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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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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Data-driven analysis and prediction of stable phases for high-entropy alloy design

High-entropy alloys (HEAs) represent a promising class of materials with exceptional structural and functional properties. However, their design and optimization pose challenges due to the large composition-phase space coupled with the complex and diverse nature of the phase formation dynamics. In this study, a data-driven approach that utilizes machine learning (ML) techniques to predict HEA phases and their composition-dependent phases is proposed. By employing a comprehensive dataset comprising 5692 experimental records encompassing 50 elements and 11 phase categories, we compare the performance of various ML models. Our analysis identifies the most influential features for accurate phase prediction. Furthermore, the class imbalance is addressed by employing data augmentation methods, raising the number of records to 1500 in each category, and ensuring a balanced representation of phase categories. The results show that XGBoost and Random Forest consistently outperform the other models, achieving 86% accuracy in predicting all phases. Additionally, this work provides an extensive analysis of HEA phase formers, showing the contributions of elements and features to the presence of specific phases. We also examine the impact of including different phases on ML model accuracy and feature significance. Notably, the findings underscore the need for ML model selection based on specific applications and desired predictions, as feature importance varies across models and phases. This study significantly advances the understanding of HEA phase formation, enabling targeted alloy design and fostering progress in the field of materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling

The accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as flux balance analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy. Relative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into FBA predictions of a multi-tissue, diel model of Arabidopsis thaliana’s central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from 13 C metabolic flux analysis compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps was measured using weighted averaged percent error values, and for parsimonious FBA this was 169%–180% for high light conditions and 94%–103% for low light conditions, depending on the gene expression dataset used. This fell to 10%-13% and 9%-11% upon incorporating expression data into the modeling process, which also substantially altered the predicted carbon and energy economy of the plant.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Accurate and efficient interatomic potentials are essential for molecular dynamics (MD) simulations of radiation damage, gas diffusion, and phase stability in complex ceramics such as LiAlO 2 , especially under extreme conditions relevant to tritium production. Here, we evaluate the performance of six machine-learned interatomic potentials (MLIPs), moment tensor potential (MTP), Gaussian approximation potential, deep potential (DeePMD), atomic cluster expansion (ACE), message-passing ACE (multilayer atomic cluster expansion (MACE) pretrained) and MACE (trained from-scratch), all trained on the same density functional theory dataset with inclusion of tritium. The MLIPs are benchmarked against traditional Buckingham and ReaxFF potentials in terms of energy accuracy, density predictions, thermal equilibration behavior, threshold displacement energy (E d ), tritium diffusivity, and computational cost. Among the models, MTP shows the best overall balance between efficiency and accuracy, with low force and energy errors and realistic E d values for Li and Al. The ACE and MACE (pretrained and trained from scratch) models exhibit high E d (>200 eV) and unphysical pair interactions. DeePMD underestimates Ed due to overly repulsive behavior even at equilibrium distances. All models over-estimate tritium diffusion but the pretrained MACE model behaves well during tritium-diffusion simulations up to 500 K, maintaining diffusivities in the physically consistent 10 −11 m 2 /s range. Finally, we quantify the computational cost of each potential in large-scale atomic/molecular massively parallel simulator, finding that only MTP is more efficient than traditional empirical potentials, while others are significantly more expensive. These findings explain the trade-offs between accuracy and computational cost in MLIP development and provide essential guidance for use in high-throughput radiation damage and gas diffusion simulations in nuclear ceramics.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Tokamak divertor plasma emulation with machine learning

Abstract Future tokamak devices that aim to create conditions relevant to power plant operations must consider strategies for mitigating damage to plasma facing components in the divertor. One of the goals of MAST-U tokamak operations is to inform these considerations by researching advanced divertor configurations that aid stable plasma detachment. Machine design, scenario planning and detachment control would all greatly benefit from tools that enable rapid calculation of scenario-relevant quantities given some input parameters. This paper presents a method for generating large, simulated scrape-off layer data sets, which was applied to generate a data set of steady-state Hermes-3 simulations of the MAST-U tokamak. A machine learning model was constructed using a Bayesian approach to hyperparameter optimisation to predict diagnosable output quantities given control-relevant input features. The resulting best-performing model, which is based on a feedforward neural network, achieves high accuracy when predicting electron temperature at the divertor target and carbon impurity radiation front position and runs in around 1 ms in inference mode. Techniques for interpreting the predictions made by the model were applied, and a high-resolution parameter scan of upstream conditions was performed to demonstrate the utility of rapidly generating accurate predictions using the emulator. This work represents a step forward in the design of machine learning-driven emulators of tokamak exhaust simulation codes in operational modes relevant to divertor detachment control and plasma scenario design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Physics-constrained Gaussian process model for prediction of hydrodynamic interactions between wave energy converters in an array

