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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

MODEL BASED BIOMASS SYSTEM DESIGN OF FEEDSTOCK SUPPLY SYSTEMS FOR BIOENERGY PRODUCTION

Engineering feedstock supply systems that deliver affordable, high-quality biomass remains a challenge for the emerging bioenergy industry. Cellulosic biomass is geographically distributed and has diverse physical and chemical properties. Because of this feedstock supply systems that deliver cellulosic biomass resources to biorefineries require integration of a broad set of engineered unit operations. These unit operations include harvest and collection, storage, preprocessing, and transportation processes. Design decisions for each feedstock supply system unit operation impact the engineering design and performance of the other system elements. These interdependencies are further complicated by spatial and temporal variances such as climate conditions and biomass characteristics. This paper develops an integrated model that couples a SQL-based data management engine and systems dynamics models to design and evaluate biomass feedstock supply systems. The integrated model, called the Biomass Logistics Model (BLM), includes a suite of databases that provide 1) engineering performance data for hundreds of equipment systems, 2) spatially explicit labor cost datasets, and 3) local tax and regulation data. The BLM analytic engine is built in the systems dynamics software package PowersimTM. The BLM is designed to work with thermochemical and biochemical based biofuel conversion platforms and accommodates a range of cellulosic biomass types (i.e., herbaceous residues, short- rotation woody and herbaceous energy crops, woody residues, algae, etc.). The BLM simulates the flow of biomass through the entire supply chain, tracking changes in feedstock characteristics (i.e., moisture content, dry matter, ash content, and dry bulk density) as influenced by the various operations in the supply chain. By accounting for all of the equipment that comes into contact with biomass from the point of harvest to the throat of the conversion facility and the change in characteristics, the BLM evaluates economic performance of the engineered system, as well as determining energy consumption and green house gas performance of the design. This paper presents a BLM case study delivering corn stover to produce cellulosic ethanol. The case study utilizes the BLM to model the performance of several feedstock supply system designs. The case study also explores the impact of temporal variations in climate conditions to test the sensitivity of the engineering designs. Results from the case study show that under certain conditions corn stover can be delivered to the cellulosic ethanol biorefinery for $35/dry ton.

09 BIOMASS FUELS↗
DOE OSTI2018

Aerosol Vertical Profiling at Oliktok Point (AVPOP) Field Campaign Report

Aerosols are an important component of the atmospheric system through their various impacts on climate. Notably, the largest uncertainty of the energy budget (i.e., radiative forcing estimate) is the aerosol indirect effect. Aerosol-induced microphysical modifications influence cloud lifetime and albedo. However, constraining aerosol-cloud impacts in climate models, specifically in regions such as the Arctic, remains a significant challenge due to limited observations. The efficacy of an aerosol to impact the arctic energy budget largely depends on its concentration, composition, and source. While ground-based measurements will provide a consistent, high-resolution time series of aerosol observations, the arctic atmosphere can be stratified, and thus what is observed on the ground may not be representative of the aerosol aloft. A key gap in arctic aerosol research is routine vertical profiling of abundance and source characterization. Previous work outside of the Arctic has focused on using launched or tethered balloon systems (LBS and TBS, respectively) to measure vertical profiles of aerosol size distributions and has demonstrated the utility of such observations (e.g., Hofmann et al. 1989; Hofmann 1993; Iwasaka et al. 2003; Kim et al. 2003; Tobo et al. 2007). More specifically, particle spectrometers have also been deployed via TBS systems (de Boer et al. 2018; Greenberg et al. 2009; Maletto et al. 2003; Renard et al. 2016; Wehner et al. 2007; Siebert et al. 2004) affording information on aerosol layer locations and evolution by means of multiple profiles. Filter samples for post-processing of aerosol composition and ice nucleation properties have also been conducted in mid-latitudes (Creamean et al. 2018). However, while these studies present novel measurements of aerosol vertical profiling via balloon systems, few exist to date in the Arctic. The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility TBS is composed of a variety of winches, balloons, and instrumentation that flies within ARM’s Restricted Airspace (R-2204) at the third ARM Mobile Facility (AMF3) at Oliktok Point, Alaska (Figure 1). The system payload comprises instruments that can simultaneously collect in situ data on horizontal winds, ice microphysics, turbulence, thermodynamic state, aerosols, and the cloud-top environment (https://www.arm.gov/capabilities/instruments/tbs). Recent campaigns at AMF3 have involved the use of the ARM helikite, which is a balloon-kite hybrid that uses lighter-than-air principles to obtain its initial lift and a kite to achieve stability and dynamic lift. Additional information on the specifications of the helikite TBS can be found in the ARM TBS handbook (Dexheimer 2018).

