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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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This presentation discusses PV soiling, including background and general mitigation approaches, state-of-the-art for extracting soiling information from PV data, data sharing, and challenges with bio-soiling.
It has become clear in recent decades that manufacturing supply chains are increasingly vulnerable to disruptions of varying geographical scales and intensities. These disruptions—whether intentional, accidental, or resulting from natural disasters—cause failures and capacity reductions to manufacturing infrastructure, with lasting effects that can cascade throughout the manufacturing network. An overall lack of understanding of solutions to mitigate disturbances has rendered the challenge of reducing manufacturing supply chain vulnerability even more difficult. Additionally, the variability of disruptions and their impacts complicates policy maker and stakeholder efforts to plan for specific disruptive scenarios. It is necessary to comprehend different kinds of disturbances and group them based on stakeholder-provided metrics to support planning processes and modeling efforts that promote adaptable, resilient manufacturing supply chains. This paper reviews existing methods for risk management in manufacturing supply chains and the economic and environmental impacts of disruptions. In addition, we develop a framework using agglomerative hierarchical clustering to classify disruptions using U.S. manufacturing network data between 2000 and 2021 and characteristic metrics defined in the literature. Our review identifies five groups of disruptions and discusses both general mitigation methods and strategies targeting each identified group. Further, we highlight gaps in the literature related to estimating and including environmental costs in disaster preparedness and mitigation planning. We also discuss the lack of easily available metrics to quantify environmental impacts of disruptions and how such metrics could be included into our methodology.
We compare two scrubbing mitigation schemes for Xilinx FPGA devices. The design of the scrubbers is briefly discussed along with an examination of mitigation limitations. Proton and Heavy Ion data are then presented and analyzed.
To develop an innovative yet practically implementable mitigation technique for the most probable impact threat of an asteroid or comet with short warning time (i.e., when we don't have sufficient warning times for a deflection mission).
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Contamination control issues are particularly challenging for long-term human spaceflight and are associated with the search for life, dynamic environmental conditions, human-robotic-environment interaction, sample collection and return, biological processes, waste management, long-term environmental disturbance, etc. These issues impact mission success, human health, planetary protection, and research and discovery. Mitigation and control techniques and strategies may include and integrate long-term environmental monitoring and reporting, contamination control and planetary protection protocols, habitation site design, habitat design, and surface exploration and traverse pathways and area access planning.
Lunar resonant orbits present several important challenges in terms of trajectory design, insertion, stability and eclipse mitigation. For some mission concepts, the placement of the initial orbital elements is fundamental to overcoming show-stopping performance values such as eclipse duration and orbit lifetime. Lunar resonant orbits need to maintain specific alignments in order to avoid undesired flybys or even recontact with Earth or GEO satellites. This paper presents methods to mitigate long eclipses while preserving achievable lunar resonant orbits that persist in the long term.
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Power generation ramping between forecasted (net) load set-points shift the generation (MWh) from its scheduled values. The Integrated Ramping is described as a method that mitigates this problem.
This study examines model-specific assumptions and projections of methane (CH 4 ) emissions in deep mitigation scenarios generated by integrated assessment models (IAMs). For this, scenarios of nine models are compared in terms of sectoral and regional CH 4 emission reduction strategies, as well as resulting climate impacts. The models’ projected reduction potentials are compared to sector and technology-specific reduction potentials found in literature. Significant cost-effective and non-climate policy related reductions are projected in the reference case (10–36% compared to a “frozen emission factor” scenario in 2100). Still, compared to 2010, CH 4 emissions are expected to rise steadily by 9–72% (up to 412 to 654 Mt CH 4 /year). Ambitious CO 2 reduction measures could by themselves lead to a reduction of CH 4 emissions due to a reduction of fossil fuels (22–48% compared to the reference case in 2100). However, direct CH 4 mitigation is crucial and more effective in bringing down CH 4 (50–74% compared to the reference case). Given the limited reduction potential, agriculture CH 4 emissions are projected to constitute an increasingly larger share of total anthropogenic CH 4 emissions in mitigation scenarios. Enteric fermentation in ruminants is in that respect by far the largest mitigation bottleneck later in the century with a projected 40–78% of total remaining CH 4 emissions in 2100 in a strong (2 °C) climate policy case.
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Since mid-2016, the frequency of low energy laser pulses emitted by the CALIPSO lidar has been slowly increasing due to pressure losses in the canister housing the laser. While originally confined primarily to the South Atlantic Anomaly (SAA) region, these low energy pulses now occur intermittently around the globe. Low energy pulses can cause calibration biases and degrade the science quality of level 2 retrievals. We describe a new low energy mitigation (LEM) algorithm that will be implemented incrementally in future versions of the CALIOP data processing to identify and reject affected profiles during calibration and feature detection. The LEM algorithm effectively eliminates low energy calibration biases, improves level 2 retrievals, and minimizes level 2 data loss.