To improve the efficiency of wave farms and achieve maximum power generation, the layout of wave energy converters (WECs) in an array needs to be carefully designed so that the hydrodynamic interactions can be positively exploited. For this, the hydrodynamic characteristics of the WEC array in different layouts need to be calculated. However, such calculations using numerical models usually entail significant computational cost, especially for large arrays of WECs. To address the computational challenge, a physics-constrained Gaussian process (GP) model is proposed to replace the original expensive numerical model and predict the hydrodynamic characteristics of the WECs for any array layout. By exploring the relationship between the WEC array (i.e., the input) and different hydrodynamic characteristics (i.e., the output), here we summarize a set of physical constraints/features, including invariance, symmetry, and additivity. This prior knowledge about the input-output relationship is then directly embedded in the constructed GP model through the design of physics-constrained kernels. In particular, a double-sum invariant kernel is first developed to incorporate the invariance and symmetry features, and then an additive kernel is developed to incorporate the additive feature of the problem. The invariant kernel and the additive kernel are then integrated to construct the physics-constrained GP model. Compared to the standard GP model, the proposed physics-constrained GP models require less training data to achieve the desired accuracy in predicting the hydrodynamic characteristics and are also less vulnerable to the curse of dimensionality (i.e., good scalability for large arrays) due to the use of an additive kernel. The efficiency, accuracy, and scalability of the proposed approach are demonstrated through an application to predict the hydrodynamic characteristics for WEC arrays of different sizes and layouts.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

A framework for strategic discovery of credible neural network surrogate models under uncertainty

The widespread integration of deep neural networks in developing data-driven surrogate models for high-fidelity simulations of complex physical systems highlights the critical necessity for robust uncertainty quantification techniques and credibility assessment methodologies, ensuring the reliable deployment of surrogate models in consequential decision-making. Here, this study presents the Occam Plausibility Algorithm for surrogate models (OPAL-surrogate), providing a systematic framework to uncover predictive neural network-based surrogate models within the large space of potential models, including various neural network classes and choices of architecture and hyperparameters. The framework is grounded in hierarchical Bayesian inferences and employs model validation tests to evaluate the credibility and prediction reliability of the surrogate models under uncertainty. Leveraging these principles, OPAL-surrogate introduces a systematic and efficient strategy for balancing the trade-off between model complexity, accuracy, and prediction uncertainty. The effectiveness of OPAL-surrogate is demonstrated through two modeling problems, including the deformation of porous materials for building insulation and turbulent combustion flow for ablation of solid fuels within hybrid rocket motors.

42 ENGINEERING↗
DOE OSTI2024

Epigenetic drift underlies epigenetic clock signals, but displays distinct responses to lifespan interventions, development, and cellular dedifferentiation

Changes in DNA methylation with age are observed across the tree of life. The stereotypical nature of these changes can be modeled to produce epigenetic clocks capable of predicting chronological age with unprecedented accuracy. Despite the predictive ability of epigenetic clocks and their utility as biomarkers in clinical applications, the underlying processes that produce clock signals are not fully resolved, which limits their interpretability. Here, we develop a computational approach to spatially resolve the within read variability or “disorder” in DNA methylation patterns and test if age-associated changes in DNA methylation disorder underlie signals comprising epigenetic clocks. We find that epigenetic clock loci are enriched in regions that both accumulate and lose disorder with age, suggesting a link between DNA methylation disorder and epigenetic clocks. We then develop epigenetic clocks that are based on regional disorder of DNA methylation patterns and compare their performance to other epigenetic clocks by investigating the influences of development, lifespan interventions, and cellular dedifferentiation. We identify common responses as well as critical differences between canonical epigenetic clocks and those based on regional disorder, demonstrating a fundamental decoupling of epigenetic aging processes. Collectively, we identify key linkages between epigenetic disorder and epigenetic clocks and demonstrate the multifaceted nature of epigenetic aging in which stochastic processes occurring at non-random loci produce predictable outcomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Branch target filtering based on memory region access count

