Computer program for design-point performance of turbojet and turbofan engine cycles
Fortran 7094 computer program for calculation of design point performance of turbojet and turbofan engine cycles, and thermodynamic equations
THERMODYNAMICS↗
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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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Technologies for Visualization in Computational Aerosciences
State-of-the-art research in computational aerosciences produces' complex, time-dependent datasets. Simulations can also be multidisciplinary in nature, coupling two or more physical disciplines such as fluid dynamics, structural dynamics, thermodynamics, and acoustics. Many diverse technologies are necessary for visualizing computational aerosciences simulations. This paper describes these technologies and how they contribute to building effective tools for use by domain scientists. These technologies include data management, distributed environments, advanced user interfaces, rapid prototyping environments, parallel computation, and methods to visualize the scalar and vector fields associated with computational aerosciences datasets.
Miceli, Kristina D.↗
A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution
Integrated computational materials engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and significantly accelerating the materials design process. However, ICME models are also computationally expensive, particularly with respect to time integration for dynamics, which hinders the ability to study statistical ensembles and thermodynamic properties of large systems for long time scales. To alleviate the computational bottleneck, we propose to model the evolution of statistical microstructure descriptors as a continuous-time stochastic process using a non-linear Langevin equation, where the probability density function (PDF) of the statistical microstructure descriptors, which are also the quantities of interests (QoIs), is modeled by the Fokker–Planck equation. In this work, we discuss how to calibrate the drift and diffusion terms of the Fokker–Planck equation from the theoretical and computational perspectives. The calibrated Fokker–Planck equation can be used as a stochastic reduced-order model to simulate the microstructure evolution of statistical microstructure descriptors PDF. Considering statistical microstructure descriptors in the microstructure evolution as QoIs, we demonstrate our proposed methodology in three integrated computational materials engineering (ICME) models: kinetic Monte Carlo, phase field, and molecular dynamics simulations.
97 MATHEMATICS AND COMPUTING↗
Experimental verification of a tank to tank He II transfer model with trade study results
A computer program has been developed to study the thermodynamics of tank to tank superfluid helium transfer. The model includes a supply and a receiver tank connected by a transfer line. The convey of He II from one tank to the other is controlled by a fountain effect pump (FEP). Phase separators are present in both the supply and receiver tank to regulate the bath temperature. Description of this model has been published elsewhere. In the present paper, data from a transfer experiment are used to verify the accuracy of this model. The experiment consisted of an FEP made of a 2-micron sintered stainless steel porous plug. Superfluid has been transferred from a liquid helium bath into a glass beaker. Bath temperatures, flowrate and heater power records are available. These results are compared to the predictions of the computer program and good agreement is found between the two. This model is very useful for the study and design of superfluid transfer systems, e.g., the Superfluid Helium Tanker (SFHT) and the Particle Astrophysics Magnet Facility (ASTROMAG).
