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Results for “improved engine efficiency”

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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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Comparative Study for Pressure Gain Combustion-Gas Turbine System Performance in NGCC Configurations

The Department of Energy (DOE) has a goal to increase natural gas combined cycle (NGCC) plant efficiency to greater than 70 percent on a lower heating value (LHV) basis. Improvement in gas turbine (GT) efficiency is key achieving this goal. While many of the traditional improvement methods (higher firing temperature, advanced materials, improved cooling methods, etc.) may increase current GT and NGCC efficiencies incrementally, combining these traditional advancements with breakthrough technologies such as pressure gain combustion (PGC) may provide significant system efficiency gains. PGC is a combustion process that results in a net pressure increase across the combustor (up to pressure ratio of 2.2) rather than the traditional (<5%) pressure drop. There are multiple PGC technologies being developed that create the pressure rise phenomena, including resonant pulse combustors (RPCs), pulse detonation engines (PDEs), wave rotor combustor (WRC) engines and rotating detonation engines (RDEs). In this study, scoping analyses of PGC concepts applied to NGCC power plants were performed by combining simplified analytical models for the RPC, PDE, WRC, and RDE types of pressure gain processes with a GT model to evaluate impacts of using each of the PGC technologies on key GT performance parameters. Sensitivity analyses were also performed. Ultimately, the PGC technologies were found to have the potential to increase simple cycle efficiency by 4–6 percent (absolute) and NGCC efficiency by 2–3 percent (absolute) based on the results of the current study.

03 NATURAL GAS↗
DOE OSTI2023

Recombinant microorganisms and uses therefor

Microorganisms are genetically engineered to produce 3-hydroxypropionate (3-HP). The microorganisms are carboxydotrophic acetogens. The microorganisms produce acetyl-coA using the Wood-Ljungdahl pathway for fixing CO/CO 2 . A β-alanine pyruvate aminotransferase from a microorganism that contains such an enzyme is introduced. Additionally, an acetyl-coA carboxylase may also be introduced. The production of 3-HP can be improved. This can be effected by improved promoters or higher copy number or enzymes that are catalytically more efficient.

Liew, Fungmin↗
DOE OSTI2024

Mild pH-decoupling aqueous flow battery with practical pH recovery

Establishing a pH difference between the two electrolytes (pH decoupling) of an aqueous redox flow battery (ARFB) enables cell voltages exceeding the 1.23 V thermodynamic water-splitting window, but acid–base crossover penalizes efficiency and lifetime. Here we employ mildly acidic and mildly alkaline electrolytes to mitigate crossover, achieving high round-trip energy efficiency with open circuit voltage >1.7 V. We implemented an acid–base regeneration system to periodically restore electrolytes to their initial pH values. The combined system exhibited capacity fade rate <0.07% per day, round-trip energy efficiency >85% and approximately 99% Coulombic efficiency during stable operation for over a week. Cost analysis shows that the tolerance of acid–base crossover could be increased if the pH-decoupling ARFB achieved a higher voltage output and lower resistance. Finally, this work demonstrates principles for improving lifespan, rate capability and energy efficiency in high-voltage pH-decoupling ARFBs and pH recovery concepts applicable for pH-decoupling systems.

25 ENERGY STORAGE↗
DOE OSTI2020

High-Temperature Particle Heat Exchanger for sCO2 Power Cycles

Particle receivers are being pursued to provide substantial performance improvements through higher temperatures (>700 °C) for more efficient and cost-effective concentrating solar power (CSP) systems with direct storage. However, the interface between the solar-collection and power-block subsystems - a high-temperature particle/supercritical CO 2 (sCO2) heat exchanger - has not been developed. The objective of this project is to design, construct, and test a first-of-a-kind particle-to-sCO2 heat exchanger. This work will enable emerging sCO2 power cycles that have the potential to meet SunShot targets of 50% thermal-to-electric efficiency, dry cooling with 40 °C ambient temperature, and $0.06/kWh for CSP systems. The development of next-generation particle-based systems and methods with potentially high consequences for improved performance and cost savings for CSP applications is an appropriate role for the government.

