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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Solving the Grid Optimization Competition Challenge 3 Problem

The Grid Optimization Competition Challenge 3 Problem posed a multiperiod security-constrained unit commitment problem with base-case AC power flow. The problem formulation includes binary unit commitment decisions, nonlinear AC power flow and balance, dispatchable loads, and linearized contingency real power flow, among other features. This talk will present a modified consensus ADMM algorithm, which splits the problem into mixed-integer linear and nonlinear components, as a heuristic solution method for this large-scale mixed integer nonlinear program. We will present some computational results from the competition for our implementation and reflect on the challenges of participating the grid optimization competition.

AC power flow↗
DOE OSTI2023

Directed Energy Deposition Process Modeling, Validation, and Process-Informed Optimization

The directed energy deposition (DED) process, one of the most popular additive manufacturing techniques in use today, involves various complex physical mechanisms that are not yet well understood. In this regard, computational tools show promise for elucidating the manufacturing process and enabling nondestructive performance evaluations of manufactured parts. To better control and optimize the DED process?thereby improving the manufactured product? the present work develops and demonstrates a novel artificial intelligence (AI)-based process control and optimization technique. Specifically, a geometry-free thermo-mechanical model with adaptive subdomain construction is developed to accurately capture the material?s thermo-mechanical response under cyclical reheating and high cooling rates [1]. The model is demonstrated and validated with experimental measurements, in light of various geometries and processing parameters. Moreover, based on this thermo-mechanical model, a physics-informed reduced-order model is developed to enable quick predictions of the temperature field at every time step. Furthermore, an AI-based controller is developed that can adapt to the ever-changing system states by automatically adjusting the manufacturing process parameters. This entire work is based on the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) [2] and its recent integration with Libtorch (the C++ frontend of PyTorch [3]). The combined development of the adaptive material deposition scheme, thermo-mechanical model, associated reduced-order model, and AI-based process controller carries great potential for improving advanced manufacturing processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Equity-driven Planning of Distributed Solar PV using Optimal Transport

Typically, distribution system planning processes do not explicitly incorporate energy equity considerations, such as identifying consumers most affected by energy costs and determining how investments in the distribution system can address existing energy burden imbalances. This paper proposes a novel optimal transport (OT)-based method to improve the energy burden distribution of consumers. The approach involves the strategic siting and sizing of solar PV in order to assist customers with high energy burden and improve the overall energy burden distribution of the community. The desired energy burden distribution is defined using the equal distribution equivalent (EDE) concept. The OT-based method is then used to estimate the distributed solar PV capacity to be installed at various locations and the tariffs to be adjusted, all while improving the energy burden distribution and providing valuable insights into distributed generation (DG) planning. The results on IEEE 37 bus test system demonstrate how DG planning, considering EDE and OT, can help reduce the energy burden of low-income consumers. Additionally, the approach also reveals optimal tariff adjustments needed to ensure revenue neutrality for distribution utilities.

Optimal transport, equal distribution equivalent, ↗
DOE OSTI2025

Optimization of a Secondary Air Injector for a Rich-Quench-Lean (RQL) Ammonia Combustor Using Computational Fluid Dynamics

Ammonia has emerged as a promising medium for moving hydrogen around the globe due to its energy density, existing infrastructure for production, transportation, and storage, and multiple applications from fertilizer to power generation. However, one of the most significant challenges with ammonia combustion for power generation is the formation of nitrogen oxides (NOx) during combustion. Several combustion strategies have been developed to minimize the formation of NOx. One such strategy, known as rich-quench-lean (RQL), is a method that combusts NH3 in a fuel-rich environment, followed by a quick mix section and a lean burnout section. Rapid mixing of secondary air before lean burnout is thought to be important to minimize the formation of NOx. A genetic algorithm (GA) is used to parametrically vary the secondary air injector design, including the diameter, count, and angles over a constrained design space. The designs are evaluated using a non-reacting OpenFOAM model, with the objective function being the uniformity index of the secondary air (modeled as a scalar). Optimization campaigns show that traditional correlation-based designs might not be adequate. After running 414 OpenFOAM models, the optimal design is 32, 1 mm diameter, angled air injectors, increasing the uniformity index at the outlet of the quick mix zone by 36.8%. The most promising designs will be manufactured and tested in a small RQL burner setup, which is expected to lead to validation and insight into the practical usage of NH3 combustion for power generation.

ammonia combustion↗
DOE OSTI2021

A method for convex black-box integer global optimization

Here we study the problem of minimizing a convex function on a nonempty, finite subset of the integer lattice when the function cannot be evaluated at noninteger points. We propose a new underestimator that does not require access to (sub)gradients of the objective; such information is unavailable when the objective is a blackbox function. Rather, our underestimator uses secant linear functions that interpolate the objective function at previously evaluated points. These linear mappings are shown to underestimate the objective in disconnected portions of the domain. Therefore, the union of these conditional cuts provides a nonconvex underestimator of the objective. We propose an algorithm that alternates between updating the underestimator and evaluating the objective function. We prove that the algorithm converges to a global minimum of the objective function on the feasible set. We present two approaches for representing the underestimator and compare their computational effectiveness. We also compare implementations of our algorithm with existing methods for minimizing functions on a subset of the integer lattice. We discuss the difficulty of this problem class and provide insights into why a computational proof of optimality is challenging even for moderate problem sizes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Cost‐Responsive Optimization of Nickel Nanoparticle Synthesis