Yuan, S. W. K.↗
Experimental and computational investigation of the bond energy of thorium dicarbonyl cation and theoretical elucidation of its isomerization mechanism to the thermodynamically most stable isomer, thorium oxide ketenylidene cation, OTh + CCO
Collision-induced dissociation (CID) of [Th,2C,2O] + with Xe is performed using a guided ion beam tandem mass spectrometer (GIBMS). The only products observed are ThCO + and Th + by sequential loss of CO ligands. The experimental findings and theoretical calculations support that the structure of [Th,2C,2O] + is the bent homoleptic thorium dicarbonyl cation, Th + (CO) 2 , having quartet spin, which is both thermodynamically and kinetically stable enough in the gas phase to be observed in our GIBMS instrument. Analysis of the kinetic energy-dependent cross sections for this CID reaction yields the first experimental determination of the bond dissociation energy (BDE) of (CO)Th + –CO at 0 K as 1.05 ± 0.09 eV. A theoretical BDE calculated at the CCSD(T) level with cc-pVXZ (X = T and Q) basis sets and a complete basis set (CBS) extrapolation is in very good agreement with the experimental result. Although the doublet spin bent thorium oxide ketenylidene cation, OTh + CCO, is calculated to be the most thermodynamically stable structure, it is not observed in our experiment where [Th,2C,2O] + is formed by association of Th + and CO in a direct current discharge flow tube (DC/FT) ion source. Potential energy profiles of both quartet and doublet spin are constructed to elucidate the isomerization mechanism of Th + (CO) 2 to OTh + CCO. The failure to observe OTh + CCO is attributed to a barrier associated with C–C bond formation, which makes OTh + CCO kinetically inaccessible under our experimental conditions. Furthermore, chemical bonding patterns in low-lying states of linear and bent Th + (CO) 2 and OTh + CCO isomers are also investigated.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Operational procedure for computer program for design point characteristics of a gas generator or a turbojet lift engine for V/STOL applications
The computer program described calculates the design-point characteristics of a gas generator or a turbojet lift engine for V/STOL applications. The program computes the dimensions and mass, as well as the thermodynamic performance of the model engine and its components. The program was written in FORTRAN 4 language. Provision has been made so that the program accepts input values in either SI Units or U.S. Customary Units. Each engine design-point calculation requires less than 0.5 second of 7094 computer time.
Krebs, R. P.↗
Thermodynamically consistent semi-compressible fluids: a variational perspective
This paper presents (Lagrangian) variational formulations for single and multicomponent semi-compressible fluids with both reversible (entropy-conserving) and irreversible (entropy-generating) processes. Semi-compressible fluids are useful in describing low-Mach dynamics, since they are soundproof. These models find wide use in many areas of fluid dynamics, including both geophysical and astrophysical fluid dynamics. Specifically, the Boussinesq, anelastic and pseudoincompressible equations are developed through a unified treatment valid for arbitrary Riemannian manifolds, thermodynamic potentials and geopotentials. By design, these formulations obey the 1st and 2nd laws of thermodynamics, ensuring their thermodynamic consistency. This general approach extends and unifies existing work, and helps clarify the thermodynamics of semi-compressible fluids. To further this goal, evolution equations are presented for a wide range of thermodynamic variables: entropy density s, specific entropy η, buoyancy b, temperature T, potential temperature θ and a generic entropic variable χ; along with a general definition of buoyancy valid for all three semicompressible models and arbitrary geopotentials. Finally, the elliptic equation for the pressure perturbation (the Lagrange multiplier that enforces semi-compressibility) is developed for all three equation sets in the case of reversible dynamics, and for the Boussinesq/anelastic equations in the case of irreversible dynamics; and some discussion is given of the difficulty in formulating it for the pseudoincompressible equations with irreversible dynamics.
97 MATHEMATICS AND COMPUTING↗
Operational procedure for computer program for design point characteristics of a compressed-air generator with through-flow combustor for V/STOL applications
The computer program described in this report calculates the design-point characteristics of a compressed-air generator for use in V/STOL applications such as systems with a tip-turbine-driven lift fan. The program computes the dimensions and mass, as well as the thermodynamic performance of a model air generator configuration which involves a straight through-flow combustor. Physical and thermodynamic characteristics of the air generator components are also given. The program was written in FORTRAN IV language. Provision has been made so that the program will accept input values in either SI units or U.S. customary units. Each air generator design-point calculation requires about 1.5 seconds of 7094 computer time for execution.
