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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Sample environment effects on synchrotron-measured temperature profiles in an approximant of optical floating zone crystal growth

Even though the growth of crystals using optical floating zone furnaces has had an immense scientific impact, the implementation of this method remains more of an art than a science due to the difficulty of obtaining quantitative information about the sample thermal profile during crystal growth. Building on recent work demonstrating that in situ synchrotron studies can be used to map sample rod temperatures during heating, investigations were carried out to better understand how the sample environment affects the sample temperature profile. Through a combination of experimental studies and modeling efforts, it is shown that the environment in the furnace can strongly influence the sample temperature at the lamp focus, the steepness of the vertical temperature gradient, and the timescale required for sample heating and cooling - effects which can combine to produce a strong history-dependence to sample temperature profiles. It is demonstrated that the furnace effects can be effectively captured in thermal models, allowing both the steady-state and time-dependent behavior of the sample to be accurately reproduced with predictive models, and providing a launching point for improved furnace designs that can more readily deliver desired thermal profiles.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The onset of alloying in Cu-Ni powders under high-shear consolidation

Friction consolidation (FC) is a solid phase processing methodology that densifies a material through high-shear deformation and pressure at elevated temperature. The method has garnered interest in the scientific community because of its ability to produce extremely refined and homogeneous microstructures, off-axis texture development, and improved material properties. This manuscript presents an investigation of Cu and Ni material mixing via evaluation of morphological evolution, grain boundary characterization, and compositional analysis to provide insights on the operational alloying mechanisms occurring under high shear and elevated temperature. Using correlative microscopy techniques, we show alloying progresses via a combination of grain boundary diffusion and interfacial roughening at heterophane boundaries. Evidence supporting Cu infiltration along Ni-Ni grain boundaries along with asymmetric diffusion of Cu into Ni grains is highlighted. The resultant, consolidated microstructure was produced directly from a powder compact in ~30 s and exhibited a submicron, equiaxed grain size.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Cloning the Dirac cones of bilayer graphene to the zone center by selenium adsorption

Dirac cones can foster extraordinary electronic effects, as exemplified by the case of graphene layers. Angle-resolved photoemission reveals that adsorption of selenium (Se) vapor on bilayer graphene creates a symmetric hybrid clone of the Dirac cones at the zone center. A detailed analysis aided by first-principles calculations shows that the adsorbed layer consists of an ordered array of Se 8 molecules. The uncovered cloning mechanism illustrates a method to generate electronic features of scientific and technological interests by gentle surface modification via van der Waals adsorption.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Data from Raczka et al., Interactions between microbial diversity and substrate chemistry determine the fate of carbon in soil, from Elizabeth Woods in Morgantown, West Virginia, USA

File types include: Soil respiration as 13C cumulative respiration from soil incubation experiment, qSIP (quantitative stable isotope probing) data that is "18S apes wide" which is the fungal atom percent excess or how much the fungi took up of the labeled litter and "16S apes wide" which is the bacterial atom percent excess or how much the bacteria took up of the labeled litter, metabolomic data from the incubated soil samples called "Metab Soil Unique Filtered", which is the metabolomic data that had an initial filter from the raw data, and lipidomic data from the incubated soil samples called "All lipids soil". These files were used according to methods in Raczka et al. 2021 Scientific Reports. Headers show soil type (Oak, Poplar) and litter type (also called substrate here, which is labeled as no litter (control), poplar, and oak). This research was done to understand interactions between ecosystem-level processes and microbial diversity that impacts the composition of decomposition products that can form stable soil organic matter.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

FAIR data infrastructure and tools for AI-assisted streamflow prediction

Focal Area(s) Areas: We discuss how the integration of AI into Earth Science models can impact streamflow predictions at both the science and data levels. Doing so, we address cross-cutting needs related to the goal of making data FAIR (Findable, Accessible, Interoperable, and Re-usable [1]) for seamless use with Artificial Intelligence/Machine Learning (AI/ML) in Earth System Science at DOE. A novel idea is that AI/ML itself can help with the FAIR data goal and address issues in targeted areas e.g. missing data, data quality and reduction. In addition, the interpretability of results obtained with new AI methods is poised to impact broader scientific challenges in hydrology..

