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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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Microwave Debinding of Ceramics Produced by Additive Manufacturing

Microwave-assisted debinding offers an innovative approach to processing 3D-printed engineering ceramics. Ceramic parts produced with photopolymer-based additive manufacturing techniques require thermal debinding to remove polymers that bind ceramic particles during the printing process. Microwave-assisted debinding significantly reduces processing times and offers energy savings over conventional thermal treatments due to higher heating rates and more uniform heating. Finite-element modeling of the debinding process allows for the optimization and prediction of temperature distribution, stress development, and potential defects, leading to improved process control and material quality.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Chirality manipulation of ultrafast phase switches in a correlated CDW-Weyl semimetal

Light engineering of correlated states in topological materials provides a new avenue of achieving exotic topological phases inaccessible by conventional tuning methods. Here we demonstrate a light control of correlation gaps in a model charge-density-wave (CDW) and polaron insulator (TaSe 4 ) 2 I recently predicted to be an axion insulator. Our ultrafast terahertz photocurrent spectroscopy reveals a two-step, non-thermal melting of polarons and electronic CDW gap via the fluence dependence of a longitudinal circular photogalvanic current. This helicity-dependent photocurrent reveals continuous ultrafast phase switches from the polaronic state to the CDW (axion) phase, and finally to a hidden Weyl phase as the pump fluence increases. Additional distinctive attributes aligning with the light-induced switches include: the mode-selective coupling of coherent phonons to the polaron and CDW modulation, and the emergence of a non-thermal chiral photocurrent above the pump threshold of CDW-related phonons. The demonstrated ultrafast chirality control of correlated topological states here holds large potentials for realizing axion electrodynamics and advancing quantum-computing applications.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Energy Efficiency of Distributed Environmental Control Systems

In this report, we present an analytical evaluation of the potential of occupant-regulated distributed environmental control systems (DECS) to enhance individual occupant thermal comfort in an office building with no increase, and possibly even a decrease in annual energy consumption. To this end we developed and applied several analytical models that allowed us to optimize comfort and energy consumption in partitioned office buildings equipped with either conventional central HVAC systems or occupant-regulated DECS. Our approach involved the following interrelated components: 1. Development of a simplified lumped-parameter thermal circuit model to compute the annual energy consumption. This was necessitated by the need to perform tens of thousands of optimization calculations involving different US climatic regions, and different occupant thermal preferences of a population of ~50 office occupants. Yearly transient simulations using TRNSYS, a time-dependent building energy modeling program, were run to determine the robustness of the simplified approach against time-dependent simulations. The simplified model predicts yearly energy consumption within approximately 0.6% of an equivalent transient simulation. Simulations of building energy usage were run for a wide variety of climatic regions and control scenarios, including traditional “one-size-fits-all” (OSFA) control; providing a uniform temperature to the entire building, and occupant-selected “have-it-your-way” (HIYW) control with a thermostat at each workstation. The thermal model shows that, un-optimized, DECS would lead to an increase in building energy consumption between 3-16% compared to the conventional approach depending on the climate regional and personal preferences of building occupants. Variations in building shape had little impact in the relative energy usage. 2. Development of a gradient-based optimization method to minimize energy consumption of DECS while keeping each occupant’s thermal dissatisfaction below a given threshold. The DECS energy usage was calculated using the simplified thermal model. OSFA control; providing a uniform temperature to the entire building, and occupant-selected HIYW control with a thermostat at each workstation were implemented for 3 cities representing 3 different climatic regions and control scenarios. It is shown that optimization allows DECS to deliver a higher level of individual and population thermal comfort while achieving annual energy savings between 14 and 26% compared to OSFA. The optimization model also allowed us to study the influence of the partitions’ thermal resistance and the variability of internal loads at each office. These influences didn’t make significant changes in the optimized energy consumption relative to OSFA. The results show that it is possible to provide thermal comfort for each occupant while saving energy compared to OSFA Furthermore, to simplify the implementation of this approach, a fuzzy logic system has been developed to generalize the overall optimization strategy. Its performance was almost as good as the gradient system. The fuzzy system provided thermal comfort to each occupant and saved energy compared to OSFA. The energy savings of the fuzzy system were not as high as for the gradient-optimized system, but the fuzzy system avoided complete connectivity, and the optimization did not have to be repeated for each population. 3. We employed a detailed CFD model of adjacent occupied cubicles to extend the thermal-circuit model in three significant ways: (a) relax the “office wall” requirement by allowing energy to flow between zones via advection as well as conduction, (b) improve the comfort model to account both for radiation as well as convection heat transfer, and (c) support ventilation systems in which the temperature is stratified, such as in underfloor air distribution systems. Initially, three-dimensional CFD simulations of several cubicle configurations, with an adjoining corridor, were performed both to understand the advection between cubicles and the resulting temperature stratification. These simulations showed that the advective flow between cubicles is very significant and severely limits the occupants’ ability to control the personal micro-environments by simply controlling the temperature of the incoming air. Subsequently, the existing thermal-circuit model was extended to include the phenomena described above. The modifications to the thermal-circuit model, which were incorporated such that the simulation time was only slightly impacted, showed that accounting for room stratification resulting from the use of floor swirl diffusers could lead to 10%-26% reduction in the annual energy consumed for HVAC in non-temperate climates. This trend was evident in both OSFA and HIYW scenarios. However, the ratio of energy usage in the two scenarios was little affected by the enhancements in the thermal model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Multi-Commodity Traffic Signal Control and Routing With Connected Vehicles