Krebs, R. P.↗
Modeling and Analysis of Stirling Power Convertors
Modeling and Analysis of Stirling Power Convertors Luis A. Rodriguez1 Steven M. Geng, Terry V. Reid, Scott D. Wilson NASA Glenn Research Center, Cleveland, OH, 44135, USA NASA Glenn’s Thermal Energy Conversion Branch is supporting the development of the next generation free-piston Stirling power convertors. American Superconductor (AMSC) and Sunpower Inc. are the two firms under contract to develop the Flexure Isotope Stirling Convertor (FISC) and the Sunpower Robust Stirling Convertor (SRSC), respectively. To comprehend and forecast convertor performance, Sage, ANSYS® Maxwell, and ANSYS® Fluent were used to model the Stirling thermodynamic cycle, alternator electromagnetics, and piston and displacer dynamics. I. Introduction Stirling convertors are being developed by NASA as a potential steady source of electrical power for NASA’s future scientific space missions. Currently, NASA Glenn Research Center has two corporations under contract, American Superconductor (AMSC) and Sunpower Inc., for the development of the next generation of free-piston Stirling convertors for dynamic radioisotope power systems. AMSC is developing the Flexure Isotope Stirling Convertor (FISC), which uses flexures to prevent side motion and rubbing of the piston. Similarly, Sunpower Inc, is developing the Sunpower Robust Stirling Convertor (SRSC). The SRSC uses gas bearings to prevent radial contact of the moving piston. As convertor development continues, it is increasingly important to understand and predict the interactions of components in the system, how they respond to one another, and how they perform as a response to changes in operating conditions. A suitable and enlightening way to demonstrate and foresee these interactions is with the use of accurate modeling software. Sage, ANSYS® Maxwell, and ANSYS® Fluent are the current modeling tools used by NASA to analytically determine convertor performance. Sage is a one-dimensional object-oriented commercial software package used for modeling and optimizing Stirling convertors for Dynamic Radioisotope Power Systems (DRPS) and it is one of the most accurate Stirling convertor codes in use by NASA. This code is the successor to GLIMPS (Globally-Implicit Stirling Cycle Simulation) and GLOP (GLIMPS Optimization) software created by Gedeon Associates [1]. Model input parameters are typically material/gas type, component physical dimensions, temperatures, frequency, charge pressure, and number of time/space nodes. Sage is used to model both the FISC’s and SRSC’s Stirling cycle thermodynamics and piston/displacer dynamics. Performance maps were created and analyzed for both power systems to better understand the relationship between the following conditions: cold-end temperature, hot-end temperature, piston/displacer amplitudes, pressure drop, and thermal input power. The synergy between these conditions will help determine parameter sensitivity. ANSYS® Maxwell was used to create a three-dimensional (3-D) axisymmetric model for both FISC and SRSC alternators. The significant physical components included in each model are the magnets, magnet carrier, outer/inner laminations, and the coil. Inputs to the model are piston amplitude, piston frequency, alternator load, coil resistance, tuning capacitance, and specific material properties. The alternator models calculate terminal voltage, current, piston/current phase, voltage/current phase, coil inductance, terminal power and efficiency. The RI2 losses, core (hysteresis and eddy) losses, and magnet/can eddy losses are also a part of the final results. ANSYS® Fluent is used to build 3-D computational fluid dynamic (CFD) models to examine the Stirling cycle thermodynamics for both the FISC and SRSC systems. Three-dimensional Computer Aided Design (CAD) models were used to create the physical components of each convertor. Steady-state simulations were conducted for hardware testing, prediction of environmental losses during testing, and generation of radiation look-up tables. The model inputs to the aforementioned analysis are the material properties and boundary thermal conditions. The steady-state model calculates temperature and heat flow distributions. Transient 3-D calculations were also part of the CFD analysis. In this study a physically reduced version of the FISC is used to obtain a prediction of available engine power. For the gas bearing SRSC, the transient effort is used to obtain a prediction of bearing pad performance and its sensitivity to micro-channel geometric variation. The model inputs to the transient simulations are the piston amplitude, displacer amplitude, frequency, displacer/piston phase angle, dynamic deforming CFD grid, temperature boundary conditions, and user defined files describing motion profile of piston/displacer. The results of the model are temperature distributions, heat distributions, and PV power produced at pre-determined conditions.
