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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Optimization of flow in additively manufactured porous columns with graded permeability

Chemical engineering systems often involve a functional porous medium, such as in catalyzed reactive flows, fluid purifiers, and chromatographic separations. Ideally, the flow rates throughout the porous medium are uniform, and all portions of the medium contribute efficiently to its function. The permeability is a property of a porous medium that depends on pore geometry and relates flow rate to pressure drop. Additive manufacturing techniques raise the possibilities that permeability can be arbitrarily specified in three dimensions, and that a broader range of permeabilities can be achieved than by traditional manufacturing methods. Using numerical optimization methods, we show that designs with spatially varying permeability can achieve greater flow uniformity than designs with uniform permeability. We consider geometries involving hemispherical regions that distribute flow, as in many glass chromatography columns. By several measures, significant improvements in flow uniformity can be obtained by modifying permeability only near the inlet and outlet.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Explicit model predictive control through robust optimization

A strategy that calculates an explicit state feedback policy to regulate constrained uncertain discrete‐time uncertain linear systems is presented. We consider uncertain processes, affected by box‐bounded multiplicative uncertainty as well as bounded additive uncertainty with linear state and inputs constraints. The proposed method includes (i) the calculation of a terminal set constraint and (ii) the robust reformulation of state constraints in the prediction horizon. These features allow the derivation of the desired policy by solving a single multiparametric quadratic programming problem that guarantees feasible operation in the presence of uncertainty. Additionally, we employ variable and constraint elimination approaches to enhance the computational performance of the strategy. We demonstrate the steps and benefits of these developments with a numerical example and a chemical engineering case study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

The scaling economics of small unit operations

We present a correlation that represents the capital costs of the components of even very small modular facilities. Conversion of distributed feedstocks (eg, associated natural gas, biomass, and carbonaceous wastes) could provide a small fraction of the liquid fuels now used for transportation in the U.S. (~6%),or nearly half of the chemical products now made from petroleum. However, those resources tend to be available at small geographically distributed sites, and they are difficult or expensive to transport to a distant processing center. Modular, and likely intensified, processes that can be numbered up promise to enable utilizing such distributed resources. Early stage economics evaluation of those processes requires cost estimates for the components, which are likely smaller in scale than can be accommodated by the 0.6 power law typically used in chemical engineering

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Mechanical and Electrochemical Implications of Drying Temperature on Lithium‐Ion Battery Electrodes

Abstract Lithium‐ion battery (LIB) electrodes are typically produced with n‐methyl‐2‐pyrrolidone, a toxic solvent that is a known carcinogen and reproductive hazard. Accordingly, aqueous processing has been an expanding area of research interest in the field of LIB manufacturing. Although aqueous processing has been widely successful in anode processing, serious challenges remain in processing the cathode. In this work, the drying mechanics of cathode processed with both solvents is investigated though implementation of a chemical‐engineering‐based model to better understand the utilization of heat provided by experimentally determining the heat and mass transfer coefficients. Electrochemical performance is also evaluated to determine the impact of drying temperature on cycling performance. Binder distribution is determined via various methods to confirm differences in binder homogeneity as a function of both solvent and drying temperature. Identified is the large difference in the efficiency in which the heat is used as well as an ideal drying temperature for both aqueous and non‐aqueous processed cathodes. Also identified is the increased sensitivity to processing temperature for aqueous processed electrodes compared to non‐aqueous processed counterparts, pointing to the possibility of tuned drying regimes which would capitalize on the potential cost savings of aqueous processing for cathodes.

25 ENERGY STORAGE↗
DOE OSTI2021

New-Generation Carbon-Capture Ionic Liquids Regulated by Metal-Ion Coordination

Development of efficient carbon capture-and-release technologies with minimal energy input is a long-term challenge in mitigating CO 2 emissions, especially via CO 2 chemisorption driven by engineered chemical bond construction. Herein, taking advantage of the structural diversity of ionic liquids (ILs) in tuning their physical and chemical properties, precise reaction energy regulation of CO 2 chemisorption was demonstrated deploying metal-ion-amino-based ionic liquids (MAILs) as absorbents. The coordination ability of different metal sites (Cu, Zn, Co, Ni, and Mg) to amines was harnessed to achieve fine-tuning on stability constants of the metal ion-amine complexes, acting as the corresponding cations in the construction of diverse ILs coupled with CO 2 -philic anions. The as-afforded MAILs exhibited efficient and controllable CO 2 release behavior with great reduction in energy input and minimal sacrifice on CO 2 uptake capacity. This coordination-regulated approach offers new prospects for the development of ILs-based systems and beyond towards energy-efficient carbon capture technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Practical choices for space-filling designs