We report a real-time traffic management policy that integrates traffic signal control and multi-commodity routing of connected vehicles in networks with multiple destinations is developed. The proposed policy is based on a multi-commodity formulation of the store-and-forward model and assumes all vehicles are able to exchange information with the infrastructure. Vehicles share information about their current location and final destination. Based on this information, the strategy determines both optimized signal timings at every intersection and vehicle-specific routing information at every link of the network. The control actions, i.e., signal times and routing information, are updated at every cycle and delivered by a finite horizon optimal control problem cast into a rolling horizon framework. The underlying optimization problem is convex, and thus the method is suitable for real-time operation in large networks. The method is validated via a micro-simulation study in networks with up to twenty intersections and, in all simulations, outperforms a real-time traffic-responsive signal control strategy that is based on a single-commodity store-and-forward model. The scalable computation effort for increasing network sizes and prediction horizon confirms the computational efficiency of the method.

42 ENGINEERING↗
DOE OSTI2016

Are atmospheric updrafts a key to unlocking climate forcing and sensitivity?

Both climate forcing and climate sensitivity persist as stubborn uncertainties limiting the extent to which climate models can provide actionable scientific scenarios for climate change. A key, explicit control on cloud–aerosol interactions, the largest uncertainty in climate forcing, is the vertical velocity of cloud-scale updrafts. Model-based studies of climate sensitivity indicate that convective entrainment, which is closely related to updraft speeds, is an important control on climate sensitivity. Updraft vertical velocities also drive many physical processes essential to numerical weather prediction. Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climate and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying vertical velocities, and parameterizations that do provide vertical velocities have been subject to limited evaluation against what have until recently been scant observations. Atmospheric observations imply that the distribution of vertical velocities depends on the areas over which the vertical velocities are averaged. Distributions of vertical velocities in climate models may capture this behavior, but it has not been accounted for when parameterizing cloud and precipitation processes in current models. New observations of convective vertical velocities offer a potentially promising path toward developing process-level cloud models and parameterizations for climate and numerical weather prediction. Taking account of the scale dependence of resolved vertical velocities offers a path to matching cloud-scale physical processes and their driving dynamics more realistically, with a prospect of reduced uncertainty in both climate forcing and sensitivity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Are Atmospheric Updrafts a Key to Unlocking Climate Forcing and Sensitivity?

Both climate forcing and climate sensitivity persist as stubborn uncertainties limiting the extent to which climate models can provide actionable scientific scenarios for climate change. A key, explicit control on cloud-aerosol interactions, the largest uncertainty in climate forcing, is the vertical velocity of cloud-scale updrafts. Model-based studies of climate sensitivity indicate that convective entrainment, which is closely related to updraft speeds, is an important control on climate sensitivity. Updraft vertical velocities also drive many physical processes essential to numerical weather prediction. Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climate and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying vertical velocities, and parameterizations which do provide vertical velocities have been subject to limited evaluation against what have until recently been scant observations. Atmospheric observations imply that the distribution of vertical velocities depends on the areas over which the vertical velocities are averaged. Distributions of vertical velocities in climate models may capture this behavior, but it has not been accounted for when parameterizing cloud and precipitation processes in current models. New observations of convective vertical velocities offer a potentially promising path toward developing process-level cloud models and parameterizations for climate and numerical weather prediction. Taking account of scale-dependence of resolved vertical velocities offers a path to matching cloud-scale physical processes and their driving dynamics more realistically, with a prospect of reduced uncertainty in both climate forcing and sensitivity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Meta-Level Design Guidance and Operator Performance Measures for Hybrid Control Rooms

