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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Relationships between fluid mixing, biodiversity, and chemosynthetic primary productivity in Yellowstone hot springs

The factors that influence biodiversity and productivity of hydrothermal ecosystems are not well understood. Here we investigate the relationship between fluid mixing, biodiversity, and chemosynthetic primary productivity in three co-localized hot springs (RSW, RSN, and RSE) in Yellowstone National Park that have different geochemistry. All three springs are sourced by reduced hydrothermal fluid, but RSE and RSN receive input of vapour phase gas and oxidized groundwaters, with input of both being substantially higher in RSN. Metagenomic sequencing revealed that communities in RSN were more biodiverse than those of RSE and RSW in all dimensions evaluated. Microcosm activity assays indicate that rates of dissolved inorganic carbon (DIC) uptake were also higher in RSN than in RSE and RSW. Together, these results suggest that increased mixing of reduced volcanic fluid with oxidized fluids generates additional niche space capable of supporting increasingly biodiverse communities that are more productive. These results provide insight into the factors that generate and maintain chemosynthetic biodiversity in hydrothermal systems and that influence the distribution, abundance, and diversity of microbial life in communities supported by chemosynthesis. These factors may also extend to other ecosystems not supported by photosynthesis, including the vast subterranean biosphere and biospheres beneath ice sheets and glaciers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Development of an Efficient Conjugate Heat Transfer Modeling Framework to Optimize Mixing-Limited Combustion of Ethanol in a Diesel Engine

Mixing controlled combustion of alcohol fuels has been identified as a promising technology based on their low propensity for particulate and NOx production, but the higher heats of vaporization and auto-ignition temperatures of these fuels make their direct use in diesel engine architectures a challenge. To realize the potential of alcohol-fueled combustion, a computational fluid dynamics (CFD) modeling framework is developed, validated, and exercised to identify designs that maximize engine thermal efficiency. To evaluate the use of thermal barrier coatings (TBCs), a simplified one-dimensional (1D) conjugate heat transfer (CHT) modeling framework is employed. The addition of the 1D CHT model only increases the computational expense by 15% relative to traditional approaches, yet offers more accurate heat transfer predictions over constant temperature boundary conditions. The validated model is then used to explore a range of injector orientations and piston bowl geometries. Using a design of experiments (DoE) approach, several designs were identified that improved fuel-air mixing, shortened the combustion duration, and increased thermal efficiency. The most promising design was fabricated and tested in a Caterpillar 1Y3700 single-cylinder oil test engine (SCOTE). Engine testing confirmed the findings from the CFD simulations and found that the co-optimized injector and piston bowl design yielded over 2-percentage point increase in thermal efficiency at the same equivalence ratio (0.96) and over 6-percentage point increase at the same engine load (10.1 bar indicated mean effective pressure (IMEP)), while satisfying design constraints for peak pressure and maximum pressure rise rate.

Magnotti, Gina M.↗
DOE OSTI2024

Estimating Scaled Cloud Height in Pulse Jet Mixed Vessels

To suspend and mix radioactive slurries without pumps or impellers, the performance of pulsed jet mixed vessels has been investigated. The slurry in the pulse tubes is expelled impinging upon the vessel floor and lifting solids to a “cloud height” in a cyclic process. Experiments measuring cloud height for a variety of waste simulants have been conducted for vessel configurations that varied vessel diameter, fill height and pulse tube number, volume, and nozzle diameter. The elevation to which suspended particles rise is termed the cloud height and derives from a balance between the fluid forces of the jet and the gravitational forces acting on the particle ensemble. Here we use scaling to estimate the cloud height based on conservation of mass and momentum and jet similarity. We find that the cloud height varies with the nozzle exit velocity to the 2/3 power in agreement with observations.

cloud height, effective cleaning radius, effective↗
DOE OSTI2022

Circularity in mixed-plastic chemical recycling enabled by variable rates of polydiketoenamine hydrolysis