Luis A Rodriguez↗
Machine learning accelerated turbulence modeling of transient flashing jets
We report modeling the sudden depressurization of superheated liquids through nozzles is a challenge because the pressure drop causes rapid flash boiling of the liquid. The resulting jet usually demonstrates a wide range of structures, including ligaments and droplets, due to both mechanical and thermodynamic effects. As the simulation comprises increasingly numerous phenomena, the computational cost begins to increase. One way to moderate the additional cost is to use machine learning surrogacy for specific elements of the calculation. This study presents a machine learning-assisted computational fluid dynamics approach for simulating the atomization of flashing liquids accounting for distinct stages, from primary atomization to secondary breakup to small droplets using the Σ - Y model coupled with the homogeneous relaxation model. Notably, the models for thermodynamic non-equilibrium (HRM) and Σ - Y are coupled, for the first time, with a deep neural network that simulates the turbulence quantities, which are then used in the prediction of superheated liquid jet atomization. The data-driven component of this method is used for turbulence modeling, avoiding the solution of the two-equation turbulence model typically used for Reynolds-averaged Navier-Stokes simulations for these problems. Both the accuracy and speed of the hybrid approach are evaluated, demonstrating adequate accuracy and at least 25% faster computational fluid dynamics simulations than the traditional approach. This acceleration suggests that perhaps additional components of the calculation could be replaced for even further benefit. Published under an exclusive license by AIP Publishing.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Insights into Active Sites and Mechanisms of Benzyl Alcohol Oxidation on Nickel–Iron Oxyhydroxide Electrodes
The electrochemical oxidation of bio-derived molecules has recently garnered interest for its potential in opening electrified synthetic pathways toward value-added products. Herein, we investigate the electrochemical conversion of benzyl alcohol (BA) to benzaldehyde and benzoate on nickel–iron (Fe ~ 7–18%) electrodes as a model system to understand reaction mechanisms and environmental conditions that can transform these molecules. Our results indicate a strong correlation between benzyl alcohol oxidation (BAO) onset potentials and Ni(II/III) redox peak positions, highlighting the potential role that lower oxidation states of nickel, i.e., Ni 3+ , can play in BAO catalysis. Our work on the Ni 2+/3+ system complements mechanisms that involve higher oxidation states of Ni as reported by others. We note that the Ni redox position and thus BAO onset is impacted by Fe incorporation during electrochemistry from unpurified electrolytes, which can resemble standard reactor operating conditions. We perform a systematic computational investigation into BAO and provide density functional theory (DFT) insights into how the redox mechanism has been such a prominent focus of alcohol oxidations. This includes the mode of BA adsorption and the nature of the adsorption site; upon conversion of the Ni 2+ surface to active Ni 3+ via hydroxyl deprotonation, BAO is thermodynamically downhill. Our DFT study also introduces the possibility of a vacancy-driven mechanism, though expected to be less prevalent during catalysis than the redox mechanism for a Ni 3+ surface. Through the systematic investigation of experimental reaction conditions and computational free energy thermodynamics, we have gained valuable insights into BAO reaction mechanisms that inform catalytic activity. Our study opens avenues for further design and development of catalyst active sites for the oxidation of related organic molecules.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Bayesian prior construction for uncertainty quantification in first-principles statistical mechanics
First-principles statistical mechanics enables the prediction of thermodynamic and kinetic properties of materials, but is computationally expensive. Many approaches require surrogate models to calculate energies within Monte Carlo or molecular dynamics simulations. Inexpensive surrogates such as cluster expansions enable otherwise intractable calculations by interpolating data from higher accuracy methods, such as Density Functional Theory (DFT). Surrogate models introduce uncertainty into downstream calculations, in addition to any uncertainty inherent to DFT calculations. Bayesian frameworks address this by quantifying uncertainty and incorporating expert knowledge through priors. However, constructing effective priors remains challenging. This work introduces and describes practical strategies for building Bayesian cluster expansions, focusing on basis truncation, hyperparameter selection, and ground state replication. We analyze multiple basis truncation schemes, compare cross-validation to the evidence-approximation for hyperparameter optimization, and provide methods to find and enforce ground-state-preserving models through priors. Additionally, we compare the uncertainties between different approximations to DFT (LDA, PBE, SCAN) against the uncertainty introduced with the use of cluster expansion surrogate models. These approaches are demonstrated on the BCC Li x Mg 1-x and Li x Al 1-x alloys, which are both of interest for solid-state Li batteries. Our results provide guidelines for constructing and utilizing Bayesian cluster expansions, thereby improving the transparency of materials modeling. Furthermore, the approaches and insights developed in this work can be transferred to a wide range of cluster expansion surrogate models, including the atomic cluster expansion and related machine-learned interatomic potential architectures.