The purpose of this grant was to refine and test methods to evaluate digital human system interfaces (HSI) for existing largely analog nuclear power plant (NPP) control rooms (CRs). We hypothesized that CR operator performance (CROP) in hybrid NPP CRs will be improved if future digital HSI is designed based on: 1) more robust design guidelines for operator situation awareness (SA) and decision-making (DM); 2) enhanced CROP metrics during HSI evaluation; 3) an ontology for selecting simulation scenarios to evaluate digital HSI; and 4) a model to predict optimal HSI design attributes given CROP objectives. The team conducted numerous nuclear power plant site visits (e.g., to Brown’s Ferry, Palo Verde, Sequoyah and Vogtle) to study control room operations and crisis management and 3 visits to the NRC Chattanooga Training Center. We observed research studies at DC Cook and NRC Chattanooga in collaboration with the Halden Reactor Project. We collected operator performance and workload data in one NPP simulator. A comprehensive human (i.e., CROP) performance literature review was published in Applied Ergonomics. We obtained operator team performance data from CAER to produce our data fusion model and a paper delineating the results have been accepted (revision submitted) in Resilience Engineering and System Safety. We analyzed interview data related to scenario ontology development; a manuscript is still in preparation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Vector-level feedforward control of LPBF melt pool area using a physics-based thermal model

Laser powder bed fusion (LPBF) is an additive manufacturing technique that has gained popularity thanks to its ability to produce geometrically complex, fully dense metal parts. However, these parts are prone to internal defects and geometric inaccuracies, stemming in part from variations in the melt pool. Here, this paper proposes a novel vector-level feedforward control framework for regulating melt pool area in LPBF. By decoupling part-scale thermal behavior from small-scale melt pool physics, the controller provides a scale-agnostic prediction of melt pool area and efficient optimization over it. This is done by operating on two coupled lightweight models: a finite-difference thermal model that efficiently captures vector-level temperature fields and a reduced-order, analytical melt pool model. Each model is calibrated separately with minimal single-track and 2D experiments, and the framework is validated on a complex 3D geometry in both Inconel 718 and 316L stainless steel. Results showed that feedforward vector-level laser power scheduling reduced geometric inaccuracy in key dimensions by 62%, overall porosity by 16.5%, and photodiode root-mean-squared deviation by 38.5% on average. Overall, this modular, data-efficient approach demonstrates that proactively compensating for known thermal effects can significantly improve part quality while remaining computationally efficient and readily extensible to other materials and machines.

Additive manufacturing↗
DOE OSTI2023

(Project 18-15502) Reducing Uncertainty in Radionuclide Transport Prediction Using Multiple Environmental Tracers (Final Report)

In order to successfully site and design a nuclear waste disposal facility, DOE scientists are required to show safe containment of the radioactive waste for up to 1 million years. A significant hurdle to accurately and convincingly demonstrating disposal safety is predicting the fate of radioactive elements once they enter the groundwater system surrounding the repository. These predictions are often made with computer models, which contain accurate physics of groundwater movement and chemical reactions that occur during groundwater flow. As computer power increases, the physics and chemistry of these computer simulators can become more and more realistic and the physically based error decreases. However, the ability of these computer models to provide accurate predictions in a specific place, over long time periods, requires the scientists and engineers to know the subsurface properties of the Earth that control groundwater movement. In particular, groundwater scientists and engineers need to know the groundwater fluid velocity, which can change over time and strongly vary with location within the groundwater system. Because the Earth’s subsurface cannot be directly seen, and can only be sampled at drilling locations, the properties of the groundwater system are never known completely, and computer models of groundwater transport will always have some amount of uncertainty. This project’s principal goal was to use chemicals and isotope “tracers”, which have been introduced to the groundwater system by natural processes over long time periods, to help inform computer models of the groundwater velocity and subsurface properties. The goal was to calculate how much better predictions of groundwater transport were when these “tracers” were used to inform the computer models.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach

Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO 2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global change scenarios for central California) on the potential activity, abundance and dominant taxa of soil nitrite-oxidizing bacteria (NOB). Using a trait-based model, we then tested whether categorizing NOB into a few functional groups unified by physiological traits enables understanding and predicting how soil NOB respond to global environmental change. Contrasted responses to global change treatments were observed between three main NOB functional types. In particular, putatively mixotrophic Nitrobacter, rare under most treatments, became dominant under the 'High CO 2 +Nitrogen+Precipitation' treatment. The mechanistic trait-based model, which simulated ecological niches of NOB types consistent with previous ecophysiological reports, helped predicting the observed effects of global change on NOB and elucidating the underlying biotic and abiotic controls. Our results are a starting point for representing the overwhelming diversity of soil bacteria by a few functional types that can be incorporated into models of terrestrial ecosystems and biogeochemical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Evaluation of Data-Driven Models for Predicting Solar Photovoltaics Power Output