Oliktok Point, North Slope of Alaska,, tethered ba↗
DOE OSTI2015

Modeling and Quantitative Assessment of Green and Sustainable Remediation Options for the M1 Air Stripper System at DOE SRS - 15660

The effectiveness of remediation systems needs to be reviewed periodically to assess their performance over the years relative to efficiency and effectiveness. New technologies and Best Management Practices have emerged that can improve existing remediation systems. From a Green and Sustainable Remediation analysis, the higher energy and/or water usage of older systems might not be justified from an environmental protection rationale. For example, remediation systems can run intermittently and with slower flow rates, use less power, and still provide the required level of environmental remediation and protection. This research by the Applied Research Center at Florida International University (FIU) is the preliminary phase of a Green and Sustainable Remediation (GSR) analysis of the operations of the M1 air stripper and the A/M Area groundwater remediation system at the Savannah River Site (SRS). An initial baseline analysis has helped identify opportunities for implementing Green and Sustainable Remediation (GSR) practices while maintaining the hydraulic containment and effectiveness of the remediation system. This study included the collection of contaminant recovery data from SRS reports into a database of trichloroethene (TCE) and tetrachloroethene (PCE) removal from contaminant recovery wells RWM-1 through RWM-12. Presented below are data and preliminary findings from these recovery wells. Findings show that 7 of 12 recovery wells have transitioned to more PCE removal than TCE removal; the nearby steam injection remediation of 'Dynamic Underground Stripping' mobilized DNAPL and resulted in increased recovery in nearby wells. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1974

Track-train dynamic analysis and test program, truck static test

A series of tests were conducted to define the characteristics of an ASF 11 Ride Truck Assembly including joint slop, friction and stiffness. Loading to the truck assembly included vertical load to simulate the car/pool loading combined with lateral or moment loading that resulted in desired truck deflections for the various phases of testing. All seven test conditions were successfully completed with load and deflection data being collected. No attempt is made to reduce the applicable data other than to provide computer plots.

Nemes, A. G.↗
DOE OSTI2022

Technical Characterization and Benefit Evaluation of 5G-Enabled Grid Data Transport and Applications

This report summarizes the Year 1 work of Pacific Northwest National Laboratory’s (PNNL’s) 5G Fabricated Resource and Asset Management Encompassment for energy infrastructure (Energy FRAME) project funded by the Department of Energy Office of Science’s Advanced Scientific Computing Research Program. 5G is a breakthrough technology that enables a fully mobile and connected society, and a 5G-enabled digital continuum will be one of the critical foundations for a clean energy economy and grid modernization. In collaboration with PNNL’s Advanced Wireless Communication team and Center for Advanced Technology Evaluation team, the project team has been evaluating the system performance of 5G testbeds in the PNNL 5G Innovation Studio, and has formulated a co-simulation test case of power system transmission, distribution, and communication (T&D&C) networks considering 5G technology and high penetration of distributed energy resources. The methodology developed in the 5G Energy FRAME project can be customized to fit different future grid scenarios to evaluate multiple (dynamic) configurations (computing, sensing, communication, environment) for different stakeholders. In summary, our main technical highlights in project Year 1 are as follows: 1) Technical characterization of 5G standalone architectures, 2) Formulation of co-simulation test case of T&D&C networks embedded with 5G, 3) Initial benefit evaluation of 5G communication platform for grid use cases, and 4) Additional extended discussions on edge computing, artificial intelligence and machine learning, and high-performance computing and cloud computing adoptions. In addition, a collection of system performance data is shared through the publicly available weblink, https://www.pnnl.gov/projects/5g-energy-frame/publications

