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DOE OSTI2017

Minimize Solvent Oxidation with NO X Pre-Scrubbing

A novel method to remove nitrogen dioxide (NO 2 ) from the flue gas of coal-fired power plants with CO 2 capture was further developed for commercial implementation. The technology leverages the equipment and chemistry in an existing (sulfur dioxide) SO 2 polishing scrubber upstream of the main CO 2 capture unit to remove the NO 2 , preventing degradation of the CO 2 capture solvent and formation of nitrosamines (environmental hazards). The research in this report focuses on further evaluation of the chemical additives and operating conditions associated with the NO 2 removal process to define conditions for commercial scale testing and deployment. Experimental work systematically evaluated a series of potential additives to minimize the oxidation of sulfite in a representative SO 2 pre-scrubber solution (sulfite, in turn, absorbs NO 2 ). The additive combinations and concentrations were varied alongside important process conditions such as temperature, oxygen concentration, and metals present in solution to mimic the conditions expected in a commercial system. Important results of the parametric experimental work include identifying a new, potent sulfite oxidation inhibitor, revealing the importance of combining inhibitors with metal chelating agents, validation of a low-cost additive process, and development of a new semi-empirical model to represent mechanisms associated with sulfite oxidation. In addition, the experimental work reveled the impact of operating at higher temperatures (representative of a field test unit), which will guide the selection and concertation of additives as well. Engineering analysis found that waste solutions from the pre-scrubber with NO 2 additives may potentially be integrated with existing processes on site (e.g., flue gas desulfurization unit). In addition, techno-economic analysis identified potential net savings as large as $1.30/tonne CO 2 captured and quantified the potential benefit of low cost additive options actively being pursued by the development team. Finally, the experimental results and engineering analysis supported the development of a detailed field testing plan and protocol to evaluate the technology at near-commercial scale. The field test preparation included development of procedures to introduce chemical additives to an existing SO 2 polishing unit and identification of representative flue gas conditions based on a review of existing plants. These activities will have direct bearing on operation and design of commercial units.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRS2005

NASA Tech Briefs, September 2005

Topivs include: Diamond-Coated Carbon Nanotubes for Efficient Field Emission; Improved Anode Coatings for Direct Methanol Fuel Cells; Advanced Ablative Insulators and Methods of Making Them; PETIs as High-Temperature Resin-Transfer-Molding Materials; Stable Polyimides for Terrestrial and Space Uses; Low-Density, Aerogel-Filled Thermal-Insulation Tiles; High-Performance Polymers Having Low Melt Viscosities; Nonflammable, Hydrophobic Aerogel Composites for Insulation; Front-Side Microstrip Line Feeding a Raised Antenna Patch; Medium-Frequency Pseudonoise Georadar; Facilitating Navigation Through Large Archives; Program for Weibull Analysis of Fatigue Data; Comprehensive Micromechanics-Analysis Code - Version 4.0; Component-Based Visualization System; Software for Engineering Simulations of a Spacecraft; LabVIEW Interface for PCI-SpaceWire Interface Card; Path Following with Slip Compensation for a Mars Rover; International Space Station Electric Power System Performance Code-SPACE; Software for Automation of Real-Time Agents, Version 2; Software for Optimizing Plans Involving Interdependent Goals; Computing Gravitational Fields of Finite-Sized Bodies; Custom Sky-Image Mosaics from NASA's Information Power Grid; ANTLR Tree Grammar Generator and Extensions; Generic Kalman Filter Software; Alignment Stage for a Cryogenic Dilatometer; Rugged Iris Mechanism; Treatments To Produce Stabilized Aluminum Mirrors for Cryogenic Uses; Making AlNx Tunnel Barriers Using a Low-Energy Nitrogen-Ion Beam; Making Wide-IF SIS Mixers with Suspended Metal-Beam Leads; Sol-Gel Glass Holographic Light-Shaping Diffusers; Automated Counting of Particles To Quantify Cleanliness; Phase Correction for GPS Antenna with Nonunique Phase Center; Compact Infrasonic Windscreen; Broadband External-Cavity Diode Laser; High-Efficiency Solar Cells Using Photonic-Bandgap Materials; Generating Solid Models from Topographical Data; Computationally Lightweight Air-Traffic-Control Simulation; Spool Valve for Switching Air Flows Between Two Beds; Partial Model of Insulator/ Insulator Contact Charging; Asymmetric Electrostatic Radiation Shielding for Spacecraft; and Reusable Hybrid Propellant Modules for Outer-Space Transport.

