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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Predictive Modeling and Uncertainty Quantification in Condition Monitoring of Active Components: A Reactor Coolant Pump Use Case

This work develops data-driven models for onset of thermal barrier leakage in reactor coolant pumps. It incorporates uncertainty quantification to enhance the reliability and robustness of pre- dictions. Using synthetic data generated by the Generic Pressurized Water Reactor simulator, realistic degradation scenarios were simulated across lifecycle stages—beginning, middle, and end of life. Key variables, including differential pressure, flow rate, vibration, and temperatures, were analyzed using machine learning framework. The fully connected neural network models demonstrated exceptional performance, achieving R2 scores exceeding 0.99 and root mean square errors as low as around 8.23 × 10-2 gallon per minute (gpm) for the three stages of the lifecy- cle. UQ analysis further validated the model’s robustness, with narrow uncertainty bounds during steady-state operations and appropriately wider bounds during transitional phases, reflecting the physical behavior of the system. This work addresses important gaps in real-time condition moni- toring and regulatory compliance by integrating advanced condition monitoring technologies with UQ into IST programs. The ability to detect thermal barrier leakage early and quantify prediction reliability supports optimizing maintenance strategies while ensuring nuclear power plants’ safe and reliable operation.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Physics-informed Deep Reinforcement Learning-based Control in Power systems

Incorporating physics information into the deep reinforcement learning (DRL) process is a promising approach for addressing the challenges faced in learning-based control design problems for physical systems. Power grid dynamics, being a physical system, adheres to specific physical laws, constraints, as well as operational and control rules. Therefore, consideration of such physics-based law improves the learning process drastically. In general, traditional grid control schemes rely on rule-based mechanisms that cannot adapt to changing operating conditions. To improve the adaptability and computation time, recent research has seen a surge of DRL-based applications in power grid control. A generic DRL-based control design imposes the system performance requirements through the design of reward functions. In some cases, some of the important physics information is injected through this reward function. However, due to the complex dynamics and large state-action space, learning an optimal DRL policy often becomes challenging. Inspired by the latest developments in general machine learning (ML) research, power system researchers have been investigating more direct ways of incorporating physics knowledge into DRL training. This chapter specifically focuses on these aspects of physics-informed DRL designs in grid control. It discusses the significance, applications, research gaps, and open problems that need to be addressed in future research.

artificial intelligence, machine learning↗
DOE OSTI2020

Performance Limits of an Alternating Current Electroluminescent Device

Abstract The use of an alternating current (AC) voltage is a simple, versatile method of producing electroluminescence from generic emissive materials without the need for contact engineering. Recently, it was shown that AC‐driven, capacitive electroluminescent devices with carbon nanotube network contacts can be used to generate and study electroluminescence from a variety of molecular materials emitting in the infrared‐to‐ultraviolet range. Here, performance trade‐offs in these devices are studied through comprehensive device simulations and illustrative experiments, enhancing understanding of the mechanism and capability of electroluminescent devices based on alternating as opposed to direct current (DC) schemes. AC‐driven electroluminescent devices can overcome several limitations of conventional DC‐driven electroluminescent devices, including the requirement for proper alignment of material energy levels and the need to process emitting materials into uniform thin films. By simultaneously optimizing device geometry, driving parameters, and material characteristics, the performance of these devices can be tuned. Importantly, the turn‐on voltage of AC‐driven electroluminescent devices approaches the bandgap of the emitting material as the gate oxide thickness is scaled, and internally efficient electroluminescence can be achieved using low‐mobility single‐layer emitter films with varying thicknesses and energy barrier heights relative to the contact.