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2024

Deciphering the Spectra of Flowers to Map Landscape-scale Blooming Dynamics

Like leaves, floral coloration is driven by inherent optical properties, which are determined by pigments, scattering structure, and thickness. However, establishing the relative contribution of these factors to canopy spectral signals is usually limited to in-situ observations. Modeling flowering dynamics (e.g., blooming duration, spatial distribution) at the landscape scale may reveal insights into ecological processes and phenological adaptations to environmental changes. Multitemporal visible to shortwave infrared (VSWIR) imaging spectroscopy observations are especially suited for such efforts. Reflectance in this spectral range is sensitive to major flower pigments, flowering phenology traces, and biophysical differences between flowers and other plant parts. We explored how flowers contribute to spectral signals using a time series of imagery from the Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) collected as part of the SBG High-Frequency Time Series (SHIFT) campaign as a case study. Airborne data were collected weekly during the spring of 2022 across two natural reserves in California. Field spectra were gathered from blooming plots at leaf, flower, and canopy levels at two time points during the campaign. The processed data was used to investigate flowering species' spectro-temporal variation and spatial distribution using Spectral Mixture Residual, Gaussian clustering techniques, and a proposed narrow-band flowering index. Linear spectral unmixing allowed the computation of the weighted contribution of four major high-variance endmembers (leaves, flowers, soil, dark) and low-variance residual signal that comprises subtle spectral features used to track biophysical processes. The reflectance residual was projected on a low principal component basis to characterize flowering clusters' variation and spatial distribution based on the Gaussian mixture model, providing an uncertainty metric to assess the results. Mapping flowering events from modeling spectro-temporal dynamics throughout the season, from pre-blooming to post-flowering stages, allowed us to identify gradient variations in spectral features within the VSWIR spectral range linked to flowering pigments. Time series of the Mixture Residual Blooming Index and the Red-Edge Normalized Difference Vegetation Index revealed specific flowering and greenness phenophases across the two main species (Coreopsis gigantea, Artemisia californica) in the flowering areas. Overall, our approach opens opportunities for future satellite monitoring of floral cycles at broader scales.

Yoseline Angel↗
DOE OSTI2021

The Urban Observatory: A Multi-Modal Imaging Platform for the Study of Dynamics in Complex Urban Systems

We describe an “Urban Observatory” facility designed for the study of complex urban systems via persistent, synoptic, and granular imaging of dynamical processes in cities. An initial deployment of the facility has been demonstrated in New York City and consists of a suite of imaging systems—both broadband and hyperspectral—sensitive to wavelengths from the visible (∼400 nm) to the infrared (∼13 micron) operating at cadences of ∼0.01–30 Hz (characteristically ∼0.1 Hz). Much like an astronomical survey, the facility generates a large imaging catalog from which we have extracted observables (e.g., time-dependent brightnesses, spectra, temperatures, chemical species, etc.), collecting them in a parallel source catalog. We have demonstrated that, in addition to the urban science of cities as systems, these data are applicable to a myriad of domain-specific scientific inquiries related to urban functioning including energy consumption and end use, environmental impacts of cities, and patterns of life and public health. We show that an Urban Observatory facility of this type has the potential to improve both a city’s operations and the quality of life of its inhabitants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Molecular Dynamics Simulations of the Adsorption of Phthalate Esters on Smectite Clay Surfaces