Despite commercial airlines mandating masks, there have been multiple documented events of COVID-19 superspreading on flights. Conventional models do not adequately explain superspreading patterns on flights, with infection spread wider than expected from proximity based on passenger seating. An important reason for this is that models typically do not consider the movement of passengers during the flight, boarding, or deplaning. Understanding the risks for each of these aspects could provide insight into effective mitigation measures. We modeled infection risk from seating and fine-grained movement patterns – boarding, deplaning, and inflight movement. We estimated infection model parameters from a prior superspreading event. We validated the model and the impact of interventions using available data from three flights, including cabin layout and seat locations of infected and uninfected passengers, to suggest interventions to mitigate COVID-19 superspreading events during air travel. Specifically, we studied: 1) London to Hanoi with 201 passengers, including 13 secondary infections among passengers; 2) Singapore to Hangzhou with 321 passengers, including 12 to 14 secondary infections; 3) a non-superspreading event on a private jet in Japan with 9 passengers and no secondary infections. Here our results show that the inclusion of passenger movement better explains the infection spread patterns than conventional models do. We also found that FFP2/N95 mask usage would have reduced infection by 95–100%, while cloth masks would have reduced it by only 40–80%. Results indicate that leaving the middle seat vacant is effective in reducing infection, and the effectiveness increases when combined with good quality masks. However, with a good mask, the risk is quite low even without the middle seats being empty. Our results suggest the need for more stringent guidelines to reduce aviation-related superspreading events of COVID-19.
Previous archaeological research in southern Idaho has suggested that climate change over the past 8000 years was not dramatic enough to alter long-term subsistence practices in the region. However, recent isotopic analyses of bison remains from cold storage caves on the Snake River Plain contest this hypothesis. Our results, when examined against an archaeoclimate model, suggest that cold storage episodes coincided with drier, warmer phases that likely reduced forage and water, and thus limited the availability of bison on the open steppe. Within this context we build a risk model to illustrate how environment might have motivated cold storage behaviors. Caching bison in cold lava tubes would have mitigated both intra-annual and inter-annual food shortages under these conditions. This analysis also suggests that skeletal fat, more than meat, may have influenced the selection, transport and storage of bison carcass parts. We deciphered when and how cold storage caves which was used to provide a more comprehensive understanding of foraging behaviors in a broad range of hunting-gathering economies.
Neural phase pickers—neural networks designed and trained to pick seismic phase arrivals—have proven to be a powerful tool for developing earthquake catalogs. However, these pickers suffer from prediction inconsistency in which the results they produce change, sometimes substantially, even under a small perturbation to the input waveform. This problem has not been addressed by the developers and users of these pickers. In this study, we show how prediction inconsistency can negatively affect the completeness of earthquake catalogs developed using neural phase pickers. Further, we show that simply using a small step size for the sliding window when processing continuous waveform data and aggregating the results significantly mitigates this problem. We also highlight the importance of training datasets for increasing the consistency and other performance metrics.
Astronauts embarking on missions beyond low Earth orbit (LEO) will be exposed to a radiation field that may increase the risks of developing cancer, cardiovascular diseases, central nervous system disorders, and immune decrements. Operational parameters will be the primary determinants of crew radiation exposure. NASA uses integrated design tools and risk models to optimize these parameters to minimize radiation exposure. NASA is also considering medical countermeasures (MCMs) to reduce radiation-associated health risks. MCMs for potential use in space-based applications can be developed from a variety of sources, including: a) population-based chemoprevention trials against targeted diseases b) drug development efforts focused on treating acute effects from accidental radiation exposures c) drug development to mitigate side effects of radiotherapy d) mechanistic studies of distinct damage caused by high charge (Z) and energy (HZE) radiation. Use of agents developed for other applications, or repurposed, is advantageous because long-term safety in humans is already established.
Finite state-machines (FSMs) are used to control operational flow in application specific integrated circuits (ASICs) and field programmable gate array (FPGA) devices. Because of their ease of interpretation, FSMs simplify the design and verification process and consequently are significant components in a synchronous design.
As NASA considers long-duration exploration missions, it is envisioned that crews will behave more autonomously as compared to low-Earth orbit missions. When considering long duration exploration missions, Flight Operations Directorate (FOD) identified the following benefits and challenges presented on this slide. As missions operate further from Earth, the communication latency between the spacecraft and Mission Control Center will increase requiring the crew to take a more active role in reacting to daily planning. However, one of the key challenges for crew self-scheduling is that we do not want to overwhelm astronauts who are not expert mission planners. This leads to our primary research objective: to first, characterize the human performance envelope for the task of planning and scheduling in the context of human spaceflight operations, then, develop countermeasure aids to mitigate adverse performance effects due to plan complexity, and finally, inform performance standards and guidelines based on research results. Our next steps are to finish summarizing our findings from the experiment in conference papers and our upcoming yearly report. In the upcoming year, we will continue to collect data in analog missions and begin summarizing our recommended standards and guidelines.