet-photoproduced organic solids synthesized under simulated Jovian conditions; the molecular analysis of organic solids produced by electrical discharge in reducing atmospheres; the organic chemistry of interstellar grains; the spectra of possible organic solids present as aerosols in planetary atmospheres; far infrared studies of organic polymers of possible astrophysical interest; organic dust synthesized in reducing environments by ultraviolet radiation or electric discharge; the diffusion of galactic civilizations; eavesdropping on galactic civilizations; Lander imaging as a detector of life on Mars; and continuing puzzles about Mars. Bibliographic data is included for four additional publications not supported by NASA grant, but related to the objectives of the program.
The Nitrogen Washout System measures nitrogen elimination on a breath basis from the body tissues of a subject breathing pure oxygen. The system serves as a prototype for a Space Shuttle Life Sciences experiment and in the Environmental Physiology Laboratory. Typically, a subject washes out body nitrogen for three hours while breathing oxygen from a mask enclosed in a positive-pressure oxygen tent. A nitrogen washout requires one test operator and the test subject. A DEC LSI-11/02 computer is used to (1) control and calibrate the mass spectrometer and Skylab spirometer, (2) gather and store experimental data and (3) provide limited real time analysis and more extensive post-experiment analysis. Five programs are used to gather and store the experimental data and perform all the real time control and analysis.
Results are given of a study to explore options for the development of a Controlled Ecological Life Support System (CELSS) for a future Space Station. In addition, study results will benefit the design of other facilities such as the Life Sciences Research Facility, a ground-based CELSS demonstrator, and will be useful in planning longer range missions such as a lunar base or manned Mars mission. The objectives were to develop weight and cost estimates for one CELSS module selected from a set of preliminary plant growth unit (PGU) design options. Eleven Space Station CELSS module conceptual PGU designs were reviewed, components and subsystems identified and a sensitivity analysis performed. Areas where insufficient data is available were identified and divided into the categories of biological research, engineering research, and technology development. Topics which receive significant attention are lighting systems for the PGU, the use of automation within the CELSS system, and electric power requirements. Other areas examined include plant harvesting and processing, crop mix analysis, air circulation and atmosphere contaminant flow subsystems, thermal control considerations, utility routing including accessibility and maintenance, and nutrient subsystem design.
NASA, Lawrence Livermore National Laboratory (LLNL) and the Department of Defense (DoD) share a common interest in compact medical diagnostic technologies—also known as “point of care testing” (POCT) technologies. For NASA, the need is to take care of astronauts on deep space missions. For LLNL, the focus is on technologies needed to support homeland security applications. For DoD, the need is to support warfighters in military theaters of operation, especially for prolonged field care and en route care. A recently patented technology, “Portable Medical Diagnosis Instrument” (U.S. Patent 9824870), co-invented by NASA and LLNL, is being developed further in a partnership between NASA, LLNL and Travis Air Force Base, with a focus on military applications. This instrument, the NASA-LLNL Medical Tricorder, can be used for a wide range of clinical samples including breath, saliva and blood. One component of the Tricorder is the NASA E-Nose, a carbon nanotube-based chemical sensor, integrated into a hand-held smart phone, that can detect and measure “breath biomarkers” in the gas phase of exhaled breath. The response of the NASA E-Nose is rapid, which makes it possible to detect various gas-phase substances in real time. The NASA E-Nose, with modification, can also be used for breath condensate analysis. Another component of the Tricorder is a miniaturized microfluidic analyzer, under development by LLNL, that does not require any pumps. The microfluidic analyzer can detect and measure biomarkers in liquid samples, such as blood, saliva and breath condensate. This microfluidic system requires small quantities of liquid sample and can perform analysis very rapidly (seconds to minutes), which makes it possible to detect relevant biomarkers in near real time in the field. The Clinical Investigation Facility (CIF) at Travis Air Force Base is currently conducting research with the goal to develop improved field-able technologies and procedures for the emergency response to life threatening injuries soldiers may receive in theater. This work includes experimental large animal systems and comprehensive collection of clinical specimens and physiological data. NASA and LLNL are partnering with Travis to test and further develop the Tricorder technology for high priority military applications, initially focused on lung injury models and traumatic brain injury.
