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DOE OSTI2026

Modeling Flow in a Microreactor Core: From High-Fidelity CFD to Subchannel Analysis

The accurate prediction of pressure drop in tightly packed, low–Reynolds number (Re) bare rod bundles is essential for the thermal-hydraulic design of the Microreactor Applications Research Validation and EvaLuation (MARVEL) reactor and other microreactor concepts. However, existing friction factor correlations, particularly the upgraded Cheng-Todreas (UCTD) correlation, have limited validation for the small pitch-to-diameter ratios (P/Ds) and transitional flow conditions characteristic of these systems. In this work, we perform high-fidelity large-eddy simulations (LESs) of both an infinite bare rod bundle and a finite bare 37-pin scalloped rod bundle across the range 1000≤ 𝑅𝑒 ≤5500. The simulations reveal strong gap vortex–driven transitional behavior and indicate that the UCTD may underpredict the friction factor by up to 28% at 𝑃/𝐷 = 1.05. Using the LES-calculated pressure drops, we formulate a new friction factor correlation that follows the Cheng-Todreas functional form but is calibrated for low-Re and tightly packed geometries representative of a MARVEL-like reactor. The correlation is implemented in the MOOSE (Multiphysics Object-Oriented Simulation Environment) subchannel module and compared against both the LES and UCTD predictions. Across all subchannel types, the proposed model reduces the streamwise velocity differences from as high as 44% (UCTD) to below 9%, and decreases the pressure gradient differences from 13% to 25% (UCTD) to 0.7% to 7% relative to the LES results. These results suggest that the new correlation has the potential to improve the pressure drop and flow field predictions for such geometries, highlighting the importance of high-fidelity simulations in supporting microreactor thermal-hydraulic model development and motivating future pressure drop experiments for compact rod bundles to further validate these findings.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

NASA NTRS2026

Thermal Inspection for the Assessment of Adhesively Bonded Metal Adherents

Certification of adhesively bonded structures is currently a challenge for aircraft manufacturers. The ability to certify bonds in primary structure can reduce dependence on fasteners and this will enable more efficient manufacturing. For example, drilling holes and installing fasteners in bonded joints can be a potentially significant bottleneck in airframe manufacturing. In addition, fasteners increase the airframe weight and can add stress concentration areas. Reducing fastener count can accelerate manufacturing and improve aircraft performance. NASA is currently investigating nondestructive evaluation (NDE) techniques to assess bond integrity. For example, bond thickness influences bond strength and therefore NDE techniques are being investigated to determine bond thickness. For bonded metal structures, there is a large difference in the thermal diffusivity between the 7075 aluminum alloy metal adherends and aircraft grade adhesive. Multi-layered thermal models show a large variation in the thermal response for bondline adhesive thicknesses that vary from 100 to 300 microns for adherend thicknesses of 0.163 cm. Experimental through-transmission thermal measurements reveal promise to quantitatively characterize the bondline thickness. Results were validated with X-ray computed tomography and optical microscopy measurements, and influence of porosity on the thermal model and measurements are investigated.

bondline thickness↗

NASA NTRS2026

Thermal Inspection for the Assessment of Adhesively Bonded Metal Adherends

X-ray computed tomography↗

NASA NTRS2012

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery

Space Propulsion↗

DOE OSTI2026

ORNL Report of Analysis for the Verification of NRMP CRM U030A

In support of the Certified Reference Material (CRM) program managed by the Nuclear Reference Material Program (NRMP), the Material Signatures and Isotopic Standards (MSIS) group of Oak Ridge National Laboratory (ORNL) was asked to prepare a set of CRM U030A units for use as standards for isotopic analysis using multicollector thermal ionization mass spectrometry (TIMS) and inductively coupled plasma mass spectrometry (ICP-MS) instruments. This report documents the results of the verification measurements performed on three randomly selected units by the MSIS group’s ISO/IEC 17025:2017 accredited operating procedure CSD-AM-CIMS-IN20, Determination of Uranium and Plutonium Isotopic Composition using Thermal Ionization Mass Spectrometry [1], and in accordance with the quality assurance plan as described in QAP-X-96-CSD/RML-001, Nuclear Analytical Chemistry Laboratory Section Quality Assurance Plan [2].

