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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fast physics-based launcher optimization for electron cyclotron current drive

With the increased urgency to design fusion pilot plants, fast optimization of electron cyclotron current drive (ECCD) launchers is paramount. Traditionally, this is done by coarsely sampling the 4D parameter space of possible launch conditions consisting of (1) the launch location (constrained to lie along the reactor vessel), (2) the launch frequency, (3) the toroidal launch angle, and (4) the poloidal launch angle. For each initial condition, a ray-tracing simulation is performed to evaluate the ECCD efficiency. Unfortunately, this approach often requires a large number of simulations (sometimes millions in extreme cases) to build up a dataset that adequately covers the plasma volume, which must then be repeated every time the design point changes. Here we adopt a different approach. Rather than launching rays from the plasma periphery and hoping for the best, we instead directly reconstruct the optimal ray for driving current at a given flux surface using a reduced physics model coupled with a commercial ray-tracing code. Repeating this throughout the plasma volume requires only hundreds of simulations, constituting a significant speedup. The new method is validated on two separate example tokamak profiles, and is shown to reliably drive localized current at the specified flux surface with the same optimal efficiency as obtained from the traditional approach.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Machine learning and atomic layer deposition: Predicting saturation times from reactor growth profiles using artificial neural networks

In this work, we explore the application of deep neural networks to the optimization of atomic layer deposition (ALD) processes. In particular, we focus on a one-shot optimization problem, where we try to predict the optimal dose time that leads to saturation everywhere in the reactor based on thickness values measured at different points of an ALD reactor after a single trial growth. In order to tackle this problem, we introduce a dataset designed to train neural networks to predict saturation times based on these inputs for a cross-flow ALD reactor. Here, we then explore the predictive ability of artificial neural networks of different depths and sizes using a separate testing dataset to evaluate their accuracies. The results obtained show that networks trained using stochastic gradient descent methods can accurately predict saturation times without requiring any additional information on the surface kinetics. This provides a viable approach to minimize the number of experiments required to optimize new ALD processes in a known reactor, and it highlights the way machine learning can be leveraged for thin film growth and manufacturing. While the datasets and training procedure depend on the reactor geometry, the trained neural networks provide a general surrogate model connecting thickness values and trial dose times with optimal saturation times that can be reused for different ALD processes within the same reactor.

36 MATERIALS SCIENCE↗
DOE OSTI2023

EV charging site day-ahead load prediction in a synthetic environment for RL based grid-informed charging

Ensuring grid health in the face of increasing demand for power is an emerging challenge especially due to transportation electrification. A free market approach to influencing electric vehicle (EV) load through grid-informed hourly dynamic pricing is introduced in this work. The setting of charging price is done by a reinforcement learning (RL) agent that learns the complicated dynamics by interacting with a synthetic environment. This synthetic environment is a combination of distribution feeder simulation, EV charger user behavior dynamics, and EV charging simulation. A key module in this synthetic environment involves obtaining the day-ahead charging profile of EV charging stations based on real-world past data. The day-ahead prediction is also useful in other traditional optimizations related to EV charge scheduling. The proposed approach involves using EV charging data from two different past time horizons – one to determine the shape of the daily profile and the other to determine a scaling value to capture actual energy consumption. Real-world charging data over many years from the ACN charging network has been used to demonstrate the ability to predict the day-ahead profile with only charge session data. Both Python and MATLAB have been used for data cleaning, processing, analysis, and prediction.

Suryanarayana, Harish↗
DOE OSTI2021

Prediction of electron density and pressure profile shapes on NSTX-U using neural networks

A new model for prediction of electron density and pressure profile shapes on NSTX and NSTX-U has been developed using neural networks. The model has been trained and tested on measured profiles from experimental discharges during the first operational campaign of NSTX-U. By projecting profiles onto empirically derived basis functions, the model is able to efficiently and accurately reproduce profile shapes. In order to project the performance of the model to upcoming NSTX-U operations, a large database of profiles from the operation of NSTX is used to test performance as a function of available data. The rapid execution time of the model is well suited to the planned applications, including optimization during scenario development activities, and real-time plasma control. A potential application of the model to real-time profile estimation is demonstrated.

