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Results for “Function Driven Optimization”

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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Strain Effects in SrHfO 3 Films Grown by Hybrid Molecular Beam Epitaxy

Perovskite oxide heterostructures host a large number of interesting phenomena such as ferroelectricity, which are often driven by octahedral distortions in the crystal that may induce polarization. SrHfO 3 (SHO) is a perovskite oxide with a pseudocubic lattice parameter of 4.08 Å that previous density functional theory (DFT) calculations suggest can be stabilized in a ferroelectric P4mm phase when stabilized with sufficient compressive strain. Additionally, it is insulating and possesses a large band gap and a high dielectric constant, making it an ideal candidate for oxide electronic devices. Here, to test the viability of epitaxial strain as a driver of ferroic phase transitions, SHO films were grown by hybrid molecular beam epitaxy (hMBE) with a tetrakis(ethylmethylamino)hafnium(IV) source on GdScO 3 and TbScO 3 substrates. Strained SHO phases were characterized using X-ray diffraction, X-ray absorption spectroscopy, and scanning transmission electron microscopy to determine the space group of the strained films, with the results compared to those of DFT-optimized models of phase stability versus strain. Contrary to past reports, we find that compressively strained SrHfO 3 undergoes octahedral tilt distortions without associated ferroelectric polarization and most likely takes on the I4/mcm phase with the a 0 a 0 c – tilt pattern.

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

Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen

The production of hydrogen fuels, via water splitting, is of practical relevance for meeting global energy needs and mitigating the environmental consequences of fossil-fuel-based transportation. Water photoelectrolysis has been proposed as a viable approach for generating hydrogen, provided that stable and inexpensive photocatalysts with conversion efficiencies over 10% can be discovered, synthesized at scale, and successfully deployed. While a number of first-principles studies have focused on the data-driven discovery of photocatalysts, in the absence of systematic experimental validation, the success rate of these predictions may be limited. We address this problem by developing a screening procedure with co-validation between experiment and theory to expedite the synthesis, characterization, and testing of the computationally predicted, most desirable materials. Here, starting with 70 150 compounds in the Materials Project database, the proposed protocol yielded 71 candidate photocatalysts, 11 of which were synthesized as single-phase materials. Experiments confirmed hydrogen generation and favorable band alignment for 6 of the 11 compounds, with the most promising ones belonging to the families of alkali and alkaline-earth indates and orthoplumbates. This study shows the accuracy of a nonempirical, Hubbard-corrected density-functional theory method to predict band gaps and band offsets at a fraction of the computational cost of hybrid functionals, and outlines an effective strategy to identify photocatalysts for solar hydrogen generation.

08 HYDROGEN↗
DOE OSTI2023

Towards Pareto optimal high entropy hydrides via data-driven materials discovery

The ability to rapidly screen material performance in the vast space of high entropy alloys is of critical importance to efficiently identify optimal hydride candidates for various use cases. Given the prohibitive complexity of first principles simulations and large-scale sampling required to rigorously predict hydrogen equilibrium in these systems, we turn to compositional machine learning models as the most feasible approach to screen on the order of tens of thousands of candidate equimolar high entropy alloys (HEAs). Critically, we show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy (e.g. a mean absolute error in desorption enthalpy prediction of ~5 kJ mol H 2 –1 ) and that explainability analyses capture the competing trade-offs that arise from feature interdependence. We can therefore elucidate the multi-dimensional Pareto optimal set of materials, i.e., where two or more competing objective properties can't be simultaneously improved by another material. This provides rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Various targets were selected from the predicted Pareto front (with saturation capacities approaching two hydrogen per metal and desorption enthalpy less than 60 kJ mol H 2 –1 ) and were experimentally synthesized, characterized, and tested amongst an international collaboration group to validate the proposed novel hydrides. Finally, additional top-predicted candidates are suggested to the community for future synthesis efforts, and we conclude with an outlook on improving the current approach for the next generation of computational HEA hydride discovery efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Data-Driven Affinely Adjustable Robust Volt/VAr Control

Recent years have seen the increasing proliferation of distributed energy resources with intermittent power outputs, posing new challenges to the voltage management in distribution networks. To this end, this paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter’s reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter’s reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Finally, numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Application of Koopman operator for model-based control of fracture propagation and proppant transport in hydraulic fracturing operation