Source record↗

DOE OSTI2018

A biophysical approach to the performance diagnosis of human–building energy interaction: Information (bits) modeling, algorithm, and indicators of energy flow complexity

Highlights: • Energy-related human-building interaction is analyzed at the biophysical system scale. • Energy flow-networking model of environmental building performance is suggested. • Information is suggested as a holistic building performance metric. - Abstract: This article addresses the utmost upstream impacts of human–building energy interaction by proposing a network-based model, algorithms, and indicators. Hypothesizing that human behavior is a key factor for the symbiotic development of building and the geobiosphere system, the author seeks emergy (spelled with an “m”)-information integrated measures to indicate dynamic system-level performance of the interaction with building energy flow topology. To validate the hypothesis and methods, four representative building cases were tested on the building form (envelope) of (i) a building with no occupant intervention (baseline), (ii) a building controlled by responsive human behavior (bioclimatic adaptation), (iii) a building with reinforced insulation and behavior-dominated control (passive design), and (iv) a net-zero energy building (NZEB). The results demonstrate that adaptive human behavior in building operation increases the information content and complexity of energy-flow networking, improving performance and sustainability. Findings also reveal that increasing information, complexity, and power (energy availability over time) parallel the general energetic features of developing biophysical systems (greater feedback, internalization, and recycling of materials and energies). It becames clear that active behavioral response is a dominant agent of sustainable environments even on a far broader system scale.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2020

State of the Profession Considerations: NASA Langley Research Center Capabilities / Technologies for Autonomous In-Space Assembly and Modular Persistent Assets

Successfully implementing OSAM into next generation revolutionary observatories requires integrating expertise and technologies in modular space structures, assembly operations, autonomy, and modeling/simulation. LaRC OSAM technologies/capabilities have been presented to inform the Planetary Science and Astrobiology Decadal Survey community of the robust and mature existing capability to support an OSAM based architecture for their next observatory. LaRC Structures and Assembly capabilities enable; a modular telescope architecture, high-performance structural modules, and robotic assembly techniques. LaRC Autonomy capabilities ensure that the robotic assembly will be accomplished in a safe and robust manner and only require humans in a supervisory role. The LaRC toolbox of Modeling and Simulation capabilities that is calibrated using module-level ground testing, will ensure that the performance of the fully assembled observatory, a very large zero-g system that will never be assembled/tested in a gravity environment, meets all performance requirements when it enters into service. Integrating all three LaRC capabilities and including embedded metrology, will enable servicing, repair, instrument upgrades (and/or replacement) while ensuring a very long lifetime for the observatory and providing a return-on-investment that is substantially greater than the initial cost. Further confidence will be achieved as OSAM technologies are validated in a new LaRC OSAM laboratory that allows large-scale collaborative testing of modular hardware, simulation software and algorithms, and autonomous agents.

Large space structures↗

DOE OSTI2023

Effect of Dual Environment Aging on Flexible Polyurethane Based Adhesive

Abstract Environmental degradation of adhesives naturally increases with the intensity of the ambient weathering agents involved. During service life, these materials are never subjected to a constant environmental loading. Real-time applications expose the material to multiple weather conditions, ranging from dry to humid or fully wet conditions. While some understanding exists about aging environments in isolation, we intend to investigate the effect of multiple environments on the constitutive behavior of the materials in question. The extent of damage can be accessed by analyzing the changes in constitutive behavior and toughness of aged material. Therefore, this aging experiment was designed around two-way aging regimes involving combinations of thermo-oxidation, hydrolytic and hygrothermal aging environments. We selected a polyurethane based flexible adhesive which is commonly used in the automotive industry as a glass sealant. The aged specimens were subjected to uniaxial tensile tests, and variation in material toughness and constitutive behavior was analyzed to ascertain the mechanics involved in the case of each environmental exposure. We outlined how different environments affect the material in isolation and how they interact with other weathering conditions. This study will help better understand and model the damage mechanics involved in exposure to multiple environmental conditions.