Space-filling designs are now commonly used as a flexible model-free strategy for providing good coverage throughout an input space of interest for a variety of computer and physical experiment design scenarios. Some of the preliminary choices about how to frame the problem and which type of design to use can have a substantial impact on the success or failure of the experiment, and yet how to make these critical choices is often under-emphasized in the literature. In this paper, we explore several of the practical choices required by the experimenter and describe a sequence of steps to help create an ideal design that matches the goals and constraints of the experiment. These choices include the specification of the input space, the scaling of the variables, the degree of uniformity of the design points across the input space, and the space-filling characteristics. In addition, some new tools for defining the weights to implement non-uniform space-filling designs are provided. Finally, the methods are demonstrated with several illustrative examples and a real-world chemical engineering experiment for carbon capture.

42 ENGINEERING↗
DOE OSTI2021

Input-response space-filling designs

Traditional space-filling designs are a convenient way to explore throughout an input space of flexible dimension and have design points close to any region where future predictions might be of interest. In some applications, there may be a model connecting the input factors to the response(s), which provides an opportunity to consider the spacing not only in the input space but also in the response space. In this paper, we present an approach for leveraging current understanding of the relationship between inputs and responses to generate designs that allow the experimenter to flexibly balance the spacing in these two regions to find an appropriate design for the experimental goals. Applications where good spacing of the observed response values include calibration problems where the goal is to demonstrate the adequacy of the model across the range of the responses, sensitivity studies where the outputs from a submodel may be used as inputs for subsequent models, and inverse problems where the outputs of a process will be used in the inverse prediction for the unknown inputs. Here, we use the multi-objective optimization method of Pareto fronts to generate multiple non-dominated designs with different emphases on the input and response space-filling criteria from which the experimenter can choose. The methods are illustrated through several examples and a chemical engineering case study.

42 ENGINEERING↗
DOE OSTI2025

Post synthetic amine functionalization of MOF-808 for CO 2 sorption via ligand exchange and Michael addition

Post synthetic modification of metal organic frameworks presents a viable route for amine functionalization which can significantly enhance CO 2 sorption capacity. We present a facile means of amine incorporation using limited synthetic steps and low-cost reagents that results in a high density of primary alkyl amines distributed through a zirconium-based metal organic framework (MOF). Both the MOF synthesis and the post synthetic modification take place under aqueous conditions and result in strongly bound molecular amines available for sorbate interaction throughout the MOF pores. Furthermore, this amine incorporation protocol results in a significantly increased CO 2 capacity compared with the unmodified MOF-808. Specifically, CO 2 isotherms collected at 298 K for the unmodified MOF-808 show uptakes of 0.06, 0.2, and 1.2 mmol/g at 4, 15, and 100 kPa, respectively, which can be compared with 0.3, 0.7, and 2.5 mmol/g for the glycine grafted MOF-808 and 0.5, 0.9, and 2.3 mmol/g for ethylenediamine grafted MOF-808.

Amine sorbent↗
DOE OSTI2025

From PINNs to PIKANs: recent advances in physics-informed machine learning

Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offering a promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Lastly, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.

Kolmogorov-Arnold networks↗
DOE OSTI2021

Assessment of model parameters in MFiX particle-in-cell approach

The limitations in numerical treatment of solids-phase in conventional methods like Discrete Element Model and Two-Fluid Model have facilitated the development of alternative techniques such as Particle-In-Cell (PIC). However, a number of parameters are involved in PIC due to its empiricism. In this work, global sensitivity analysis of PIC model parameters is performed under three distinct operating regimes common in chemical engineering applications, viz. settling bed, bubbling fluidized bed and circulating fluidized bed. Simulations were performed using the PIC method in Multiphase Flow with Interphase eXchanges (MFiX) developed by National Energy Technology Laboratory (NETL). A non-intrusive uncertainty quantification (UQ) based approach is applied using Nodeworks to first construct an adequate surrogate model and then identify the most influential parameters in each case. This knowledge will aid in developing an effective design of experiments and determine optimal parameters through techniques such as deterministic or statistical calibration.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Dynamic, hollow nanotubular networks with superadjustable pH-responsive and temperature resistant rheological characteristics