Mathew, Kattathu [Oak Ridge National Laboratory (O↗

DOE OSTI2026

ResStock Measure Documentation: Residential Variable-Speed Geothermal Heat Pump (4.4 COP, 30.9 EER) With Light Envelope Improvements

This report is part of series describing a variety of different ResStock™ measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Residential Variable-Speed Geothermal Heat Pump (4.4 COP, 30.9 EER) with Light Envelope Improvements" measure upgrade methodology and briefly discusses key results. All results can be accessed on the ResStock Open Energy Data Initiative "End-Use Load Profiles for the U.S. Building Stock" data lake and on the data viewer at resstock.nlr.gov.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2026

CFD-Assisted Nodal Modeling of Sloshing in a Cryogenic Propellant Tank

During autogenous pressurization, tank sloshing causes a significant increase in pressurant consumption to maintain constant ullage pressure during draining of the tank. This increase in pressurant consumption is caused by a significant increase in condensation at the liquid-vapor interface. Sloshing strongly affects the liquid side heat transfer coefficient and thereby the condensation rate. Traditionally, sloshing is modeled by CFD code using the VOF (Volume of Fluid) method to track the liquid vapor interface during sloshing. CFD calculations require a very fine grid to accurately compute the heat and mass transfer at the interface. Therefore, computations are time consuming and prohibit performing many parametric studies often needed during the design of a new system. This paper describes an alternative approach by developing a CFD-assisted nodal model to predict system parameters more economically with reasonable accuracy. In this approach, a multi-node model of tank pressurization was developed using GFSSP. A multi-node model was needed to account for stratification. The model computes heat and mass transfer at the interface to calculate the condensation rate. The liquid side heat transfer is computed using the parameters of sloshing dynamics such as frequency, wave amplitude, and interface area. The parameters of sloshing dynamics are computed by the CFD code LOCI-Stream. The model predictions were compared with test data for several cases.

Cryogenic Tank Sloshing↗

DOE OSTI2026

Application of Ibuprofen Sodium Dihydrate for Thermochemical Energy Storage

Thermochemical energy storage (TCES) offers a transformative approach to address grid instability by harnessing reversible chemical reactions for efficient heat storage and release. Here, we introduce pharmaceutical organic salt hydrates as a class of materials with exceptional performance for low-grade waste heat recovery. We demonstrate ibuprofen sodium dihydrate (ISD) as an example organic hydrate exhibiting a dehydration temperature range of 60-110 °C and a remarkable dehydration enthalpy of up to 59.5 kJ/mol of water, ideally suited for capturing industrial and residential waste heat. Using rigorous multimodal characterization, including thermogravimetric analysis, differential scanning calorimetry, in-situ FTIR, in-situ PXRD, and NMR, we demonstrate ISD's superior thermal, chemical, and structural stability over 150 hydration-dehydration cycles, achieving an unprecedented cycling efficiency of ~99.9%. Compared to conventional inorganic salt hydrates like strontium chloride hexahydrate and calcium oxalate monohydrate, ISD showcases enhanced durability without deliquescence or pulverization, even under high-humidity conditions. In-situ analyses confirm the transition from ISD to ibuprofen sodium anhydrous (ISA) proceeds with structural reorganization, thereby combining the dehydration mechanism with phase transitions, resulting in higher energy storage capacity. Microstructural analyses reveal that repeated water intercalation and structural transitions aid in creating significant porosity that enhances water transport kinetics, further improving the hydration/dehydration performance. By combining the phase change and chemical dehydration mechanisms, ISD paves the way for designing a new class of organic salt hydrates, offering tunable properties to meet diverse thermal energy storage demands and supporting sustainable grid resilience.

Thangaraj, Kavin C.↗

DOE OSTI2026

A hybrid Monte Carlo-deterministic second moment method with efficient variance reduction

In this work, we present a hybrid method that combines Monte Carlo with deterministic finite element methods to solve a linear Boltzmann transport equation. Our hybrid method runs orders of magnitude faster than Monte Carlo, without sacrificing accuracy, for a proxy problem from radiative transfer that contains both optically-thick and optically-thin material. We believe that this is the first demonstration of a hybrid Second Moment Method in more than one spatial dimension, the first to consider more than one material, and the first to use variance reduction. Our variance reduction approach arises from an asymptotic analysis in which we show that the magnitude of the scattering source grows without bound. We transform the problem to compute the deviation of the radiation intensity from isotropy. The magnitude of the source in the transformed problem is bounded, and the quality of the hybrid method solution is dramatically improved by a substantial reduction in the variance.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2006