This research was undertaken to evaluate different inverse models for predicting power output of solar photovoltaic (PV) systems under different practical scenarios. In particular, we have investigated whether PV power output prediction accuracy can be improved if module/cell temperature was measured in addition to climatic variables, and also the extent to which prediction accuracy degrades if solar irradiation is not measured on the plane of array but only on a horizontal surface. We have also investigated the significance of different independent or regressor variables, such as wind velocity and incident angle modifier in predicting PV power output and cell temperature. The inverse regression model forms have been evaluated both in terms of their goodness-of-fit, and their accuracy and robustness in terms of their predictive performance. Given the accuracy of the measurements, expected CV-RMSE of hourly power output prediction over the year varies between 3.2% and 8.6% when only climatic data are used. Depending on what type of measured climatic and PV performance data is available, different scenarios have been identified and the corresponding appropriate modeling pathways have been proposed. The corresponding models are to be implemented on a controller platform for optimum operational planning of microgrids and integrated energy systems.

14 SOLAR ENERGY↗
DOE OSTI2013

Tracking control of colloidal particles through non-homogeneous stationary flows

We consider the problem of controlling the trajectory of a single colloidal particle in a fluid with steady non-homogeneous flow. We use a Langevin equation to describe the dynamics of this particle, where the friction term is assumed to be given by the Faxén's Theorem for the force on a sphere immersed in a stationary flow. We use this description to propose an explicit control force field to be applied on the particle such that it will follow asymptotically any given desired trajectory, starting from an arbitrary initial condition. We show that the dynamics of the controlled particle can be mapped into a set of stochastic harmonic oscillators and that the velocity gradient of the solvent induces an asymmetric coupling between them. We study the particular case of a Brownian particle controlled through a plane Couette flow and show explicitly that the velocity gradient of the solvent renders the dynamics non-stationary and non-reversible in time. We quantify this effect in terms of the correlation functions for the position of the controlled particle, which turn out to exhibit contributions depending exclusively on the non-equilibrium character of the state of the solvent. In order to test the validity of our model, we perform simulations of the controlled particle moving in a simple shear flow, using a hybrid method combining molecular dynamics and multi-particle collision dynamics. We confirm numerically that the proposed guiding force allows for controlling the trajectory of the micro-sized particle by obligating it to follow diverse specific trajectories in fluids with homogeneous shear rates of different strengths. In addition, we find that the non-equilibrium correlation functions in simulations exhibit the same qualitative behavior predicted by the model, thus revealing the presence of the asymmetric non-equilibrium coupling mechanism induced by the velocity gradient.

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

Explainable discrepancy checker and diagnosis for digital Twin-based supervisory control system

By virtually representing a physical object and process, a digital twin (DT) enables optimal autonomous operations by combining classical and novel frameworks in sensors, state predictions, and multi-input/multi-output systems. A DT’s values depend on how well models estimate quantities of interest and on how uncertainty is handled. Moreover, DTs often combine physics-based and data-driven models with mixed fidelities, where classical uncertainty quantification (UQ) struggles with many sources of uncertainty and real-time constraints. Here, this work presents a UQ-based discrepancy checking and diagnosis tool for a DT-based supervisory control system. The tool is developed using metadata from an automated DT development process to learn correlations between sources of uncertainties and outcomes. During operation, it compares predictions with measurements, attributes discrepancies to dominant sources, and recommends parameter and configuration updates. We verify the workflow on a synthetic temperature-control problem and deploy it on a virtual Thermal Energy Delivery System, reducing mismatch and improving control robustness.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Numerical framework for integrated additive manufacturing-compression molding (AM-CM) of thermoplastic composites