A branch predictor of a processor includes one or more prediction structures, including a predicted branch address and predicted branch direction, that identify predicted branches. To reduce power consumption, the branch predictor selects one or more of the prediction structures that are not expected to provide useful branch prediction information and filters the selected structures such that the filtered structures are not used for branch prediction. The branch predictor thereby reduces the amount of power used for branch prediction without substantially reducing the accuracy of the predicted branches.

Kalamatianos, John↗
DOE OSTI2025

A Data-Driven Framework for Predicting the Sorting and Screening Performance of an Integrated Biomass Feedstock Preprocessing System

The characteristics of mechanically sorted and screened lignocellulosic biomass, such as the mass contents of corn stover anatomical fractions (leaves, husks, stalks, cobs, etc.), can be used to calculate the intermediate feedstock quality attributes “yield” and “purity” that indicate the conversion efficiency of biocrude. No prior study has investigated the correlations from the characteristics of raw biomass and preprocessing unit operation parameters to those intermediate feedstock quality attributes. This work presents a data-driven framework for assessing and predicting the intermediate feedstock quality attributes in an integrated biomass feedstock preprocessing system. Our study used corn stover as a typical type of herbaceous biomass because of its abundance in the U.S. It began with data acquisition of moisture content, particle size distribution, and anatomical fractions of the materials after each unit operation in the system. The objective of this preprocessing system is to minimize husks and leaves and maximizing cobs and stalks by mechanically separating the materials into three streams via disc screen and air separator. Prototype neural network models were then developed to evaluate the feasibility of predicting process outcomes based on measurable parameters. It is found that incorporating physical constraints into these prediction models significantly enhances the accuracy of the predicted yield and purity against the ground truth data. The experimental data and model predictions indicate that decreasing throughput increases purity, while higher throughput results in lower purity. Finally, an optimization problem was introduced to search optimal combinations of feed material properties and preprocessing unit operation parameters, as the intermediate feedstock quality attributes – yield and purity, appeared to be competing factors. The study also suggests the continual need to improve the data-driven framework’s predictability by incorporating more accurate physical models to describe the dynamics in the preprocessing units such as the air separator.

09 - BIOMASS FUELS↗
DOE OSTI2023

A constitutive model for sheared dense fiber suspensions

Here we propose a constitutive model to predict the viscosity of fiber suspensions, which undergoes shear thinning, at various volume fractions, aspect ratios, and shear stresses/rates. We calibrate the model using the data from direct numerical simulation and prove the accuracy by predicting experimental measurements from the literature. We use a friction coefficient decreasing with the normal load between the fibers to quantitatively reproduce the experimentally observed shear thinning in fiber suspensions. In this model, the effective normal contact force, which is directly proportional to the bulk shear stress, determines the effective friction coefficient. A rise in the shear stress reduces the effective friction coefficient in the suspension. As a result, the jamming volume fraction increases with the shear stress, resulting in a shear thinning in the suspension viscosity. Moreover, we extend the model to quantify the effects of fiber volume fraction and aspect ratio in the suspension. We calibrate this model using the data from numerical simulations for the rate-controlled shear flow. Once calibrated, we show that the model can be used to predict the relative viscosity for different volume fractions, shear stresses, and aspect ratios. The model predictions are in excellent agreement with the available experimental measurements from the literature. The findings of this study can potentially be used to tune the fiber size and volume fraction for designing the suspension rheology in various applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A Tanks-in-Series Approach to Estimate Parameters for Lithium-Ion Battery Models

