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Results for “THERMODYNAMIC 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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Efficient real gas Navier-Stokes computations of high speed flows using an LU scheme

An efficient method to account for the chemically frozen thermodynamic and transport properties of air in three dimensional Navier-Stokes calculations was demonstrated. This approach uses an explicitly specified equation of state (EOS) so that the fluid pressure, temperature and transport properties are directly related to the flow variables. Since the pressure is explicitly known as a general function of the flow variables no assumptions are made regarding the pressure derivatives in the construction of the flux Jacobians. The method is efficient since no sub-iterations are required to deduce the pressure and temperature from the flux variables and allows different equations of state to be easily supplied to the code. The flexibility of the EOS approach is demonstrated by implementing a high order TVD upwinding scheme based upon flux differencing and Van Leer's flux vector splitting. The EOS approach is demonstrated by computing the hypersonic flow through the corner region of two mutually perpendicular flat plates and through a simplified model of a scramjet module gap-seal configuration.

Coirier, William J.↗
NASA NTRS2002

Probabilistic Analysis of Gas Turbine Field Performance

A gas turbine thermodynamic cycle was computationally simulated and probabilistically evaluated in view of the several uncertainties in the performance parameters, which are indices of gas turbine health. Cumulative distribution functions and sensitivity factors were computed for the overall thermal efficiency and net specific power output due to the thermodynamic random variables. These results can be used to quickly identify the most critical design variables in order to optimize the design, enhance performance, increase system availability and make it cost effective. The analysis leads to the selection of the appropriate measurements to be used in the gas turbine health determination and to the identification of both the most critical measurements and parameters. Probabilistic analysis aims at unifying and improving the control and health monitoring of gas turbine aero-engines by increasing the quality and quantity of information available about the engine's health and performance.

Gorla, Rama S. R.↗
DOE OSTI2020

What fuel properties enable higher thermal efficiency in spark-ignited engines?

The Co-Optimization of Fuels and Engines (Co-Optima) initiative from the US Department of Energy aims to co-develop fuels and engines in an effort to maximize energy efficiency and the utilization of renewable fuels. Many of these renewable fuel options have fuel chemistries that are different from those of petroleum-derived fuels. Because practical market fuels need to meet specific fuel-property requirements, a chemistry-agnostic approach to assessing the potential benefits of candidate fuels was developed using the Central Fuel Property Hypothesis (CFPH). The CFPH states that fuel properties are predictive of the performance of the fuel, regardless of the fuel's chemical composition. In order to use this hypothesis to assess the potential of fuel candidates to increase efficiency in spark-ignition (SI) engines, the individual contributions towards efficiency potential in an optimized engine must be quantified in a way that allows the individual fuel properties to be traded off for one another. This review article begins by providing an overview of the historical linkages between fuel properties and engine efficiency, including the two dominant pathways currently being used by vehicle manufacturers to reduce fuel consumption. Then, a thermodynamic-based assessment to quantify how six individual fuel properties can affect efficiency in SI engines is performed: research octane number, octane sensitivity, latent heat of vaporization, laminar flame speed, particulate matter index, and catalyst light-off temperature. The relative effects of each of these fuel properties is combined into a unified merit function that is capable of assessing the fuel property-based efficiency potential of fuels with conventional and unconventional compositions.

42 ENGINEERING↗
NASA NTRS1996

Computation of Kinetics for the Hydrogen/Oxygen System Using the Thermodynamic Method

A new method for predicting chemical rate constants using thermodynamics has been applied to the hydrogen/oxygen system. This method is based on using the gradient of the Gibbs free energy and a single proportionality constant D to determine the kinetic rate constants. Using this method the rate constants for any gas phase reaction can be computed from thermodynamic properties. A modified reaction set for the H/O system is determined. A11 of the third body efficiencies M are taken to be unity. Good agreement was obtained between the thermodynamic method and the experimental shock tube data. In addition, the hydrogen bromide experimental data presented in previous work is recomputed with M's of unity.

Marek, C. John↗
DOE OSTI2024

Development of a self-lubricating high-efficiency hybrid seal composed of carbon nanotube-coated metal meshes for CSP turbomachinery (SETO CPS #36333 Final Report)