Shaafaey, Mamoon↗

NASA NTRS2014

NASA Tech Briefs, March 2014

Topics include: Data Fusion for Global Estimation of Forest Characteristics From Sparse Lidar Data; Debris and Ice Mapping Analysis Tool - Database; Data Acquisition and Processing Software - DAPS; Metal-Assisted Fabrication of Biodegradable Porous Silicon Nanostructures; Post-Growth, In Situ Adhesion of Carbon Nanotubes to a Substrate for Robust CNT Cathodes; Integrated PEMFC Flow Field Design for Gravity-Independent Passive Water Removal; Thermal Mechanical Preparation of Glass Spheres; Mechanistic-Based Multiaxial-Stochastic-Strength Model for Transversely-Isotropic Brittle Materials; Methods for Mitigating Space Radiation Effects, Fault Detection and Correction, and Processing Sensor Data; Compact Ka-Band Antenna Feed with Double Circularly Polarized Capability; Dual-Leadframe Transient Liquid Phase Bonded Power Semiconductor Module Assembly and Bonding Process; Quad First Stage Processor: A Four-Channel Digitizer and Digital Beam-Forming Processor; Protective Sleeve for a Pyrotechnic Reefing Line Cutter; Metabolic Heat Regenerated Temperature Swing Adsorption; CubeSat Deployable Log Periodic Dipole Array; Re-entry Vehicle Shape for Enhanced Performance; NanoRacks-Scale MEMS Gas Chromatograph System; Variable Camber Aerodynamic Control Surfaces and Active Wing Shaping Control; Spacecraft Line-of-Sight Stabilization Using LWIR Earth Signature; Technique for Finding Retro-Reflectors in Flash LIDAR Imagery; Novel Hemispherical Dynamic Camera for EVAs; 360 deg Visual Detection and Object Tracking on an Autonomous Surface Vehicle; Simulation of Charge Carrier Mobility in Conducting Polymers; Observational Data Formatter Using CMOR for CMIP5; Propellant Loading Physics Model for Fault Detection Isolation and Recovery; Probabilistic Guidance for Swarms of Autonomous Agents; Reducing Drift in Stereo Visual Odometry; Future Air-Traffic Management Concepts Evaluation Tool; Examination and A Priori Analysis of a Direct Numerical Simulation Database for High-Pressure Turbulent Flows; and Resource-Constrained Application of Support Vector Machines to Imagery.

Source record↗

DOE OSTI2021

AlphaBuilding ResCommunity: A multi-agent virtual testbed for community-level load coordination

Training and validating algorithms in a simulation testbed can accelerate research and applications of optimal control of residential loads to improve energy flexibility and grid resilience. We developed an open-source simulation environment, AlphaBuilding ResCommunity, that can be used to train and validate algorithms to control a single thermostatically controlled load (TCL) or coordinate a group of TCLs. We used reduced-order models to simulate the thermodynamics of TCLs, and the parameter values were determined from the connected smart thermostat data of real households. The environment was built upon the standardized OpenAI Gym interface. Ancillary functions, such as retrieving the parameters and weather forecasts, are provided to facilitate control strategies that require predictive information. Compared with existing efforts, AlphaBuilding ResCommunity has three advantages: (1) more realistic model settings because the parameter values are identified from actual household operating data, and modelling and measurement uncertainty are considered; (2) passive thermal storage control; and (3) ease of use due to a simple software dependency and standardized interface. We demonstrated the applications of the environment by implementing a Kalman Filter and Model Predictive Control on a single TCL and a Priority-Stack-Based Control and Alternating Direction Method of Multipliers to coordinate multiple TCLs for load tracking.

