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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Understanding the use of message passing interface in exascale proxy applications

The Exascale Computing Project (ECP) focuses on the development of future exascale-capable applications. Most ECP applications use the message passing interface (MPI) as their parallel programming model with mini-apps serving as proxies. This paper explores the explicit usage of MPI in such ECP proxy applications. We empirically analyze 14 proxy applications from the ECP Proxy Apps Suite. We use the MPI profiling interface (PMPI) to collect MPI usage patterns in ECP proxy apps. Our analysis shows that a small subset of features from MPI is commonly used in the proxies of exascale-capable applications, even when they reference third-party libraries. Overall, this study is intended to provide a better understanding of the use of MPI in current exascale applications. The findings can help focus software investments made for exascale systems in the MPI middleware including optimization, fault-tolerance, tuning, and hardware-offload.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High-throughput mass spectrometry analysis using immediate drop-on-demand technology coupled with an open port sampling interface

Rationale: The sampling throughput of immediate drop-on-demand technology (I.DOT) coupled with an open port sampling interface (OPSI) is limited by software communication. To enable much-needed high-throughput mass spectrometry (MS) analysis capabilities, a novel software was developed that allows for flexible sample selection from a 96-well plate and for maximized analysis throughput using I.DOT/OPSI-MS coupling. Methods: Wells of a 96-well I.DOT plate were filled with propranolol solution and were used to test maximum sampling throughput strategies to minimize analysis time. Demonstration of chemical reaction monitoring was done using acid-catalyzed ring closure of 2,3-diaminonaphthalene (DAN) with nitrite to form 2,3-naphthotriazole (NAT). Analytes were detected in positive electrospray ionization mode using selected reaction monitoring. Results: A maximum throughput of 1.54 s/sample (7.41 min/96-well plate with three technical replicates) was achieved, and it was limited by the peak width of the MS signal resulting in an occasional slight overlap between the peaks. Further, relative standard deviation was 10 ± 1% with all tested sampling strategies. Chemical reaction monitoring of DAN to NAT using nitrite was successfully accomplished with 2 s/sample throughout showing almost complete transformation in 10 min with no signal overlap. Conclusions: This work illustrates the development of a noncontact, automated I.DOT/OPSI-MS system with improved throughput achieved through an optimized software interface. Its achievable analysis time and precision make it a viable approach for drug discovery and in situ reaction monitoring studies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Extrusion parameter control optimization for DIW 3D printing using image analysis techniques

Material extrusion is a well-recognized facet of additive manufacturing that involves the fabrication of parts through the deposition of structural material from an extrusion head from a bulk supply. In the subdivision of Direct Ink Writing (DIW) additive manufacturing, challenges arise when the structural material is flowable, synchronous extrusion control and tool movement becomes critical for achieving high-quality parts with low defect populations. DIW techniques are most used in laboratory settings using expensive custom instruments and may require specialized 3D slicing software. Here, in this study, the fabrication of an inexpensive, consumer-friendly progressive cavity pump dispensing system is detailed, in which can create high-quality parts by executing G-code commands produced from a commercial slicing software. The precision and repeatability of the movement-synchronized material extrusion is demonstrated through a series of optimization schemes, entailing the alteration of various control parameters, which directly affect the extrusion properties demonstrated during a print. In situ diagnostics were implemented to evaluate the results of the established optimization experiment. Using a machine vision technique, images of the optimization prints are processed. Following this, a supervised machine learning model was trained to autonomously judge whether or not the extrusion parameters produced a passing or failing result. The machine learning scheme serves as a preliminary benchmark for future layer-by-layer evaluation of more complex DIW parts. The construction of the printer and development of in situ characterization capabilities demonstrates the ability for this printer to create high-fidelity DIW parts for a fraction of the price of other systems.