14 SOLAR ENERGY↗
DOE OSTI2022

Modification of a Silicon Photomultiplier for Reduced High Temperature Dark Count Rate

In this work we present a novel method for improving the high-temperature performance of silicon photomultipliers (SiPMs) via focused ion beam (FIB) modification of individual microcells. The literature suggests that most of the dark count rate (DCR) in a SiPM is contributed by a small percentage (<5%) of microcells. By using a FIB to electrically deactivate this relatively small number of microcells, we believe we can greatly reduce the overall DCR of the SiPM at the expense of a small reduction in overall photodetection efficiency, thereby improving its high temperature performance. In this report we describe our methods for characterizing the SiPM to determine which individual microcells contribute the most to the DCR, preparing the SiPM for FIB, and modifying the SiPM using the FIB to deactivate the identified microcells.

42 ENGINEERING↗
DOE OSTI2020

Spatial Engineering Direct Cooperativity between Binding Sites for Uranium Sequestration

Preorganization is a basic design principle used by nature that allows for synergistic pathways to be expressed. Herein, a full account of the conceptual and experimental development from randomly distributed functionalities to a convergent arrangement that facilitates cooperative binding is given, thus conferring exceptional affinity toward the analyte of interest. The resulting material with chelating groups populated adjacently in a spatially locked manner displays up to two orders of magnitude improvement compared to a random and isolated manner using uranium sequestration as a model application. This adsorbent shows exceptional extraction efficiencies, capable of reducing the uranium concentration from 5 ppm to less than 1 ppb within 10 min, even though the system is permeated with high concentrations of competing ions. The efficiency is further supported by its ability to extract uranium from seawater with an uptake capability of 5.01 mg g -1 , placing it among the highest-capacity seawater uranium extraction materials described to date. The concept presented here uncovers a new paradigm in the design of efficient sorbent materials by manipulating the spatial distribution to amplify the cooperation of functions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Development of HVPE-Grown III-V Solar Cells Passivated with AlInP

We present the first AlInP-passivated solar cells grown by dynamic hydride vapor phase epitaxy (D-HVPE). Single junction (1J) GaAs and GaInP/GaAs (2J) tandems with AlInP passivation achieve AM1.5G efficiencies of 26.0% and 28.5%, respectively, which are the highest reported efficiencies for HVPE-grown devices of each type. 1J devices passivated with either AlInP or GaInP exhibit similar VOC and long wavelength current collection, indicating that both windows provide similar passivation. Adding AlInP passivation to the 2J solar cell improves the VOC by -50 mV relative to the device passivated by a GaInP emitter. These achievements remove one of the last barriers limiting parity of HVPE device efficiencies with state-of-the-art.

AlInP↗
DOE OSTI2022

Role of carboxysomes in cyanobacterial CO 2 assimilation: CO 2 concentrating mechanisms and metabolon implications

Many carbon-fixing organisms have evolved CO 2 concentrating mechanisms (CCMs) to enhance the delivery of CO 2 to RuBisCO, while minimizing reactions with the competitive inhibitor, molecular O 2 . These distinct types of CCMs have been extensively studied using genetics, biochemistry, cell imaging, mass spectrometry, and metabolic flux analysis. Highlighted in this paper, the cyanobacterial CCM features a bacterial microcompartment (BMC) called ‘carboxysome’ in which RuBisCO is co-encapsulated with the enzyme carbonic anhydrase (CA) within a semi-permeable protein shell. Further, the cyanobacterial CCM is capable of increasing CO 2 around RuBisCO, leading to one of the most efficient processes known for fixing ambient CO 2 . The carboxysome life cycle is dynamic and creates a unique subcellular environment that promotes activity of the Calvin–Benson (CB) cycle. The carboxysome may function within a larger cellular metabolon, physical association of functionally coupled proteins, to enhance metabolite channelling and carbon flux. In light of CCMs, synthetic biology approaches have been used to improve enzyme complex for CO 2 fixations. Research on CCM-associated metabolons has also inspired biologists to engineer multi-step pathways by providing anchoring points for enzyme cascades to channel intermediate metabolites towards valuable products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Charting the Path to Electrification: Analyzing the Economic and Technological Potential of Advanced Vehicle Powertrains

The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) is driving advancements in highway transportation by targeting energy efficiency, environmental sustainability, and cost reductions. This study investigates the fuel economy potential and cost implications of advanced powertrain technologies using comprehensive system simulations. Leveraging tools such as Autonomie and TechScape, developed by Argonne National Laboratory, this study evaluates multiple timeframes (2023–2050) and powertrain types, including conventional internal combustion engines, hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). Simulations conducted across standard regulatory driving cycles provide detailed insights into fuel economy improvements, cost trajectories, and total cost of ownership. The findings highlight key innovations in battery energy density, lightweighting, and powertrain optimization, demonstrating the growing viability of BEVs and their projected economic competitiveness with conventional vehicles by 2050. This work delivers actionable insights for policymakers and industry stakeholders, underscoring the transformative potential of vehicle electrification in achieving sustainable transportation goals.

