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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 OSTI2020

Hot-Carrier Extraction in Nanowire-Nanoantenna Photovoltaic Devices

Nanowires bring new possibilities to the field of hot-carrier photovoltaics by providing flexibility in combining materials for band engineering and using nanophotonic effects to control light absorption. Previously, an open-circuit voltage beyond the Shockley–Queisser limit was demonstrated in hot-carrier devices based on InAs–InP–InAs nanowire heterostructures. However, in these first experiments, the location of light absorption, and therefore the precise mechanism of hot-carrier extraction, was uncontrolled. In this study, we combine plasmonic nanoantennas with InAs–InP–InAs nanowire devices to enhance light absorption within a subwavelength region near an InP energy barrier that serves as an energy filter. From photon-energy- and irradiance-dependent photocurrent and photovoltage measurements, we find that photocurrent generation is dominated by internal photoemission of nonthermalized hot electrons when the photoexcited electron energy is above the barrier and by photothermionic emission when the energy is below the barrier. We estimate that an internal quantum efficiency up to 0.5–1.2% is achieved. Insights from this study provide guidelines to improve internal quantum efficiencies based on nanowire heterostructures.

14 SOLAR ENERGY↗
DOE OSTI2023

University Coalition for Fossil Fuel Energy Research

Following a nationwide open competition, the University Coalition for Fossil Energy Research (UCFER) was established in October 2015 through a cooperative agreement between Penn State and the Department of Energy (DOE) National Energy Technology Laboratory (NETL). Penn State lead UCFER with the objective of advancing basic and applied research for clean and low-carbon energy based on fossil fuels in support of the DOE’s mission. UCFER focused on research that improves the efficiency of production and use of fossil energy resources, while minimizing the environmental impacts and reducing greenhouse gas emissions. Penn State lead a team of nine universities (Massachusetts Institute of Technology, The Pennsylvania State University, Princeton University, Texas A&M University, University of Kentucky, University of Southern California, The University of Tulsa, University of Wyoming, and Virginia Polytechnic and State University) during the competition stage, adding seven more universities in 2017 (Carnegie Mellon University, Louisiana State University, The Ohio State University, University of North Dakota, University of Pittsburgh, University of Utah, and West Virginia University). This Coalition exhibited a wide geographical distribution across the U.S. bringing a wide variety of fossil energy expertise. This national university alliance was a major collaborative effort with NETL to address specific topics of R&D in NETL’s mission area, which involved one or more of NETL’s five core competencies (Geologic and Environmental Systems, Materials Engineering and Manufacturing, Energy Conversion Engineering, Systems Engineering and Analysis, and Computational Science and Engineering). The first five to six months of the project was the definitization stage. During this period, Penn State worked closely with NETL to finalize the Coalition organizational structure and By- Laws, prepare a statement of substantial involvement and a statement of project objectives, and develop operations and membership plans. A major component of this stage included preparing an execution plan to solicit research, evaluate proposals, recommend selected projects to NETL, and award projects. In addition, a plan was prepared to monitor projects, review projects, disseminate knowledge from research projects and develop an online system for Coalition research portfolio management. This included developing a website and several databases. The first of six rounds of solicitations started in mid-2016. Projects from the sixth solicitation started February 1, 2021, and ended January 31, 2023. Projects that were selected represented twelve technology lines. Approximately $16.6 million in funding was available for the six solicitations. Most of the funding was provided by DOE, Office of Fossil Energy (DOEFE) with the DOE Fuel Cells Technologies Office (DOE-FCTO) providing funding for a few projects. Coalition universities submitted 259 proposals in response to the solicitations, requesting approximately $67.0 million in funding, and forty-three projects were selected. However, one project withdrew after the principal investigator left the university. The management of the Coalition projects was a major activity by Penn State. Managing the Coalition projects consisted of monitoring the projects, reviewing the projects through annual technical review meetings, disseminating knowledge from the research projects, and developing an online system for Coalition research portfolio management. Penn State’s OMT monitored projects to ensure that all milestones (technical, schedule, budget) were met, expenditures were allowable, cost share (when applicable) were reported, and all technical reports were submitted. The OMT also posted the technical reports electronically on a secure members-only website for access and review by the Coalition members. Disseminating knowledge from the research projects was done through a website, newsletters, various meetings, conferences, journal articles, publicity/press releases, and project summaries that were prepared after each project was completed. Penn State kept NETL apprised of UCFER progress through quarterly reports (thirtyone were submitted by Penn State), verbal and written communications, yearly updates at the annual technical review meetings, and cost accrual reports. The UCFER project had a significant impacty. The UCFER program established the first national university alliance in fossil energy research with a major collaboration effort with DOE NETL that addressed specific topics in NETL’s research and development mission areas. It generated inter-university collaborations, which was another program interest. Twenty-two out of 259 proposals contained collaborations (≈8.5%) and three of forty-two funded projects involved inter-university collaborations (≈7.0%). The forty-two funded projects provided support at fourteen universities involving 269 personnel. Research was conducted by 106 faculty, 115 graduate and undergraduate students, forty-six research staff and post-doctoral scholars, and two visiting scholars. Students and post-doctoral scholars were also on-site at NETL through CRADAs. In addition, non-Coalition participants included six universities and sixteen companies and national laboratories. The non-Coalition participants were involved as subcontractors, providers of cost share, performed unpaid consultation and sample analysis, served as advisory board members, or were providers of samples and materials for testing. UCFER also produced visibility in that fifty-six refereed journal articles were published, fifty-seven conference papers and twenty-three posters were prepared, 190 presentations were given, eight patent applications were filed, two books/book chapters were written, and ten software codes were developed. Collaboration between NETL and the individual projects was a major requirement for all funded projects. This included NETL staff time to support collaboration, consultation, technical guidance, sample preparation and analysis, internships at NETL, on-site testing and equipment usage by Coalition participants at NETL, co-mentoring students, and coauthoring journal articles and conference papers. Collaboration was impacted by COVID-19 in that not all on-site activities could be performed. NETL personnel were coauthors on eight of the conference papers (fourteen percent of the conference papers that were prepared) and seventeen of the journal articles (thirty percent of the journal articles that were prepared). A website was developed for an online proposal solicitation and review process and to provide exposure to UCFER. A website analysis highlighted the large amount of member and general public interest in UCFER by interpreting access statistics from March 2016 through June 2023. Visitors to the site originated from many different organizations, businesses, and countries. The website provided a means to disseminate information to both the general public and the UCFER members and was successfully used for outreach activities. In addition, NETL required that RFP release, proposal submission, and proposal reviews all be performed online. Penn State successfully developed these capabilities in a secure section of the website, which were used throughout the UCFER project. It is recognized that each project had its technical successes. In addition, highlighted successes were compiled and summarized from the research projects. Information was requested from the PIs of completed projects. In addition, Penn State’s Operations Management Team reviewed subcontract reports to identify project successes. Examples of information requested from PIs included (not all-inclusive): new projects that have been funded as a result of UCFER funding; new commercial products; establishment of a new center; new patent; new software; best paper awards; highly-cited work; and graduate student successes. A total of forty-eight highlighted successes were reported.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