A concept for a science-driven robotic mission to an exoplanet was developed by a team of scientists and engineers from NASA and academia. The concept and scope were based on key mission and science requirements designed to address the question: “What makes a flight mission to an exoplanet compelling, in terms of science return, compared to what we will be able to learn in the next few decades with large near-Earth telescopes or other remote sensing techniques such as a telescope at the Solar Gravity Lens Focus?” By thinking systematically through mission and science goals and objectives, key requirements were developed that would drive technology developments in all necessary aspects, not just on propulsion. Unique science measurements would be performed en route to the exoplanet, including exploring the environment in the outer regions of our solar system, the Oort Cloud, the local interstellar medium, and the astrospheric environment around the host star. One of the key mission science objectives, and one that addresses why a mission to an exoplanet is compelling, was to confirm and characterize life. This objective is fundamental and drives the need for a precursor exoplanet characterization program to search for Earth-centric biosignatures and also drives key aspects of the mission concept. The team concluded that a direct confirmation of life would require in situ observations and measurements which cannot be performed on a fast (~10% of the speed of light) flyby; thus, the mission would require a method to slow down, orbit, or send a probe to the exoplanet’s surface. This capability drives a trade between interstellar travel velocity, trip duration, and propulsion architecture, as well as a high level of onboard autonomy, including adaptive science data collection, on-board data processing, and analysis. This paper describes our mission concept, the key requirements, and open trades.
Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.
Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.
The constant fragment (Fc) of the immunoglobulin G1 (IgG1) subtype is increasingly recognized as a crucial scaffold in the development of advanced therapeutics due to its enhanced specificity, efficacy, and extended half‐life. A prime example is VEGF‐Trap (Aflibercept), a recombinant fusion protein that merges the Fc region of the IgG1 subtype with the binding domains of vascular endothelial growth factor receptors (VEGFR)‐1 and VEGFR‐2. The Fc region's role in N‐glycosylation is particularly important, as it significantly influences protein stability. Herein, the first near‐physiological temperature structures of the N‐glycan‐bound Fc fragment of IgG1 subtype from a biosimilar VEGF‐Trap are presented, determined using the SPring‐8 Angstrom Compact free electron LAser (SACLA) and the Turkish Light Source (Turkish DeLight). Comparative analysis with cryogenic structures, including existing data, reveals alternate conformations within the glycan‐binding pocket. Furthermore, molecular dynamics simulations indicate the presence of a high degree of structural plasticity, explaining how the protein adapts its structure through conformational changes. The observed structural fluctuations/conformational changes demonstrate the effect of N‐glycans on protein stability. These findings offer new insights into the molecular basis of Fc‐mediated functions and provide valuable information for the design of next‐generation therapeutics.
There are numerous applications for microalgae in spaceflight missions and on Earth, such as oxygen production, carbon dioxide removal, nutrition, wastewater processing, and biofuel production. Space Algae-2 aims to test the genetic stability of Arthrospira platensis, commonly known as spirulina, during six-months of continuous culture on the International Space Station. Long-duration exposure to ionizing radiation and microgravity may impact growth, nutrient composition, and genetic stability. The high protein, vitamin, antioxidant content, and radiation resistance make spirulina a promising candidate for bioregenerative life support systems. A concept of operations was developed to grow and harvest algal biomass in space. Preflight testing experiments optimized conditions for an extended growth period in a gas permeable bioreactor bag. Preflight and post-harvest storage methods were developed in addition to a novel cryopreservation method. After sample return, multi-omics analysis will be conducted to determine the mutation rate, gene expression, and the protein and metabolite profile. The concept of operations for Space Algae-2 was tested during a lunar mission simulation within a semi-controlled environment. During a six-day lunar analog mission at the Hawai’i Space Exploration Analog and Simulation (HI-SEAS) A. platensis was successfully grown using flight-like hardware. The cyanobacteria were harvested and used to supplement bread as an example of spirulina biomass utilization. Overall, the data collected from Space Algae-2 will inform potential bioengineering of spirulina for space and terrestrial applications.