Alloy theory↗
Rapid Prediction of a Liquid Structure from a Single Molecular Configuration Using Deep Learning
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A Hybrid Quantum–Classical Study of Ion Adsorption at the Copper Electrode
Here, we present a set of computational studies that surveys the molecular structure and thermodynamics of simple ions at the electrode–electrolyte interface. A hybrid quantum–classical approach is used to obtain free energy profiles and solvent structure as a function of the ion’s position relative to the electrochemical liquid/solid interface and system potential. Model systems consist of a copper electrode and single halide or alkali metal ion in contact with an implicit solvent and reveal new insights into the interplay of forces that drive or resist ion absorption at the electrode/ electrolyte interface. We also expand on subtleties of absorption site preference and describe the complex relationship between the energetic barrier to absorption and the ion–electrode binding energy as a function of electrode potential.
36 MATERIALS SCIENCE↗
Solvent-Mediated Control of Nanocellulose Dispersion: An Integrated Computational and Experimental Investigation
Fibrillated cellulose derived from forestry feedstocks represents a renewable and high-strength materials platform for circular bioeconomies. However, its practical implementation is hindered by the irreversible aggregation of nanocellulose architectures, including cellulose nanofibers (CNFs). Solvent-based dispersion offers a simple and practical route to prevent CNF aggregation. Here, in this work, we integrate classical and enhanced sampling molecular dynamics (MD) simulations with experimental suspension rheology and atomic force microscopy (AFM) to elucidate how solvent environments tune CNF–CNF interactions and dispersion stability. CNF–CNF contact free energies computed from MD simulations reveal reduced aggregation in acetone/water, γ-valerolactone (GVL)/water, and tetrahydrofuran (THF)/water and pure acetone compared with pure water, reflecting stronger CNF-solvent relative to inter-CNF interactions. Correspondingly, CNF-solvent suspensions in these solvent systems exhibit stronger inter-fibril network structures and enhanced recovery compared to water, indicating improved CNF-solvent affinity. Liquid cell AFM imaging in acetone–water mixtures and in pure acetone further confirm the presence of well-dispersed CNFs. By combining multiscale computation with targeted experiments, this study establishes a rational framework for solvent design to achieve stable nanocellulose dispersions for high-strength biobased materials and efficient bioenergy conversion.
cellulose↗
Accelerated discovery of a large family of quaternary chalcogenides with very low lattice thermal conductivity
The development of efficient thermal energy management devices such as thermoelectrics and barrier coatings often relies on compounds having low lattice thermal conductivity (κ l ). Here, we present the computational discovery of a large family of 628 thermodynamically stable quaternary chalcogenides, AMM'Q 3 (A = alkali/alkaline earth/post-transition metals; M/M' = transition metals, lanthanides; Q = chalcogens) using high-throughput density functional theory (DFT) calculations. We validate the presence of low κ l in these materials by calculating κ l of several predicted stable compounds using the Peierls–Boltzmann transport equation. Our analysis reveals that the low κ l originates from the presence of either a strong lattice anharmonicity that enhances the phonon-scatterings or rattler cations that lead to multiple scattering channels in their crystal structures. Our thermoelectric calculations indicate that some of the predicted semiconductors may possess high energy conversion efficiency with their figure-of-merits exceeding 1 near 600 K. Our predictions suggest experimental research opportunities in the synthesis and characterization of these stable, low κ l compounds.
36 MATERIALS SCIENCE↗
Finding predictive models for singlet fission by machine learning
Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set.
36 MATERIALS SCIENCE↗
Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility
One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g. mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.
domain of applicability↗