New U.S. Data Tools are Playing a Crucial Role in Decarbonizing Buildings at Speed, Scale, and Low Cost

Preparing buildings for retrofits traditionally requires expensive on-site audits or timeintensive simulation models. As a result, the majority of buildings fail to pursue cost-saving retrofits. To address these barriers, the U.S. Department of Energy (DOE) has introduced the Building Efficiency Targeting Tool for Energy Retrofits (BETTER)-a new, free, on-line tool that utilizes a data-driven analytical engine and user-friendly web interface to automatically analyze a building's monthly energy usage in response to weather conditions. The tool benchmarks a building's electric and fossil energy usage against peers; estimates energy, cost, and emissions reductions at the building and portfolio levels; recommends energy efficiency measures; and prioritizes buildings for net-zero energy retrofits. Thanks to interoperability with the DOE's Standard Energy Efficiency Data (SEED) platform, BETTER is supporting U.S. jurisdictions to prepare buildings for retrofit at speed, scale, and low cost to comply with energy policies. This paper discusses the use of BETTER and SEED by one of the branches of the California state government to streamline a retrofit program across 455 public non-residential buildings to align with state goals to reduce greenhouse gas emissions. It describes the organization's challenge to reduce energy consumption across a geographically diverse, aging portfolio; explores how BETTER and SEED improved workflow efficiency; presents preliminary results, including avoiding audit costs of $3.28 million and developing the groundwork for retrofit projects estimated to prevent emission of 2,271 t CO2e annually; and provides guidance for other jurisdictions seeking similar results.