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

HRMT-67 Radiate II Material Studies

This poster covers the current status of the HRMT-67 experiment conducted in August of 2025. The poster contains information on the scientific motivation, experimental assembly, data acquisition methods including some data that was recorded as reference, and the next steps for the experiment concerning Post Irradiation Examination (PIE) work.

Peterson, Q. [Fermilab]↗
DOE OSTI2020

Measure Utility, Gain Trust: Practical Advice for XAI Researchers

Research into explanation of machine learning models, i.e. explainable AI (XAI), has seen a sympathetic exponential growth alongside deep artificial neural networks throughout the past decade. For historical reasons explanation and trust have been intertwined. However this focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that lead to more defensible scientific knowledge about people and explanations. To address this, we contribute a practical path forward for researchers in the XAI field. We recommend researchers focus on the utility and impact of their explanations instead of trust. We outline five broad use cases where explanations are useful and, for each, we describe pseudo-experiments that rely on objective empirical measurements and falsifiable hypotheses. We believe that this experimental rigor is necessary to contribute to scientific knowledge in the field of XAI.

Davis, Brittany F.↗
DOE OSTI2023

Trustworthy Physics-Informed Deep Learning for Predictive Scientific Computing

This project has developed powerful trustworthy physics-informed deep learning (TPiDL) models and methods to fundamentally enhance the scale and power of computational modeling in the scientific and engineering domains. Deep learning (DL) has radically advanced the state-of-the-art in machine learning, computer vision, natural language processing, and also scientific computing. Nevertheless, progress has been driven almost entirely by empirical observations, hacks, and tricks. Under the support of this project, the graph operator learning tools and advanced trustworthy physical informed neural networks have been developed. In addition, stochastic gradient replica-exchange Markov Chain Monte Carlo (MCMC) sampling algorithms have been designed to quantify the uncertainties and speed up the training of large-scale neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Nanomechanics and Electronic Structure of Organic Photovoltaics in Real Application Conditions by Advanced Scanning Probe Microscopy (Final Technical Report for Project DE-SC0018041)

This project report consists of the following three sections: A.) Investigation of Charge Transport Properties in Semiconducting Conjugated Polymer Blends Using Computational Chemistry and Experiments: This section provides unpublished but valuable research results that correlate the electronic structure of PCBM:PCDTBT organic photovoltaic systems with their current-voltage characteristics. The presentation is structured in the form of a scientific publication. B.) Scanning Probe Microscopy Methodology Development Efforts for Organic Photovoltaic Materials: This section summarizes various published works focusing on advancements in scanning probe microscopy methods. Each sub-section corresponds to a scientific publication for which the abstract and citation are provided. C.) Other Publications Supported by the Project: This section lists additional published works partially supported by the project, which are not included in the previous two sections.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Multidimensional Distributional Neural Network Output Demonstrated in Super‐Resolution of Surface Wind Speed

Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high-dimensional, correlated data. While existing methods capture either aleatoric or epistemic uncertainty, few offer closed-form, multidimensional distributions that preserve spatial correlation while remaining computationally tractable. In this work, we present a framework for training neural networks with a multidimensional Gaussian loss, generating a closed-form predictive distribution over outputs informed by non-identically distributed training data. Our approach captures aleatoric uncertainty by iteratively estimating the means and covariance matrices, and is demonstrated on a super-resolution example out-of-training-sample. We leverage a Fourier representation of the covariance matrix to stabilize network training and preserve spatial correlation. We introduce a novel regularization strategy—referred to as information sharing—that interpolates between image-specific and global covariance estimates, enabling convergence of the super-resolution downscaling network trained on image-specific distributional loss functions. This framework allows for efficient sampling, explicit correlation modeling, and extensions to more complex distribution families all without disrupting prediction performance. We demonstrate the method on a surface wind speed downscaling task and discuss its broader applicability to uncertainty-aware prediction in scientific models.

