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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Development and Validation of a Process Model and Open-Source Process Simulator for Microalgae-Based Tertiary Phosphorus Recovery

Microalgae-based tertiary wastewater treatment has the potential to meet stringent effluent phosphorus limits, with the added benefit of producing a marketable feedstock. However, the lack of validated mechanistic models and their implementation in process simulators have limited the adoption of this technology. In this study, an updated lumped pathway metabolic model (Phototrophic-Mixotrophic Process Model, PM 2 ), including both photoautotrophic and heterotrophic metabolisms of microalgae, was developed to predict effluent phosphorus concentration and biomass yield in response to dynamic influent and varying environmental conditions. The model was implemented in QSDsan – an open-source, Python-based design and simulation platform – for robust simulation under uncertainty. A global sensitivity analysis was performed to prioritize model parameters for calibration. The model was then calibrated and validated using batch experimental data and 45 days of continuous online monitoring data from a full-scale (568 m 3 ·d -1 ) microalgae-based tertiary wastewater treatment plant (EcoRecover process). In particular, along with dynamic influent composition, temperature and light intensity data with diel variation were provided as model inputs to reflect the microalgal behavior under day-night cycling. Overall, the QSDsan-based microalgae process simulator was able to predict effluent phosphorus within 0.02–0.04 mg-P·L -1 , while also capturing the general trends of state variables according to nutrient availability.

Lumped pathway metabolic model↗
NASA NTRS2017

A Probabilistic Asteroid Impact Risk Model

Asteroid threat assessment requires the quantification of both the impact likelihood and resulting consequence across the range of possible events. This paper presents a probabilistic asteroid impact risk (PAIR) assessment model developed for this purpose. The model incorporates published impact frequency rates with state-of-the-art consequence assessment tools, applied within a Monte Carlo framework that generates sets of impact scenarios from uncertain parameter distributions. Explicit treatment of atmospheric entry is included to produce energy deposition rates that account for the effects of thermal ablation and object fragmentation. These energy deposition rates are used to model the resulting ground damage, and affected populations are computed for the sampled impact locations. The results for each scenario are aggregated into a distribution of potential outcomes that reflect the range of uncertain impact parameters, population densities, and strike probabilities. As an illustration of the utility of the PAIR model, the results are used to address the question of what minimum size asteroid constitutes a threat to the population. To answer this question, complete distributions of results are combined with a hypothetical risk tolerance posture to provide the minimum size, given sets of initial assumptions. Model outputs demonstrate how such questions can be answered and provide a means for interpreting the effect that input assumptions and uncertainty can have on final risk-based decisions. Model results can be used to prioritize investments to gain knowledge in critical areas or, conversely, to identify areas where additional data has little effect on the metrics of interest.

Asteroid↗
NASA NTRSDate not supplied

Squeezing Every Last 'Bit' of Information from Enceladus Mass Spectrometry

Potential opportunities to return to Enceladus in Discovery and Flagship class missions inspire development of next-generation instruments and creative approaches to sample collection, sample analysis, and data analysis and transmission strategies. Mass spectrometers (MS) are ideally suited to future Enceladus missions due to their analytical power in identifying a range of molecular and ionic compositions – including complex organics – and potentially astrobiologically-important features such as isotope ratios, chirality, and enantiomeric excess. However, long communication delays from Enceladus and limited bandwidth limits the data transmission from these higher-data-volume instruments, likely delaying mission-related response to new data. We explore the utility of data science and machine learning (ML) on isotope ratio (IR)MS data collected from laboratory analogs of Enceladus to: 1) process data quickly for rapid ground-based analyses, 2) understand if compositional and biosignature information could be extracted from IRMS data, and 3) evaluate whether onboard ML techniques could improve sample analysis, cadence, and transmission prioritization. Laboratory analogs analyzed isotopes of volatile CO2 that interacted with seawaters of varying composition, and include both abiotic and biotic (microbially-influenced) experiments. Enceladus’s alkaline oceans promote speciation of carbon into multiple forms (e.g., H2CO3 / CO2, HCO3-, and CO32-), each of which could be isotopically fractionated by abiotic or biotic reactions. Large (>2‰) changes in carbon isotopes (δ13C) are observed from some biotic experiments inoculated with complex microbial ecosystems relative to the abiotic seawaters. ML training and classification suggests that microbial samples can be distinguished from abiotic samples, yet that a broad range of microbial experiments are necessary to train ML models to cover a range of complexities including disequilibria, and isotopic and compositional fractionation.