Islam, Ehsan Sabri (ORCID:0000000200220180)↗
DOE OSTI2020

Recent Progress in Engineering Metal Halide Perovskites for Efficient Visible‐Light‐Driven Photocatalysis

Abstract Artificial photosynthesis has attracted increasing attention due to recent environmental and energy concerns. Metal halide perovskites (MHPs) demonstrating excellent optoelectronic properties have currently emerged as novel and efficient photocatalytic materials. Herein, the structural features of MHPs that are responsible for the photoinduced charge separation and charge migration properties are briefly introduced, and then important and necessary photophysical and photochemical aspects of MHPs related to photoredox catalysis are summarized. Subsequently, the applications of MHPs for solar energy harvesting and photocatalytic conversion, including H 2 evolution, CO 2 reduction, degradation of organic pollutants, and photoredox organic synthesis, are extensively demonstrated, with a focus on strategies for improving the performance (e.g., selectivity, activity, stability, recyclability, and environmental compatibility) of these MHP‐based photocatalytic systems. To conclude, existing challenges and prospects on the future development of MHP‐based materials towards photoredox catalysis applications are detailed.

Han, Chuang↗
DOE OSTI2021

The Moderately (D)efficient Enzyme: Catalysis-Related Damage In Vivo and Its Repair

Enzymes have in vivo life spans. Analysis of life spans, i.e., lifetime totals of catalytic turnovers, suggests that nonsurvivable collateral chemical damage from the very reactions that enzymes catalyze is a common but underdiagnosed cause of enzyme death. Analysis also implies that many enzymes are moderately deficient in that their active-site regions are not naturally as hardened against such collateral damage as they could be, leaving room for improvement by rational design or directed evolution. Enzyme life span might also be improved by engineering systems that repair otherwise fatal active-site damage, of which a handful are known and more are inferred to exist. Unfortunately, the data needed to design and execute such improvements are lacking: there are too few measurements of in vivo life span, and existing information about the extent, nature, and mechanisms of active-site damage and repair during normal enzyme operation is too scarce, anecdotal, and speculative to act on. Fortunately, advances in proteomics, metabolomics, cheminformatics, comparative genomics, and structural biochemistry now empower a systematic, data-driven approach for identifying, predicting, and validating instances of active-site damage and its repair. These capabilities would be practically useful in enzyme redesign and improvement of in-use stability and could change our thinking about which enzymes die young in vivo, and why.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Plug & play directed evolution of proteins with gradient-based discrete MCMC

Abstract A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence. By composing these models, we aim to improve our ability to evaluate unseen mutations and constrain search to regions of sequence space likely to contain functional proteins. Our framework achieves this without any model fine-tuning or re-training by constructing a product of experts distribution directly in discrete protein space. Instead of resorting to brute force search or random sampling, which is typical of classic directed evolution, we introduce a fast Markov chain Monte Carlo sampler that uses gradients to propose promising mutations. We conduct in silico directed evolution experiments on wide fitness landscapes and across a range of different pre-trained unsupervised models, including a 650 M parameter protein language model. Our results demonstrate an ability to efficiently discover variants with high evolutionary likelihood as well as estimated activity multiple mutations away from a wild type protein, suggesting our sampler provides a practical and effective new paradigm for machine-learning-based protein engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A high-order computational framework for particle-resolved simulations of disperse multiphase flows

This work presents a high-order numerical approach for particle-resolved simulations of disperse multiphase flows, where the Navier-Stokes equations for fluid flow are solved using a high-order spectral element method in the Eulerian framework, and the particle phase is directly simulated with a discrete element method. The coupling between particles and fluids is explicitly handled using an adapted direct-forcing immersed boundary method. Unlike the conventional schemes, a high-order barycentric Lagrange interpolation method and a Gaussian projection kernel are used to ensure accurate momentum exchange between local boundary points and surrounding fluid nodes in the framework of high-order fluid solver. Benchmark tests of increasing complexity are conducted to demonstrate the accuracy and efficiency of our method. Here, it is found that our approach exhibits an excellent convergence performance, as the fluid element/grid is refined and the number of boundary points increases. Compared to conventional low-order methods, the proposed high-order framework enables the use of substantially larger fluid elements while maintaining high accuracy in modeling fluid-particle interactions, owing to the enhanced resolution of high-order basis functions. Moreover, since the primary unknowns are stored at element or grid nodes, the high-order approach offers improved efficiency in both CPU memory usage and total computational cost.