Boyer, Mark↗
DOE OSTI2021

Joint inversion of electromagnetic measurements for the determination of water saturation profiles in concrete structures

Highlights: • DC-electrical and dielectric data are combined to estimate concrete saturation degree. • A joint inversion approach of the electromagnetic measurements is proposed. • The sensitivity of the measurements to the saturation model parameters is analysed. • The new joint approach was developed and is applied to synthetic and real data. • The benefits of the joint approach over the inversion of one data type are highlighted. Water saturation profiles in concrete are essential to assess its durability and can be determined using non-destructive techniques, especially the electric and the capacitive methods. In this paper, we propose a new inversion scheme where both resistivity and permittivity measurements are inverted jointly to retrieve the saturation profile. The finite element method is used to model the measurements in 3D, the concrete having a saturation profile with depth, represented by a continuous model taking the form of a Weibull curve with four parameters. A non-linear least-squares optimization based on the Levenberg-Marquardt scheme is developed for the inversion of measurements. Results show that information gathered from both measurements enriches the reconstructed profile, leading to a more reliable estimation of saturation profiles. We believe that the joint inversion method herein developed could lead to the study of more complex phenomena, such as the coupled water-chloride ingress.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Study on Surface Texture and Wettability of Femtosecond Laser Treated Aluminum Alloys

This research aims to study the effects of femtosecond laser treatment on surface texture and wettability properties. The first portion of this research studies 2024-T3 aluminum alloy concerning surface texture (e.g., roughness), wettability, and surface free energy. SEM images help aid the researchers in detecting minuscule changes in surface texture. Profilometry extracts topographical data from the surface. A contact angle goniometer measures contact angle, which follows the sessile drop method. Owens, Wendt, Rabel, and Kaelble's method calculates the surface free energy in conjunction with the contact angle. Femtosecond laser modification produces relatively uniform surface morphology at the macroscale. When looking at the microscale, surface morphology differs depending on the interplay between material melting and self-assembly, where the surface becomes rougher after laser treatment. Contact angle results show that all but the control sample and sample 7 exhibit hydrophobic behavior, i.e., contact angle >90°, with Sample 1 having the most significant angle of 115°. The effects of laser parameters on surface energy is apparent. More work is needed to establish correlations among these three quantities: contact angle, surface energy, and laser parameters. The second portion of this research studies aluminum alloy 6061 as the subject material with the primary goal of creating a superhydrophobic surface. A central composite design creates the laser surface texturing design of experiments. SEM images help aid the researchers in detecting minuscule changes in surface texture. A 3D optical profiler extracts topographical data from the surface. The sessile drop method incorporates a contact angle goniometer to measure the contact angle. The response surface method builds a second-order polynomial model for the contact angle and obtains optimized parameters to maximize the contact angle. This research shows that laser surface texturing can generate a wide range of surface profiles and roughness values with geometric features ranging from hundreds of μm to submicron. All three laser parameters (pulse energy, pulse duration, repetition rate) affect surface roughness and contact angle to some degree. To quantify the relationship between the contact angle, pulse energy, and pulse repetition rate, a response surface model for Al 6061 is identified and used to find the optimal conditions of E=214 μJ, tp=10 ps, and fp=2427 Hz with a predicted maximum contact angle of 161°. A confirmation experiment produces a contact angle of 168°, in good agreement with the predicted value.