17 WIND ENERGY↗
DOE OSTI2025

In situ multi-tier auto-ignition detection applied to dual-fuel combustion simulations

Here we use an anomaly detection methodology that is centered on analyzing fourth-order joint moments (co-kurtosis), particularly focusing on its application in auto-ignition of combustion problems with large numbers of species. Unsupervised anomaly detection is challenging to generalize across problem types and domains. A recent technique, centered on analyzing information in the fourth-order joint moment co-kurtosis, has shown promise, especially for high-dimensional scientific data. In this work we present developments to the co-kurtosis based anomaly detection method needed to make it effective and scalable for large-scale distributed scientific data, such as those generated by massively parallel simulations. An in situ co-kurtosis algorithm is employed as the anomaly detection method for identifying ignition kernels in simulations of turbulent combustion. Here, we extend an existing methodology which identifies regions of the domain where anomalies are present, and add another tier of anomaly detection where the individual samples contributing to the anomaly are identified. We apply this algorithm on-the-fly to a variety of turbulent reacting flow problems and compare it to the widely used (but significantly more expensive) chemical explosive mode analysis (CEMA). We demonstrate the ability of the method to detect and identify the onset of low and high temperature ignition which can be used for computational steering, as chemical and combustion anomalies occur intermittently at spatio-temporal locations unknown a priori. Finally, we apply our lightweight in situ algorithm to an exascale high-fidelity simulation with a total of 2.4 Trillion degrees of freedom, performed using an adaptive mesh refinement solver. Furthermore, through a scalability analysis, we show that the relative computational cost of this in-situ anomaly detection algorithm compared to an iteration of the reacting flow solver is negligible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Square Kilometre Array Science Data Challenge 1: analysis and results

ABSTRACT As the largest radio telescope in the world, the Square Kilometre Array (SKA) will lead the next generation of radio astronomy. The feats of engineering required to construct the telescope array will be matched only by the techniques developed to exploit the rich scientific value of the data. To drive forward the development of efficient and accurate analysis methods, we are designing a series of data challenges that will provide the scientific community with high-quality data sets for testing and evaluating new techniques. In this paper, we present a description and results from the first such Science Data Challenge 1 (SDC1). Based on SKA MID continuum simulated observations and covering three frequencies (560, 1400, and 9200 MHz) at three depths (8, 100, and 1000 h), SDC1 asked participants to apply source detection, characterization, and classification methods to simulated data. The challenge opened in 2018 November, with nine teams submitting results by the deadline of 2019 April. In this work, we analyse the results for eight of those teams, showcasing the variety of approaches that can be successfully used to find, characterize, and classify sources in a deep, crowded field. The results also demonstrate the importance of building domain knowledge and expertise on this kind of analysis to obtain the best performance. As high-resolution observations begin revealing the true complexity of the sky, one of the outstanding challenges emerging from this analysis is the ability to deal with highly resolved and complex sources as effectively as the unresolved source population.

Bonaldi, A.↗
DOE OSTI2026

Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark—A Bayesian Inverse UQ-Based Approach for Data Assimilation

The Organisation for Economic Co-operation and Development Working Party on Nuclear Criticality Safety has proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian inverse uncertainty quantification (IUQ) employing scientific machine learning surrogate models as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of generalized linear least squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed agreement with GLLS and MOCABA for linear applications. Here, when comparing GLLS, MOCABA, and IUQ posterior predictions to computed model responses using adjusted parameters, we observe that the GLLS predictions failed to replicate the computed response distributions for nonlinear applications, while MOCABA showed near agreement, and IUQ used the computed model responses directly. We also discuss observations on why experiments with low correlation to applications can be informative to nuclear data adjustments and identify some properties useful in selecting experiments for inclusion in nuclear data adjustment. Performance in this benchmark indicates potential for Bayesian IUQ in nuclear data adjustments.

Bayesian calibration↗
DOE OSTI2025

Leveraging interpolation models and error bounds for verifiable scientific machine learning