Wang, Z↗

NASA NTRS2001

Ontolology Negotiation Between Scientific Archives

This paper describes an approach to ontology negotiation between information agents. Ontologies are declarative (data driven) expressions of an agent's "world": the objects, operations, facts, and rules that constitute the logical space within which an agent performs. Ontology negotiation enables agents to cooperate in performing a task, even if they are based on different ontologies. 'Me process allows agents to discover ontology conflicts and then, though incremental interpretation, clarification, and explanation, establish a common basis for communicating with each other. The need for ontology negotiation stems from the proliferation of information sources and of agents with widely varying specialty expertise. The unmanageability of massive amounts of web-based information is already becoming apparent. It is starting to have an impact on professions that rely on distributed archived information. If the expansion continues at its present rate without an ontology negotiation process being introduced, there will soon be no way to ensure the accuracy and completeness of information that scientists obtain from sources other than their own experiments. Ontology negotiation is becoming increasingly recognized as a crucial element of scalable agent technology. This is because agents, by their very nature, are supposed to operate with a fair amount of autonomy and independence from their end-users. Part of this independence is the ability to enlist other agents for help in performing a task (such as locating information on the web). The agents enlisted for help may be "owned" by a different end-user or organization (such as a document archive), and there is no guarantee that they will use the same terminology or understand the same concepts (objects, operators, theorems, rules) as the recruiting agent. For NASA, the need for ontology negotiation arises at the boundaries between scientific disciplines. For example: modeling the effects of global warming might involve knowledge about imaging, climate analysis, ecology, demographics, industrial economics, and biology. The need for ontology negotiation also arises at the boundaries between scientific programs. For example, a Principal Investigator may want to use information from a previous mission to complement downloads from the instruments currently deployed.

Bailin, Sidney C.↗

DOE OSTI2024

Visual feedback and guided balance training in an immersive virtual reality environment for lower extremity rehabilitation

Balance training is essential for physical rehabilitation procedures, as it can improve functional mobility and enhance cognitive coordination. However, conventional balance training methods may have limitations in terms of motivation, real-time objective feedback, and personalization, which a virtual reality (VR) setup may provide a better alternative. In this work, we present an immersive VR training environment for lower extremity balance rehabilitation with real-time guidance and feedback. The VR training environment immerses the user in a 3D ice rink model where a virtual coach (agent) leads them through a series of balance poses, and the user controls a trainee avatar with their own movements. Here we developed two coaching styles: positive-reinforcement and autonomous-supportive, and two viewpoints of the trainee avatar: first-person and third-person. The proposed environment was evaluated in a user study with healthy, non-clinical participants (n = 16, 24.4 ± 5.7 years old, 9 females). Our results show that participants showed stronger performance in the positive-reinforcement style compared to the autonomous-supportive style. Additionally, in the third-person viewpoint, the participants exhibited more stability in the positive-reinforcement style compared to the autonomous-supportive style. For viewpoint, participants exhibited stronger performance in the first-person viewpoint compared to third-person in the autonomous-supportive style, while they were comparable in the positive-reinforcement style. We observed no significant effects on the foot height and number of mistakes. Furthermore, we report the analysis of user performance with balance training poses and subjective measures based on questionnaires to assess the user experience, usability, and task load. The proposed VR balance training could offer an interactive, adaptive, and engaging environment and open new potential research directions for lower extremity rehabilitation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Evaluating the role of Burkholderia pseudomallei K96243 toxins BPSS0390, BPSS0395, and BPSS1584 in persistent infection