In turbomachinery, internal leakage flow accounts for up to 3% of the total thermodynamic cycle energy loss. Tradeoff must be made between the sealing efficiency (smaller clearance) and the friction and wear issues for interfering with the shaft (larger clearance). This ORNL-Danfoss joint effort developed a novel hybrid seal composed of carbon nanotube (CNT)-coated metal meshes. The CNT growth process was based on a self-catalyzing chemical vapor deposition and these multiwall CNTs were well aligned with high crystallinity. This hybrid material structure takes advantage of the CNT’s low-friction nature and uses the metal mesh as an extendable backbone. Full-scale experimental seals were designed, fabricated, and optimized for sealing performance and durability. The CNT-coated metal mesh seal demonstrated superior gas sealing efficiency to the baseline labyrinth seal and significantly improved shaft surface protection compared with the state-of-the-art superalloy brush seal on the static rig and full-scale compressor dynamometer tests. The CNT-metal mesh seal is low-cost and scalable and can potentially benefit wide applications, including CSP and other power generation, marine, automotive, and HVAC.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Experiments on a vapor compression air conditioner with liquid desiccants for efficient dehumidification

Buildings require air conditioning systems that not only cool and dehumidify supply air but also provide sufficient ventilation to ensure indoor air quality and occupant comfort. However, standard recirculation systems-which introduce about a 10 % to 20 % fraction of outdoor air-often fail to deliver air that is precisely cooled and dry, particularly because 80-90 % of the ventilation cooling load is latent. Mixing humid ventilation air with recirculated indoor air increases the energy and costs required to condition the air to comfortable levels. Dedicated outdoor air systems (DOASs) are designed to handle this latent dominated ventilation load and thus need to have efficient humidity removal. Many cooling cycles can perform this task. Here we describe a liquid desiccant DOAS, which combines a vapor compression cycle and a liquid desiccant absorber and desorber pair. We present its performance at 26 operating conditions and a thermodynamic model which can accurately predict the moisture removal efficiency. The model's performance predictions have a mean percentage error of 2.5 % and a coefficient of variation of the root mean square error of 7.5 %. We also compare the performance of this vapor-compression-coupled liquid desiccant system with a standard vapor compression system with the same components but no liquid desiccant. For the 26 conditions tested in this study, this comparison shows that adding liquid desiccants lowers the required evaporator cooling load by 21 %, allows for 25 % lower compressor volumetric capacity, and 25 % lower electricity use. Future work will leverage this model to quantify the reduction in annual electricity use across different climates, including the need for a standard vapor compression system to reheat the air during some of the year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Solventless, Ambient-Pressure Production of Bio-Based Lactones Over Earth-Abundant, Mixed Metal Oxide Catalysts for Circular Polyesters

Transitioning to a circular plastics economy will require use of renewable feedstocks, energy-efficient processes, and closed-loop recyclable polymers, such as polyesters. A key challenge lies in sustainably sourcing monomers used to make recyclable polyesters. This work presents a catalytic platform utilizing earth-abundant Cu(x)Ca(1-x)O mixed metal oxides for the oxidative dehydrocyclization of bio-based diols to lactones, which are advantaged for energy-efficient ring-opening polymerization. Operating below 200 degrees C, at ambient pressure, and without solvent, the process uses air as the sole oxidant, achieving high yields of lactones across a broad substrate scope of C4-8 diols in the liquid phase. The oxidative dehydrocyclization reaction is thermodynamically downhill due to water formation and energy-efficient compared to incumbent, non-redox pathways utilized in fossil carbon-based industrial processes for lactone production. Mechanistic studies reveal facile redox cycling of Cu2+-O(Ca2+)-Cu2+ interfacial sites unique to the developed catalyst. Techno-economic analysis and life cycle assessment estimate 40% lower energy demand and 15% lower GHG intensity per mass of butyrolactone produced compared to the fossil carbon-based route. Liquid-phase oxidative dehydrocyclization offers a promising approach for scalable lactone production from renewable, bio-based diols to enable circular polyesters.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Editors’ Choice—Molten Salt Electrolysis in Chloride Melts for Energy-Efficient Iron Metal Production

This study explores chloride molten salt electrolysis (CMSE) as a promising route for energy-efficient iron metal (Fe) production. Moderate temperature (500 °C) LiCl-KCl molten salts offer excellent thermodynamic stability, high ionic conductivity and diffusivity, and high solubility for FeCl 3 , thereby enabling efficient Fe metal extraction at high electrowinning rates. Here, we demonstrate the two essential steps for converting taconite ore into Fe metal. First, Fe 2 O 3 from taconite pellets was selectively leached in HCl yielding a high-purity FeCl 3 aqueous solution, while the gangue components settled at the bottom. Then, anhydrous FeCl 3 was electrolyzed in a LiCl-KCl eutectic molten salt at 500 °C at high current density (1 A cm −2 ) and at high Coulombic efficiency (>85%). Analysis of the electrowon Fe deposits revealed dendritic structures with purity of >99 wt%, which could be further improved to nearly 100 wt% through arc re-melting. CMSE offers low specific energy consumption (3.7 kWhr kg −1 ), competitive with H 2 -DRI and other electrolytic approaches being pursued globally. Our findings underscore the potential of CMSE as an energy-efficient route for electrosynthesis of Fe metal.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A systematic analysis of phase stability in refractory high entropy alloys utilizing linear and non-linear cluster expansion models