The partitioning of organic contaminants between water and solid surfaces is a key process controlling their fate and transport in natural environments. A novel methodology was developed to predict the adsorption of organic contaminants by smectite clay minerals (high specific surface area adsorbents abundant in natural soils) using molecular dynamics (MD) simulations. The methodology models a stack of flexible Ca- montmorillonite lamellae in direct contact with a bulk aqueous reservoir and uses the metadynamics technique to facilitate the exploration of the free energy landscape. The methodology was tested and validated in the case of six phthalate esters, widely used chemical plasticizers with endocrine disrupting properties. Simulation predictions reveal strong phthalate adsorption, especially for the larger and more hydrophobic phthalates. Predicted partition coefficients (K d values) are consistent with collected batch experimental data. Adsorption was observed on both the exterior basal surfaces and within the interlayer nanopore, with phthalate molecules predominately adopting a flat orientation on the clay surface. Intercalation was also detected in complementary X-ray diffraction (XRD) experiments. A strong inverse relationship between extent of adsorption and clay surface charge density was observed, as phthalate molecules preferentially occupied the more hydrophobic uncharged patches on each surface. Detailed analysis of the free energy of adsorption revealed that phthalate affinity for the clay surface results from a small favorable van der Waals contribution and a large favorable entropic contribution. Overall, this research demonstrates the substantial affinity of smectite clays for phthalate esters and establishes a computational methodology capable of predicting the water-clay partition coefficients of organic contaminants, a key parameter in environmental fate and transport models.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Kinetic Deep Learning v0.1

Here, we present a method that uses protein levels to predict times series of metabolite concentrations. Understanding this type of pathway dynamics is important in order to predict the behavior of the pathway and, more pragmatically, to be able to design biological systems (such as strains bioengineered to produce chemical products) reliably. Typically, for this purpose, kinetic models consisting of differential equations based on the Michaelis-Menten dynamics have been used in the past. However, these methods can rarely produce good fits to measured data time series. Possibly, this happens because the kinetic constants are unknown or are different from the ones measured in vivo, or perhaps because Michaelis-Menten dynamics is not a satisfactory description. In order to improve the predictive nature of these kinetic models we have eliminated the Michaelis-Menten description of pathway dynamics and we have substituted it by algorithms that automatically learn these dynamics from previously obtained metabolomics and proteomics data using machine learning approaches. Specifically, kinetic deep learning uses deep learning to map proteomics time series to metabolite concentration time series, instead of learning the first metabolite derivative and integrating in (as in the first version of kinetic learning). This approach is shown to provide good to excellent results with a data set specifically collected for this purpose.

Garcia Martin, Hector [Joint BioEnergy Institute (↗
DOE OSTI2013

Analysis of High-Penetration Levels of Photovoltaics into the Distribution Grid on Oahu, Hawaii: Detailed Analysis of HECO Feeder WF1

Renewable generation is growing at a rapid rate due to the incentives available and the aggressive renewable portfolio standard (RPS) targets implemented by state governments. Distributed generation in particular is seeing the fastest growth among renewable energy projects, and is directly related to the incentives. Hawaii has the highest electricity costs in the country due to the high percentage of oil burning steam generation, and therefore has some of the highest penetration of distributed PV in the nation. The High Penetration PV (HiP-PV) project on Oahu aims to understand the effects of high penetration PV on the distribution level, to identify penetration levels creating disturbances on the circuit, and to offer mitigating solutions based on model results. Power flow models are validated using data collected from solar resources and load monitors deployed throughout the circuit. Existing interconnection methods and standards such as IEEE 1547, Hawaii Rule 14H and California Rule 21 are evaluated in these emerging high penetration scenarios. A key finding is a shift in the level of detail to be considered and moving away from steady-state peak time analysis towards dynamic and time varying simulations. Each level of normal interconnection study is evaluated and enhanced to a new level of detail, allowing full understanding of each issue.