geochemistry↗
NASA NTRS2001

CSMA Versus Prioritized CSMA for Air-Traffic-Control Improvement

OPNET version 7.0 simulations are presented involving an important application of the Aeronautical Telecommunications Network (ATN), Controller Pilot Data Link Communications (CPDLC) over the Very High Frequency Data Link, Mode 2 (VDL-2). Communication is modeled for essentially all incoming and outgoing nonstop air-traffic for just three United States cities: Cleveland, Cincinnati, and Detroit. There are 32 airports in the simulation, 29 of which are either sources or destinations for the air-traffic of the aforementioned three airports. The simulation involves 111 Air Traffic Control (ATC) ground stations, and 1,235 equally equipped aircraft-taking off, flying realistic free-flight trajectories, and landing in a 24-hr period. Collisionless, Prioritized Carrier Sense Multiple Access (CSMA) is successfully tested and compared with the traditional CSMA typically associated with VDL-2. The performance measures include latency, throughput, and packet loss. As expected, Prioritized CSMA is much quicker and more efficient than traditional CSMA. These simulation results show the potency of Prioritized CSMA for implementing low latency, high throughput, and efficient connectivity.

Robinson, Daryl C.↗
NASA NTRS2016

Spaceport Command and Control System - Support Software Development

The Information Architecture Support (IAS) Team, the component of the Spaceport Command and Control System (SCCS) that is in charge of all the pre-runtime data, was in need of some report features to be added to their internal web application, Information Architecture (IA). Development of these reports is crucial for the speed and productivity of the development team, as they are needed to quickly and efficiently make specific and complicated data requests against the massive IA database. These reports were being put on the back burner, as other development of IA was prioritized over them, but the need for them resulted in internships being created to fill this need. The creation of these reports required learning Ruby on Rails development, along with related web technologies, and they will continue to serve IAS and other support software teams and their IA data needs.

Information Architecture↗
NASA NTRSDate not supplied

Assessing Sediment Inputs into the Shoshone River in Wyoming to Determine Areas for Protection and Restoration Practices

In 2016, a routine repair operation at the Willwood Dam released tons of built-up sediment into the Shoshone River, polluting the river and killing thousands of fish. This release greatly concerned the communities that rely on the river for farming, recreation, and tourism. In partnership with the Wyoming Department of Environmental Quality (WDEQ), Shoshone River Partners, and the United States Geological Survey (USGS) Wyoming-Montana Water Science Center, this multipart project illustrated the application of high-resolution satellite imagery and hydrological modeling techniques to identify major tributaries contributing to sediment influxes into the Shoshone River between the Buffalo Bill Dam and the Willwood Dam. The team used PlanetScope and Sentinel-2 Multi-Spectral Instrument (MSI) satellite images to assess changes in the surface reflectance of the river in response to precipitation events. To determine these storm events, the team selected dates of interest using Global Precipitation Measurement (GPM) Integrated Multi-Spectral Retrieval (IMERG) and gauge precipitation data from the Global Historical Climatology Network Daily (GHCNd). The continuation of this research further quantified sediment influx in the Shoshone River following snowfall and melt events using the Soil & Water Assessment Tool (SWAT). The precipitation analysis and sediment contribution maps helped the partner organizations prioritize their current decision making and best management practices to specific sites along the river. The results demonstrated the feasibility of using PlanetScope data for categorizing sediment in the Shoshone River.

Caroline Williams↗
DOE OSTI2023

Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning

In overhead image segmentation tasks, including additional spectral bands beyond the traditional RGB channels can improve model performance. However, it is still unclear how incorporating this additional data impacts model robustness to adversarial attacks and natural perturbations. For adversarial robustness, the additional in-formation could improve the model’s ability to distinguish malicious inputs, or simply provide new attack avenues and vulnerabilities. For natural perturbations, the additional information could better inform model decisions and weaken perturbation effects or have no significant influence at all. In this work, we seek to characterize the performance and robustness of a multispectral (RGB and near infrared) image segmentation model subjected to adversarial attacks and natural perturbations. While existing adversarial and natural robustness research has focused primarily on digital perturbations, we prioritize on creating realistic perturbations designed with physical world conditions in mind. For adversarial robustness, we focus on data poisoning attacks whereas for natural robustness, we focus on extending ImageNet-C common corruptions for fog and snow that coherently and self-consistently perturbs the input data. Overall, we find both RGB and multispectral models are vulnerable to data poisoning attacks regardless of input or fusion architectures and that while physically-realizable natural perturbations still degrade model performance, the impact differs based on fusion architecture and input data.