Wang, Vivian↗
DOE OSTI2024

Multi‐Material Gradient Printing Using Meniscus‐enabled Projection Stereolithography (MAPS)

Light‐based additive manufacturing methods are widely used to print high‐resolution 3D structures for applications in tissue engineering, soft robotics, photonics, and microfluidics, among others. Despite this progress, multi‐material printing with these methods remains challenging due to constraints associated with hardware modifications, control systems, cross‐contamination, waste, and resin properties. Here, a new printing platform coined Meniscus‐enabled Projection Stereolithography (MAPS) is reported, a vat‐free method that relies on generating and maintaining a resin meniscus between a crosslinked structure and bottom window to print lateral, vertical, discrete, or gradient multi‐material 3D structures with no waste and user‐defined mixing between layers. MAPS is compatible with a wide range of resins shown and can print complex multi‐material 3D structures without requiring specialized hardware, software, or complex washing protocols. MAPS's ability to print structures with microscale variations in mechanical stiffness, opacity, surface energy, cell densities, and magnetic properties provides a generic method to make advanced materials for a broad range of applications.

bioprinting↗
DOE OSTI2023

New Chemical Dopant and Counterion Mechanism for Organic Electrochemical Transistors and Organic Mixed Ionic–Electronic Conductors

Abstract Organic mixed ionic–electronic conductors (OMIECs) have varied performance requirements across a diverse application space. Chemically doping the OMIEC can be a simple, low‐cost approach for adapting performance metrics. However, complex challenges, such as identifying new dopant materials and elucidating design rules, inhibit its realization. Here, these challenges are approached by introducing a new n‐dopant, tetrabutylammonium hydroxide (TBA‐OH), and identifying a new design consideration underpinning its success. TBA‐OH behaves as both a chemical n‐dopant and morphology additive in donor acceptor co‐polymer naphthodithiophene diimide‐based polymer, which serves as an electron transporting material in organic electrochemical transistors (OECTs). The combined effects enhance OECT transconductance, charge carrier mobility, and volumetric capacitance, representative of the key metrics underpinning all OMIEC applications. Additionally, when the TBA + counterion adopts an “edge‐on” location relative to the polymer backbone, Coulombic interaction between the counterion and polaron is reduced, and polaron delocalization increases. This is the first time such mechanisms are identified in doped‐OECTs and doped‐OMIECs. The work herein therefore takes the first steps toward developing the design guidelines needed to realize chemical doping as a generic strategy for tailoring performance metrics in OECTs and OMIECs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Effect of Antagonistic Binder–Catalyst Interactions on Catalytic Activity in Lithium–Sulfur Batteries

Lithium-sulfur (Li-S) batteries are a promising next-generation energy storage solution, as they can reduce reliance on critical transition metals while offering high energy densities. However, their deployment is hindered by low sulfur utilization and the formation/diffusion of lithium polysulfides (LiPSs). While transition-metal catalysts and polymeric binders have been independently developed to enhance redox kinetics and LiPS adsorption, their mutual compatibility has remained largely unexplored. We show here that binder-catalyst interactions can significantly impact catalytic performance. Employing TiO 2 as a generic catalyst, the electrochemical performance is shown to depend strongly on the binder environment. TiO 2 paired with lithiated polyacrylic acid (LiPAA) shows benign interactions, resulting in enhanced cycle life. In contrast, pairing TiO 2 with protonated PAA produces antagonistic interactions that hinder Li 2 S growth. A mechanistic analysis unveils that the carboxylic H atom in PAA promotes COO − coordination to Ti sites, occupying catalytic centers and suppressing LiPS adsorption, increasing charge transfer and diffusion resistances. This phenomenon is observed across multiple catalysts, indicating that COOH-functionalized binders may broadly hinder catalytic activity. Overall, this study underscores the need for holistic cathode design and identifies binder-catalyst compatibility as an important parameter for high-performance Li-S batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

PtCo/MWCNTs Prepared by a Microwave‐assisted Polyol Method for Selective Cinnamaldehyde Hydrogenation