Additive manufacturing-compression molding (AM-CM) has emerged as a transformative technology in advanced composite manufacturing. Additive manufacturing (AM) offers high design flexibility and the ability to produce complex geometries with precisely aligned fibers in the preferred orientation. Compression molding (CM) enhances composite materials by providing excellent dimensional stability, reduced porosity, high production rates, and a smooth surface finish. Despite these advantages, extensive integrated analysis is required to optimize processing conditions for improved fiber orientation distribution (FOD) and porosity control. Here, this study develops a comprehensive numerical model to simulate the AM-CM manufacturing process. The model isolates the effects of both the AM and CM phases while also capturing their integration. Additionally, it accounts for heat transfer, temperature-dependent viscosity, and fiber orientation in the extruded fiber-filled polymer, accurately representing material behavior during processing. This approach enables the analysis of interactions between deposited beads of complex strand shapes and their interface regions after full compression. Moreover, the model predicts key parameters such as polymer flowability, fiber orientation, and temperature evolution in AM-CM parts. By optimizing processing conditions, it facilitates a controlled and predictable microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Spin-on deposition of amorphous zeolitic imidazolate framework films for lithography applications

Amorphous zeolitic imidazolate framework (aZIF) films have been recently introduced as resists for electron beam and extreme ultraviolet lithography. aZIFs are also being considered for separation applications, including thin film membranes. However, the reported methods for aZIF deposition are currently based on highly empirical trial-and-error approaches that hinder control of film composition, thickness and uniformity as well as scale-up and transferability to different coating geometries. This work presents a method for depositing aZIF films with controllable thickness using dilute precursors mixed immediately before encountering the substrate. Importantly, the method is amenable to quantitative analysis by computational fluid dynamics to extract intrinsic deposition rates and limiting reactant transport diffusivities, enabling predictive physics-based modeling of the deposition process. This allows the deposition method to be adapted for spin coating on silicon wafers to prepare high-quality aZIF films with consistently controlled thickness. Using this approach, high-resolution resist performance and wafer-scale use for beyond extreme-ultraviolet lithography of aZIF films is demonstrated.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

A Privacy Preserving Model-Free Optimization and Control Framework for Demand Response from Residential Thermal Loads

We consider the problem of optimizing the cost of procuring electricity for a large collection of homes managed by a load serving entity, by pre-cooling or pre-heating the thermal inertial loads in the homes to avoid procuring power during periods of peak electricity pricing. We would like to accomplish this objective in a completely privacy-preserving and model-free manner, that is, without direct access to the state variables (temperatures or power consumption) or the dynamical models (thermal characteristics) of individual homes, while guaranteeing personal comfort constraints of the consumers. We propose a two-stage optimization and control framework to address this problem. In the first stage, we use a long short-term memory (LSTM) network to predict hourly electricity prices, based on historical pricing data and weather forecasts. Given the hourly price forecast and thermal models of the homes, the problem of designing an optimal power consumption trajectory that minimizes the total electricity procurement cost for the collection of thermal loads can be formulated as a large-scale integer program (with millions of variables) due to the on-off cyclical dynamics of such loads. We provide a simple heuristic relaxation to make this large-scale optimization problem model-free and computationally tractable. In the second stage, we translate the results of this optimization problem into distributed open-loop control laws that can be implemented at individual homes without measuring or estimating their state variables, while simultaneously ensuring consumer comfort constraints. We demonstrate the performance of this approach on a large-scale test case comprising of 500 homes in the Houston area and benchmark its performance against a direct model-based optimization and control solution.

Sivaranjani, S.↗
DOE OSTI2018

Effect of Pore Size, Morphology and Orientation on the Bulk Stiffness of a Porous Ti35Nb4Sn Alloy

The metal foams of a titanium alloy were designed to study porosity as well as pore size and shape independently. These were manufactured using a powder metallurgy/space-holder technique that allowed a fine control of the pore size and morphology; and then characterized and tested against well-established models to predict a relationship between porosity, pore size and shape, and bulk stiffness. Among the typically used correlations, existing power-law models were found to be the best fit for the prediction of macropore morphology against compressive elastic moduli, outperforming other models such as exponential, polynomial or binomial. Other traditional models such as linear ones required of updated coefficients to become relevant to metal porous sintered macrostructures. The new coefficients reported in this study contribute toward a design tool that allows the tailoring of mechanical properties through porosity macrostructure. The results show that, for the same porosity range, pore shape and orientation have a significant effect on mechanical performance and that they can be predicted. Conversely, pore size has only a mild impact on bulk stiffness.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A Review of Texture Evolution Mechanisms During Deformation by Rolling in Aluminum Alloys

The current understanding of texture evolution during deformation by rolling in aluminum alloys was summarized. This included understanding the evolution mechanisms and several key factors of initial texture, microstructure, alloy composition, deformation temperature, stress–strain condition, and rolling geometry. Related models on predicting texture evolution during rolling were also discussed. Finally, for this research field, the recommendations for controlling the formation of rolling textures were proposed.

36 MATERIALS SCIENCE↗