Burkholderia pseudomallei is the causative agent of melioidosis, a disease with a mortality rate of up to 40% even with treatment. Despite the ability of certain antibiotics to control initial infection, relapse occurs in treated patients. The inability of antibiotics to clear this bacterial infection is in part due to persistence, an evasion mechanism against antibiotics and the effect of host defenses. Evaluation of antibiotic efficacy against B. pseudomallei revealed that up to 48% of in vitro grown populations can survive in a persister state. Toxin–antitoxin (TA) systems have been previously implicated in modulating bacterial persistence. Here, we generated three isogenic TA mutants and found that loss of each toxin gene did not alter antibiotic persistence or macrophage survival. In response to macrophage–induced persistence, all three toxin mutants demonstrated increased intracellular susceptibility to levofloxacin which in part was due to the inability of the mutants to induce persistence after nitric oxide or nutrient starvation. In an inhalational model of murine melioidosis, both ΔBPSS0395 and ΔBPSS1584 strains were attenuated, and treatment with levofloxacin led to significant reduction in lung colonisation and reduced splenic colonisation by ΔBPSS0395. Based on our findings, these toxins deserve additional evaluation as putative therapeutic targets.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2024

Piezoelectric impedance-based high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis

Two elements are essential in structural health monitoring utilizing dynamic responses: response measurement with high-frequency contents, i.e., small characteristic wavelengths, that can adequately reflect damage features, and effective inverse identification analysis that is however oftentimes under-determined. The advancement of smart structure integration has led to active interrogation through frequency-sweeping piezoelectric impedance measurement at high frequency range. In this research we develop a multi-objective optimization formulation for the identification of damage location and severity utilizing piezoelectric impedance. While one optimization objective is to match the response measurement with finite element model prediction in the damage parametric space, the other is the number of locations of damage, i.e., the sparsity of damage index as the solution vector, since damage usually occurs within a small number of locations. This multi-objective formulation fits well the under-determined nature of damage identification, as it naturally provides multiple solutions as basis for further elucidation. The challenge remaining is how to find a small solution set that can include the actual damage scenario. Here we develop a novel inverse identification framework utilizing the intelligent swarm optimizer which possesses flexibility for enhancement. We first embed a sparsity enforcement process into the population generation of the optimizer, which yields a solution repository intrinsically possessing sparsity. We then apply reinforcement learning so the agents can adaptively opt for local strategies with the aim of enriching the searching patterns to diversify the solutions. Through the incorporation of a Q-table, searching toward more promising directions will be rewarded. Our case analyses employing experimental data indicate that this sparsity-conscious multi-objective particle swarm optimization technique can lead to a small solution set which generally encompasses the true damage scenario. This effectively solves the structural damage identification problem with piezoelectric impedance measurement.

Yang Zhang↗

DOE OSTI2021

Chloropyridinyl Esters of Nonsteroidal Anti-Inflammatory Agents and Related Derivatives as Potent SARS-CoV-2 3CL Protease Inhibitors

We report the design and synthesis of a series of new 5-chloropyridinyl esters of salicylic acid, ibuprofen, indomethacin, and related aromatic carboxylic acids for evaluation against SARS-CoV-2 3CL protease enzyme. These ester derivatives were synthesized using EDC in the presence of DMAP to provide various esters in good to excellent yields. Compounds are stable and purified by silica gel chromatography and characterized using 1 H-NMR, 13 C-NMR, and mass spectral analysis. These synthetic derivatives were evaluated in our in vitro SARS-CoV-2 3CLpro inhibition assay using authentic SARS-CoV-2 3CLpro enzyme. Compounds were also evaluated in our in vitro antiviral assay using quantitative VeroE 6 cell-based assay with RNAqPCR. A number of compounds exhibited potent SARS-CoV-2 3CLpro inhibitory activity and antiviral activity. Compound 9a was the most potent inhibitor, with an enzyme IC 50 value of 160 nM. Compound 13b exhibited an enzyme IC 50 value of 4.9 µM. However, it exhibited a potent antiviral EC 50 value of 24 µM in VeroE6 cells. Remdesivir, an RdRp inhibitor, exhibited an antiviral EC 50 value of 2.4 µM in the same assay. We assessed the mode of inhibition using mass spectral analysis which suggested the formation of a covalent bond with the enzyme. To obtain molecular insight, we have created a model of compound 9a bound to SARS-CoV-2 3CLpro in the active site.

