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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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The goal of our project is to integrate diverse global and local air quality data sources using the cloud computing platform of Google Earth Engine to provide synthesized estimates and forecasts of air quality at a local scale but with a global scope freely accessible by air quality managers worldwide, facilitating their decision-making processes.
At NASA, the Crew Health and Performance (CHP) system represents the span of countermeasures, capabilities, interventions, and tested processes and procedures that in combination work to mitigate the human component of spaceflight mission risk. Across the varying NASA mental models of the CHP system, the different functionalities needed to meet human flight systems standards can be broken down into specific categories (i.e. medical capability, environmental health, behavioral health). These categories can be further broken down into specific subgroups generally associated with the CHP functionalities meant to mitigate or buy down individual human system risks. Taking a similar development approach we seek to leverage dynamic probabilistic risk assessment as a means to quantify and relatively assess the human risk state within the crew health and performance domain. By utilizing existing tools as integrators, we propose a rapid development strategy for incorporating research and operational data that represent the influence of the CHP system functionalities, in order to provide order of magnitudes estimates of the influence on most human system risks outcomes. The model system utilizes a modest cumulative risk approach and that limits the scope to primary paths of influence between the CHP functionalities and human system risks, thus enabling quick prototypes of the integrative effects of CHP functional combinations to solicit valuable feedback from stakeholders and customers on the data, relationship, and structure of the integration.
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Interest in novel aircraft configurations has continued to grow in response to increasing demand for improved fuel efficiency. One such configuration of interest is the Transonic Truss-Braced Wing (TTBW). Because the improvement of the TTBW comes from increased efficiency during cruise, it offers increasing returns as the mission distance increases. As a result, there is often a crossover point which represents the minimum mission range where the TTBW will offer a fuel burn reduction relative to a similar Tube and Wing aircraft with similar component technology levels. Aircraft rarely fly missions at their maximum range, so it is important to investigate not only the maximum benefit, but also the most likely benefit. Design decisions and analysis assumptions that significantly impact this crossover point include whether to put fuel in body tanks, how weight build-up estimation is calibrated, engine technology selection, and flight Mach number. This study, conducted under NASA’s Sustainable Flight Demonstration project and inspired by differing assumptions made by different research teams, details the impacts of changing these design decisions and assumptions.
Interest in novel aircraft configurations has continued to grow in response to increasing demand for improved fuel efficiency. One such configuration of interest is the Transonic Truss-Braced Wing (TTBW). Because the improvement of the TTBW comes from increased efficiency during cruise, it offers increasing returns as the mission distance increases. As a result, there is often a crossover point which represents the minimum mission range where the TTBW will offer a fuel burn reduction relative to a similar Tube and Wing aircraft with similar component technology levels. Aircraft rarely fly missions at their maximum range, so it is important to investigate not only the maximum benefit, but also the most likely benefit. Design decisions and analysis assumptions that significantly impact this crossover point include whether to put fuel in body tanks, how weight build-up estimation is calibrated, engine technology selection, and flight Mach number. This study, conducted under NASA’s Sustainable Flight Demonstration project and inspired by differing assumptions made by different research teams, details the impacts of changing these design decisions and assumptions.
Estimates of the total uncertainty for empirically determined loudness levels are documented when GRS (Ground Recording System) noise monitors are used to record X 59 sonic boom waveforms. The total uncertainty is characterized by combining nine different sources of uncertainty that may affect the apparent gain of the measurement chain. These uncertainty estimates are presented as expected measurement error relative to the true loudness level, and separate error estimates are provided for eight different noise metrics in which NASA has interest. The behavior of the Perceived Level (PL) metric is studied within the report body, while the total uncertainties for the seven other noise metrics are summarized in appendices for brevity. The effects of four sources of uncertainty are estimated simply from information found on hardware specification sheets provided by the manufacturer. However, mock acoustic recordings are created to estimate the effects of other sources because those effects are expected to induce spectral coloration, so they may vary with noise metric type and sound level. These sources are not well modeled by simple gain adjustments. Importantly, measurement error is computable when processing mock recordings since the true levels are knowable, which is not the case when processing data recorded in the field. Specifically, the true levels are knowable because the components of the mock recordings are separable – e.g., loudness levels of booms can be computed with or without superimposed background noise. Mock acoustic recordings also have the benefit of allowing analysis of sonic booms from vehicles that are not yet flying, like the X-59, since the mock recordings are created by combining vehicle-specific predicted waveforms with other audio sources. The estimates of total measurement error are documented as a function of the signal-to-noise ratio (SNR) of the loudness level, where the corrected SNR is computed while accounting for the effects of the method that is used to correct for background noise contamination when computing the noise metric values. The corrected SNR calculations used here can be applied to both mock recordings and in-field measurements, so the uncertainty of in-field recordings can be found using pre-computed lookup tables that identify the relationship between metric type, corrected SNR, and the expected measurement error.
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