Angell, Dakota↗
DOE OSTI2023

Revisiting the definition of field capacity as a functional parameter in a layered agronomic soil profile beneath irrigated maize

The soil water content at the condition of field capacity (θ FC ) is a key parameter in irrigation scheduling and has been suggested to be determined by running a synthetic drainage experiment until the flux rate (q) at the bottom of the soil profile achieves a predefined negligible value (q FC ). We question the impact of q FC on the assessment of field capacity. Moreover, calculating θ FC as the integral mean of the water content profile when q is equal to q FC is strictly valid only for uniform soil profiles. By contrast, this practice is ambiguous and biased for stratified soil profiles due to the soil water content discontinuity at the layer interfaces. In this study, the concept of field capacity was revisited and adapted to practical agronomic heuristics. By resorting to the assessment of root-zone water storage capacity (W), we envision field capacity as a functional hydraulic parameter derived from synthetic irrigation scheduling scenarios to minimize drought stress, drainage, and nitrate leachate below the root zone. A functional analysis was carried out on a 135-cm-thick layered soil profile beneath maize in eastern Nebraska. On-farm irrigation scheduling applications and agricultural practices were recorded for 20 years (2001–2020) at a daily time step. Hydrus-1D was calibrated and validated with direct measurements of the soil water retention curve and soil water content data, respectively, in each soil layer. A set of functional field capacity values was derived from 24 irrigation scheduling scenarios, and the optimal water storage capacity at field capacity (W FC ) was approximately 50 cm (corresponding to about 80% saturation in the soil profile). An average irrigation amount of 217.5 mm distributed over 21 events was obtained by using optimal irrigation scheduling, which was initiated when the matric pressure head took on a value of –700 cm and the irrigation rate was set at 1.0 cm d –1 . This irrigation practice ensured water storage at approximately the same level (ideally at W FC ) by sustaining only evapotranspiration fluxes in the uppermost portion of the root zone and by limiting excessive drainage. This protocol can be transferred to other agricultural fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Model predictive current profile control in tokamaks by exploiting spatially moving electron cyclotron current drives

The plasma control systems in next-generation tokamaks like ITER will balance competing control objectives to achieve the desired level of performance in advanced scenarios while preventing magnetohydrodynamic instabilities and disruptions. During normal tokamak operation, the points of incidence of the electromagnetic waves generated by the electron cyclotron heating and current drives (EC H&CDs) are usually fixed in space. However, the points of incidence can be modified in real-time by changing the angles of the mirrors that reflect the EC H&CD waves. Altering the points of incidence, in turn, varies the ability of the plasma control system to regulate a plasma property. For instance, changing the EC H&CD wave incidence location may place the power demands necessary to achieve a particular plasma target within saturation limits. Therefore, using the EC H&CD deposition location, which is related to the EC H&CD mirror angle, as a supplementary controllable variable may facilitate access to a given target scenario. However, active scenario-control algorithms have not been designed so far to fully exploit this capability in real time. In this work, a model predictive controller that can handle actuation locations as control inputs is developed. In particular, the controller is designed to regulate both the auxiliary powers and the EC H&CD deposition locations in a pre-defined optimal sense to achieve the control objective of attaining and sustaining a target current profile. Further, the proposed controller is tested for a DIII-D tokamak scenario in nonlinear simulations using the Control Oriented Transport SIMulator (COTSIM).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Retrieval of temperature and humidity profiles from ground-based high-resolution infrared observations using an adaptive fast iterative algorithm

Various retrieval algorithms have been developed for retrieving temperature and water vapor profiles from Atmospheric Emitted Radiance Interferometer (AERI) observations. The physical retrieval algorithm, named AERI Optimal Estimation (AERIoe), outperforms other retrieval algorithms in many aspects except the retrieval time, which is significantly increased due to the complex radiative transfer process. The calculation of the Jacobian matrix is the most computationally intensive step of the physical retrieval algorithm. Interestingly, an analysis of the change in AERI observations' information content with respect to Jacobians revealed that the AERIoe algorithm's performance presents negligible dependence on these metrics. Thus, the Jacobian matrix could remain unchanged when the variation in the atmospheric state is small in the retrieval process to reduce the most time-consuming computation. On the basis of the above findings, a fast physical–iterative retrieval algorithm was proposed by adaptively recalculating Jacobians in keeping with the changes in the atmospheric state. Experiments with synthetic observations demonstrate that the proposed method experiences an average reduction in retrieval time by an impressive 59 % compared to the original AERIoe algorithm while achieving maximum root-mean-square errors of less than 0.95 K and 0.22 log(ppmv) for heights below 3 km for the temperature and water vapor profile, respectively. Further analyses revealed that the fast-retrieval algorithm reached an acceptable convergence rate of 98.7 %, marginally lower than AERIoe's 99.9 % convergence rate for the 826 cases used in this study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Virtual Volumetric Additive Manufacturing (VirtualVAM)