BETTER↗
DOE OSTI2024

New U.S. Data Tools are Playing a Crucial Role in Decarbonizing Buildings at Speed, Scale, and Low Cost

Preparing buildings for retrofits traditionally requires expensive on-site audits or time- intensive simulation models. As a result, the majority of buildings fail to pursue cost-saving retrofits. To address these barriers, the U.S. Department of Energy (DOE) has introduced the Building Efficiency Targeting Tool for Energy Retrofits (BETTER)—a new, free, on-line tool that utilizes a data-driven analytical engine and user-friendly web interface to automatically analyze a building’s monthly energy usage in response to weather conditions. The tool benchmarks a building’s electric and fossil energy usage against peers; estimates energy, cost, and emissions reductions at the building and portfolio levels; recommends energy efficiency measures; and prioritizes buildings for net-zero energy retrofits. Thanks to interoperability with the DOE’s Standard Energy Efficiency Data (SEED) platform, BETTER is supporting U.S. jurisdictions to prepare buildings for retrofit at speed, scale, and low cost to comply with energy policies. This paper discusses the use of BETTER and SEED by one of the branches of the California state government to streamline a retrofit program across 455 public non-residential buildings to align with state goals to reduce greenhouse gas emissions. It describes the organization’s challenge to reduce energy consumption across a geographically diverse, aging portfolio; explores how BETTER and SEED improved workflow efficiency; presents preliminary results, including avoiding audit costs of $3.28 million and developing the groundwork for retrofit projects estimated to prevent emission of 2,271 t CO2e annually; and provides guidance for other jurisdictions seeking similar results.

Li, han↗
DOE OSTI2021

An embedded variable step IMEX scheme for the incompressible Navier–Stokes equations

This report presents a series of implicit–explicit (IMEX) variable stepsize algorithms for the incompressible Navier–Stokes equations (NSE). Here, IMEX means the nonlinear term is treated fully explicitly while the remaining terms are treated implicitly. With the advent of new computer architectures there has been growing demand for low memory solvers of this type. The addition of time adaptivity improves the accuracy and greatly enhances the efficiency of the algorithm. We prove energy stability of an embedded first–second order IMEX pair. For the first order member of the pair, we prove stability for variable stepsizes, and analyze convergence. We believe this to be the first proof of this type for a variable stepsize IMEX scheme for the incompressible NSE. We then define and test a variable stepsize, variable order IMEX scheme using these methods. Our work contributes several firsts for IMEX NSE schemes, including an energy argument and error analysis of a two-step, variable stepsize method, and embedded error estimation for an IMEX multi-step method.

42 ENGINEERING↗
DOE OSTI2025

Fuel cells for single-aisle regional aircraft: System configuration, performance and cost

A hydrogen fuel cell propelled electric aircraft can compete with incumbent turbofan technologies for single-aisle regional aircraft by coupling design of stack, air handling, thermal management, propulsion, and airframe to optimize performance. The stack operates at 95°C to facilitate heat rejection during take-off and below 75°C during cruise to extend lifetime and is oversized to satisfy power requirements at end of life. A multi-stage turbocompressor with a compression ratio >10 is selected to reach high stack power density at 11,300-m cruise altitude. The propulsion system is configured to accommodate air handling within the core duct, an inclined heat exchanger in the outer duct to limit the nacelle size, and variable area nozzles to independently control mass flows through the core and bypass ducts. The airframe is modified for maximum lift coefficient and longer balanced field length for dramatically reduced thrust during take-off, and the fuselage is stretched by 20% to store liquid hydrogen (LH 2 ). Modularization of power systems promotes safety in one engine inoperative scenarios and allows reaching specific power metrics for stack, balance-of-plant and fuel cell system (FCS), necessary for acceptable take-off weight. In conclusion, cost parity requires increase in FCS lifetime, LH 2 cost reduction, and improved FCS specific power.