Footwear, carpet, automotive interiors, and multilayer packaging are examples of products manufactured from several types of polymers whose inextricability poses substantial challenges for recycling at the end of life. Here, we show that chemical circularity in mixed-polymer recycling becomes possible by controlling the rates of depolymerization of polydiketoenamines (PDK) over several orders of magnitude through molecular engineering. Stepwise deconstruction of mixed-PDK composites, laminates, and assemblies is chemospecific, allowing a prescribed subset of monomers, fillers, and additives to be recovered under pristine condition at each stage of the recycling process. We provide a theoretical framework to understand PDK depolymerization via acid-catalyzed hydrolysis and experimentally validate trends predicted for the rate-limiting step. The control achieved by PDK resins in managing chemical and material entropy points to wide-ranging opportunities for pairing circular design with sustainable manufacturing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Chemical recycling of mixed textile waste

Globally, less than 0.5% of postconsumer textile waste is recycled, with the majority incinerated or ending up in landfills. Most postconsumer textiles are mixed fibers, complicating mechanical recycling due to material blends and contaminants. Here, we demonstrate the chemical conversion of postconsumer mixed textile waste using microwave-assisted glycolysis over a ZnO catalyst followed by solvent dissolution. This approach electrifies the process heat while allowing rapid depolymerization of polyester and spandex to their monomers in 15 minutes. A simple solvent dissolution enables the separation of cotton and nylon. We assess the quality of all components through extensive material characterization, discuss their potential for sustainable recycling, and provide a techno-economic analysis of the economic feasibility of the process.

Science & Technology - Other Topics↗
DOE OSTI2025

Alternate routes to acetate tolerance lead to varied isoprenol production from mixed carbon sources in Pseudomonas putida

ABSTRACT Lignocellulose is a renewable resource for the production of a diverse array of platform chemicals, including the biofuel isoprenol. Although this carbon stream provides a rich source of sugars, other organic compounds, such as acetate, can be used by microbial hosts. Here, we examined the growth and isoprenol production in a Pseudomonas putida strain pre-tolerized (“PT”) background where its native isoprenol catabolism pathway is deleted, using glucose and acetate as carbon sources. We found that PT displays impaired growth in minimal medium containing acetate and often fails to grow in glucose-acetate medium. Using a mutant recovery-based approach, we generated tolerized strains that overcame these limitations, achieving fast growth and isoprenol production in the mixed carbon feed. Changes in the glucose and acetate assimilation routes, including an upregulation in PP_0154 (SpcC, succinyl-CoA:acetate CoA-transferase) and differential expression of the gluconate assimilation pathways, were key for higher isoprenol titers in the tolerized strains, whereas a different set of mechanisms were likely enabling tolerance phenotypes in media containing acetate. Among these, a coproporphyrinogen-III oxidase (HemN) was upregulated across all tolerized strains and in one isolate required for acetate tolerance. Utilizing a defined glucose and acetate mixture ratio reflective of lignocellulosic feedstocks for isoprenol production in P. putida allowed us to obtain insights into the dynamics and challenges unique to dual carbon source utilization that are obscured when studied separately. Together, this enabled the development of a P. putida bioconversion chassis able to use a more complex carbon stream to produce isoprenol. IMPORTANCE Acetate is a relatively abundant component of many lignocellulosic carbon streams and has the potential to be used together with sugars, especially in microbes with versatile catabolism such as P. putida . However, the use of mixed carbon streams necessitates additional optimization. Furthermore, the use of P. putida for the production of the biofuel target, isoprenol, requires the use of engineered strains that have additional growth and production constraints when cultivated in acetate and glucose mixtures. In this study, we generate acetate-tolerant P. putida strains that overcome these challenges and examine their ability to produce isoprenol. We show that acetate tolerance and isoprenol production, although independent phenotypes, can both be optimized in a given P. putida strain. Using proteomics and whole genome sequencing, we examine the molecular basis of both phenotypes and show that tolerance to acetate can occur via alternate routes and result in different impacts on isoprenol production.

de Siqueira, Guilherme M. V. (ORCID:00000002364563↗
DOE OSTI2025

Patchy burn severity explains heterogeneous soil viral and prokaryotic responses to fire in a mixed conifer forest