Deep learning, multispectral images, multimodal fu↗
NASA NTRS2012

Earth System Modeling and Field Experiments in the Arctic-Boreal Zone - Report from a NASA Workshop

Early climate modeling studies predicted that the Arctic Ocean and surrounding circumpolar land masses would heat up earlier and faster than other parts of the planet as a result of greenhouse gas-induced climate change, augmented by the sea-ice albedo feedback effect. These predictions have been largely borne out by observations over the last thirty years. However, despite constant improvement, global climate models have greater difficulty in reproducing the current climate in the Arctic than elsewhere and the scatter between projections from different climate models is much larger in the Arctic than for other regions. Biogeochemical cycle (BGC) models indicate that the warming in the Arctic-Boreal Zone (ABZ) could lead to widespread thawing of the permafrost, along with massive releases of CO2 and CH4, and large-scale changes in the vegetation cover in the ABZ. However, the uncertainties associated with these BGC model predictions are even larger than those associated with the physical climate system models used to describe climate change. These deficiencies in climate and BGC models reflect, at least in part, an incomplete understanding of the Arctic climate system and can be related to inadequate observational data or analyses of existing data. A workshop was held at NASA/GSFC, May 22-24 2012, to assess the predictive capability of the models, prioritize the critical science questions; and make recommendations regarding new field experiments needed to improve model subcomponents. This presentation will summarize the findings and recommendations of the workshop, including the need for aircraft and flux tower measurements and extension of existing in-situ measurements to improve process modeling of both the physical climate and biogeochemical cycle systems. Studies should be directly linked to remote sensing investigations with a view to scaling up the improved process models to the Earth System Model scale. Data assimilation and observing system simulation studies should be used to guide the deployment pattern and schedule for inversion studies as well. Synthesis and integration of previously funded Arctic-Boreal projects (e.g., ABLE, BOREAS, ICESCAPE, ICEBRIDGE, ARCTAS) should also be undertaken. Such an effort would include the integration of multiple remotely sensed products from the EOS satellites and other resources.

Sellers, Piers↗
NASA NTRS2018

Towards Design Principles for Visual Analytics in Operations Contexts

Operations engineering teams interact with complex data systems to make technical decisions that ensure the operational efficacy of their missions. To support these decision-making tasks, which may require elastic prioritization of goals dependent on changing conditions, custom analytics tools are often developed. We were asked to develop such a tool by a team at the NASA Jet Propulsion Laboratory, where rover telecom operators make decisions based on models predicting how much data rovers can transfer from the surface of Mars. Through research, design, implementation, and informal evaluation of our new tool, we developed principles to inform the design of visual analytics systems in operations contexts. We offer these principles as a step towards understanding the complex task of designing these systems. The principles we present are applicable to designers and developers tasked with building analytics systems in domains that face complex operations challenges such as scheduling, routing, and logistics.

Lombeyda, Santiago↗
DOE OSTI2021

Prioritizing Countries by Concern Regarding Access to Weapons of Mass Destruction Materials

This article presents a global prioritization methodology that evaluates the relative risks of non-state actor acquisition of materials that could be used in chemical, biological, radiological, nuclear and high explosive Weapons of Mass Destruction (WMD) from the country’s relevant infrastructure. Prioritization is based on three domains: 1. Assessing relative scale of materials in each country, 2. The country’s corresponding security posture, and 3. The presence of threat actors. The output is a list of countries prioritized from greatest risk to least. Rather than providing an overall 1 to N ranking, however, the results are placed into tiers based upon their natural groupings within the three domains. The countries in the highest tiers are flagged as potential US national security concern; those scoring in the middle and at the bottom are flagged as posing lower US national security concern. A systematic approach assesses each country by leveraging many disciplines, such as risk and decision analysis, as well as expert judgement. A quantitative value model based on Multi-Attribute Value Theory (MAVT) organizes the objectives scoring criteria into a value tree using lessons learned from previous studies, published literature, and expert judgement. The article presents the prioritization categories and corresponding value model scoring criteria to include measurement type, weight, range, and value preference. Country names and data are notional in order to share the details on the underlying methodology and model without identification of actual security risks. A deliberative process addresses factors external to the model and scrutinizes inputs, methodology, model, and results.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS2008