This work explores the application of the recently developed Koopman operator approach for model identification and feedback control of a hydraulic fracturing process. Controlling fracture propagation and proppant transport with precision is a challenge due in large part to the difficulty of constructing approximate models that accurately capture the characteristic moving boundary and highly-coupled dynamics exhibited by the process. Koopman operator theory is particularly attractive here as it offers a way to explicitly construct linear representations for even highly nonlinear dynamics. The method is data-driven and relies on lifting the states to an infinite-dimensional space of functions called observables where the dynamics are governed by a linear Koopman operator. Here this work considers two problems: (a) fracture geometry control, and (b) proppant concentration control. In both cases, an approximate linear model of the corresponding dynamics is constructed and used to design a model predictive controller (MPC). The manuscript shows that in the case of highly nonlinear dynamics, as observed in the proppant concentration, use of canonical functions in the observable basis fails. In such cases, a priori system knowledge can be leveraged to choose the required basis. The numerical experiments demonstrate that the Koopman linear model shows excellent agreement with the real system and successfully achieves the desired target values maximizing the oil and gas productivity. Additionally, due to its linear structure, the Koopman models allow convex MPC formulations that avoid any issues associated with nonlinear optimization.

42 ENGINEERING↗
DOE OSTI2025

Evaluation of probability density function descriptions for three-component Rayleigh–Taylor mixing

Results from simulations of a three-component Rayleigh–Taylor (RT) mixing problem are presented. These simulations are conducted in heavy–light–heavy and heavy–intermediate–light configurations, and each of these configurations are further considered in high- and low-Reynolds-number regimes. This results in RT-unstable flow with one or both interfaces initially unstable, permitting the influence of problem configuration on the statistical description of three-component RT-driven mixing to be considered. Mass fraction covariances are observed to undergo a sign change through the mixing layer in all four configurations considered. This appears to be unique to the multi-component case and represents another way in which multi-component RT mixing differs from the two-component case. Qualitative and quantitative comparisons of joint and marginal probability density function (PDF) descriptions of species concentration are made. Three-, five-, and six-parameter model PDFs are compared against simulation data to assess how accurately they describe the mixing, and it is found that three-component mixing requires at least a five-parameter model PDF to accurately describe the mixing. Notably, the marginal distributions of three-component mixing do not appear to conform to a beta distribution, representing a departure from the classical two-component RT case. In conclusion, statistical neutrality also appears to influence the optimal choice of model PDF, which is found to be a function of problem configuration.

Large-eddy simulation↗
DOE OSTI2024

The Overlooked Potential of Sulfated Zirconia: Reexamining Solid Superacidity Toward the Controlled Depolymerization of Polyolefins

Closed-loop recycling via an efficient chemical process can help alleviate the global plastic waste crisis. However, conventional depolymerization methods for polyolefins, which compose more than 50% of plastics, demand high temperatures and pressures, employ precious noble metals, and yield complex mixtures of products limited to single-use fuels or oils. Superacidic sulfated zirconia (SZrO) with Hammet Acidity Functions (H 0 ) = -12 (i.e., stronger than 100% H 2 SO 4 ) is an industrially deployed heterogeneous catalyst capable of activating hydrocarbons under mild conditions and is shown to decompose polyolefins at ambient pressure and temperatures near 200 °C. Additionally, confinement of active sites in porous supports is known to radically increase selectivity, coking and sintering resistance, and acid site activity, presenting a possible approach to low-energy polyolefin depolymerization. However, a critical examination of the literature on SZrO led us to a surprising conclusion: despite 40 years of catalytic study, engineering, and industrial use, the surface chemistry of SZrO is poorly understood. Ostensibly spurred by SZrO's impressive catalytic activity, the application-driven study of SZrO has resulted in deleterious ambiguity in requisite synthetic conditions and insufficient characterization of acidity, porosity, and active site structure. Here, this ambiguity has produced significant knowledge gaps surrounding the synthesis, structure, and mechanisms of hydrocarbon activation for optimized SZrO, stunting the potential of this catalyst in olefin cracking and other industrially relevant reactions, such as isomerization, esterification, and alkylation. Toward mitigating these long extant issues, we herein identify and highlight these current shortcomings and knowledge gaps, propose explicit guidelines for characterization of and reporting on characterization of solid acidity, and discuss the potential of pore-confined superacids in the efficient and selective depolymerization of polyolefins.