We report that obtaining surrogate models for various types of materials has long been a goal of computational material scientists and physicists, as they allow the calculation of thermodynamic quantities and properties of interest far more efficiently than DFT. Many surrogate models, including interatomic potentials generated by machine learning algorthims and cluster expansions, rely on incorporating many-body interactions such as three-body or four-body interactions. When utilizing machine learning algorithms for interatomic potentials, it is common to start with two-body interactions and systematically include three- and four-body clusters to improve the fit, stopping when including the additional clusters does not measurably change the predictive power of the model. In cluster expansions for FCC and related lattices, it is often enough to include mostly two-body interactions and a handful of three-body interactions. However, it remains to be seen whether those will be enough in the case of lattices with a lower packing fraction, such as BCC.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Towards chemical equilibrium in thermochemical water splitting. Part 1: Thermal reduction

The efficiency of many processes strongly depends on their thermodynamic reversibility, i.e., proximity to equilibrium throughout the process. In thermochemical cycles for water and/or carbon dioxide splitting, thermochemical air separation, and thermochemical energy storage, operating near equilibrium means that the oxygen chemical potential of the solid and gas phases must not differ significantly. Herein we show that approaching this ideal is possible in thermal reduction only if the reaction step occurs at a specific, reaction coordinate- and material-dependent temperature. The resulting thermal reduction temperature profile also depends on the ratio of gas and solid flows.

08 HYDROGEN↗
DOE OSTI2021

Nickel B-site substitution in bulk Sr 1-x Ca x FeO 3 perovskite oxygen carriers: Benefits and limitations

We report oxygen (O 2 ) storage materials often rely on the presence of cobalt (Co) to reduce the thermodynamic penalty and increase the kinetics necessary for efficient O 2 storage and release. In this work, we investigate nickel (Ni) as an alternative B-site dopant in Sr 1-x Ca x FeO 3 to identify Co-free carriers that still show improved kinetics at low temperatures. In fact, we show a substantial increase in the reversible O2 release rate through mild Ni B-site substitution (y = 0.06) in select Sr 1-x Ca x Fe 1-y Ni y O 3 systems at 400 to 500 °C, reaching 2.00 wt.% O 2 release up to approximately 75% faster than Ni-free systems. To explain the role of Ni in these systems, we use density functional theory to calculate the O 2 vacancy (V O ) formation energy from separate metal-oxygen (M-O) bonding and relaxation components. We computationally show elongated Ni-O bonds are directly responsible for the decrease in V O upon Ni substitution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Ethylene oxidation on unpromoted silver catalysts: Reaction pathway and selectivity analysis using DFT calculations

Chemical conversions in catalytic partial oxidation processes of light hydrocarbons are responsible for the production of numerous industrial chemicals, plastics, and intermediates. These processes are relatively expensive to perform, and are typically operated at high thermodynamic inefficiency, so the development of novel, highly efficient catalysts would prove to be very cost effective. Herein, our study focused on surface catalytic mechanisms of the ethylene oxide (EO) formation process. Periodic plane-wave Density Functional Theory (DFT) methods were used to analyze related reaction mechanisms on the Ag(111) surface facet with low coverage. Energetic changes of related species and pathways were calculated. Key surface species are identified to suggest the factors for the observed selectivity during EO formation. Our results are consistent with previous kinetic modeling efforts in the literature which did not employ DFT analysis. Lastly, our study demonstrates how fundamental theoretical investigations and multi-scale modeling techniques are currently impacting the advancement of rational catalyst design and microkinetic modeling techniques in the light hydrocarbon processing industry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials

As a corollary of the rapid advances in computing, ab initio simulation is playing an increasingly important role in modeling materials at the atomic scale. Two strategies are possible, ab initio Monte Carlo (AIMC) and molecular dynamics (AIMD) simulation. The former benefits from exact sampling from the correct thermodynamic distribution, while the latter is typically more efficient with its collective all-atom coordinate updates. In this study, using a relatively simple test model comprised of helium and argon, we show that AIMC can be brought up to, and even above, the performance levels of AIMD via a hybrid nested sampling/machine learning (ML) strategy. Here, ML provides an accurate classical reference potential (up to three-body explicit interactions) that can pilot long collective Monte Carlo moves that are accepted or rejected in toto à la nested Monte Carlo (NMC); this is in contrast to the single move nature of a naive implementation. Our proposed method only requires a small up front expense from evaluating the ab initio energies and forces of (100) random configurations for training. Importantly, our method does not totally rely on the trained, assuredly imperfect, interaction. We show that high performance and exact sampling at the desired level of theory can be realized even when the trained interaction has appreciable differences from the ab initio potential. Remarkably, at the highest levels of performance realized via our approach, a pair of statistically uncorrelated atomic configurations can be generated with (1) ab initio calculations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Techno-Economic Analysis of CSP Incorporating sCO2 Brayton Power Cycles: Trade-Off Between Cost and Performance