14 SOLAR ENERGY↗
DOE OSTI2015

Strategy and gaps for modeling, simulation, and control of hybrid systems

The purpose of this report is to establish a strategy for modeling and simulation of candidate hybrid energy systems. Modeling and simulation is necessary to design, evaluate, and optimize the system technical and economic performance. Accordingly, this report first establishes the simulation requirements to analysis candidate hybrid systems. Simulation fidelity levels are established based on the temporal scale, real and synthetic data availability or needs, solution accuracy, and output parameters needed to evaluate case-specific figures of merit. Accordingly, the associated computational and co-simulation resources needed are established; including physical models when needed, code assembly and integrated solutions platforms, mathematical solvers, and data processing. This report first attempts to describe the figures of merit, systems requirements, and constraints that are necessary and sufficient to characterize the grid and hybrid systems behavior and market interactions. Loss of Load Probability (LOLP) and effective cost of Effective Cost of Energy (ECE), as opposed to the standard Levelized Cost of Electricty (LCOE), are introduced as technical and economical indices for integrated energy system evaluations. Financial assessment methods are subsequently introduced for evaluation of non-traditional, hybrid energy systems. Algorithms for coupled and iterative evaluation of the technical and economic performance are subsequently discussed. This report further defines modeling objectives, computational tools, solution approaches, and real-time data collection and processing (in some cases using real test units) that will be required to model, co-simulate, and optimize; (a) an energy system components (e.g., power generation unit, chemical process, electricity management unit), (b) system domains (e.g., thermal, electrical or chemical energy generation, conversion, and transport), and (c) systems control modules. Co-simulation of complex, tightly coupled, dynamic energy systems requires multiple simulation tools, potentially developed in several programming languages and resolved on separate time scales. Whereas further investigation and development of hybrid concepts will provide a more complete understanding of the joint computational and physical modeling needs, this report highlights areas in which co-simulation capabilities are warranted. The current development status, quality assurance, availability and maintainability of simulation tools that are currently available for hybrid systems modeling is presented. Existing gaps in the modeling and simulation toolsets and development needs are subsequently discussed. This effort will feed into a broader Roadmap activity for designing, developing, and demonstrating hybrid energy systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

COVID-19 biomarkers based on respiratory microbiome content

COVID-19 patient care management would greatly benefit from new tools that enable accurate assessment of disease severity and stage, potentially enabling a personalized medicine approach. Detection of the SARS-CoV-2 virus itself, or even quantitation of viral loads, is not sufficient for accurate assessment of disease state beyond diagnosis of infection [eg, doi:10.1093/cid/ciaa344]. Levels of usual suspect protein biomarkers associated with host response to infection [eg, C-reactive protein (CRP); cytokines like IL-6, TNF-alpha, and IL-10; complement proteins like C3a and C5a], and of individual blood cell types (eg, leukocytes, lymphocytes, and subsets thereof), show limited correlation with disease severity and stage, with high patient-to-patient and study-to-study variability [eg, doi:10.1093/cid/ciaa248]. High-dimensional panels of biomarkers should have greater predictive power and resilience to unavoidable sources of variability; however, their assembly from proteins and cell types is extremely difficult, due to technical limitations in analyte measurement, especially with regard to starting material requirements and detection sensitivity. Host response profiling through Next Generation Sequencing (NGS) of gene expression patterns (ie, RNA-Seq) is a promising approach, but at the time of this project there were only two publicly available datasets of relevance [doi:10.1093/cid/ciaa203, doi:10.1080/22221751.2020.1747363], and close inspection of them revealed that each had at least one major flaw that severely undermined its value in supporting robust analysis of host response to SARS-CoV- 2 infection. However, the first of these studies [doi:10.1093/cid/ciaa203] fortuitously collected NGS data not only from host cells, but also from bacteria present in bronchoalveolar lavage fluid (BALF) recovered from COVID-19 patients; and because the respiratory microbiome (in terms of bacterial species content) is far less complex than the human transcriptome, the NGS data collected were sufficient to provide coverage depth supporting robust analysis. Surprisingly, the authors of the study did not carry out a detailed analysis of these data and their potential for revealing important new information about COVID-19. Therefore, we carried out a meta-analysis of the dataset as a first step in evaluating the potential for profiling of respiratory microbiome dynamics as a means of accurately assessing COVID-19 disease state.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Image Collection Simulation Using High-Resolution Atmospheric Modeling