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

Using Particle Image Velocimetry to Determine Turbulence Model Parameters

The primary parameter of a standard k-ϵ model, C μ , was calculated from stereoscopic particle image velocimetry (PIV) data for a supersonic jet exhausting into a transonic crossflow. This required the determination of turbulent kinetic energy, turbulent eddy viscosity, and turbulent energy dissipation rate. Image interrogation was optimized, with different procedures used for mean strain rates and Reynolds stresses, to produce useful turbulent eddy viscosity fields. The eddy viscosity was calculated by a least-squares fit to all components of the three-dimensional strain-rate tensor that were available from the PIV data. This eliminated artifacts and noise observed when using a single strain component. Local dissipation rates were determined via Kolmogorov’s similarity hypotheses and the second-order structure function. The eddy viscosity and dissipation rates were then combined to determine C μ . Considerable spatial variation was observed in C μ , with the highest values found in regions where turbulent kinetic energy was relatively low but where turbulent mixing was important, e.g., along the high-strain jet edges and in the wake region. This suggests that use of a constant C μ in modeling may lead to poor Reynolds stress predictions at mixing interfaces. A data-driven modeling approach that can predict this spatial variation of C μ based on known state variables may lead to improved simulation results without the need for calibration.

42 ENGINEERING↗
NASA NTRS1984

On the utilization of engineering knowledge in design optimization

Some current research work conducted at the University of Michigan is described to illustrate efforts for incorporating knowledge in optimization in a nontraditional way. The incorporation of available knowledge in a logic structure is examined in two circumstances. The first examines the possibility of introducing global design information in a local active set strategy implemented during the iterations of projection-type algorithms for nonlinearly constrained problems. The technique used algorithms for nonlinearly constrained problems. The technique used combines global and local monotinicity analysis of the objective and constraint functions. The second examines a knowledge-based program which aids the user to create condigurations that are most desirable from the manufacturing assembly viewpoint. The data bank used is the classification scheme suggested by Boothroyd. The important aspect of this program is that it is an aid for synthesis intended for use in the design concept phase in a way similar to the so-called idea-triggers in creativity-enhancement techniques like brain-storming. The idea generation, however, is not random but it is driven by the goal of achieving the best acceptable configuration.

Papalambros, P.↗
DOE OSTI2023

Changing Directions: Influence of Ligand Electronics on the Directionality and Kinetics of Photoinduced Charge Transfer in Cu(I)Diimine Complexes

A key challenge to the effective utilization of solar energy is to promote efficient photoinduced charge transfer, specifically avoiding unproductive, circuitous electron-transfer pathways and optimizing the kinetics of charge separation and recombination. We hypothesize that one way to address this challenge is to develop a fundamental understanding of how to initiate and control directional photoinduced charge transfer, particularly for earth-abundant first-row transition-metal coordination complexes, which typically suffer from relatively short excited-state lifetimes. Here, in this paper, we report a series of functionalized heteroleptic copper(I)bis(phenanthroline) complexes, which have allowed us to investigate the directionality of intramolecular photoinduced metal-to-ligand charge transfer (MLCT) as a function of the substituent Hammett parameter. Ultrafast transient absorption suggests a complicated interplay of MLCT localization and solvent interaction with the Cu(II) center of the MLCT state. This work provides a set of design principles for directional charge transfer in earth-abundant complexes and can be used to efficiently design pathways for connecting the molecular modules to catalysts or electrodes and integration into systems for light-driven catalysis.