Abstract Tomographic volumetric additive manufacturing (VAM) produces arbitrary 3D geometries by exposure of a rotating volume of photopolymer resin to tomographically‐patterned illumination. This enables high speed, layer‐less printing of parts from a wide range of photopolymers not amenable to layer‐by‐layer processes. Since the entire geometry is produced at once over the course of a few seconds to minutes, molecular diffusion length scales become significant to the printing process. Understanding these molecular reaction and diffusion processes is imperative for advancing VAM to a usable technology. These processes are experimentally very difficult to monitor and measure. Herein, VirtualVAM ‐ a simulation framework for modeling the tomographic VAM process, is developed and experimentally validated. VirtualVAM simulates reaction, diffusion, and heat generation processes over the course of a print with single‐voxel resolution. From a few experimentally‐determined input parameters and a set of images for projection, VirtualVAM is able to generate a large spatio‐temporal data set for any given tomographic VAM print. Using VirtualVAM, a number of experimentally‐unattainable aspects of the VAM process are investigated such as single‐voxel conversion profiles, effect of molecular oxygen, and stopping time determination. VirtualVAM also enables the optimization of exposure patterns to further improve contrast between in‐part and out‐of‐part delivered dose.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Parametric and sensitivity analysis of a PCM-integrated wall for optimal thermal load modulation in lightweight buildings

Load modulation in buildings is becoming increasingly important due to growing disparity in energy demand during peak and off-peak hours. Integrating phase change material (PCM) in building envelopes and using a controlled precooling strategy can provide substantial thermal load modulation; however, it may greatly increase the total energy use. Previous studies have employed PCM in building envelopes primarily for energy savings and, to some extent, peak load shedding and shifting. However, the load modulation capacity of a PCM-integrated envelope has not been well explored in the literature. In this study, we perform an extensive parametric and sensitivity analysis on PCM-integrated lightweight building walls and examine the combinatory effects of various PCM parameters on thermal load modulation and wall-related heat gains in buildings. Using numerical simulations, we investigate eight PCM parameters: PCM location in the wall, transition temperature, thickness, latent heat, transition range, density, specific heat, and thermal conductivity. Here, we evaluate their impact and relative importance to achieve maximum load modulation in buildings without compromising occupants’ thermal comfort or total energy use. The results show that the optimized PCM proposed in this study can completely invert the transient heat gain profile of the wall, providing up to 70% reduction of wall-related heat gain during peak hours without a major increase in the cumulative heat gain.

42 ENGINEERING↗
DOE OSTI2019

Simulating Dynamic Roots in the Energy Exascale Earth System Land Model

Abstract Roots are important contributors to plant development, functioning to provide nutrients and water for plant growth. However, roots and their functions are often simplified in Earth system models, which limit the feedback of root foraging strategy on plant productivity, and their impacts on the carbon cycle. The goal of this study is to introduce a new method to resolve the vertical structure of roots over time. The method allows plasticity of rooting depth distribution under nonuniform profiles of water and nitrogen, which influences aboveground dynamics. The dynamic root model optimizes root distribution for both water and nitrogen uptake but gives priority to plant water demands. I implement this new method in the Energy Exascale Earth System model. The resulting root distribution maintains agreement with observations in most ecosystems and marginally improves the gross primary productivity estimated by the model, compared to satellite observations. Increases in gross primary productivity are simulated in desert and boreal ecosystems. However, the model does not capture deep roots in the dry tropics, and therefore, productivity losses are observed in parts of the Amazon and the African savannah. I discuss details of the model algorithm, along with some sensitivity studies that shed light on the model behavior in water‐limited ecosystems. The study shows that additional model processes, such as climate dependent root depth, root hydraulics, root form and function, and better nitrogen uptake, should be considered to improve the root water uptake in the Energy Exascale Earth System Land Model (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The regulatory and transcriptional landscape associated with carbon utilization in a filamentous fungus