Catalyst durability↗
DOE OSTI2022

Modeling of Supercritical CO2 Shell-and-Tube Heat Exchangers Under Extreme Conditions: Part II: Heat Exchanger Model

Abstract Heat exchangers play a critical role in supercritical CO2 Brayton cycles by providing necessary waste heat recovery. Supercritical CO2 thermal cycles potentially achieve higher energy density and thermal efficiency operating at elevated temperatures and pressures. Accurate and computationally efficient estimation of heat exchanger performance metrics at these conditions is important for the design and optimization of sCO2 systems and thermal cycles. In this paper (Part II), a computationally efficient and accurate numerical model is developed to predict the performance of shell-and-tube heat exchangers (STHXs). Highly accurate correlations reported in Part I of this study are utilized to improve the accuracy of performance predictions, and the concept of volume averaging is used to abstract the geometry and reduce computation time. The numerical model is validated by comparison with computational fluid dynamics (CFD) simulations and provides high accuracy and significantly lower computation time compared to existing numerical models. A preliminary optimization study is conducted and the advantage of using supercritical CO2 as a working fluid for energy systems is demonstrated.

Engineering↗
DOE OSTI2022

Hot Electron-Driven Photocatalysis Using Sub-5 nm Gap Plasmonic Nanofinger Arrays

Semiconductor photocatalysis has received increasing attention because of its potential to address problems related to the energy crisis and environmental issues. However, conventional semiconductor photocatalysts, such as TiO2 and ZnO, can only be activated by ultraviolet light due to their wide band gap. To extend the light absorption into the visible range, the localized surface plasmon resonance (LSPR) effect of noble metal nanoparticles (NPs) has been widely used. Noble metal NPs can couple incident visible light energy to strong LSPR, and the nonradiative decay of LSPR generates nonthermal hot carriers that can be injected into adjacent semiconductor material to enhance its photocatalytic activity. Here we demonstrate that nanoimprint-defined gap plasmonic nanofinger arrays can function as visible light-driven plasmonic photocatalysts. The sub-5 nm gaps between pairs of collapsed nanofingers can support ultra-strong plasmon resonance and thus boost the population of hot carriers. The semiconductor material is exactly placed at the hot spots, providing an efficient pathway for hot carrier injection from plasmonic metal to catalytic materials. This nanostructure thus exhibits high plasmon-enhanced photocatalytic activity under visible light. The hot carrier injection mechanism of this platform was systematically investigated. The plasmonic enhancement factor was calculated using the finite-difference time-domain (FDTD) method and was consistent with the measured improvement of the photocatalytic activity. This platform, benefiting from the precise controllable geometry, provides a deeper understanding of the mechanism of plasmonic photocatalysis.

42 ENGINEERING↗
DOE OSTI2023

IDAES-PSE 2.2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. New features in the 2.2 release include: DiagnosticsToolbox DiagnosticsToolbox (idaes.core.util.model_diagnostics. DiagnosticsToolbox is a new self-guiding toolbox for assisting with diagnosing modeling issues. The toolbox contains a wide range of tools for identifying common modeling issues and conveying these to the user, and more features will be added in the coming months. To try the new toolbox, use the following steps: from idaes.core.util import DiagnosticsToolbox Build an instance of your model Ensure your models has zero degrees of freedom (as best you can) dt = DiagnosticsToolbox(model) dt.report_structural_issues() Follow the prompts Deprecations DegeneracyHunter is being deprecated in favor of the new DiagnosticsToolbox. The features and capabilities of DegeneracyHunter will be moved over to the DiagnositcsToolbox in the coming months. Removal of usage of Pyomo's expr.current due to deprecation upstream. Removed support for radial basis functions in ALAMOpy as these did not work in the current implementation. Those wishing to use radial basis functions should use Pysmo instead. New Capabilities MSContactor model for unit operations involving mass transfer between 2 or more streams, such as membrane, solvent extraction and solids leaching operations. Other Improvements Improved formulation for solubility products with additional parameters for better scaling of terms Better backward compatibility, verification and robustness testing of core models and APIs Added support for Separator models without energy balances

DiagnosticsToolbox↗
DOE OSTI2020

Microsimulation of Energy and Flow Effects from Optimal Automated Driving in Mixed Traffic