ABSTRACT Effects of fire on soil viruses and virus–host dynamics are largely unexplored, despite known microbial contributions to biogeochemical processes and ecosystem recovery. Here, we assessed how viral and prokaryotic communities responded to a prescribed burn in a mixed conifer forest. We sequenced 91 viral-size fraction metagenomes (viromes) and 115 16S rRNA gene amplicon libraries from 120 samples: four samples at five timepoints (two before fire and three after fire) at six sites (four treatment, two control). We hypothesized that compositional differences would be most significant between burned and unburned soils, but instead, plot location best distinguished viral communities, more than treatment (burned or not), depth (0–3 or 3–6 cm), or timepoint. For both viruses and prokaryotes, some burned communities resembled unburned controls, while others were significantly different, revealing heterogeneous responses to fire. These patterns were explained by burn severity, here defined by soil chemistry. Viral but not prokaryotic richness decreased significantly with burn severity, and low viromic DNA yields indicated substantial loss of viral biomass at higher severity. The relative abundances of Firmicutes, Actinobacteriota, and the viruses predicted to infect them increased significantly with burn severity, suggesting survival and viral infection of these fire-responsive and potentially spore-forming taxa. The degree of burn severity experienced by each patch of soil, rather than burn status alone, differed over mere meters in the same fire. Therefore, our analyses highlight the importance of high-resolution, paired biogeochemical data to explain soil community responses to fire. IMPORTANCE The impact of fire on the soil microbiome, particularly on understudied soil viral communities, warrants investigation, given known microbial contributions to biogeochemical processes and ecosystem recovery. Here, we collected 120 soil samples before and after a prescribed burn in a mixed conifer forest to assess the impacts of this disturbance on soil viral and prokaryotic communities. We show that simple categorical comparisons of burned and unburned areas were insufficient to reveal the underlying community response patterns. The patchy nature of the fire (indicated by soil chemistry data) led to significant changes in viral and prokaryotic community composition in areas of high burn severity, while communities that experienced lower burn severity were indistinguishable from those in unburned controls. Our results highlight the importance of considering highly resolved burn severity and biogeochemical measurements, even in nearby soils after the same fire, in order to understand soil microbial responses to prescribed burns.

Microbiology↗
DOE OSTI2020

Mixed higher-order anisotropic flow and nonlinear response coefficients of charged particles in $\mathrm {PbPb}$ collisions at $\sqrt{\smash [b]{s_{_{\mathrm {NN}}}}} = 2.76$ and 5.02$\,\text {TeV}$

Anisotropies in the initial energy density distribution of the quark-gluon plasma created in high energy heavy ion collisions lead to anisotropies in the azimuthal distributions of the final-state particles known as collective anisotropic flow. Fourier harmonic decomposition is used to quantify these anisotropies. The higher-order harmonics can be induced by the same order anisotropies (linear response) or by the combined influence of several lower order anisotropies (nonlinear response) in the initial state. The mixed higher-order anisotropic flow and nonlinear response coefficients of charged particles are measured as functions of transverse momentum and centrality in $\mathrm {PbPb}$ collisions at nucleon-nucleon center-of-mass energies $\sqrt{\smash [b]{s_{_{\mathrm {NN}}}}} = 2.76$ and 5.02$\,\text {TeV}$ with the CMS detector. The results are compared with viscous hydrodynamic calculations using several different initial conditions, as well as microscopic transport model calculations. None of the models provides a simultaneous description of the mixed higher-order flow harmonics and nonlinear response coefficients.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Approaches for Synthesis and Deployment of Controller Models on Automated Vehicles for Car-following in Mixed Autonomy

This paper describes the software design patterns and vehicle interfaces that were employed to transition vehicle controllers from simulation environments to open-road field experiments. The approach relies on a life cycle that utilizes model-based design and code generation, along with agile software development, and both software and hardware-in-the-loop testing, with additional safety margins. Autonomous designs should consider the dynamics of mixed autonomy in traffic to safely operate among humans. The software that provides a vehicle’s behavior intelligence is often developed through simulation, which may have a mismatch between dynamics, or as a result of a reinforcement learning workflow, which may be a black box with challenges to analyze. In each of these cases, it is important to have research interfaces that provide strongly typed data streams accessible to researchers who are not software experts while continuing to satisfy safety and liveness constraints. This paper describes how we design the hardware platform interfaces and software design process for a mixed autonomy traffic experiment with a leader-follower scenario. Controller synthesis for these vehicles requires clearly articulated vehicle interfaces and software design patterns for successful onboard deployment. Testing strategies for such controllers are also described before algorithms are transitioned to full-scale field experiments with safety operators for the vehicles. Testing strategies include software-in-the-loop simulation testing, hardware-in-the-loop simulation, ghost-car testing, and read-only testing in live traffic. With our approach, we were not only able to validate our controller synthesized in scripts and simulation, but also able to scale deployment to multiple vehicles.