42 ENGINEERING↗
DOE OSTI2020

Improving CRISPR/Cas9-mediated genome editing efficiency in Yarrowia lipolytica using direct tRNA-sgRNA fusions

We report Yarrowia lipolytica is an important oleaginous yeast currently used in the production of specialty chemicals and has a great potential for further applications in lipid biotechnology. Harnessing the full potential of Y. lipolytica is, however, limited by its inherent recalcitrance to genetic manipulation. In contrast to Saccharomyces cerevisiae, Y. lipolytica is poor in homology-mediated DNA repair and thus in homologous recombination, which limits site-specific gene editing in this yeast. Recently developed CRISPR/Cas9-based methods using tRNA-sgRNA fusions succeeded in editing some genomic loci in Y. lipolytica. Nonetheless, the majority of other tested loci either failed editing or editing was achieved but at very low efficiency using these methods. Using tools of secondary RNA structure prediction, we were able to improve the design of the tRNA-sgRNA fusions used for the expression of single guide RNA (sgRNA) in such methods. This resulted in high efficiency CRISPR/cas9 gene editing at chromosomal loci that failed gene editing or were edited at very low efficiencies with previous methods. In addition, we characterized the gene editing performance of our newly designed tRNA-sgRNA fusions for both chromosomal gene integration and deletion. As such, this study presents an efficient CRISPR/Cas9-mediated gene-editing tool for efficient genetic engineering of Yarrowia lipolytica.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗
DOE OSTI2021

Oxygenated Fuel Blending Effects in a Mixing-Controlled Compression-Ignition Engine Equipped with Ducted Fuel Injection [Slides]

Continued creation of harmful emissions such as NO x and soot from compression-ignition engines utilizing mixing-controlled combustion systems (i.e., diesel engines) remains a problem and is the subject of on-going research. The inherently high efficiency, relatively low cost, and numerous other desirable attributes of such engines, coupled with a widely supported infrastructure, motivates their continued advancement. Recently, a scientifically distinct and mechanically simple technology called ducted fuel injection (DFI) has shown a robust ability to allow such engines to operate with simultaneously low engine-out soot and NO x emissions when it is employed with simulated exhaust-gas recirculation. To better understand the property ranges of sustainable, oxygenated-fuel blending stocks that will most improve engine performance, two oxygenated blendstocks were separately blended with a commercial diesel base fuel and tested within a heavy-duty diesel optical engine equipped with a four-duct DFI configuration. Conventional and crank-angle-resolved optical diagnostics were used to elucidate the effects of fuel ignition quality, oxygenate molecular structure, and overall oxygen content on engine performance.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Accelerating Combustion and Surface Chemistry Simulations

Design of modern combustion systems relies on computer models to predict how changes in design will affect performance. These models have largely displaced previous methods that rely on the designer’s intuition or costly and time-consuming physical testing. By using improved models, design cycles can be shortened, and cleaner and more efficient combustion devices can be created. This project aims to improve computer simulations of transportation fuels with the goal of making these simulations faster and more accurate for predicting combustion in vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Design and Performance of an InAs Quantum Dot Scintillator with Integrated Photodetector

A new scintillation material composed of InAs quantum dots (QDs) hosted within a GaAs matrix was developed, and its performance with different types of radiation is evaluated. A methodology for designing an integrated photodetector (PD) with a low defect density and that is optically matched to the QD’s emission spectrum is introduced, utilizing an engineered epitaxial InAlGaAs metamorphic buffer layer. The photoluminescence (PL) collection efficiency of the integrated PD is examined using two-dimensional scanning laser excitation. The detector response to 5.5 MeV α-particles and 122 keV photons is presented. Yields of 13 electrons/keV for α-particles and 30–60 electrons/keV for photons were observed. The energy resolution of 12% observed with α-particles was mainly limited by noise- and geometry-related optical losses. The radiation hardness of an InAs QDs hosted within GaAs and a wider band gap AlGaAs ternary alloy was studied under a 1 MeV proton implantation up to a 10 14 cm –2 dose. The integrated PL responses were compared to evaluate PL quenching due to non-radiative defects. The QDs embedded in the AlGaAs demonstrated improved radiation hardness compared to QDs in the GaAs matrix and in the InGaAs quantum wells.

36 MATERIALS SCIENCE↗