2005 NASA Seal/Secondary Air System Workshop

The 2005 NASA Seal/Secondary Air System workshop covered the following topics: (i) Overview of NASA’s new Exploration Initiative program aimed at exploring the Moon, Mars, and beyond; (ii) Overview of the NASA-sponsored Propulsion 21 Project; (iii) Overview of NASA Glenn’s seal project aimed at developing advanced seals for NASA’s turbomachinery, space, and reentry vehicle needs; (iv) Reviews of NASA prime contractor, vendor, and university advanced sealing concepts including tip clearance control, test results, experimental facilities, and numerical predictions; and (v) Reviews of material development programs relevant to advanced seals development. Turbine engine studies have shown that reducing high-pressure turbine (HPT) blade tip clearances will reduce fuel burn, lower emissions, retain exhaust gas temperature margin, and increase range. Several organizations presented development efforts aimed at developing faster clearance control systems and associated technology to meet future engine needs. The workshop also covered several programs NASA is funding to develop technologies for the Exploration Initiative and advanced reusable space vehicle technologies. NASA plans on developing an advanced docking and berthing system that would permit any vehicle to dock to any on-orbit station or vehicle. Seal technical challenges (including space environments, temperature variation, and seal-on-seal operation) as well as plans to develop the necessary “androgynous” seal technologies were reviewed. Researchers also reviewed tests completed for the shuttle main landing gear door seals.

Hypersonic↗

NASA NTRS1965

Symposium on Thermal Radiation of Solids

The Symposium was intended to provide for presentation and discussion of significant developments in the theory of thermal radiation of solids and in our understanding of the important effects of surfaces and interfaces and of surface imperfections on this radiation, of the effects of the space environment on radiation properties, and of the application of this information to the thermal designer's problems. Presentation of data as such-for example, absorptances and emittances of various types of coatings-was not considered a primary purpose of the Symposium; nevertheless, as the reader will observe, an appreciable amount of significant data of this type is contained in some of the papers. Developments in measurement techniques were also not considered basic to the main theme, especially since it was the theme of the preceding Symposium. However, because the subject remains of continuing concern, one session was devoted to it. These Proceedings contain all the papers presented orally at the Symposium together with the discussions that followed them, and, in addition, nine papers that were accepted for publication only. Each paper was reviewed by the session chairman and by the editor, and any apparent technical discrepancies were called to the attention of the author for clarification. As a further aid to the reader, the editor has tried to achieve some degree of uniformity in organization and format of the papers—at least to the extent permitted by the subject matter, and he has also striven for clarity and readability of the text. In this task he has had not only the cooperation of the authors but also extensive help from the Langley Technical Editing Section, the Langley Library (where most of the cited references were checked), and the NASA Headquarters Technical Publications Branch.

Solids↗

NASA NTRS1994

Review of European Microgravity Measurements

AA In a French/Russion cooperation, CNES developed a microgravity detection system for analyzing the Mir space station micro-g-environment for the first time. European efforts to characterize the microgravity (1/9) environment within a space laboratory began in the late seventies with the design of the First Spacelab Mission SL-1. Its Material Science Double Rack was the first payload element to carry its own tri-axial acceleration package. Even though incapable for any frequency analysis, the data provided a wealth of novel information for optimal experiment and hardware design and operations for missions to come. Theoretical investigations under ESA contract demonstrated the significance of the detailed knowledge of micro-g data for a thorough experiment analysis. They especially revealed the high sensitivity of numerous phenomena to low frequency acceleration. Accordingly, the payloads of the Spacelab missions D-1 and D-2 were furnished with state-of-the-art detection systems to ensure frequency analysis between 0.1 and 100 Hz. The Microgravity Measurement Assembly (MMA) of D-2 was a centralized system comprising fixed installed as well as mobile tri-axial packages showing real-time data processing and transmission to ground. ESA's free flyer EURECA carried a system for continuous measurement over the entire mission. All EURECA subsystems and experimental facilities had to meet tough requirements defining the upper acceleration limits. In a French/Russion cooperation, CNES developed a mi crogravity detection system for analyzing the Mir space station micro-g-environment for the first time. An approach to get access to low frequency acceleration between 0 and 0.02 Hz will be realized by QSAM (Quasi-steady Acceleration Measurement) on IML-2, complementary to the NASA system Spacelab Acceleration Measurement System SAMS. A second flight of QSAM is planned for the Russian free flyer FOTON.

H Hamacher↗

NASA NTRS2009

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, satellite vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed, or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery.

Satellites↗

DOE OSTI2026

EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.