42 ENGINEERING↗
DOE OSTI2024

Single-atom materials boosting wearable orthogonal uric acid detection

Abstract Uric acid (UA) is a vital biomarker for the diagnosis and management of various health conditions, including cardiovascular diseases, gout, kidney disorders, metabolic syndrome, and wound healing. Despite significant advances in wearable sensor technology, challenges persist in developing wearable sensors that are capable of maintaining high sensitivity, selectivity, and stability. In this study, we present an epidermal sensing platform enhanced with single-atom materials (SAMs) designed for flexible and orthogonal electrochemical detection of UA. We designed and synthesized an SAM with Fe-N 5 active sites to boost the electrochemical sensing signals, integrating it with laser-engraved graphene (LEG) to fabricate a wearable SAM-based UA patch sensor. This design provides superior UA detection performance compared to sensors based on conventional nanomaterials. In addition, we enhanced the detection accuracy and range by using an orthogonal approach that combines direct oxidation through differential pulse voltammetry (DPV) along with parallel biocatalytic amperometric detection. The resulting SAM-based UA orthogonal sensor patch demonstrated exceptional performance in wearable applications through tests measuring sweat UA levels in subjects before and after consuming a purine-rich diet. Graphical Abstract

Ding, Shichao↗
DOE OSTI2025

Damage progression and failure of SiC/SiC composite tubes under hard-contact radial expansion

The response of silicon carbide (SiC) fiber-reinforced SiC matrix (SiC/SiC) composite cladding to mechanical interaction with fissile fuel is a knowledge gap that must be overcome to design and assess SiC-based cladding systems for advanced nuclear applications. This study developed the relevant mechanical testing capability and identified the failure behavior and the critical microstructural features and processing defects. Sections of SiC composite tube were subjected to a modified expansion-due-to-compression (EDC) test in an X-ray computed tomography microscope: a polyurethane plug pressed surrogate Al 2 O 3 into the inner walls of the SiC/SiC composite tubes to achieve hard contact. A pure EDC test with just a polyurethane plug was also performed as a reference. Through the use of displacement fields, digital volume correlation revealed inhomogeneous deformation fields in the tubes, even for pure EDC, which was related to the inherent defects in the structure. Deep learning–aided segmentation and systematic data analysis revealed that the presence of inhomogeneous deformation applied by the hard contact was exaggerated by the presence of inner surface imperfections left behind from the matrix densification process. In conclusion, the findings provide insights into the applications, highlighting the necessity for improvements in inner surface roughness and the incorporation of localized contacts in pellet–cladding mechanical interaction computational models.

Composites↗
DOE OSTI2026

Development of physics-consistent conditional diffusion model to overcome data scarcity in critical heat flux

Deep generative modeling provides a powerful pathway to overcome data scarcity in energy-related applications where experimental data are often limited. By learning the underlying probability distribution of the training dataset, deep generative models, such as the diffusion model, can generate high-fidelity synthetic samples that statistically resemble the training data. Such synthetic data generation can significantly enrich the size and diversity of the available training data, and more importantly, improve the robustness of downstream machine learning models in predictive tasks. The objective of this paper is to investigate the effectiveness of diffusion models for overcoming data scarcity in nuclear energy applications. By leveraging a public dataset on critical heat flux which covers a wide range of commercial nuclear reactor operational conditions, we developed a diffusion model that can generate an arbitrary amount of synthetic samples. Since a vanilla diffusion model can only generate samples randomly, we also developed a conditional diffusion model capable of generating targeted critical heat flux data under user-specified thermal-hydraulic conditions. The performance of the diffusion model was evaluated based on its ability to capture empirical feature distributions and pair-wise correlations, as well as to maintain physical consistency. The results showed that both the diffusion model and conditional diffusion model can successfully generate realistic and physics-consistent critical heat flux data. Furthermore, uncertainty quantification results demonstrate that the conditional diffusion model is highly effective in augmenting critical heat flux data while maintaining acceptable levels of uncertainty.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

A techno-economic assessment framework for hydrogen energy storage toward multiple energy delivery pathways and grid services

Hydrogen energy storage (HES) transforms and stores electric energy from the grid into hydrogen, and supplements other energy storage and demand response resources in addressing challenges in renewable-intensive power systems. Understanding how to optimally utilize an HES system to maximize its economic benefits from stacked value streams is highly important to its development and deployment. Here, in this paper, we present a techno-economic assessment framework for an HES system considering three common energy delivery pathways and multiple grid and end-user services. Models are developed to capture the operational capability, flexibility, and constraints associated with hydrogen production, compression, storage, and utilization as well as different grid services in an economic assessment. To define the technically achievable benefits, an optimal dispatch formulation is proposed to maximize the economic benefits over a representative year with an hourly time step considering the trade-offs among different value streams. Representative case studies are designed and carried out to show how system configuration, energy delivery pathways, and grid services may affect economic benefits. It was found that value streams from bundling grid services account for up to 76% of the total benefits and are critical for an HES project to be financially viable.