Abstract Using microwave irradiation, PtCo alloy nanoparticles were deposited within a few minutes on COOH‐functionalized MWCNT supports. The obtained catalysts were used for selective hydrogenation of cinnamaldehyde, a reaction whose products are widely used in various fields. In the selective cinnamaldehyde hydrogenation to cinnamyl alcohol, microwave‐prepared catalysts (generically, Pt x Co y ‐MW) outperformed a catalyst prepared by the conventional method (Pt 1 Co 2 ‐con). The highest selective hydrogenation to cinnamyl alcohol, 89%, was obtained using Pt 1 Co 2 ‐MW, while Pt 1 Co 2 ‐con showed a selectivity of 76%. Characterization results confirmed that the microwave prepared samples had a stronger interaction between Pt and Co than that in the Pt 1 Co 2 ‐con sample. The alloyed Co altered the electronic structure of Pt, leading to favorable adsorption of the C=O bond by the lone‐pair electrons of its oxygen atom. Moreover, the Pt 1 Co 2 ‐MW sample showed neglectable change in catalytic performance (e. g., cinnamaldehyde conversion and selective hydrogenation to cinnamyl alcohol) during recycling experiments.

Wang, Kaiying↗
DOE OSTI2022

Formation of Point Shocks for 3D Compressible Euler

We consider the 3D isentropic compressible Euler equations with the ideal gas law. We provide a constructive proof of the formation of the first point shock from smooth initial datum of finite energy, with no vacuum regions, with nontrivial vorticity present at the shock, and under no symmetry assumptions . We prove that for an open set of Sobolev‐class initial data that are a small L ∞ perturbation of a constant state, there exist smooth solutions to the Euler equations which form a generic stable shock in finite time. The blowup time and location can be explicitly computed, and solutions at the blowup time are smooth except for a single point , where they are of cusp‐type with Hölder C 1/3 regularity. Our proof is based on the use of modulated self‐similar variables that are used to enforce a number of constraints on the blowup profile, necessary to establish global existence and asymptotic stability in self‐similar variables. © 2022 Wiley Periodicals LLC.

Mathematics↗
DOE OSTI2022

Computational models of direct and indirect X‐ray breast imaging detectors for in silico trials

Abstract Background To facilitate in silico studies that investigate digital mammography (DM) and breast tomosynthesis (DBT), models replicating the variety in imaging performance of the DM and DBT systems, observed across manufacturers are needed. Purpose The main purpose of this work is to develop generic physics models for direct and indirect detector technology used in commercially available systems, with the goal of making them available open source to manufacturers to further tweak and develop the exact in silico replicas of their systems. Methods We recently reported on an in silico version of the SIEMENS Mammomat Inspiration DM/DBT system using an open‐source GPU‐accelerated Monte Carlo x‐ray imaging simulation code (MC‐GPU). We build on the previous version of the MC‐GPU codes to mimic the imaging performances of two other Food and Drug Administration (FDA)‐approved DM/DBT systems, such as Hologic Selenia Dimensions (HSD) and the General Electric Senographe Pristina (GSP) systems. In this work, we developed a hybrid technique to model the optical spread and signal crosstalk observed in the GSP and HSD systems. MC simulations are used to track each x‐ray photon till its first interaction within the x‐ray detector. On the other hand, the signal spread in the x‐ray detectors is modeled using previously developed analytical equations. This approach allows us to preserve the modeling accuracy offered by MC methods in the patient body, while speeding up secondary carrier transport (either electron–hole pairs or optical photons) using analytical equations in the detector. The analytical optical spread model for the indirect detector includes the depth‐dependent spread and collection of optical photons and relies on a pre‐computed set of point response functions that describe the optical spread as a function of depth. To understand the capabilities of the computational x‐ray detector models, we compared image quality metrics like modulation transfer function (MTF), normalized noise power spectrum (NNPS), and detective quantum efficiency (DQE), simulated with our models against measured data. Please note that the purpose of these comparisons with measured data would be to gauge if the model developed as part of this work could replicate commercially used direct and indirect technology in general and not to achieve perfect fits with measured data. Results We found that the simulated image quality metrics such as MTF, NNPS, and DQE were in reasonable agreement with experimental data. To demonstrate the imaging performance of the three DM/DBT systems, we integrated the detector models with the VICTRE pipeline and simulated DM images of a fatty breast model containing a spiculated mass and a calcium oxalate cluster. In general, we found that the images generated using the indirect model appeared more blurred with a different noise texture and contrast as compared to the systems with direct detectors. Conclusions We have presented computational models of three commercially available FDA‐approved DM/DBT systems, which implement both direct and indirect detector technology. The updated versions of the MC‐GPU codes that can be used to replicate three systems are available in open source format through GitHub.

