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Results for “Computational thermodynamics”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A mathematical framework for thermodynamic computing with applications to chemical reaction networks

The widespread adoption of energy-intensive computing applications has led to a growing need for energy-efficient computing approaches. Thermodynamic computing offers a promising approach for low-energy computation by leveraging the intrinsic computational capabilities of physical, chemical, or biological systems. However, the mathematical foundations of thermodynamic computing require further development to fully realize the potential energy efficiencies, as well as to assess factors like noise and operational speed. In this paper, we establish a mathematical framework for utilizing thermodynamic processes to perform fundamental operations, including addition, subtraction, multiplication, and division. We highlight the use of chemical reactions as potential computational units and explore synthetic chemical and biochemical systems as practical implementations. Additionally, we demonstrate how these principles can be applied to solving complex mathematical problems, such as ordinary differential equations (ODEs) and suggest the necessary components to implement the thermodynamic computing framework using chemical reactions based in a microfluidic device. This work enhances our understanding of thermodynamic processes for natural computing as a basis for scalable, energy-efficient computation in paradigm disruptive next-generation systems.

Cannon, William R. [Pacific Northwest National Lab↗
DOE OSTI2026

Nonlinear thermodynamic computing out of equilibrium

We present the design for a thermodynamic computer that can perform arbitrary nonlinear calculations in or out of equilibrium. Simple thermodynamic circuits, fluctuating degrees of freedom in contact with a thermal bath and confined by a quartic potential, display an activity that is a nonlinear function of their input. Such circuits can therefore be regarded as thermodynamic neurons, and can serve as the building blocks of networked structures that act as thermodynamic neural networks, universal function approximators whose operation is powered by thermal fluctuations. We simulate a digital model of a thermodynamic neural network, and show that its parameters can be adjusted by genetic algorithm to perform nonlinear calculations at specified observation times, regardless of whether the system has attained thermal equilibrium. This work expands the field of thermodynamic computing beyond the regime of thermal equilibrium, enabling fully nonlinear computations, analogous to those performed by classical neural networks, at specified observation times.

Whitelam, Stephen [Lawrence Berkeley National Labo↗
DOE OSTI2026

Generative Thermodynamic Computing

Here, we introduce a generative modeling framework for thermodynamic computing, in which structured data are synthesized from noise by the natural time evolution of a physical system governed by Langevin dynamics. While conventional diffusion models use neural networks to perform denoising, here the information needed to generate structure from noise is encoded by the dynamics of a thermodynamic system. Training proceeds by maximizing the probability with which the computer generates the reverse of a noising trajectory, which ensures that the computer generates data with minimal heat emission. We demonstrate this framework within a digital simulation of a thermodynamic computer. If realized in analog hardware, such a system would function as a generative model that produces structured samples without the need for artificially injected noise or active control of denoising.

Whitelam, Stephen [Lawrence Berkeley National Labo↗
NASA NTRS1994

Computing Thermodynamic And Transport Properties Of Air

EQAIRS computer program is set of FORTRAN 77 routines for computing thermodynamic and transport properties of equilibrium air for temperatures from 100 to 30,000 K. Computes properties from 11-species, curve-fit mathematical model. Successfully implemented on DEC VAX-series computer running VMS, Sun4-series computer running SunOS, and IBM PC-compatible computer running MS-DOS.

Thompson, Richard A.↗
NASA NTRS2001

Computational Thermodynamic Study to Predict Complex Phase Equilibria in the Nickel-Base Superalloy Rene N6

A previous study by Ritzert et al. on the formation and prediction of topologically closed packed (TCP) phases in the nickel-base superalloy Rene' N6 is re-examined with computational thermodynamics. The experimental data on phase distribution in forty-four alloys with a composition within the patent limits of the nickel-base superalloy Rene' N6 provide a good basis for comparison to and validation of a commercial nickel superalloy database used with ThermoCalc. Volume fraction of the phases and partitioning of the elements are determined for the forty-four alloys in this dataset. The baseline heat treatment of 400 h at 1366 K was used. This composition set is particularly interesting since small composition differences lead to dramatic changes in phase composition. In general the calculated values follow the experimental trends. However, the calculations indicated no TCP phase formation when the experimental measurements gave a volume percent of TCP phase less than 2 percent. When TCP phases were predicted, the calculations under-predict the volume percent of TCP phases by a factor of 2 to 8. The calculated compositions of the gamma and gamma' phases show fair agreement with the measurements. However, the calculated compositions of the P Phase do not agree with those measured. This may be due to inaccuracies in the model parameters for P phase and/or issues with the microprobe analyses of these phases. In addition, phase fraction diagrams and sigma and P phase solvus temperatures are calculated for each of the alloys. These calculations indicate that P phase is the primary TCP phase formed for the alloys considered here at 1366 K. Finally, a series of isopleths are calculated for each of the seven alloying elements. These show the effect of each alloying element on creating TCP phases.