Concentrating solar power (CSP) plants, thanks to the implementation of cost-competitive thermal energy storage, represent a dispatchable zero-emission alternative to traditional fossil fuel power plants. Next generation solar towers are expected to adopt high temperature receivers (>700 degrees C) coupled to sCO2-based power blocks, which optimal design is generally pushed towards the maximum cycle efficiency, often neglecting the economic impact with the justification that the main share of the capital cost is represented by the heliostat field. As result, the scientific literature lacks in comprehensive studies on techno-economic evaluation of CSP+sCO2 power plants addressing the important correlation that exists between system cost and performances. This work provides a preliminary techno-economic analysis of a solar power tower comparing four different cycle configurations for the sCO2 power block. Results have been reported on a Pareto front, highlighting the tradeoff between the plant investment cost and the solar-to-electricity plant efficiency. The trends of the optimization variables and cycle results have been reported to give useful insights about proper assumptions for the sCO2 power block design. The recompressed cycle with intercooling resulted as the most promising configuration and it has been further analyzed through a comparison of different solutions on the Pareto front. The cost breakdown of the sCO2 power block has been reported to highlight which components have the greatest impact on the overall plant cost and how they vary along the optimal solutions front. Eventually, the optimization has been repeated introducing a correlation to compute the turbomachinery isentropic efficiencies, to investigate their effect on the techno- economic analysis.

concentrated solar power↗
DOE OSTI2022

Characterizing coherences in chemical dynamics with attosecond time-resolved x-ray absorption spectroscopy

Coherence can drive wave-like motion of electrons and nuclei in photoexcited systems, which can yield fast and efficient ways to exert materials’ functionalities beyond the thermodynamic limit. The search for coherent phenomena has been a central topic in chemical physics although their direct characterization is often elusive. Here, we highlight recent advances in time-resolved x-ray absorption spectroscopy (tr-XAS) to investigate coherent phenomena, especially those that utilize the eminent light source of isolated attosecond pulses. The unparalleled time and state sensitivities of tr-XAS in tandem with the unique element specificity render the method suitable to study valence electronic dynamics in a wide variety of materials. The latest studies have demonstrated the capabilities of tr-XAS to characterize coupled electronic–structural coherence in small molecules and coherent light–matter interactions of core-excited excitons in solids. Furthermore, we address current opportunities and challenges in the exploration of coherent phenomena, with potential applications for energy- and bio-related systems, potential crossings, strongly driven solids, and quantum materials, and so with the ongoing developments in both theory and light sources, tr-XAS holds great promise for revealing the role of coherences in chemical dynamics.

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

Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials

Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. In conclusion, our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly, and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

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

Group-equivariant autoencoder for identifying spontaneously broken symmetries

We introduce the group-equivariant autoencoder (GE autoencoder), a deep neural network (DNN) method that locates phase boundaries by determining which symmetries of the Hamiltonian have spontaneously broken at each temperature. We use group theory to deduce which symmetries of the system remain intact in all phases, and then use this information to constrain the parameters of the GE autoencoder such that the encoder learns an order parameter invariant to these “never-broken” symmetries. This procedure produces a dramatic reduction in the number of free parameters such that the GE-autoencoder size is independent of the system size. We include symmetry regularization terms in the loss function of the GE autoencoder so that the learned order parameter is also equivariant to the remaining symmetries of the system. By examining the group representation by which the learned order parameter transforms, we are then able to extract information about the associated spontaneous symmetry breaking. We test the GE autoencoder on the 2D classical ferromagnetic and antiferromagnetic Ising models, finding that the GE autoencoder (1) accurately determines which symmetries have spontaneously broken at each temperature; (2) estimates the critical temperature in the thermodynamic limit with greater accuracy, robustness, and time efficiency than a symmetry-agnostic baseline autoencoder; and (3) detects the presence of an external symmetry-breaking magnetic field with greater sensitivity than the baseline method. Lastly, we describe various key implementation details, including a quadratic-programming-based method for extracting the critical temperature estimate from trained autoencoders and calculations of the DNN initialization and learning rate settings required for fair model comparisons.

42 ENGINEERING↗