Sengupta, Aunnasha↗
DOE OSTI2025

AAPM Truth‐based CT (TrueCT) reconstruction grand challenge

Background: This Special Report summarizes the 2022, AAPM grand challenge on Truth-based CT image reconstruction. Purpose: To provide an objective framework for evaluating CT reconstruction methods using virtual imaging resources consisting of a library of simulated CT projection images of a population of human models with various diseases. Methods: Two hundred unique anthropomorphic, computational models were created with varied diseases consisting of 67 emphysema, 67 lung lesions, and 66 liver lesions. The organs were modeled based on clinical CT images of real patients. The emphysematous regions were modeled using segmentations from patient CT cases in the COPDGene Phase I dataset. For the lung and liver lesion cases, 1–6 malignant lesions were created and inserted into the human models, with lesion diameters ranging from 5.6 to 21.9 mm for lung lesions and 3.9 to 14.9 mm for liver lesions. The contrast defined between the liver lesions and liver parenchyma was 82 ± 12 HU, ranging from 50 to 110 HU. Similarly, the contrast between the lung lesions and the lung parenchyma was defined as 781 ± 11 HU, ranging from 725 to 805 HU. For the emphysematous regions, the defined HU values were −950 ± 17 HU ranging from −918 to −979 HU. The developed human models were imaged with a validated CT simulator. The resulting CT sinograms were shared with the participants. The participants reconstructed CT images from the sinograms and sent back their reconstructed images. Further, the reconstructed images were then scored by comparing the results against the corresponding ground truth values. The scores included both task-generic (root mean square error [RMSE] and structural similarity matrix [SSIM]), and task-specific (detectability index [d’] and lesion volume accuracy) metrics. For the cases with multiple lesions, the measured metric was averaged across all the lesions. To combine the metrics with each other, each metric was normalized to a range of 0 to 1 per disease type, with “0” and “1” being the worst and best measured values across all cases of the disease type for all received reconstructions. Results: The True-CT challenge attracted 52 participants, out of which 5 successfully completed the challenge and submitted the requested 200 reconstructions. Across all participants and disease types, SSIM absolute values ranged from 0.22 to 0.90, RMSE from 77.6 to 490.5 HU, d’ from 0.1 to 64.6, and volume accuracy ranged from 1.2 to 753.1 mm3. The overall scores demonstrated that participant “A” had the best performance in all categories, except for the metrics of d’ for lung lesions and RMSE for liver lesions. Participant “A” had an average normalized score of 0.41 ± 0.22, 0.48 ± 0.32, and 0.42 ± 0.33 for the emphysema, lung lesion, and liver lesion cases, respectively. Conclusions: The True-CT challenge successfully enabled objective assessment of CT reconstructions with the unique advantage of access to a diverse population of diseased human models with known ground truth. This study highlights the significant potential of virtual imaging trials in objective assessment of medical imaging technologies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Implementation and verification of a user-defined element (UEL) for coupled thermal-hydraulic-mechanical-chemical (THMC) processes in saturated geological media