Recently, the interest in stimuli-responsive and adaptable materials has continuously grown in various fields and applications. For such responsive systems, different triggers, including pH, light, pressure, temperature, and electric field, have been utilized to control dynamics and assembly. Among these, pH is one of the most convenient, energy-efficient, and economic modalities. Besides, plenty of traditional materials have poor thermal and salt stability, limiting their applications. Herein, we report a new design of a pH-responsive viscoelastic supramolecular complex (VSC) based on commplexation of a new long-chain amino-amide and maleic acid. In this study, the system demonstrated a sol–gel-sol transition from pH 2 to 10, with the largest static viscosity occurring at pH 6 (~1000 Pa·s) and the smallest viscosity at pH 4 (~3.3 Pa·s), indicating ~ 300-fold control over the viscosity. For a given concentration, the static viscosity of VSC was about 15 times larger than that of CTAB/NaSal, a well-established dynamic viscoelastic system, and no pH-responsiveness was observed for the traditional system. In addition, the VSC demonstrated a superior temperature tolerance and lower temperature dependence. The potential of these intriguing dynamics viscoelastic systems was evaluated for hydraulic fracturing and enhanced oil recovery applications. Proppant settling velocity of DMAA/MA was 500 ~ 1000 times lower than that of CTAB/NaSal and common traditional polymers. Likewise, the oil recovery percentage could be significantly improved with the utilization of DMAA/MA compared to the CTAB/NaSal (86 % vs 52 %). Aside from applications in hydraulic fracturing and enhanced oil recovery, we anticipate that the intriguing rheological properties of this viscoelastic system can be beneficial for other chemical engineering applications including personal care products, cosmetics, lubricants, and biomedical gels.

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

Bayesian optimization for chemical products and functional materials

The design of chemical-based products and functional materials is vital to modern technologies, yet remains expensive and slow. Artificial intelligence and machine learning offer new approaches to leverage data to overcome these challenges. Herein this review focuses on recent applications of Bayesian optimization (BO) to chemical products and materials including molecular design, drug discovery, molecular modeling, electrolyte design, and additive manufacturing. Numerous examples show how BO often requires an order of magnitude fewer experiments than Edisonian search. The essential equations for BO are introduced in a self-contained primer specifically written for chemical engineers and others new to the area. Finally, the review discusses four current research directions for BO and their relevance to product and materials design.

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

From clutter to clarity: Emergent neural operators via questionnaire metrics

Real-world datasets in chemical engineering and bioengineering processes—such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials—can often be unlabeled or disorganized, rendering the training of existing supervised learning models ineffective at learning the underlying dynamics. To salvage these datasets for decision-making, we first seek to obtain clarity from the cluttered data. Here, we present a framework for developing “structural” generative models, discovering emergent equations, and constructing efficient emulators from scrambled datasets by integrating unsupervised organizational learning techniques (Questionnaires) with advanced deep learning architectures (Deep Hidden Physics Models and Deep Operator Networks). Our approach is demonstrated on two illustrative model systems: (a) a 1D advection–diffusion partial differential equation representing a winding underground pipe and (b) an ensemble of Stuart–Landau oscillators, an agent-based system of coupled ordinary differential equations. In both cases, we successfully reconstruct meaningful spatial, temporal, and parameter embeddings from scrambled data, enabling good predictions of system dynamics. As a result, we highlight the framework’s potential for broader applications, enabling data-driven system identification in fields with inherently disorganized or hidden parameter spaces.

42 ENGINEERING↗
DOE OSTI2025

Hyperplane decision trees as piecewise linear surrogate models for chemical process design

Recent trends in chemical engineering research point towards an increasing reliance on data-driven modeling approaches. Neural networks, for instance, have proven to be accurate when data is plentiful and high-dimensional, but in many cases, they require computationally-intensive training procedures. Here, in this work, we describe hyperplane decision trees (HT) as a highly expressive and low-compute machine learning model architecture. These models are locally linear and have linear decision boundaries, resulting in a piecewise linear model of the data. This property allows them to be converted into mixed-integer linear constraints which can be globally optimized. Our open-source PyTorch implementation of this method is a fast, flexible, and accessible way to build accurate piecewise linear models of data.