3CLpro inhibitors↗

DOE OSTI2017

Probing Small-Molecule Degradation to Counter Enzyme Promiscuity

Enzymes that degrade specific small molecules could save lives by neutralizing threats from chemical agents in the blood or environment, or by starving pathogenic cells, but promiscuous interactions with other molecules typically limit their effectiveness by blocking the enzyme active site. An obvious solution would be to re-engineer the enzyme to enhance catalytic fidelity, but lack of understanding about how enzymes discriminate between molecules remains a formidable challenge to this approach. Our recent work in collaboration with the University of Texas (UT) suggested a new approach and a model system for understanding enzyme specificity. Asparaginase enzymes catalyze degradation of asparagine, which forms the basis of a medical treatment. Competition by the abundant and chemically similar molecule, glutamine, interferes with asparagine decomposition, thus hindering enzyme efficacy. Asparaginase is advantageous as a model degradation enzyme because variants that demonstrate different binding affinities and catalytic rates can be compared. Here, we leveraged Sandia and the University of Maryland's strengths in molecular simulation, and UT experimental expertise in asparaginase modification and functional assays, to understand asparaginase specificity. Our results advanced a new hypothesis about asparaginase catalytic mechanism that explains for the first time why proximity between the substrate's alpha-carboxyl and carboxamide is absolutely required for catalysis. Based on those insights, we developed the first mutant (Q59L) asparaginase from E. coli that lacks activity toward glutamine. We used that mutant to show that glutaminase activity is required to kill cancer cells that have asparagine synthetase enzymes (ASNS), but not ASNS-negative cancer cells.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

High Temperature, High Pressure Stability of Aqueous Foams for Potential Application in Enhanced Geothermal System (EGS)

Enhanced Geothermal Systems (EGS) are typically constructed by injecting high-pressure water into deep hot dry rocks (HDR) under carefully controlled conditions to create new or re-open existing fractures, which usually uses an immense quantity of water. In lieu, a more sustainable technique is to utilize aqueous foams as fracturing fluid to reduce water consumption and waste-water treatments of conventional hydraulic fracturing. Although foam-based fracturing has shown promising results in oil and gas industries, its feasibility is not demonstrated in EGS conditions that usually involve high temperature and high pressures. One potential barrier of utilizing foams in EGS applications is that foams are thermodynamically unstable and will become more unstable with increasing temperature due to liquid drainage, bubble coarsening, and coalescence. This work focuses on evaluation of the stability of selected aqueous foams under high-temperature and high-pressure conditions. Specifically, foams generated with surfactant alfa olefin sulfonate (AOS) were studied at temperature up to 200oC, while the pressure ranged between 100 psi and 1000 psi. The effect of additional stabilizing agents was also examined, including guar gum, bentonite clay, borate salt crosslinker, silicon dioxide nanoparticles (SiO2), and graphene oxide (GO) dispersion. Results showed that the stabilizing agents can enhance the foam thermal stability. Foams made with AOS and the borate salt crosslinker exhibited the longest half-life of 20 min at 200°C when the pressure was at 1000 psi. Data fitting showed that foam stability decreased exponentially as temperature increased. On the other hand, pressure increased the foam half-life which followed a power model. This study indicates that it may be possible to obtain highly stable foams at high-temperature and high-pressure conditions with appropriate stabilizing agents.

Thakore, Viren↗

DOE OSTI2020

Advanced Engine and Fuel Technologies Annual Progress Report (FY2019)