Filamentous fungi, such as Neurospora crassa , are very efficient in deconstructing plant biomass by the secretion of an arsenal of plant cell wall-degrading enzymes, by remodeling metabolism to accommodate production of secreted enzymes, and by enabling transport and intracellular utilization of plant biomass components. Although a number of enzymes and transcriptional regulators involved in plant biomass utilization have been identified, how filamentous fungi sense and integrate nutritional information encoded in the plant cell wall into a regulatory hierarchy for optimal utilization of complex carbon sources is not understood. Here, we performed transcriptional profiling of N. crassa on 40 different carbon sources, including plant biomass, to provide data on how fungi sense simple to complex carbohydrates. From these data, we identified regulatory factors in N. crassa and characterized one (PDR-2) associated with pectin utilization and one with pectin/hemicellulose utilization (ARA-1). Using in vitro DNA affinity purification sequencing (DAP-seq), we identified direct targets of transcription factors involved in regulating genes encoding plant cell wall-degrading enzymes. In particular, our data clarified the role of the transcription factor VIB-1 in the regulation of genes encoding plant cell wall-degrading enzymes and nutrient scavenging and revealed a major role of the carbon catabolite repressor CRE-1 in regulating the expression of major facilitator transporter genes. These data contribute to a more complete understanding of cross talk between transcription factors and their target genes, which are involved in regulating nutrient sensing and plant biomass utilization on a global level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Control of core–shell nanoparticles properties through plasma synthesis: a computational study

The improved properties of core–shell nanoparticles (CSNPs) over homogeneous nanoparticles (NPs) have expanded and diversified the applications of these nanomaterials. However, controlling the properties of CSNPs can be a challenging task. Low temperature plasmas have proven to be an effective method of producing NPs with uniform size and morphology, and high yield. That said, NP transport and growth dynamics are sensitive to LTP properties. We report on a computational investigation of the evolution of Ge–Si CSNP properties as a function of operating conditions through the modeling of a flowing, two-zone inductively coupled plasma (ICP) reactor. Ar/GeH 4 and Ar/SiH 4 gas mixtures were supplied to separate plasma zones at a pressure of 1 Torr to promote growth of Ge cores and Si shells. The negatively charged CSNPs are trapped electrostatically in the vicinity of the antennas where the plasma is generated and where the majority of particle growth occurs. Particles that grow to a critical size are then de-trapped by fluid drag due to neutral gas flow. A two-dimensional hybrid plasma model coupled with a three-dimensional kinetic NP transport model were utilized to resolve plasma chemistry and NP growth processes that take place on distinct timescales. The trends in CSNP properties and trapping mechanisms associated with flow rate, applied ICP power and inlet precursor fraction are discussed. While the spatial distribution of plasma produced radical species can have significant impact on the NP growth process, the NP transport dynamics are what ultimately dictates the growth environment that is unique to each particle and so determines their final dimension and composition. The key to optimizing reactor conditions involves controlling the spatial density of growth species and plasma profile as a means to tailor particle trapping dynamics suitable to produce CSNPs for a specific application.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection

Communication overhead in federated learning (FL) poses a significant challenge for network anomaly detection systems, where the myriad of client configurations and network conditions can severely impact system efficiency and detection accuracy. While existing approaches attempt to address this through individual optimization techniques, they often fail to maintain the delicate balance between reduced overhead and detection performance. This paper presents an adaptive FL framework that dynamically combines batch size optimization, client selection, and asynchronous updates to achieve efficient anomaly detection. Through extensive profiling and experimental analysis on two distinct datasets-UNSW-NBIS for general network traffic and ROAD for automotive networks-our framework reduces communication overhead by 97.6%; (from 700.0s to 16.8s) compared to synchronous baseline approaches while maintaining comparable detection accuracy (95.10%; vs. 95.12%;). Statistical validation using Mann-Whitney U test confirms significant improvements (p < 0.05) over existing FL approaches across both datasets, demonstrating the framework's adaptability to different network security contexts. Detailed profiling analysis reveals the efficiency gains through dramatic reductions in GPU operations and memory transfers while maintaining robust detection performance under varying client conditions.