25 ENERGY STORAGE↗
DOE OSTI2023

A study of adhesive bonding in metal–metal, metal–CFRP, and CFRP–CFRP material combinations under shear deformation: Fracture morphologies and damage mechanisms

Safe design of adhesive joining in multi-materials in engineered structures requires the accumulation of numerous experimental data on the failure behavior of various adhesively-bonded material combinations under different loading conditions. The deep understanding of mechanical performance, fracturing morphologies, and main damage mechanisms is also quintessential for accelerating the development of proper physics-based and multi-scale models for assisting the design. Towards this goal, this work presents a comprehensive characterization of the failure behavior of adhesively-bonded metal–metal, metal–CFRP, and CFRP–CFRP material combinations under global shear deformation via single lap shear testing. Thanks to a synergistic combination of measurement methods by using Digital Imaging Correlation (DIC) and 3D optical profilometry, adhesive features on the adherend after failure were quantified and the main progressive damage mechanisms were identified. The characterization performed in this work provides quantitative data that contributes to a better understanding of shear failure in adhesive bonding across different bi-material combinations. Finally, the obtained results have practical implications, including the potential to enhance adhesive bonding design, identify failure causes in adhesive joints, and develop or validate computational models capable of capturing the observed behavior in various adhesively-bonded materials under global shear deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Real-time monitoring of the orthohydrogen fraction in a liquid hydrogen moderator

Here, we present the first direct measurements of the orthohydrogen fraction in an operating liquid hydrogen moderator loop at a high-powered neutron source. We have developed a Raman immersion probe capable of characterizing the orthohydrogen and parahydrogen makeup of a liquid hydrogen loop approximately every ten seconds, which can see orthohydrogen fractions lower than 0.20 without difficulty. At the Spallation Neutron Source, where the time-averaged power deposition in the studied moderator system is around 0.02 W/cm 3 , the orthohydrogen fraction increases with beam power and equilibrates at levels of 0.40 and responds to beam trips in a dynamic fashion. Additionally, we find that without deliberate catalyst systems in place, the relaxation due to the piping in the system has insignificant effect in catalyzing the relaxation of freshly condensed hydrogen toward thermodynamic equilibrium, and that the radiation levels associated with activation in a non-operating spallation neutron source target-moderator-reflector assembly likewise has insignificant effect on the liquid hydrogen kinetics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Zincate-Blocking-Functionalized Polysulfone Separators for Secondary Zn–MnO 2 Batteries

Alkaline zinc–manganese dioxide (Zn–MnO 2 ) batteries are well suited for grid storage applications because of their inherently safe, aqueous electrolyte and established materials supply chain, resulting in low production costs. With recent advances in the development of Cu/Bi-stabilized birnessite cathodes capable of the full 2-electron capacity equivalent of MnO 2 (617 mA h/g), there is a need for selective separators that prevent zincate (Zn(OH) 4 ) 2– transport from the anode to the cathode during cycling, as this electrode system fails in the presence of dissolved zinc. In this work, we present the synthesis of N-butylimidazolium-functionalized polysulfone (NBI-PSU)-based separators and evaluate their ability to selectively transport hydroxide over zincate. We then examine their impact on the cycling of high depth of discharge Zn/(Cu/Bi–MnO 2 ) batteries when inserted in between the cathode and anode. Initially, we establish our membranes’ selectivity by performing zincate and hydroxide diffusion tests, showing a marked improvement in zincate-blocking (D Zn (cm 2 /min): 0.17 ± 0.04 × 10 –6 for 50-PSU, our most selective separator vs 2.0 ± 0.8 × 10 –6 for Cellophane 350P00 and 5.7 ± 0.8 × 10 –6 for Celgard 3501), while maintaining similar crossover rates for hydroxide (D OH (cm 2 /min): 9.4 ± 0.1 × 10 –6 for 50-PSU vs 17 ± 0.5 × 10 –6 for Cellophane 350P00 and 6.7 ± 0.6 × 10 –6 for Celgard 3501). We then implement our membranes into cells and observe an improvement in cycle life over control cells containing only the commercial separators (cell lifetime extended from 21 to 79 cycles).