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

Layer Time Control for Large Scale Additive Manufacturing Using High Performance Computing

This work proposes to optimize an additive manufacturing AM process to reduce energy and printing cost. The polymer AM process is inherently dependent on the time-temperature history of each layer to maintain geometric tolerances and mechanical integrity. Our preliminary study shows that regression-based layer time control model using thermal images could result in up to 30% build time reduction for simple geometries. This proposed work would use high-performance computing (HPC) to couple the data-driven model with thermal simulation for better predicting layer temperature profiles, improving throughput of large-scale additive manufacturing, and reducing its energy cost. We have developed a method to optimize a layer deposition time (a.k.a. layer time) for large-scale AM via physics-based simulations. A long layer time leads to an over-cooled surface on which a new layer is deposited, and therefore, it may result in a weak bonding or debonding between layers, cracking, or warping. A short layer time leads to a high temperature of the structure due to insufficient cooling, and therefore, the structure may not be stiff enough and may collapse during manufacturing. Therefore, it is important to estimate the optimal layer time in additive manufacturing for a high-quality product. The temperature of a top layer right before deposition is recommended to be slightly higher than the glass temperature of the material. A temperature cooling was approximated to an exponential function of time, and the optimized layer time was obtained based on a target temperature while maintaining a minimal printing time. The material used is carbon fiber-reinforced polycarbonate (CF/PC), and the large-scale deposition system used is LSAM TM from Thermwood Corporation. Three different layer time cases were used for experiments, and a series of thermal images were obtained via an infra-red (IR) camera during the entire AM processes. AM process simulations were performed using a finite element method and the temperature profiles from the simulation were in good agreements with those from experiments. The layer time optimization was performed based on the temperature profiles from the simulations. A layer temperature with the optimal layer time was confirmed as the target temperature through simulation. In addition to the development of a layer time optimization method, we have developed a numerical framework for AM simulation with element activations in sync with toolpath, based on an open source finite element framework, DEAL.II. A major portion of this work was presented at SAMPE 2022 Conference and Exhibition on May 2022, and published in Proceedings of SAMPE 2022.

42 ENGINEERING↗
DOE OSTI2025

Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials

In contrast to their empirical counterparts, machine-learning interatomic potentials (MLIAPs) promise to deliver near-quantum accuracy over broad regions of configuration space. However, due to their generic functional forms and extreme flexibility, they can catastrophically fail to capture the properties of novel, out-of-sample configurations, making the quality of the training set a determining factor, especially when investigating materials under extreme conditions. We propose a novel automated dataset generation method based on the maximization of the information entropy of the feature distribution, aiming at an extremely broad coverage of the configuration space in a way that is agnostic to the properties of specific target materials. The ability of the dataset to capture unique material properties is demonstrated on a range of unary materials, including elements with the FCC (Al), BCC (W), HCP (Be, Re and Os), graphite (C), and trigonal (Sb, Te) ground states. MLIAPs trained to this dataset are shown to be accurate over a range of application-relevant metrics, as well as extremely robust over very broad swaths of configurations space, even without dataset fine-tuning or hyper-parameter optimization, making the approach extremely attractive to rapidly and autonomously develop general-purpose MLIAPs suitable for simulations in extreme conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗
NASA NTRS2013

Development of a High Reliability Compact Air Independent PEMFC Power System

Autonomous Underwater Vehicles (AUV's) have received increasing attention in recent years as military and commercial users look for means to maintain a mobile and persistent presence in the undersea world. Compact, neutrally buoyant power systems are needed for both small and large vehicles. Historically, batteries have been employed in these applications, but the energy density and therefore mission duration are limited with current battery technologies. Vehicles with stored energy requirements greater than approximately 10 kWh have an alternate means to get long duration power. High efficiency Proton Exchange Membrane (PEM) fuel cell systems utilizing pure hydrogen and oxygen reactants show the potential for an order of magnitude energy density improvement over batteries as long as the subsystems are compact. One key aspect to achieving a compact and energy dense system is the design of the fuel cell balance of plant (BOP). Recent fuel cell work, initially focused on NASA applications requiring high reliability, has developed systems that can meet target power and energy densities. Passive flow through systems using ejector driven reactant (EDR) circulation have been developed to provide high reactant flow and water management within the stack, with minimal parasitic losses compared to blowers. The ejectors and recirculation loops, along with valves and other BOP instrumentation, have been incorporated within the stack end plate. In addition, components for water management and reactant conditioning have been incorporated within the stack to further minimize the BOP. These BOP systems are thermally and functionally integrated into the stack hardware and fit into the small volumes required for AUV and future NASA applications to maximize the volume available for reactants. These integrated systems provide a compact solution for the fuel cell BOP and maximize the efficiency and reliability of the system. Designs have been developed for multiple applications ranging from less than 1 kWe to 70 kWe. These systems occupy a very small portion of the overall energy system, allowing most of the system volume to be used for reactants. The fuel cell systems have been optimized to use reactants efficiently with high stack efficiency and low parasitic losses. The resulting compact, highly efficient fuel cell system provides exceptional reactant utilization and energy density. Key design variables and supporting test data are presented. Future development activities are described.