Marfo, William [University of Texas at El Paso]↗
DOE OSTI2024

Doping engineering: Next step toward room temperature performance of terahertz quantum cascade lasers

We hereby offer a comprehensive analysis of various factors that could potentially enable terahertz quantum cascade lasers (THz QCLs) to achieve room temperature performance. We thoroughly examine and integrate the latest findings from recent studies in the field. Our work goes beyond a mere analysis; it represents a nuanced and comprehensive exploration of the intricate factors influencing the performance of THz QCLs. Through a comprehensive and holistic approach, we propose novel insights that significantly contribute to advancing strategies for improving the temperature performance of THz QCLs. This all-encompassing perspective allows us not only to present a synthesis of existing knowledge but also to offer a fresh and nuanced strategy to improve the temperature performance of THz QCLs. We draw new conclusions from prior works, demonstrating that the key to enhancing THz QCL temperature performance involves not only optimizing interface quality but also strategically managing doping density, its spatial distribution, and profile. This is based on our results from different structures, such as two experimentally demonstrated devices: the spit-well resonant-phonon and the two-well injector direct-phonon schemes for THz QCLs, which allow efficient isolation of the laser levels from excited and continuum states. In these schemes, the doping profile has a setback that lessens the overlap of the doped region with the active laser states. Our work stands as a valuable resource for researchers seeking to gain a deeper understanding of the evolving landscape of THz technology. Furthermore, we present a novel strategy for future endeavors, providing an enhanced framework for continued exploration in this dynamic field. This strategy should pave the way to potentially reach higher temperatures than the latest records reached for T max of THz QCLs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Atmospheric Emitted Radiance Interferometer Optimal Estimation (AERIoe) Value-Added Product Report

The Atmospheric Emitted Radiance Interferometer (AERI) Optimal Estimation (AERIoe) algorithm (Turner and Löhnert 2014, Turner and Blumberg 2019) retrieves profiles of temperature and water vapor mixing ratio, together with cloud properties for a single-layer cloud (i.e., liquid water path [LWP], effective radius), from AERI-observed infrared radiance spectrum and other measurements. The data can be used to characterize the evolution of the planetary boundary layer and boundary-layer clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Development of ~25% Efficient Double Side Screen Printed Poly-Si/SiO x Passivated Contact Solar Cells

This program aims to overcome these challenges and develop high-efficiency (24-25%) double-side (DS) TOPCon solar cells by maximizing passivation on both sides while mitigating light absorption losses. To achieve this, the program will implement either thin (≤ 20 nm) homogeneous n-TOPCon on the entire front surface or selective area thick (≥ 100 nm) n-TOPCon only underneath the front metal contact with ~90% field region composed of dielectric passivated textured n-Si in between the poly-Si/metal grid. The rear side will feature ~250 nm-thick full-area planar p-TOPCon, which functions as the rear junction. Recombination and parasitic absorption losses in the front and rear TOPCon layers will be minimized by tailoring their doping profiles and thickness. Additionally, the device performance will be further enhanced through the optimization of bulk parameters, including the carrier lifetime, resistivity, and thickness of the n-type Si absorber. Finally, advanced metallization techniques, such as fine-line printing, and floating busbar or busbar-less designs, will be employed to reduce recombination, resistive, and optical losses. The program started with the development of a technology roadmap for DS-TOPCon cells to achieve target efficiency.

14 SOLAR ENERGY↗