crossover↗
DOE OSTI2025

Developing Fluorescence-Based Sensors to Support Rare Earth Element Separation

Rare earth elements (REEs) are essential to most renewable energy technologies. Unfortunately, as we transition to sustainable energy production, the demand for REEs is rapidly growing well beyond current rates of production. As a result, novel means of efficient, scalable, and easily adaptable methods for processing primary and recycle feedstocks are needed. Development and integration of sensors for highly selective in-line monitoring can support more efficient design and testing of such novel separation processes, as well as more cost-effective deployment of those separation flowsheets. Work here will explore the application of fluorescence spectroscopy, a highly sensitive and selective technique, to quantify multiple lanthanides in complex mixtures including known interferents or quenching agents. Results include identification of the optimal excitation wavelength and the limit of detection of various rare earth elements as well as the performance of data-science-based quantification approaches in streams where “unknowns” are present. Overall, the data science tools in conjunction with optical sensor data were able to quantify analytes in the presence of other lanthanides which can be anticipated in the actual industrial stream. Here we include characterization of lanthanides in a microfluidic device similar to those used in new process development. This study demonstrates the capability of utilizing fluorescence spectroscopy to quantify analytes in a complicated solution matrix, suggesting this is a successful approach for in-line monitoring to optimize the separation efficiency in an industrial stream.

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

Recommendations for Comprehensive and Independent Evaluation of Machine Learning‐Based Earth System Models

Abstract Machine learning (ML) is a revolutionary technology with demonstrable applications across multiple disciplines. Within the Earth science community, ML has been most visible for weather forecasting, producing forecasts that rival modern physics‐based models. Given the importance of deepening our understanding and improving predictions of the Earth system on all time scales, efforts are now underway to develop Earth‐system models (ESMs) capable of representing all components of the coupled Earth system (or their aggregated behavior) and their response to external changes over long timescales. Building trust in ESMs is a much more difficult problem than for weather forecast models, not least because the model must represent the alternate (e.g., future or paleoclimatic) coupled states of the system for which there are no direct observations. Given that the physical principles that enable predictions about the response of the Earth system are often not explicitly coded in these ML‐based models, demonstrating the credibility of ML‐based ESMs thus requires us to build evidence of their consistency with the physical system. To this end, this paper puts forward five recommendations to enhance comprehensive, standardized, and independent evaluation of ML‐based ESMs to strengthen their credibility and promote their wider use.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity and selectivity. However, BDE computations at sufficiently high levels of quantum mechanical theory require substantial computing resources. In this paper, we develop a machine learning model capable of accurately predicting BDEs for organic molecules in a fraction of a second. We perform automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory for 42,577 small organic molecules, resulting in 290,664 BDEs. A graph neural network trained on a subset of these results achieves a mean absolute error of 0.58 kcal mol-1 (vs DFT) for BDEs of unseen molecules. We further demonstrate the model on two applications: first, we rapidly and accurately predict major sites of hydrogen abstraction in the metabolism of drug-like molecules, and second, we determine the dominant molecular fragmentation pathways during soot formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The impact of bilateral injuries on the pathophysiology and functional outcomes of volumetric muscle loss