3D molecular generation↗

DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Multifunctional electrochemical memory stabilized by phase coexistence

Our growing computing needs, especially in applications that heavily rely on artificial intelligence (AI), motivate a search for new components that could substantially augment the performance of general-purpose digital computers. Beyond ON/OFF switching, new components with linear multistate analog resistive tuning, nonlinear volatile switching, spiking, oscillatory, stochastic and other complex functionalities could enable highly efficient neuromorphic computing schemes for AI information processing. Compared to the extreme multifunctionality of biological neurons, realizing all the above characteristics in a single, scalable analog component remains a grand challenge. Here we investigate electrochemical gating combined with localized thermal activation to program and switch a single, vertically integrated and dimensionally scaled electrothermal chemical random access memory (ETCRAM) with a channel and reservoir composed of phase-separated vanadium oxide. Closely related to electrochemical RAM (ECRAM), ETCRAM uses an integrated gate-heater electrode to overcome kinetic barriers that help retain states at ambient temperatures. In addition to synapse-like stable and programmable analog resistance states arising from redox-tunable phase coexistence, a single component exhibits neuron-like nonlinear conductance switching with a tunable threshold and self-driven dynamics owing to the thermally driven metal-insulator phase transition in vanadium dioxide. More broadly, we demonstrate that electrochemically stabilized phase coexistence could unlock analog electronics with novel functionality, stability, reconfigurability, and scalability.

Oh, Sangheon [Sandia National Lab. (SNL-CA), Liver↗

DOE OSTI2024

Machine learning the electric field response of condensed phase systems using perturbed neural network potentials

Abstract The interaction of condensed phase systems with external electric fields is of major importance in a myriad of processes in nature and technology, ranging from the field-directed motion of cells (galvanotaxis), to geochemistry and the formation of ice phases on planets, to field-directed chemical catalysis and energy storage and conversion systems including supercapacitors, batteries and solar cells. Molecular simulation in the presence of electric fields would give important atomistic insight into these processes but applications of the most accurate methods such as ab-initio molecular dynamics (AIMD) are limited in scope by their computational expense. Here we introduce Perturbed Neural Network Potential Molecular Dynamics (PNNP MD) to push back the accessible time and length scales of such simulations. We demonstrate that important dielectric properties of liquid water including the field-induced relaxation dynamics, the dielectric constant and the field-dependent IR spectrum can be machine learned up to surprisingly high field strengths of about 0.2 V Å −1 without loss in accuracy when compared to ab-initio molecular dynamics. This is remarkable because, in contrast to most previous approaches, the two neural networks on which PNNP MD is based are exclusively trained on molecular configurations sampled from zero-field MD simulations, demonstrating that the networks not only interpolate but also reliably extrapolate the field response. PNNP MD is based on rigorous theory yet it is simple, general, modular, and systematically improvable allowing us to obtain atomistic insight into the interaction of a wide range of condensed phase systems with external electric fields.

Science & Technology - Other Topics↗

DOE OSTI2026

Heterotrophic respiration by soil microbes in a changing climate

Soil microbes strongly influence the soil organic carbon (SOC) pool, which globally stores ~2,000 PgC. Specifically, the balance between microbial heterotrophic respiration (R H ), which degrades SOC, and plant–microbe interactions that stabilize SOC determines whether terrestrial ecosystems are a net source or sink of CO 2 to the atmosphere. Here, in this Review, we evaluate how climate change alters these competing processes. R H is approximately half of total soil respiration, at ~50 PgC yr −1 , with 70% occurring in topsoils. Warming accelerates microbial metabolism, with a 10 °C temperature increase estimated to raise R H by ~50%, an effect that is particularly strong in Arctic soils. Warming also reduces soil moisture, further modulating R H , which responds nonlinearly to soil moisture, being limited by saturation and desiccation and meeting a maximum at intermediate levels. Consequently, R H is highly sensitive to future precipitation changes and drought. However, soil management strategies could enhance SOC stocks and persistence under climate change. Bacterial and fungal inoculants can promote SOC production and stabilization, while deep-rooting plants increase SOC inputs to deeper layers that experience lower R H . Agricultural practices and biochar amendments can also enhance SOC and reduce R H . Expanding field trials across regions, climates and soil types would improve empirical understanding of these responses and support better representation of R H in predictive models, enabling more accurate assessments of climate impacts on SOC storage.

Jansson, Janet K. [Pacific Northwest National Labo↗