Image Compression Algorithm Altered to Improve Stereo Ranging

A report discusses a modification of the ICER image-data-compression algorithm to increase the accuracy of ranging computations performed on compressed stereoscopic image pairs captured by cameras aboard the Mars Exploration Rovers. (ICER and variants thereof were discussed in several prior NASA Tech Briefs articles.) Like many image compressors, ICER was designed to minimize a mean-square-error measure of distortion in reconstructed images as a function of the compressed data volume. The present modification of ICER was preceded by formulation of an alternative error measure, an image-quality metric that focuses on stereoscopic-ranging quality and takes account of image-processing steps in the stereoscopic-ranging process. This metric was used in empirical evaluation of bit planes of wavelet-transform subbands that are generated in ICER. The present modification, which is a change in a bit-plane prioritization rule in ICER, was adopted on the basis of this evaluation. This modification changes the order in which image data are encoded, such that when ICER is used for lossy compression, better stereoscopic-ranging results are obtained as a function of the compressed data volume.

Kiely, Aaron↗
NASA NTRSDate not supplied

Creating Portfolio Management Concepts for Highly Agile and Innovative Government Research Programs Using Design Thinking and Lean Startup Methodologies

Managing a research and development (R&D) portfolio presents numerous challenges, such as prioritizing research areas, remaining agile, strategic workforce planning, and measuring return on investment, impact, and innovation. One government R&D program is charged with producing a high degree of innovative, transformational breakthroughs in aviation technology. A practical, structured methodology for strategically prioritizing emerging aviation R&D in such an environment is lacking. The existing multi-criteria decision aid tools are primarily utilized at an enterprise level, take months to set up and collect data, require large teams of experts, are used on an infrequent basis, and are not conducive to a highly innovative, high-risk portfolio. Prior to audaciously creating a new portfolio prioritization process, the exact challenges with portfolio management for R&D projects were identified using the design thinking and lean start-up methods. A key part of this discovery process was interviewing stakeholders, as well as other managers of organizations charged with producing innovative portfolios. These interviews, as well as additional techniques, were used to develop a deeper understanding of the challenges and, subsequently, lay the foundation for development of an effective portfolio prioritization process. Four potential concepts that represent key findings emerged: carefully selected criteria for portfolio assessment and selection, targeted portfolio turnover rate, dynamic portfolio prioritization framework, and streamlined transition or commercialization of R&D. Acting on any one of the resulting portfolio management concepts will increase the transparency and confidence in portfolio decisions and, ideally, result in a greater degree of transformational breakthroughs in aviation technology.

project portfolio management↗
DOE OSTI2026

A U.S. scientific community review of carbon cycle science gaps and opportunities to better support earth system science and carbon management

Greenhouse gas (GHG) emissions continue to grow, while natural carbon reservoirs are becoming increasingly vulnerable to anthropogenic pressures, climate extremes, and disturbance. These changes are impacting humans, ecosystems, and natural resources worldwide. Tracking and mitigating GHG emissions require a pivot to operational monitoring of regional carbon flux and stock changes. The current GHG observing system is addressing needs at two distinct scales: 1) Local scale (< 1 km), related to anthropogenic point source emissions, and 2) global scales (> 1000 km), related to land and ocean carbon sinks. More focus on intermediate (10–1000 km) scales is needed to more effectively monitor progress in reducing carbon emissions, enhancing removals, and maintaining sinks. Representatives from carbon cycle biomass and flux communities across United States government agencies and academic institutions met in September 2024 to discuss the rationale and scientific context for more effectively implementing an operational system for GHG monitoring in support of urban and national carbon management needs. To guide development of this system, we propose a multi-tiered global spaceborne observing framework for carbon flux and stock, prioritizing: 1) frequent GHG partial columns for carbon emissions and removals; 2) continuous time series and data fusion of biomass from Lidar and Synthetic Aperture Radar (SAR) for carbon stocks, and 3) expanded coverage of tropical, high latitude, and oceanic regions to monitor carbon cycle tipping points and feedbacks. This system should be complemented by expanded surface and airborne networks for oceanic and terrestrial/aquatic ecosystems for calibration, ground truthing, and study of under-sampled regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fast jet tagging with MLP-Mixers on FPGAs