Bhadani, Rahul↗
DOE OSTI2026

Detect the Unobservable: Abnormality Detection in mixed Autonomy for Lane Change Maneuver with Following Vehicles’ Trajectories Only

Highly Automated Vehicles (HAVs) and Advanced Driver-Assistance Systems (ADAS) are transforming modern transportation with enhanced mobility, safety, and efficiency. Despite their advantages, cybersecurity vulnerabilities in these systems can lead to abnormal behavior, posing significant risks to surrounding human-driven vehicles (HDVs) in mixed traffic environments. Here, this article addresses the challenge of detecting abnormal lateral movements of HAVs/ADAS vehicles using only trajectory profiles of following HDVs. Specifically, we propose a novel modeling approach that captures both normal and abnormal lateral behaviors through vehicle kinematics, integrated decision-making processes, vehicle control using symbolic regression for lane change vehicles. Additionally, we introduce an abnormality detection framework that relies on observable HDV data, even in occlusion scenarios. The framework evaluates the sensitivity of various car-following models to detect abnormal behaviors, providing insights into the interaction between HAVs/ADAS and HDVs in mixed autonomy systems.

Connected and Automated vehicles↗
DOE OSTI2023

Comparison of Machine Learning Algorithms for Natural Gas Identification with Mixed Potential Electrochemical Sensor Arrays

Mixed-potential electrochemical sensor arrays consisting of indium tin oxide (ITO), La 0.87 Sr 0.13 CrO 3 , Au, and Pt electrodes can detect the leaks from natural gas infrastructure. Algorithms are needed to correctly identify natural gas sources from background natural and anthropogenic sources such as wetlands or agriculture. We report for the first time a comparison of several machine learning methods for mixture identification in the context of natural gas emissions monitoring by mixed potential sensor arrays. Random Forest, Artificial Neural Network, and Nearest Neighbor methods successfully classified air mixtures containing only CH 4 , two types of natural gas simulants, and CH 4 +NH 3 with >98% identification accuracy. The model complexity of these methods were optimized and the degree of robustness against overfitting was determined. Finally, these methods are benchmarked on both desktop PC and single-board computer hardware to simulate their application in a portable internet-of-things sensor package. The combined results show that the random forest method is the preferred method for mixture identification with its high accuracy (>98%), robustness against overfitting with increasing model complexity, and had less than 10 ms training time and less than 0.1 ms inference time on single-board computer hardware.

03 NATURAL GAS↗
DOE OSTI2024

Field Testing of a Mixed Potential IoT Sensor Platform for Methane Quantification

Emissions of CH 4 from natural gas infrastructure must urgently be addressed to mitigate its effect on global climate. With hundreds of thousands of miles of pipeline in the US used to transport natural gas, current methods of surveying for leaks are inadequate. Mixed potential sensors are a low cost, field deployable technology for remote and continuous monitoring of natural gas infrastructure. We demonstrate for the first time a field trial of a mixed potential sensor device coupled with machine learning and internet-of-things platform at Colorado State University’s Methane Emissions Technology Evaluation Center (METEC). Emissions were detected from a simulated buried underground pipeline source. Sensor data was acquired and transmitted from the field test site to a remote cloud server. Quantification of concentration as a function of vertical distance is consistent with previously reported transport modelling efforts and experimental surveys of methane emissions by more sophisticated CH 4 analyzers.

03 NATURAL GAS↗
DOE OSTI2021

RF mixing modules for superconducting qubit room temperature control systems v1.0

We have developed a series of compact RF mixing boards by integrating I/Q quadrature mixing, IF(intermediate frequency)/LO(local oscillator)/RF power level adjustments, and DC (direct current) bias fine tuning on a 40 mm × 80 mm 4-layer PCB (printed circuit board) board with EMI (electromagnetic interference) shielding.