A new method is described for simulating the passive remote sensing image collection of ground targets that includes effects from atmospheric physics and dynamics at fine spatial and temporal scales. The innovation in this research is the process of combining a high-resolution weather model with image collection simulation to attempt to account for heterogeneous and high-resolution atmospheric effects on image products. The atmosphere was modeled on a 3D voxel grid by a Large-Eddy Simulation (LES) driven by forcing data constrained by local ground-based and air-based observations. The spatial scale of the atmospheric model (10–100 m) came closer than conventional weather forecast scales (10–100 km) to approaching the scale of typical commercial multispectral imagery (2 m). This approach was demonstrated through a ground truth experiment conducted at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. In this experiment, calibrated targets (colored spectral tarps) were placed on the ground, and the scene was imaged with WorldView-3 multispectral imagery at a resolution enabling the tarps to be visible in at least 9–12 image pixels. The image collection was simulated with Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, using the 3D atmosphere from the LES model to generate a high-resolution cloud mask. The high-resolution atmospheric model-predicted cloud coverage was usually within 23% of the measured cloud cover. The simulated image products were comparable to the WorldView-3 satellite imagery in terms of the variations of cloud distributions and spectral properties of the ground targets in clear-sky regions, suggesting the potential utility of the proposed modeling framework in improving simulation capabilities, as well as testing and improving the operation of image collection processes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

GD SDR Automatic Gain Control Characterization Testing

The General Dynamics (GD) S-Band software defined radio (SDR) in the Space Communications and Navigation (SCAN) Testbed on the International Space Station (ISS) will provide experimenters an opportunity to develop and demonstrate experimental waveforms in space. The GD SDR platform and initial waveform were characterized on the ground before launch and the data will be compared to the data that will be collected during on-orbit operations. A desired function of the SDR is to estimate the received signal to noise ratio (SNR), which would enable experimenters to better determine on-orbit link conditions. The GD SDR does not have an SNR estimator, but it does have an analog and a digital automatic gain control (AGC). The AGCs can be used to estimate the SDR input power which can be converted into a SNR. Tests were conducted to characterize the AGC response to changes in SDR input power and temperature. This purpose of this paper is to describe the tests that were conducted, discuss the results showi ng how the AGCs relate to the SDR input power, and provide recommendations for AGC testing and characterization.

Nappier, Jennifer M.↗
NASA NTRS2013

GD SDR Automatic Gain Control Characterization Testing

The General Dynamics (GD) S-Band software defined radio (SDR) in the Space Communications and Navigation (SCAN) Testbed on the International Space Station (ISS) will provide experimenters an opportunity to develop and demonstrate experimental waveforms in space. The GD SDR platform and initial waveform were characterized on the ground before launch and the data will be compared to the data that will be collected during on-orbit operations. A desired function of the SDR is to estimate the received signal to noise ratio (SNR), which would enable experimenters to better determine on-orbit link conditions. The GD SDR does not have an SNR estimator, but it does have an analog and a digital automatic gain control (AGC). The AGCs can be used to estimate the SDR input power which can be converted into a SNR. Tests were conducted to characterize the AGC response to changes in SDR input power and temperature. This purpose of this paper is to describe the tests that were conducted, discuss the results showing how the AGCs relate to the SDR input power, and provide recommendations for AGC testing and characterization.