Advanced Battery Management Systems (BMS) play a vital role in monitoring, predicting, and controlling the performance of lithium-ion batteries. BMS employing sophisticated electrochemical models can help increase battery cycle life and minimize charging time. However, in order to realize the full potential of electrochemical model-based BMS, it is critical to ensure accurate predictions and proper model parameterization. The accuracy of the predictions of an electrochemical model is dependent on the accuracy of its parameters, the values of which might change with battery cycling and aging. Parameter estimation for an electrochemical model is generally challenging due to the nonlinear nature and computational complexity of the model equations. To this end, this work utilizes the recently proposed Tanks-in-Series model for Li-ion batteries (J.Electrochem. Soc., 167, 013534 (2020)) to perform parameter estimation. The Tanks-in-Series approach allows for substantially faster parameter estimation compared to the original pseudo two-dimensional (p2D) model. The objective of this work is thus to demonstrate the gain in computational efficiency from the Tanks-in-Series approach. A sensitivity analysis of model parameters is also performed to benchmark the fidelity of the Tanks-in-Series model.

25 ENERGY STORAGE↗
DOE OSTI2025

Mechanistic Fission Gas Release Uncertainty Induced by Microstructure Data

Fission Gas Release (FGR) is an important engineering safety parameter for nuclear fuel. While fuel performance modeling with BISON currently relies on mechanistic models to predict it, comparison with experimental data shows both under or over prediction depending on operation mode (steady or transient).Predicting microstructure data is essential to accurately predicts the engineering scale parameters. An important source of uncertainty in mechanistic models arises from the missing captured physics. Continuous validation and refinement of these models against experimental data are also necessary to ensure their reliability and accuracy in predicting engineering parameters.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Performance evaluation of automated data-driven feature extraction and selection methods for practical and scalable building energy consumption prediction models

Here, this study quantifies the impact of automated feature engineering methods (feature extraction and selection) on the quality and accuracy of machine learning models that predict building energy consumption. The case study compares model performance for three main scenarios: baseline (no feature extraction and selection), feature extraction only, and feature extraction combined with feature selection (filter and/or wrapper methods) for fully trained machine learning models for 200 metered/sub-metered energy measurements across 118 real buildings. For consistency, the same machine learning model architecture (a black box deep learning neural network with probabilistic forecast output) was used for all scenarios. Based on results, all feature engineering methods provided noticeable prediction accuracy improvements (e.g., 29%-68% median prediction improvement) compared to baseline scenarios. However, in this application, feature selection methods provide little practical value due to their limited performance gains and high computational cost. Smarter algorithm development supported by better computational environments will be needed before feature selection methods can reliably and efficiently improve predictive model performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Enhanced descriptor identification and mechanism understanding for catalytic activity using a data-driven framework: revealing the importance of interactions between elementary steps

We report accurate identification of descriptors for catalytic activities has long been essential to the in-depth understanding of catalysis and recently to set the basis for catalyst screening. However, commonly used methods suffer from low accuracy in predictability. This study reports an enhanced approach to accurately identify the descriptors from a kinetic dataset using a machine learning (ML) surrogate model. CO hydrogenation to methanol over Cu-based catalysts was taken as a case study. Our model captures not only the contribution from individual elementary steps but also the interaction between relevant steps within a reaction network, which was found to be essential for high accuracy. As a result, six effective descriptors are identified, which are accurate enough to ensure the trained gradient boosted regression (GBR) model for good prediction of the methanol turnover frequency (TOF) over metal (M)-doped Cu(111) model surfaces (M = Au, Cu, Pd, Pt, Ni). More importantly, going beyond the purely mathematical ML model, the catalytic role of each identified descriptor can be revealed by using model-agnostic interpretation tools, which enhances the insight into the promoting effect of alloying. The trained GBR model outperforms the conventional derivative-based methods in terms of both the predictability and the mechanism understanding. It opens alternative possibilities toward accurate descriptor-based rational catalyst optimization.

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

Modeling salt adsorption in electrical double layer for capacitive deionization

Abstract This work presents an electrical double layer (EDL) model for capacitive deionization (CDI) by intercorrelating the salt adsorption, the electrode voltage, and the surface charge density. The counterion condensation is considered a crucial contribution to salt adsorption because of the high surface charge density in the charged micropores. The counterions condense when the surface charge density exceeds a critical value, which is predicted by the counterion condensation theory. The EDL model accurately correlates the NaCl salt adsorption and electrode voltage with various surface charge densities and well predicts the salt adsorption in a wide range of external salt concentrations. The EDL model reproduces the non‐monotonic relationship between the salt adsorption and the specific surface area observed in experiments and provides an explanation from the modeling perspective. Exhibiting superior accuracy and predictability for salt adsorption, the EDL model could serve as an enabling tool contributing to the development of CDI processes.