Volumetric muscle loss (VML)—defined as the irrecoverable loss of skeletal muscle tissue with associated persistent functional deficits—is among the most common and highly debilitating combat-related extremity injuries. This is particularly true in cases of severe polytrauma wherein multiple extremities may be involved as a result of high energy wounding mechanisms. As such, significant investment and effort has been made toward developing a clinically viable intervention capable of restoring the form and function of the affected musculature. While these investigations conducted to date have varied with respect to the species, breed, and sex of the chosen pre-clinical in-vivo model system, the majority of these studies have been performed in unilateral injury models, an aspect which may not fully exemplify the clinical representation of the multiply injured patient. Furthermore, while various components of the basal pathophysiology of VML (e.g., fibrosis and inflammation) have been investigated, relatively little effort has focused on how the pathophysiology and efficacy of pro-regenerative technologies is altered when there are multiple VML injuries. Thus, the purpose of this study was two-fold: (1) to investigate if/how the pathophysiology of unilateral VML injuries differs from bilateral VML injuries and (2) to interrogate the effect of bilateral VML injuries on the efficacy of a well-characterized regenerative therapy, minced muscle autograft (MMG). In contrast to our hypothesis, we show that bilateral VML injuries exhibit a similar systemic inflammatory response and improved muscle functional recovery, compared to unilateral injured animals. Furthermore, MMG treatment was found to only be effective at promoting an increase in functional outcomes in unilateral VML injuries. The findings presented herein add to the growing knowledge base of the pathophysiology of VML, and, importantly, reiterate the importance of comprehensively characterizing preclinical models which are utilized for early-stage screening of putative therapies as they can directly influence the translational research pipeline.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Long-term, non-invasive FTIR detection of low-dose ionizing radiation exposure

Non-invasive methods of detecting radiation exposure show promise to improve upon current approaches to biological dosimetry in ease, speed, and accuracy. Here we developed a pipeline that employs Fourier transform infrared (FTIR) spectroscopy in the mid-infrared spectrum to identify a signature of low dose ionizing radiation exposure in mouse ear pinnae over time. Mice exposed to 0.1 to 2 Gy total body irradiation were repeatedly measured by FTIR at the stratum corneum of the ear pinnae. We found significant discriminative power for all doses and time-points out to 90 days after exposure. Classification accuracy was maximized when testing 14 days after exposure (specificity > 0.9 with a sensitivity threshold of 0.9) and dropped by roughly 30% sensitivity at 90 days. Infrared frequencies point towards biological changes in DNA conformation, lipid oxidation and accumulation and shifts in protein secondary structure. Since only hundreds of samples were used to learn the highly discriminative signature, developing human-relevant diagnostic capabilities is likely feasible and this non-invasive procedure points toward rapid, non-invasive, and reagent-free biodosimetry applications at population scales.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

The ocean’s nuclear energy reserve

Oceanic uranium represents a vast fuel resource that could ensure the long-term sustainability of nuclear power. Here, a new study seeks to harness that potential by developing a bioinspired adsorbent membrane capable of capturing uranium from seawater.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Numerical simulation of spall in lead accounting for its occurrence in solid as well as liquid phases

The capabilities of a recently-developed continuum-level model developed for the purpose of predicting the evolution of spall, including the compaction of damaged material, and applicable to the formation of spall in the material that is fully solid as well as material that is locally liquid, are evaluated against empirical data for lead. Here, the model accounts for inertial forces, elastoplastic constitutive behavior, viscosity, and surface tension. Predictions of spall layer thickness are shown to match the empirical data reasonably well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Hybrid quantum-classical simulations of magic angle spinning dynamic nuclear polarization in very large spin systems

Solid-state nuclear magnetic resonance can be enhanced using unpaired electron spins with a method known as dynamic nuclear polarization (DNP). Fundamentally, DNP involves ensembles of thousands of spins, a scale that is difficult to match computationally. This scale prevents us from gaining a complete understanding of the spin dynamics and applying simulations to design sample formulations. We recently developed an ab initio model capable of calculating DNP enhancements in systems of up to ~1000 nuclei; however, this scale is insufficient to accurately simulate the dependence of DNP enhancements on radical concentration or magic angle spinning (MAS) frequency. We build on this work by using ab initio simulations to train a hybrid model that makes use of a rate matrix to treat nuclear spin diffusion. We show that this model can reproduce the MAS rate and concentration dependence of DNP enhancements and build-up time constants. Finally, we then apply it to predict the DNP enhancements in core–shell metal-organic-framework nanoparticles and reveal new insights into the composition of the particles’ shells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