Nappier, Jennifer M.↗
NASA NTRS2017

Temporal Consistency Between Gross Primary Production and Solar-Induced Chlorophyll Fluorescence in the Ten Most Populous Megacity Areas over Years

The gross primary production (GPP) of vegetation in urban areas plays an important role in the study of urban ecology. It is difficult however, to accurately estimate GPP in urban areas, mostly due to the complexity of impervious land surfaces, buildings, vegetation, and management. Recently, we used the Vegetation Photosynthesis Model (VPM), climate data, and satellite images to estimate the GPP of terrestrial ecosystems including urban areas. Here, we report VPM-based GPP (GPPvpm) estimates for the world's ten most populous megacities during 2000-2014. The seasonal dynamics of GPPvpm during 2007-2014 in the ten megacities track well that of the solar-induced chlorophyll fluorescence (SIF) data from GOME-2 at 0.5deg x 0.5deg resolution. Annual GPPvpm during 2000-2014 also shows substantial variation among the ten megacities, and year-to-year trends show increases, no change, and decreases. Urban expansion and vegetation collectively impact GPP variations in these megacities. The results of this study demonstrate the potential of a satellite-based vegetation photosynthesis model for diagnostic studies of GPP and the terrestrial carbon cycle in urban areas.

urban↗
NASA NTRS2021

LMI Automated Air Cargo Operations Market Research and Forecast

Air cargo companies and aircraft manufacturers are making significant investments to enable the movement of cargo via various levels of automated aircraft, such as aircraft with simplified operations requiring a pilot, remotely monitored or piloted aircraft, and fully autonomous aircraft. These investments will enable greater utilization of aircraft while unlocking new air markets traditionally served by ground transportation only. Many cargo companies and aerospace experts envision an operating environment where a single pilot can remotely pilot numerous aircraft for significant increases in aircraft utilization. The future operating environment is also expected to include air cargo companies flying smaller aircraft from airports and distribution centers outside of major U.S. cities directly to city centers, avoiding congested roads and increasing the velocity of cargo shipments, particularly those that are high-value, time-sensitive, and security sensitive (e.g., pharmaceuticals). These are just a few benefits and use cases cargo companies and aerospace experts see with the advancement of automated aircraft. To better understand industry’s direction, NASA asked the LMI team to research the forecasted market, timeline, risks, and opportunities for integrating unmanned air cargo vehicles into the National Airspace System (NAS) for the development and prioritization of the NASA Air Traffic Management Exploration’s research portfolio. To begin the market assessment, we gathered data via numerous interviews with key stakeholders and subject matter experts and literature reviews. We then incorporated the data into a custom-developed systems dynamics model and visualization dashboard. The systems dynamics model classifies the size of the market (e.g., overall fleet size of automated aircraft) for four distinct use cases over the next 20 years. The model projects the year in which various types of automated aircraft will enter the commercial cargo market based on our team’s collective research on when the aircraft will become viable due to manufacturing and certification timelines and the lifespan of current, traditional aircraft in service, to name a few factors. While this report defines our team’s estimated timeline of entry and growth, the model is dynamic—it enables NASA users to change variables based on future-year events. If the necessary technology does not mature in accordance with our assumptions, then NASA can change the entry of service point to a future year to evaluate the changes in market size in the out years. This flexibility will be key to deciding when and how NASA should invest in various areas.

air traffic management↗
DOE OSTI2024

Theory of capillary tension and interfacial dynamics of motility-induced phases

The statistical mechanics of equilibrium interfaces has been well-established for over a half century. In the past decade, a wealth of observations have made increasingly clear that a new perspective is required to describe interfaces arbitrarily far from equilibrium. In this work, beginning from microscopic particle dynamics that break time-reversal symmetry, we derive the linear interfacial dynamics of coexisting motility-induced phases. Doing so allows us to identify the athermal energy scale that excites interfacial fluctuations and the nonequilibrium surface tension that resists these excitations. Our theory identifies that, in contrast to equilibrium fluids, this active surface tension contains contributions arising from nonconservative forces which act to suppress interfacial fluctuations and, crucially, is distinct from the mechanical surface tension of Kirkwood and Buff. Here we find that the interfacial stiffness scales linearly with the intrinsic persistence length of the constituent active particle trajectories, in agreement with simulation data. We demonstrate that at wavelengths much larger than the persistence length, the interface obeys surface-area minimizing Boltzmann statistics with our derived nonequilibrium interfacial stiffness playing a role identical to that of equilibrium systems.

36 MATERIALS SCIENCE↗