Lin, Yu‐Jeng↗
DOE OSTI2022

Demographic composition, not demographic diversity, predicts biomass and turnover across temperate and tropical forests

The growth and survival of individual trees determine the physical structure of a forest with important consequences for forest function. However, given the diversity of tree species and forest biomes, quantifying the multitude of demographic strategies within and across forests and the way that they translate into forest structure and function remains a significant challenge. Here, we quantify the demographic rates of 1961 tree species from temperate and tropical forests and evaluate how demographic diversity (DD) and demographic composition (DC) differ across forests, and how these differences in demography relate to species richness, aboveground biomass (AGB), and carbon residence time. We find wide variation in DD and DC across forest plots, patterns that are not explained by species richness or climate variables alone. There is no evidence that DD has an effect on either AGB or carbon residence time. Rather, the DC of forests, specifically the relative abundance of large statured species, predicted both biomass and carbon residence time. Our results demonstrate the distinct DCs of globally distributed forests, reflecting biogeography, recent history, and current plot conditions. Linking the DC of forests to resilience or vulnerability to climate change, will improve the precision and accuracy of predictions of future forest composition, structure, and function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Computing x-ray absorption spectra from linear-response particles atop optimized holes

State specific orbital optimized density functional theory (OO-DFT) methods, such as restricted open-shell Kohn–Sham (ROKS), can attain semiquantitative accuracy for predicting x-ray absorption spectra of closed-shell molecules. OO-DFT methods, however, require that each state be individually optimized. In this Communication, we present an approach to generate an approximate core-excited state density for use with the ROKS energy ansatz, which is capable of giving reasonable accuracy without requiring state-specific optimization. Herein, this is achieved by fully optimizing the core-hole through the core-ionized state, followed by the use of electron-addition configuration interaction singles to obtain the particle level. This hybrid approach can be viewed as a DFT generalization of the static-exchange (STEX) method and can attain ~0.6 eV rms error for the K-edges of C–F through the use of local functionals, such as PBE and OLYP. This ROKS(STEX) approach can also be used to identify important transitions for full OO ROKS treatment and can thus help reduce the computational cost of obtaining OO-DFT quality spectra. ROKS(STEX), therefore, appears to be a useful technique for the efficient prediction of x-ray absorption spectra.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

CFD modeling of near-wall combustion and unburned methane prediction in natural gas spark ignition engines

Natural gas-powered engines play a critical role in gas drilling, compression, and transmission sectors, but methane (CH 4 ) from engine combustion slip can be significant over their lifespan, contributing to atmospheric pollution and signaling reduced engine efficiency. Here, to address this challenge, computational fluid dynamics (CFD) simulations offer valuable insights into the in-cylinder combustion process, enabling the optimization of combustion strategies and engine designs to minimize unburned CH 4 slip. This study aims to evaluate and improve combustion models for simulating the combustion process and predicting unburned CH 4 concentrations in natural gas spark-ignition (SI) engines, including engines that are part of combined reformer-engine systems. Specifically, the performance of two flamelet-based combustion models—the Extended Coherent Flame Model (ECFM) and the G-equation model—was assessed using experimental engine data collected under varying excess-air ratio (λ) conditions and fuel compositions, including natural gas and syngas blends. In addition, to enhance the predictive capabilities of the G-equation model, a flame-wall interaction (FWI) sub-model was integrated into its framework. The effects of its model parameters, such as quenching and influence distance, on combustion behavior and unburned methane predictions were analyzed in detail. The ECFM tended to predict delayed combustion phasing under diluted mixture conditions, resulting in overprediction of unburned CH 4 concentrations. In contrast, the G-equation model provided reasonable predictions of combustion pressure, while representing higher the CH 4 reduction rate across the operating condition compared to experimental data. Incorporating the FWI sub-model—with the quenching distance calculated based on a pressure-dependent relation (P -0.48 ) and a fixed influence distance of 1.5 mm—further improved the G-equation model’s accuracy in predicting CH 4 reduction rates without compromising its ability to simulate the combustion process.

Combustion model↗