Copland, Evan H.↗
NASA NTRS1992

Computing Thermodynamics Of Cryostorage Tanks In Orbit

No-vent filling, boiloff, and other phenomena predicted. Cryogenic On-Orbit Liquid Analytical Tool (COOLANT) computer program developed to analyze thermodynamic processes required in maintaining and utilizing cryogen depot tank in microgravity environment. Code contains four major analysis modules, tank-geometry module, and data base of properties of fluids and other materials. COOLANT is modular, so algorithms and data bases upgraded and expanded as required. Written in FORTRAN 77.

Brown, N.↗
NASA NTRS1993

Computing Thermodynamic And Transport Properties

CET89 calculates compositions in chemical equilibrium and properties of mixtures of any chemical system for which thermodynamic data available. Provides following options: obtains chemical-equilibrium compositions and corresponding thermodynamic mixture properties for assigned thermodynamic states; calculates dilute-gas transport properties of complex chemical mixtures; obtains Chapman-Jouguet detonation properties for gaseous mixtures; calculates properties of incident and reflected shocks in terms of assigned velocities; and calculates theoretical performance of rocket for both equilibrium and frozen compositions during expansion. Rocket performance based on optional models of finite or infinite area combustor.

Mcbride, B.↗
NASA NTRS1994

Program Computes Thermodynamic Functions

PAC91 is latest in PAC (Properties and Coefficients) series. Two principal features are to provide means of (1) generating theoretical thermodynamic functions from molecular constants and (2) least-squares fitting of these functions to empirical equations. PAC91 written in FORTRAN 77 to be machine-independent.

Mcbride, Bonnie J.↗
DOE OSTI2024

Computational thermodynamics-guided alloy design and phase stability in CoCrFeMnNi-based medium-and high-entropy alloys: An experimental-theoretical study

A computational thermodynamics approach has been employed to design CoCrFeMnNi-based medium- and high-entropy alloys (M/HEAs) with systematically varied compositions (Co(( 80-X )/ 2 )Cr(( 80-X )/ 2 )Fe X Mn 10 Ni 10 with x = 30, 40, and 50 at.%) and phase stability. Since the formation of sigma phase, usually brittle and undesirable, is a common concern, when this class of alloys is subjected to elevated temperatures (600–1000 °C), predicting its formation becomes essential. Thus, its formation and the phase equilibria were studied using the CALPHAD method, and two empirical methods, namely, valence electron concentration (VEC) and paired sigma-forming element (PSFE). Isothermal aging treatments at 900–1100 °C for 20 h were performed, since CALPHAD and VEC/PSFE predictions diverged. Both prediction methods were compared with experimental characterization by a combination of scanning electron microscopy and high-energy synchrotron X-ray diffraction. In conclusion, the predictions from the VEC/PSFE and CALPHAD calculations (depending on the database used) were shown to be quite accurate.

36 MATERIALS SCIENCE↗
DOE OSTI2026

ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling

Traditional entropy-based methods—such as cross-entropy loss in classification problems—have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe3Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature–pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.

36 MATERIALS SCIENCE↗
NASA NTRS1986

A computer aided thermodynamic approach for predicting the formation of Z-DNA in naturally occurring sequences

The ease with which a particular DNA segment adopts the left-handed Z-conformation depends largely on the sequence and on the degree of negative supercoiling to which it is subjected. We describe a computer program (Z-hunt) that is designed to search long sequences of naturally occurring DNA and retrieve those nucleotide combinations of up to 24 bp in length which show a strong propensity for Z-DNA formation. Incorporated into Z-hunt is a statistical mechanical model based on empirically determined energetic parameters for the B to Z transition accumulated to date. The Z-forming potential of a sequence is assessed by ranking its behavior as a function of negative superhelicity relative to the behavior of similar sized randomly generated nucleotide sequences assembled from over 80,000 combinations. The program makes it possible to compare directly the Z-forming potential of sequences with different base compositions and different sequence lengths. Using Z-hunt, we have analyzed the DNA sequences of the bacteriophage phi X174, plasmid pBR322, the animal virus SV40 and the replicative form of the eukaryotic adenovirus-2. The results are compared with those previously obtained by others from experiments designed to locate Z-DNA forming regions in these sequences using probes which show specificity for the left-handed DNA conformation.