Decision trees↗
DOE OSTI2024

Direct Air Capture of CO 2 via Reactive Crystallization

Atmospheric CO 2 removal using engineered chemical processes, aka direct air capture (DAC), has become an essential component of our available portfolio for mitigating climate change. Here we describe a promising approach to DAC based on reactive crystallization of atmospheric CO 2 (RC-DAC) with aqueous guanidine and amino acid. Compared to the previously studied phase-changing DAC processes involving initial CO 2 absorption by an aqueous alkaline solvent followed by carbonate crystallization in a second step, RC-DAC combines the CO 2 absorption and carbonate crystallization into a single step. Thus, as the insoluble carbonate salts are removed from solution by crystallization, more CO 2 is pulled from the air into solution, further driving the DAC process. The RC-DAC was performed in a household humidifier as the air–liquid contactor, which can handle solid–liquid slurries effectively. The crystallization was monitored in situ by pH measurements, real-time imaging with a microscope probe, and by Raman spectroscopy, and ex situ by NMR spectroscopy, powder X-ray diffraction, and total inorganic carbonate analysis. Further, the investigation provided a detailed mechanistic picture of the RC-DAC process, involving formation of carbamate and carbonate anions in solution, followed by sequential crystallization of different guanidinium carbonate phases.

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

Bridging Scales in Bioenergy and Catalysis: A Review of Mesoscale Modeling Applications, Methods, and Future Directions

Between the molecular and reactor scales, which are familiar to the chemical engineering community, lies an intermediate regime, here termed the “mesoscale,” where transport phenomena and reaction kinetics compete on similar time scales. Bioenergy and catalytic processes offer particularly important examples of mesoscale phenomena owing to their multiphase nature and the complex, highly variable porosity characteristic of biomass and many structured catalysts. In this review, we overview applications and methods central to mesoscale modeling as they apply to reaction engineering of biomass conversion and catalytic processing. A brief historical perspective is offered to put recent advances in context. Applications of mesoscale modeling are described, and several specific examples from biomass pyrolysis and catalytic upgrading of bioderived intermediates are highlighted. Methods including reduced order modeling, finite element and finite volume approaches, geometry construction and import, and visualization of simulation results are described; in each category, recent advances, current limitations, and areas for future development are presented. Owing to improved access to high-performance computational resources, advances in algorithm development, and sustained interest in reaction engineering to sustainably meet societal needs, we conclude that a significant upsurge in mesoscale modeling capabilities is on the horizon that will accelerate design, deployment, and optimization of new bioenergy and catalytic technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Catalytic Reaction Triggered by Magnetic Induction Heating Mechanistically Distinguishes Itself from the Standard Thermal Reaction

As a recent advancement in chemical engineering, magnetic induction heating (MIH) is utilized to initiate the intended reactions by enabling the self-heating of the ferromagnetic catalyst particles. While MIH can be energy-efficient and industrially scalable, its full potential has been underappreciated in catalysis because of the perception that MIH is merely an alternative heating approach. Unexpectedly, we show that the MIH-triggered reaction could go beyond standard thermal catalysis. Specifically, by probing the representative Pt/Fe 3 O 4 catalysts with CO oxidation in both thermal and MIH modes with consistent temperature profiles and catalyst structures, we found that the MIH mode boosts the reactivity more than 25 times by modifying Pt-FeO x interfacial synergies and promoting facile oxidation of the adsorbed carbonyl species by atomic oxygen. Further, as we preliminarily observed, this beneficial MIH-catalysis can be translational to other thermal reactions, potentially paving the way to launch MIH-catalysis as a distinct reaction category.

CO oxidation↗
DOE OSTI2024

Achieving gigawatt-scale green hydrogen production and seasonal storage at industrial locations across the U.S

Onsite production of gigawatt-scale wind- and solar-sourced hydrogen (H 2 ) at industrial locations depends on the ability to store and deliver otherwise-curtailed H 2 during times of power shortages. Thousands of tonnes of H 2 will require storage in regions where subsurface storage is scarce, which may only be possible using liquid organic H 2 carriers. We evaluate aboveground system with a focus on providing technical insights into toluene/methylcyclohexane (TOL/MCH) storage systems in locations suitable for gigawatt-scale wind- and solar-powered electrolyzer systems in the United States. Here we show that the levelized cost of storage, at a national median of US dollar $1.84/kg-H 2 is spatially heterogeneous, causing minor impact on the cost of H 2 supply in the Midwest, and significant impact in Central California and the Southeast. While TOL/MCH may be the cheapest aboveground bulk storage solution evaluated, upfront capital costs, modest energy efficiency, reliance on critical materials and pre-sulfided catalysts, and greenhouse gas emissions from heating are opportunities for further development.

14 SOLAR ENERGY↗