Vasquez, Arturo↗
DOE OSTI2024

An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

25 ENERGY STORAGE↗
NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗
DOE OSTI2026

A Review on Direct Air Capture of Carbon Dioxide: Sorbent Materials, Process Engineering, Industrial Scale-Up, and Future Perspectives

The relentless accumulation of anthropogenic greenhouse gases has driven atmospheric carbon dioxide concentrations to approximately 426 ppm, necessitating the aggressive deployment of negative-emission technologies to achieve net zero by 2050. Direct air capture (DAC) offers a scalable, location-independent approach to atmospheric carbon removal; however, it is fundamentally constrained by the significant thermodynamic barriers associated with capturing CO 2 from ultradilute ambient conditions, requiring minimum thermodynamic energy inputs substantially higher than those for postcombustion point sources. This comprehensive review critically examines the technological landscape of DAC, focusing on the interdependent triad of sorbent material design, contactor engineering, and regeneration thermodynamics. We evaluate the fundamental boundaries of adsorption, emphasizing that an optimal adsorption enthalpy and isosteric heat of adsorption must balance the high CO 2 uptake capacity with the energetic penalties of sorbent regeneration. A systematic, comparative analysis of state-of-the-art sorbents is presented, encompassing mesoporous silicas, zeolites, carbon-based materials (CBMs), metal−organic frameworks (MOFs), porous organic polymers (POPs), and polymeric membranes. Special attention is devoted to surface functionalization strategies, particularly amine grafting and impregnation, which transition capture mechanisms from physisorption to chemisorption to enhance selectivity under ambient moisture and low partial pressures. Furthermore, we assess the operational merits of various reactor configurations, including gas−solid, gas−liquid, and membrane contactors, alongside regeneration cycles such as temperature, vacuum, pressure, and moisture swing adsorption. Finally, the review bridges fundamental materials science with industrial application by chronicling the scale-up milestones of pioneering entities and providing a strategic roadmap for advancing DAC technology readiness levels toward global deployment.

Adsorption↗
DOE OSTI2025

Process intensification approach to enhancing heat and mass transfer during drying: Ultrasonic (US) assisted drying of paper and board

Drying of paper and board is conventionally achieved through alternating conduction (steam-heated cylinders) and pocket convection (heated air over the paper web surface). These conventional drying systems rely heavily on steam from fossil fuels, resulting in inefficiencies, high energy usage, and thermal losses due to surface-driven mechanisms. Here, to address these challenges, an experimental system, with in-situ drying characteristics measurements, was developed to investigate process intensification using ultrasonic-based dewatering—a volumetric, pressure-driven acoustic energy system—integrated with conventional drying. The objectives of this study are to assess the impact of ultrasonics (US) on dewatering; compare performances to conventional drying systems; identify improvements in drying rate and energy use as a function of moisture content; and gain potential insights on heat and mass transfer mechanisms during US-assisted drying. US performance was evaluated across frequencies, power levels, pulp types, and basis weights. Results show that improvements to ultrasonic applications in conjunction with convection were 30-43% in drying rate and 20-35% in drying time over continuous and intermittent applications. When combined with conduction and convection, ultrasonics yielded up to 20% improvement in both rate and time and up to 20% reduction in energy consumption. Observations support a hypothesis of extension of the constant rate period due to improved capillary flow at higher moisture content and enhancing vapor diffusion and boundary layer disruption at lower moisture contents during falling rate period. These findings will inform future modeling, simulation, design and optimization of advanced drying systems.

42 ENGINEERING↗