Non-NASA Center↗
NASA NTRS2012

Compositional Effects on Nickel-Base Superalloy Single Crystal Microstructures

Fourteen nickel-base superalloy single crystals containing 0 to 5 wt% chromium (Cr), 0 to 11 wt% cobalt (Co), 6 to 12 wt% molybdenum (Mo), 0 to 4 wt% rhenium (Re), and fixed amounts of aluminum (Al) and tantalum (Ta) were examined to determine the effect of bulk composition on basic microstructural parameters, including gamma' solvus, gamma' volume fraction, volume fraction of topologically close-packed (TCP) phases, phase chemistries, and gamma - gamma'. lattice mismatch. Regression models were developed to describe the influence of bulk alloy composition on the microstructural parameters and were compared to predictions by a commercially available software tool that used computational thermodynamics. Co produced the largest change in gamma' solvus over the wide compositional range used in this study, and Mo produced the largest effect on the gamma lattice parameter and the gamma - gamma' lattice mismatch over its compositional range, although Re had a very potent influence on all microstructural parameters investigated. Changing the Cr, Co, Mo, and Re contents in the bulk alloy had a significant impact on their concentrations in the gamma matrix and, to a smaller extent, in the gamma' phase. The gamma phase chemistries exhibited strong temperature dependencies that were influenced by the gamma and gamma' volume fractions. A computational thermodynamic modeling tool significantly underpredicted gamma' solvus temperatures and grossly overpredicted the amount of TCP phase at 982 C. Furthermore, the predictions by the software tool for the gamma - gamma' lattice mismatch were typically of the wrong sign and magnitude, but predictions could be improved if TCP formation was suspended within the software program. However, the statistical regression models provided excellent estimations of the microstructural parameters based on bulk alloy composition, thereby demonstrating their usefulness.

MacKay, Rebecca A.↗
NASA NTRS2005

Computing the Thermodynamic State of a Cryogenic Fluid

The Cryogenic Tank Analysis Program (CTAP) predicts the time-varying thermodynamic state of a cryogenic fluid in a tank or a Dewar flask. CTAP is designed to be compatible with EASY5x, which is a commercial software package that can be used to simulate a variety of processes and equipment systems. The mathematical model implemented in CTAP is a first-order differential equation for the pressure as a function of time.

Willen, G. Scott↗
DOE OSTI2024

Physics-coupled data-driven design of high-temperature alloys

We present a materials design loop, which streamlines physics-coupled machine learning (ML) surrogate models to discover new alloy chemistries with improved properties. The efficacy is demonstrated by discovering a high-temperature alumina-forming austenitic (AFA) stainless steel with enhanced creep, followed by experimental validation. The ML models have been trained using a well-curated, highly consistent experimental dataset augmented with synthetic microstructural features from a computational thermodynamic approach. We have populated a large number of hypothetical AFA alloys to explore the high-dimensional composition space and have predicted their creep properties by providing the same synthetic input features obtained from the trained ML models. Uncertainties from the ML training were taken as thresholds for truncating predicted results to identify alloys with improved or deteriorated creep. Individual elemental compositions have been determined via probability density distribution analysis from the group of alloys at the top and bottom of the predicted creep values for further virtual and experimental validations. In conclusion, we anticipate that this workflow can be applied to screen desired conditions, such as chemistry and processing parameters, in high-dimensional space through physics-guided data analytics.

Alloy design↗
NASA NTRSDate not supplied

Uncertainty Quantification of Classical Theories of Dendritic Growth Kinetics Applied to Nickel-Based Alloys

The solidification velocity in a model nickel-alloy single crystal during laser spot melting was recently characterized using synchrotron X-ray imaging. The measured solidification velocity was found to exceed the absolute stability threshold predicted by the Kurz-Giovanola-Trivedi (KGT) model. The discrepancies between the model and experiments motivate the further assessment of accurate material properties. This work quantifies the impact material property uncertainty has on model predictions of the absolute stability threshold velocities. Properties from the literature are reviewed and compared to those calculated using computational thermodynamics to provide uncertainty estimates on input properties to the KGT model. Global sensitivity analysis is used to quantify the influence of each uncertain input property on the predicted threshold velocity. This work supports the understanding of the nickel-alloy solidification during powder bed fusion additive manufacturing and identifies the solidification material properties that are the most important to assess from first-principles computations and experiments.

Computational thermodynamics↗