In this paper we study the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the unconnected mode, active when following a human-driven vehicle, and 2. the connected mode, active when following another automated vehicle equipped with connectivity. Probabilistic constraints balance safety considerations with inter-vehicle compactness, and vehicle constraints for acceleration capabilities are expressed through the use of powertrain maps. Emergent highway traffic scenarios are then modeled using time headway distributions from empirical traffic data. To study the impact of automation over a range of demands of free-flow to stop-and-go, we vary vehicle flux from low to high and vary automated vehicle penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with connected automated vehicles introduced, network capacity was improved to support the high-volume scenarios. Finally, we examine energy efficiencies of the fleet for conventional, electric, and hybrid vehicles. We find that automated vehicles perform at a 10%-20% higher energy efficiency over human drivers when considering conventional powertrains, and find that automated vehicles perform at a 3%-9% higher energy efficiency over human drivers when considering electric and hybrid powertrains. Due to secondary effects of smoothing traffic flow and reducing unnecessary braking, energy benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more energy-efficiently than they did in the baseline all-human scenario.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Hyperconjugation-controlled molecular conformation weakens lithium-ion solvation and stabilizes lithium metal anodes

Tuning the solvation structure of lithium ions via electrolyte engineering has proven effective for lithium metal (Li) anodes. Further advancement that bypasses the trial-and-error practice relies on the establishment of molecular design principles. Expanding the scope of our previous work on solvent fluorination, we report here an alternative design principle for non-fluorinated solvents, which potentially have reduced cost, environmental impact, and toxicity. By studying non-fluorinated ethers systematically, we found that the short-chain acetals favor the [gauche, gauche] molecular conformation due to hyperconjugation, which leads to weakened monodentate coordination with Li + . The dimethoxymethane electrolyte showed fast activation to >99% coulombic efficiency (CE) and high ionic conductivity of 8.03 mS cm -1 . The electrolyte performance was demonstrated in anode-free Cu$∥$LFP pouch cells at current densities up to 4 mA cm -2 (70 to 100 cycles) and thin-Li$∥$high-loading-LFP coin cells (200–300 cycles). Overall, we demonstrated and rationalized the improvement in Li metal cyclability by the acetal structure compared to ethylene glycol ethers. We expect further improvement in performance by tuning the acetal structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Spark-Assisted HCCI Residential CHP

A transformative small spark ignition (SI) internal combustion (IC) engine fueled with natural gas was developed for combined heat and power (CHP) applications using a combination of cycle simulations, computational fluid dynamics (CFD) modeling, and engine experiments. The resulting 1 kW CHP engine was demonstrated to achieve 36.1% brake thermal efficiency (BTE) while meeting aggressive exhaust emissions targets using a low-cost three-way catalyst. Starting from a “clean-sheet” design, modeling tools were used to select the optimal engine parameters and operating characteristics. The disruptive technologies developed for the CHP system have a target lifetime of at least 10 years. The engine cost at volume of 10,000 units was estimated to be $\$$1,050. A combination of experimental and simulation results were used to identify a path to the program target of 38.6% BTE. Base engine design parameters (i.e., displacement, speed, bore, stroke, valve timings, and compression ratio) were optimized to reduce friction, heat transfer, and exhaust losses. The base clean sheet prototype engine achieved 32% BTE. Optimization of the lube oil and coolant temperatures added 1.8% BTE. The addition of exhaust gas recirculation added 1.1% BTE. Novel thermal barrier coatings (TBCs) developed over the course of the project added 1.2% BTE. Further improvements in engine friction reduction (1.3% BTE) and improved combustion (1.2% BTE) are estimated to meet the program target 38.6% BTE, while also meeting the stringent criteria pollutant emissions targets.

42 ENGINEERING↗
DOE OSTI2021

Inverse Design of Two-Dimensional Airfoils Using Conditional Generative Models and Surrogate Log-Likelihoods

Abstract This paper shows how to use conditional generative models in two-dimensional (2D) airfoil optimization to probabilistically predict good initialization points within the vicinity of the optima given the input boundary conditions, thus warm starting and accelerating further optimization. We accommodate the possibility of multiple optimal designs corresponding to the same input boundary condition and take this inversion ambiguity into account when designing our prediction framework. To this end, we first employ the conditional formulation of our previous work BézierGAN–Conditional BézierGAN (CBGAN)—as a baseline, then introduce its sibling conditional entropic BézierGAN (CEBGAN), which is based on optimal transport regularized with entropy. Compared with CBGAN, CEBGAN overcomes mode collapse plaguing conventional GANs, improves the average lift-drag (Cl/Cd) efficiency of airfoil predictions from 80.8% of the optimal value to 95.8%, and meanwhile accelerates the training process by 30.7%. Furthermore, we investigate the unique ability of CEBGAN to produce a log-likelihood lower bound that may help select generated samples of higher performance (e.g., aerodynamic performance). In addition, we provide insights into the performance differences between these two models with low-dimensional toy problems and visualizations. These results and the probabilistic formulation of this inverse problem justify the extension of our GAN-based inverse design paradigm to other inverse design problems or broader inverse problems.