Efficient and accurate modeling of the coupled thermal-hydraulic-mechanical-chemical (THMC) processes in various rock formations is indispensable for designing energy geo-structures such as underground repositories for high-level nuclear wastes. Here, this work focuses on developing and verifying an implicit finite element solver for generic coupled THMC problems in geological settings. Starting from the mass, momentum, and energy balance laws, a specialized set of governing equations and a thermoporoelastic constitutive model is derived. This system is then solved by an implicit finite element (FE) scheme. Specifically, the residuals and the Jacobians are scripted in a user-defined element (UEL) subroutine which is then combined with the general-purpose FE software Abaqus Standard to solve initial-boundary value problems. Considering the complexity of the system, the UEL development follows a stepwise manner by first solving the coupled hydraulic-mechanical (HM) and thermal-hydraulic-mechanical (THM) equations before moving on to the full THMC problem. Each implementation step consists of at least one verification test by comparing computed results with closed-form analytical solutions to ensure that the various coupling effects are correctly realized. To demonstrate the robustness of the algorithm and to validate the UEL, a three-dimensional case study is performed with reference to the in-situ heating test of ATLAS at Belgium in 1980s. A hypothetical radionuclide leakage event is then simulated by activating the chemical-concentration degree of freedom and prescribing a constant high concentration at the heater's surface. The model predicts a limited contaminated regime after six years considering both diffusion and advection effects on species transport.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Development of data‐driven exponential integrators with application to modeling of delay photocurrents

Abstract Radiation‐induced photocurrent effects represent a threat to microelectronic components operating in space, manmade and terrestrial radiation environments. Analysis of these threats by circuit simulations requires accurate and computationally efficient compact models. Most existing compact models are based on closed form analytic solutions of the governing equations and require empirical assumptions and idealizations that can limit their validity. In this paper we formulate an alternative numerical, data‐driven approach that learns a compact model from data representative of the type of measurements one can obtain in an experimental facility. To develop the model we start from a generic discrete‐time dynamical system and then use physics knowledge to refine its structure. Numerical studies demonstrate the potential of the model and establish some empirical guidelines for its training.

Bochev, Pavel↗
DOE OSTI2020

Linear viscoelastic characterization of electrically conductive adhesives used as interconnect in photovoltaic modules

Electrically conductive adhesives (ECAs) are incorporated into recent designs of photovoltaic (PV) modules and replace the traditional metallic solders as interconnects. This transition depicts a significant material change, and a proper understanding of the interconnects' mechanical response has not yet been established. However, such an understanding is necessary to (a) identify the driving forces for module degradation and failure, (b) allow for module design optimization, and (c) enable accurate lifetime predictions. This study summarizes the framework for the mechanical materials characterization and modeling of ECAs for PV applications. Only high-fidelity material models are able to capture the rate and temperature dependency of the ECA interconnect and allow for accurate modeling of the materials response. Furthermore, a linear viscoelastic representation is found to describe the mechanical response of the ECAs sufficiently well. The effects of curing conditions and environmental exposure are investigated, and material models for a variety of ECAs are reported and prepared for the use in numerical simulations. Overall, a finite element simulation of a generic submodel of a shingled cell module is used to highlight the need for high-fidelity material models and demonstrates the error made in the predicted stress states by using less sophisticated models.

14 SOLAR ENERGY↗
DOE OSTI2020

Polyphosphonates as ionic conducting polymers

Polyphosphonates, a class of polymers with the generic formula –[P(R)(X)–OR'O] n –, exhibit a high degree of modularity due to the range of R, R', and X groups that can be incorporated. As such, these polymers may be designed with a polyethylene oxide (PEO) backbone (R' group) and employed as solid polymer electrolytes (SPEs). Two PEO-containing polyphosphonate analogs (R = Ph; X = S or Se) were doped with LiPF 6 and their conductivities were measured. Conductivities were similar (X = S) to or exceeding (X = Se) those of standard PEO systems (just below 10 -4 S/cm at 100°C). Binding models for Li + were generated using 31 P{ 1 H}NMR titration experiments. Binding of Li + by these polyphosphonates followed a positive cooperativity model, and varying the X group (S or Se) affected the observed cooperativity (Hill coefficient = 1.73 and 4.16, respectively). The presence of Se also leads to an increase in conductivity as temperature is raised above the T g , which is likely an effect of reduced Columbic interactions. Finally, because of their modularity and ease with which cation binding can be evaluated using 31 P{ 1 H} NMR titration experiments, polyphosphonates offer a unique approach for the modification of Li + ion battery technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Spatial analysis of cell patterning to aid genetic and phenotypic understanding of grass stomatal density: A case study in maize

Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Hidden Sectors from Multiple Line Bundles for the B−L$B-L$ MSSM

Abstract We give a formalism for constructing hidden sector bundles as extensions of sums of line bundles in heterotic M‐theory. Although this construction is generic, we present it within the context of the specific Schoen threefold that leads to the physically realistic MSSM model. We discuss the embedding of the line bundles, the existence of the extension bundle, and a number of necessary conditions for the resulting bundle to be slope‐stable and thus supersymmetric. An explicit example is presented, where two line bundles are embedded into the factor of the maximal subgroup of the hidden sector E 8 gauge group, and then enhanced to a non‐Abelian bundle by extension. For this example, there are in fact six inequivalent extension branches, significantly generalizing that space of solutions compared with hidden sectors constructed from a single line bundle.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Quantitative methods and modeling to assess COVID–19–interrupted in vivo pharmacokinetic bioequivalence studies with two reference batches

The coronavirus disease 2019 (COVID-19) has presented unprecedented challenges to the generic drug development, including interruptions in bioequivalence (BE) studies. Per guidance published by the US Food and Drug Administration (FDA) during the COVID-19 public health emergency, any protocol changes or alternative statistical analysis plan for COVID-19-interrupted BE study should be accompanied with adequate justifications and not lead to biased equivalence determination. In this study, we used a modeling and simulation approach to assess the potential impact of study outcomes when two different batches of a Reference Standard (RS) were to be used in an in vivo pharmacokinetic BE study due to the RS expiration during the COVID-19 pandemic. Simulations were performed with hypothetical drugs under two scenarios: (1) uninterrupted study using a single batch of an RS, and (2) interrupted study using two batches of an RS. The acceptability of BE outcomes was evaluated by comparing the results obtained from interrupted studies with those from uninterrupted studies. The simulation results demonstrated that using a conventional statistical approach to evaluate BE for COVID-19-interrupted studies may be acceptable based on the pooled data from two batches. An alternative statistical method which includes a “batch” effect to the mixed effects model may be used when a significant “batch” effect was found in interrupted four-way crossover studies. However, such alternative method is not applicable for interrupted two-way crossover studies. Overall, the simulated scenarios are only for demonstration purpose, the acceptability of BE outcomes for the COVID19-interrupted studies could be case-specific.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Aero‐servo‐elastic co‐optimization of large wind turbine blades with distributed aerodynamic control devices

Abstract This work introduces automated wind turbine optimization techniques based on full aero‐servo‐elastic models and investigates the potential of trailing edge flaps to reduce the levelized cost of energy (LCOE) of wind turbines. The Wind Energy with Integrated Servo‐control (WEIS) framework is improved to conduct the presented research. Novel methods for the generic implementation and tuning of trailing edge flap devices and their controller are also introduced. Primary flap and controller parameters are optimized to demonstrate potential maximum blade tip deflection reductions of 21 % . Concurrent design optimization (i.e., co‐design) of a novel segmented wind turbine blade with trailing edge flaps and its controller is then conducted to demonstrate blade cost savings of 5 % . Additionally, rotor diameter co‐design optimization is demonstrated to reduce the LCOE by 1.3 % without significant load increases to the tower. These results demonstrate the efficacy of control co‐design optimization using trailing edge flaps, and the entirety of this work provides a foundation for numerous control co‐design‐oriented studies for distributed aerodynamic control devices.

17 WIND ENERGY↗