On behalf of the Vehicle Technologies Office of the U.S. Department of Energy, we are pleased to introduce the Fiscal Year (FY) 2019 Annual Progress Report for the Advanced Engine and Fuel Technologies Program. In support of the Vehicle Technology Office’s goal for future U.S. economic growth, the Program focuses on early-stage research and development to improve understanding of combustion processes, fuel properties, and emissions control technologies, generating knowledge and insight necessary for industry to cost-effectively develop the next generation of engines and fuels. One of the most promising and cost-effective approaches to improving the fuel economy of the U.S. vehicle fleet is to introduce the next generation of higher-efficiency, very-low-emission combustion engines that meet future federal emissions regulations into the passenger and commercial vehicle markets. Advanced fuel formulations that can incorporate non-petroleum-based blending agents could further enhance engine efficiency, reduce greenhouse gas emissions, and provide fuel diversification. Also, innovations in combustion, fuels, emissions control, air control, turbomachinery, and energy recovery could potentially increase fuel economy considerably compared to today’s vehicles. The expected national economic, environmental, and energy security benefits from these next-generation engines and fuels would be significant inasmuch as the majority of vehicles sold over the next several decades will still include an engine. The Program has set the following goals for passenger and commercial vehicle fuel economy improvements. By 2030, increase light-duty engine efficiency to demonstrate 35% improvement in passenger vehicle fuel economy (25% improvement from engine efficiency and 10% from fuel co-optimization) relative to a 2015 baseline vehicle, while meeting the U.S. Environmental Protection Agency Tier 3 Emission and Fuel Standards. By 2030, improve heavy-duty engine efficiency by 35% relative to a 2009 baseline vehicle and identify cost-effective high-performance fuels that can further increase efficiency up to an additional 4%, while meeting prevailing U.S. Environmental Protection Agency emissions standards. The Program utilized advanced combustion processes to increase engine efficiency, resulting in a modeled passenger vehicle fuel economy improvement of 19.4% (over a Model Year 2015 baseline) in FY 2019. This report highlights progress achieved by the Advanced Engine and Fuel Technologies Program during FY 2019. The nature, current focus, and recent progress of the Program are described together with summaries of National Laboratory, industry, and university projects that provide an overview of the exciting work being conducted to address critical technical barriers and challenges to commercializing the next generation of higher-efficiency engine, emissions control, and fuel technologies for passenger and commercial vehicles.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRSDate not supplied

Aerial Vehicle Routing and Scheduling for UAS Traffic Management: A Monte Carlo Tree Search Approach

Numerous unmanned aircraft systems operating at low altitudes to deliver goods and services may one day become ubiquitous in our cities. In the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) framework, such a concept is envisioned, where aerial vehicles operate beyond visual line of sight (BVLOS) within specifically reserved and time stamped “corridors” in the airspace. For example, these corridors or operational intent volumes can connect an aerial vehicle’s origin site to its destination site for package delivery operations. There may also be more than one corridor available for an aerial vehicle to choose from and often different corridors may intersect with one another. Thus, it is imperative to ensure flight trajectories belonging to different aerial vehicles are not in conflict. Per the UTM CONOPs, we assume that a vehicle almost always stays inside its corridor or operational volume. This work provides a framework for strategic deconfliction of UTM or package delivery drones, where we schedule the departure time of all vehicles subject to various temporal constraints (including the corridor deconfliction at the intersections). We present the “multi-route weighted package delivery problem” which serves as an exemplifying model for strategic deconfliction in UTM. In the multi-route weighted package delivery problem, a graph network is given which consists of a set of depots (source) and drop-off (destination) nodes, with multiple routes (defined as a sequence of waypoints) connecting the depots to drop-off nodes. In addition, routes are weighted by the associated ground risk and total travel distance for package delivery. The goal is for a known set of aerial vehicles to depart from the depots, choose a route and take off time, while avoiding conflicts with other aerial vehicles, and minimizing both risk and distance traveled. We provide a mixed integer linear programming (MILP) formulation of the problem, as well as a heuristic solution based on Monte Carlo Tree Search (MCTS) – a method used in game theory and artificial intelligence – to overcome limitations inherent to optimal solvers. Computational results show the advantages of using MCTS over the MILP formulation; the former can provide a sub-optimal solution quickly, and may sometimes even reach an optimal solution, whereas the latter may not even produce a solution in reasonable time. Furthermore, results from both the MILP formulation and MCTS methods were validated using a preliminary agent-based simulator implementing the UTM concept of operations. Thus, the MCTS method can be seen as a scalable solution to the complex multi-route weighted package delivery problem and may possibly be extended to similar complex optimization problems.