Engineering↗
DOE OSTI2025

FEM Modeling and Simulation of 2-D High Specific Heat Nb 3 Sn Wires

In the past few years, new high specific heat Nb 3 Sn wires have gained much focus at FNAL. Indeed, they have proved to be more stable against thermal perturbations with respect to standard wires. Nevertheless, a trade off exists between their thermal efficiency and production feasibility. In this report I describe the thermal and structural models that I have developed by exploiting ANSYS Mechanical APDL ® , which I got acquainted with at the beginning of my training. The aim has been to optimize the location of high specific heat elements in order to obtain an optimal thermal stability, while minimizing the risk of wire breakage during drawing. FEM results are compared with experimental ones made beforehand. Other minimum quench energy (MQE) thermal models have been developed for expected new experimental results. New data may improve the understanding of the embedded physical uncertainties in the model.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Metabolic engineering of Zymomonas mobilis for anaerobic isobutanol production

Biofuels and value-added biochemicals derived from renewable biomass via biochemical conversion have attracted considerable attention to meet global sustainable energy and environmental goals. Isobutanol is a four-carbon alcohol with many advantages that make it attractive as a fossil-fuel alternative. Zymomonas mobilis is a highly efficient, anaerobic, ethanologenic bacterium making it a promising industrial platform for use in a biorefinery. In this study, the effect of isobutanol on Z. mobilis was investigated, and various isobutanol-producing recombinant strains were constructed. The results showed that the Z. mobilis parental strain was able to grow in the presence of isobutanol below 12 g/L while concentrations greater than 16 g/L inhibited cell growth. Integration of the heterologous gene encoding 2-ketoisovalerate decarboxylase such as kdcA from Lactococcus lactis is required for isobutanol production in Z. mobilis. Moreover, isobutanol production increased from nearly zero to 100-150 mg/L in recombinant strains containing the kdcA gene driven by the tetracycline-inducible promoter Ptet. In addition, we determined that overexpression of a heterologous als gene and two native genes (ilvC and ilvD) involved in valine metabolism in a recombinant Z. mobilis strain expressing kdcA can divert pyruvate from ethanol production to isobutanol biosynthesis. This engineering improved isobutanol production to above 1 g/L. Finally, recombinant strains containing both a synthetic operon, als-ilvC-ilvD, driven by Ptet and the kdcA gene driven by the constitutive strong promoter, Pgap, were determined to greatly enhance isobutanol production with a maximum titer about 4.0 g/L. Finally, isobutanol production was negatively affected by aeration with more isobutanol being produced in more poorly aerated flasks. This study demonstrated that overexpression of kdcA in combination with a synthetic heterologous operon, als-ilvC-ilvD, is crucial for diverting pyruvate from ethanol production for enhanced isobutanol biosynthesis. Moreover, this study also provides a strategy for harnessing the valine metabolic pathway for future production of other pyruvate-derived biochemicals in Z. mobilis.

09 BIOMASS FUELS↗
DOE OSTI2025

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗
DOE OSTI2026

FleetREDI Insight: Intrastate Coach Bus Dataset

Capturing real-world data is critical to improving efficiency and supporting technology advancements in commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores coach buses operating in Colorado. Coach buses are a primary mover for intrastate transit and are primarily used for longer trips with more comfortable seats and a restroom. All Aboard America! Holdings Inc. offers various fixed-service and charter routes across Colorado on its Bustang fleet out of its depot in Golden, Colorado. NLR installed logging devices and collected operational data on nine 40-foot Bustang motorcoaches operating on fixed routes from May through August 2022. Using NLR’s FleetREDI data platform, this dataset provides a summary of daily operation to help understand duty cycle characteristics. This includes daily distance, fuel use, and estimated engine-produced energy consumption for nine motorcoaches that operated more than 33,000 miles. These vehicles primarily operated on Interstate 25 and Interstate 70. ![FleetREDI interstate bus](FleetREDI-interstate-bus.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