We explore the innovative use of MLP-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs. MLP-Mixers excel in processing sequences of jet constituents, achieving state-of-the-art performance on datasets mimicking Large Hadron Collider conditions. By using advanced optimization techniques such as High-Granularity Quantization and Distributed Arithmetic, we achieve unprecedented efficiency. These models match or surpass the accuracy of previous architectures, reduce hardware resource usage by up to 97%, double the throughput, and half the latency. Additionally, non-permutation-invariant architectures enable smart feature prioritization and efficient FPGA deployment, setting a new benchmark for machine learning in real-time data processing at particle colliders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

The Yucatan Peninsula Energy Assessment: Pathways for a Clean and Sustainable Power System

This report begins with an overview of the status of the power sector in Mexico's Yucatán peninsula, including key challenges, followed by policy and program solutions that pave the way for a cleaner, more sustainable power sector in the Yucatan. This work is intended to provide information to the states of the Yucatán peninsula - Yucatán, Campeche, and Quintana Roo - for prioritization and future technical assistance from the 21st Century Power Partnership (21CPP) program, in Mexico including data gathering, capacity building, modelling and analysis, program and policy setting to help the states and the region overall best utilize its resources to meet its electricity needs while implementing energy efficiency measures towards a more sustainable, clean, reliable system with lower prices. This report is also available in Spanish: https://www.nrel.gov/docs/fy21osti/81142.pdf.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Energy Assessment of the Yucatan Peninsula: Pathways to a Clean and Sustainable Energy System; Evaluación Energética de la Península de Yucatán: Vías para un Sistema Energético Limpio y Sustentable

Quintana Roo - for prioritization and future technical assistance from the 21st Century Power Partnership (21CPP) program, in Mexico including data gathering, capacity building, modelling and analysis, program and policy setting to help the states and the region overall best utilize its resources to meet its electricity needs while implementing energy efficiency measures towards a more sustainable, clean, reliable system with lower prices.; Este trabajo tiene como objetivo brindar información a los estados de la península de Yucatán - Yucatán, Campeche y Quintana Roo - para priorizar y dar asistencia técnica futura del programa 21st Century Power Partnership (21CPP), en México, incluyendo recolección de datos, desarrollo de capacidades, modelación y análisis, y el establecimiento de programas y políticas para ayudar a los estados y la región en general a utilizar mejor sus recursos, para satisfacer sus necesidades de energía eléctrica, implementando también medidas de eficiencia energética para obtener un sistema más sustentable, limpio y confiable con precios más bajos. This report is also available in English: https://www.nrel.gov/docs/fy21osti/79680.pdf.

21CPP↗
NASA NTRS1987

Cryogenic Fluid Management Flight Experiment (CFMFE)

Since its foundation, NASA has excelled in the study and development of microgravity fluid management technology. With the advent of space-based vehicles and systems, the use of and the ability to efficiently manage subcritical cryogens in the space environment has become necessary to our growing space program. The NASA Lewis Research Center is responsible for the planning and execution of a program which will provide advanced in-space cryogenic fluid management technology. A number of future space missions have been identified that will require or could benefit from this technology. These technology needs have been prioritized and the Cryogenic Fluid Management Flight Experiment (CFMFE) is being designed to provide the experimental data necessary for the technological development effort.

Defelice, David M.↗
NASA NTRS2004

Science-based Region-of-Interest Image Compression

As the number of currently active space missions increases, so does competition for Deep Space Network (DSN) resources. Even given unbounded DSN time, power and weight constraints onboard the spacecraft limit the maximum possible data transmission rate. These factors highlight a critical need for very effective data compression schemes. Images tend to be the most bandwidth-intensive data, so image compression methods are particularly valuable. In this paper, we describe a method for prioritizing regions in an image based on their scientific value. Using a wavelet compression method that can incorporate priority information, we ensure that the highest priority regions are transmitted with the highest fidelity.

Wagstaff, K. L.↗