Huang, Gang↗
DOE OSTI2023

OpenMxP-Opensource Mixed Precision Computing

This is an opensource library for benchmarking the system's GPU mixed precision capabilities. The software calculates solution of the system of linear equation in 64bit accuracy using mixed precision techniques and iterative refinement. Original benchmark designed is done by ICL, and it is name HPL-MxP (HPL-AI)

Lu, Hao↗
DOE OSTI2025

Radsource Mr: Mixed Reality Planning Tool For Radioactive Recovery

The RadSource MR system leverages Meta Quest 3's advanced mixed reality capabilities to create a comprehensive spatial planning platform for end-of-life sealed radioactive source recovery operations. The application utilizes the Quest 3's high-resolution passthrough cameras and spatial mapping algorithms to generate accurate 3D environmental models. Core technical components include: (1) Real-time spatial measurement algorithms calculating distances, angles, slopes, and surface areas with sub-centimeter accuracy; (2) Virtual object placement system allowing users to position digital representations of recovery equipment (trailers, containment vessels, protective barriers) within the real environment; (3) Voice recording and annotation system for hands-free documentation in protective equipment; (4) 3D mesh capture and storage capabilities for post-operation analysis and regulatory documentation. (5) Procedure documentation is available for viewing in Mixed Reality, providing an innovative and convenient way to access the information during pre-visit and inspection activities. (6) Support for screen capture for the view for real world and virtual objects together to use it later for planning. The system integrates computer vision techniques for environmental understanding, spatial mathematics for precise measurements, and human-computer interaction principles optimized for hazardous environment operations. Data persistence allows teams to save and share planning sessions across multiple stakeholders while maintaining operational security requirements.

Khadka, Rajiv [Idaho National Laboratory (INL), Id↗
DOE OSTI2020

Relationships between Immersion Freezing and Crystal Habit for Arctic Mixed-Phase Clouds—A Numerical Study

Abstract The number concentration of ice particles in Arctic mixed-phase clouds is a major controlling factor of cloud lifetime. The relationships between ice nucleation mode and ice crystal habit development are not yet constrained by observations. This study uses a habit-predicting microphysical scheme within a 3D large-eddy simulation model to evaluate the relationship between immersion freezing and ice habit in a simulated Arctic mixed-phase cloud case. Three immersion freezing parameterizations are considered: a volume-dependent freezing scheme (VF), a parameterization limited to activated droplets (C-AC), and a parameterization limited to coarse aerosol particles (C-CM). Both C-AC and C-CM are based on classical nucleation theory. The freezing rate with VF is found to be greater in downdraft regions than in updraft regions due to the downdraft having a higher number concentration of large droplets. The C-AC cases show active freezing of small droplets near cloud top, whereas in the C-CM cases, mainly the 8–32- μ m-sized droplets freeze in updraft regions near the cloud base. Because the initial crystal size is assumed to affect the axis ratio of hexagonal plates, the VF cases produce crystals with larger axis ratios, resulting in smaller mode radii than the C-AC cases. In all cases, irregular polycrystals dominate near cloud top and a band-like structure develops within the cloud, which qualitatively agrees with previous observations. In the VF and C-CM cases, unactivated large droplets arising from coarse-mode aerosol particles contributed significantly to the freezing rate, producing an important influence on crystal habit.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evidence of Mixed Scaling for Mean Profile Similarity in the Stable Atmospheric Surface Layer

A new mixed scaling parameter Z = z/(Lh)1/2 is proposed for similarity in the stable atmospheric surface layer, where z is the height, L is the Obukhov length, and h is the boundary layer depth. In comparison with the parameter ζ = z/L from Monin–Obukhov similarity theory (MOST), the new parameter Z leads to improved mean profile similarity for wind speed and air temperature in large-eddy simulations. It also yields the same linear similarity relation for CASES-99 field measurements, including in the strongly stable (but still turbulent) regime where large deviations from MOST are observed. Results further suggest that similarity for turbulent energy dissipation rate depends on both Z and ζ. The proposed mixed scaling of Z and relevance of h can be explained by physical arguments related to the limit of z-less stratification that is reached asymptotically above the surface layer. The presented evidence and fitted similarity relations are promising, but the results and arguments are limited to a small sample of idealized stationary stable boundary layers. Here, corroboration is needed from independent datasets and analyses, including for complex and transient conditions not tested here.

54 ENVIRONMENTAL SCIENCES↗