Effective verification and validation techniques for modern scientific machine learning workflows are challenging to devise. Statistical methods are abundant and easily deployed, but often rely on speculative assumptions about the data and methods involved. Error bounds for classical interpolation techniques can provide mathematically rigorous estimates of accuracy, but often are difficult or impractical to determine computationally. Here, in this work, we present a best-of-both-worlds approach to verifiable scientific machine learning by demonstrating that (1) multiple standard interpolation techniques have informative error bounds that can be computed or estimated efficiently; (2) comparative performance among distinct interpolants can aid in validation goals; (3) deploying interpolation methods on latent spaces generated by deep learning techniques enables some interpretability for black-box models. We present a detailed case study of our approach for predicting lift-drag ratios from airfoil images. Code developed for this work is available in a public Github repository.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to purely data-driven methods, {PIML} models can be trained from additional information obtained by enforcing physical laws such as energy and mass conservation. More broadly, {PIML} models can include abstract properties and conditions such as stability, convexity, or invariance. The basic premise of {PIML} is that the integration of ML and physics can yield more effective, physically consistent, and data-efficient models. This paper aims to provide a tutorial-like overview of the recent advances in {PIML} for dynamical system modeling and control. Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of {PIML} models; and 4) physics-informed digital twins. The paper is concluded with a perspective on open challenges and future research opportunities.

Nghiem, Truong↗
DOE OSTI2022

Empirical Performance Analysis of HPC Applications with Portable Hardware Counter Metrics [Thesis]

In this dissertation, we demonstrate that it is possible to develop methods of empirical hardware-counter-based performance analysis for scientific applications running on diverse types of CPUs. Although hardware counters have been used in performance analysis for at least 30 years, the methods used are still limited to particular CPU vendors or even particular generations of CPUs from the same vendor. Our motivating hypothesis is that hardware counter-based measurements could be developed to provide consistent performance information on diverse CPU types. This dissertation proves the hypothesis was correct by demonstrating one such set of metrics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

New technologies as decision aids for the advancement of ecological risk assessment

Moore's law states that the number of transistors that can be placed on an integrated circuit doubles every two years (Moore, 1975). This has led to a steady increase in the processing power of computers over time, and technology is now enhancing and advancing software and scientific applications, which has enabled computationally intensive methods such as machine learning, data science, modeling, and simulation. The advancement of computers and data-driven algorithms is profoundly impacting people's lives. It is changing the way we work, the way we learn, and the way we interact with the world around us. Here, this editorial will discuss how scientists can benefit from the latest technology advancements and related tools by incorporating them into the ecological risk assessment (ERA) to study ecosystems as a way to create refined assessments and accelerate the turnaround times.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Microstructurally Strained Pyrochlore–Perovskite Biphasic Electrocatalysts for the Oxygen Evolution Reaction

Efficiency of water splitting for hydrogen production is often limited by the sluggish kinetics of multiple electronic transfers required in the heterogeneous oxygen evolution reaction (OER). Catalyst design for reducing the high OER overpotential remains a major scientific challenge. Lattice-strain engineering, a method for tuning the electronic structure and surface geometric configuration of active sites, may greatly affect the interaction between adsorbates and catalytic surfaces for high activity and stability. Here, in this study, we present the synthesis of biphasic oxides of YPrSrRuMnO x , which consists of distinct phases of Y 2 Ru 2 O 7 pyrochlore and (Pr 0.7 Sr 0.3 )MnO 3 perovskite, and the development of a suitable analytical approach to study the strain–catalytic property relationship. Linear sweep voltammetry results reveal that the biphasic oxide exhibits approximately 3.1 times greater mass activity and 2.4 times larger turnover frequency (TOF) than single-phase Y 2 Ru 2 O 7 in the 0.1 M HClO 4 electrolyte. The biphasic catalyst is also about 3 times more stable than the single-phase oxide under acidic conditions. X-ray photoelectron spectroscopy, nitrogen isotherm, and electrochemical surface area analyses indicate that the oxidation state, specific surface area, and electrochemical surface area do not cause enough difference in the observed enhancement of OER performance. We examined the effects of microstrain on electrocatalysis, originating from lattice mismatch between different phases, using three different structural models. Specifically, we compared the Williamson–Hall method, standard stress–strain analysis, and Rietveld refinement in analyzing the structure–property relationship. Strain mapping using geometric phase analysis (GPA) further revealed significant microstrain and lattice dislocations localized near phase boundaries in the biphasic oxide, in contrast to the uniform strain in single-phase materials. The results reveal that the increased microstrain correlates well with the improved OER performance, as the biphasic oxide catalyst exhibits 2–3 times greater microstrain than Y 2 Ru 2 O 7 pyrochlore.

electrocatalysts↗