Kenny Chour↗

DOE OSTI2024

Determining the Crosslinking and Degradation Reaction Kinetics in Photovoltaic Encapsulants Using Accelerated Aging: Preprint

Degradation of photovoltaic (PV) module encapsulant mechanical characteristics that lead to embrittlement and delamination remains a cause of failure in solar installations. A multiscale reliability model based on detailed molecular degradation reaction kinetics was previously published, connecting the encapsulant mechanical properties (elastic modulus, yield strength, and adhesion energy) to the degraded molecular structure and interfacial bond density to adjacent solar cell and glass substrates. The model, developed primarily for poly(ethylene-co-vinyl acetate) (EVA) encapsulants, remains to be experimentally validated. Determining the degradation and crosslinking kinetics of alternative encapsulants, such as polyolefin elastomer (POE) and EVA/POE/EVA composites (EPE), can further generalize the model. Importantly, the activation energy for crosslinking of fully cured PV encapsulant products from temperature or UV is presently unknown or unavailable. In this work, we subject EVA, POE, and EPE encapsulants to a set of accelerated aging conditions, varying the temperature, UV intensity, and relative humidity. We use DSC, FTIR, and Soxhlet extraction (gel content) to characterize the encapsulants' changing molecular structure. This allows for determination of the photochemical degradation and crosslinking kinetics of the encapsulants. Preliminary results show an increase in gel content (crosslinking) and a decrease in crystallinity of EVA, POE, and EPE encapsulants under hot-aerobic (90 deg C, 22% RH) and hot-anaerobic (90oC, sealed in N2 air) accelerated aging, even in the absence of UV and crosslinking initiating agents. For a least squares regression with an assumed first-order crosslinking kinetics, the crosslinking rate parameters were computed for the three encapsulants under hot-aerobic and hot-anaerobic aging conditions. FTIR analysis showed insignificant encapsulant degradation for hot-aerobic and hot-anaerobic aging, demonstrating the critical role of UV and moisture in accelerating degradation. Aging conditions with UV exposure and elevated humidity are presently in progress.

adhesion↗

DOE OSTI2021

Coarse Woody Debris Decomposition Assessment Tool: Model validation and application

Coarse woody debris (CWD) is a significant component of the forest biomass pool; hence a model is warranted to predict CWD decomposition and its role in forest carbon (C) and nutrient cycling under varying management and climatic conditions. A process-based model, CWDDAT (Coarse Woody Debris Decomposition Assessment Tool) was calibrated and validated using data from the FACE (Free Air Carbon Dioxide Enrichment) Wood Decomposition Experiment utilizing pine ( Pinus taeda ), aspen ( Populous tremuloides ) and birch ( Betula papyrifera ) on nine Experimental Forests (EF) covering a range of climate, hydrology, and soil conditions across the continental USA. The model predictions were evaluated against measured FACE log mass loss over 6 years. Four widely applied metrics of model performance demonstrated that the CWDDAT model can accurately predict CWD decomposition. The R 2 (squared Pearson’s correlation coefficient) between the simulation and measurement was 0.80 for the model calibration and 0.82 for the model validation ( P <0.01). The predicted mean mass loss from all logs was 5.4% lower than the measured mass loss and 1.4% lower than the calculated loss. The model was also used to assess the decomposition of mixed pine-hardwood CWD produced by Hurricane Hugo in 1989 on the Santee Experimental Forest in South Carolina, USA. The simulation reflected rapid CWD decomposition of the forest in this subtropical setting. The predicted dissolved organic carbon (DOC) derived from the CWD decomposition and incorporated into the mineral soil averaged 1.01 g C m -2 y -1 over the 30 years. The main agents for CWD mass loss were fungi (72.0%) and termites (24.5%), the remainder was attributed to a mix of other wood decomposers. These findings demonstrate the applicability of CWDDAT for large-scale assessments of CWD dynamics, and fine-scale considerations regarding the fate of CWD carbon.

54 ENVIRONMENTAL SCIENCES↗