Abstract Early‐stage cost evaluation during catalyst development holds the potential to accelerate the commercialization and deployment of advanced catalytic materials for sustainable chemical processes. The modeling and assessment of manufacturing costs as early as the laboratory synthesis scale, for example, focusing on materials costs and synthesis performance metrics, can support the development of an experimental–economic feedback loop that enables rapid insight into cost drivers associated with catalyst synthesis and highlights areas that require focused research and development effort. Ultimately, this feedback loop supports the realization of an economic understanding of the overall synthetic process and highlights opportunities to reduce costs, serving as the foundation for the scale‐up of catalyst manufacturing. Herein, a case study is presented utilizing CatCost, a free and publicly available estimation tool for the evaluation of catalyst manufacturing costs, to perform a cost‐responsive optimization of the synthesis of nickel nanoparticles (Ni NPs). It is demonstrated that reagent substitutions with more cost‐effective analogs, coupled with stoichiometric optimization, afford a 58% reduction in raw materials cost without changing the product yield or properties.

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

An Optimized Fibril Network Morphology Enables High‐Efficiency and Ambient‐Stable Polymer Solar Cells

Abstract Morphological stability is crucially important for the long‐term stability of polymer solar cells (PSCs). Many high‐efficiency PSCs suffer from metastable morphology, resulting in severe device degradation. Here, a series of copolymers is developed by manipulating the content of chlorinated benzodithiophene‐4,8‐dione (T1‐Cl) via a random copolymerization approach. It is found that all the copolymers can self‐assemble into a fibril nanostructure in films. By altering the T1‐Cl content, the polymer crystallinity and fibril width can be effectively controlled. When blended with several nonfullerene acceptors, such as TTPTT‐4F, O‐INIC3, EH‐INIC3, and Y6, the optimized fibril interpenetrating morphology can not only favor charge transport, but also inhibit the unfavorable molecular diffusion and aggregation in active layers, leading to excellent morphological stability. The work demonstrates the importance of optimization of fibril network morphology in realizing high‐efficiency and ambient‐stable PSCs, and also provides new insights into the effect of chemical structure on the fibril network morphology and photovoltaic performance of PSCs.

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

Electrolyte Design and Optimization for Alkali Metal‐Sulfur Batteries

Alkali metal-sulfur batteries, including lithium-sulfur (Li-S), sodium-sulfur (Na-S), and potassium-sulfur (K-S) systems, have garnered significant attention as promising electrochemical energy storage (EES) technologies. Among them, Li-S batteries stand out as strong contenders for next-generation energy storage, owing to their high energy density and the cost-effectiveness of sulfur-based cathodes. However, with the rapid technological advances and the escalating energy demand, lithium resources are becoming increasingly scarce, making it imperative to explore alternative metal anodes to replace lithium. Therefore, Na-S and K-S batteries, serving as counterparts to Li-S systems, are emerging as formidable contenders for next-generation energy storage technologies due to the abundant and cost-effective nature of sodium and potassium. Although Na-S and K-S batteries possess considerable potential in the energy sector, their development is still in its infancy, with performance constrained by the nascent state of electrolyte design and optimization. This review article provides a comprehensive overview of recent advancements and developments in liquid electrolytes for alkali metal-sulfur batteries. Additionally, it identifies key challenges and proposes future research directions aimed at enhancing electrolyte stability, optimizing interfacial compatibility, and improving the overall performance of alkali metal-sulfur batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Direct air capture with amino acid solvent: Operational optimization using a crossflow air‐liquid contactor

Direct air capture (DAC) is a negative emission technology for removing CO2 from the atmosphere to maintain the CO 2 level within a reasonable range so as to address greenhouse effects. In this study, the operational optimization of lab-scale DAC has been investigated using a crossflow air-liquid contactor loaded with a three dimensionally printed Gyroid packing structure and a potassium sarcosinate solvent. The effects of various parameters, including feed air flow rate, liquid solvent flow rate, contactor geometry, and ambient temperature, are examined. The results demonstrate that the Gyroid packing design achieves comparable CO 2 capture performance to conventional packed beds but with a significantly lower pressure drop of up to 77.8%, suggesting its potential as an efficient and cost-effective solution for gas–liquid contactors in DAC. Additionally, the study explores the climate impact on CO 2 capture performance and finds that as the air temperature increases from 35 to 95°F at a fixed relative humidity of 80%, the CO 2 capture rate increased from 23.2% to 46.8% with better stability. The research highlights the importance of optimizing contactor design and operational conditions to improve the CO 2 capture rate and feasibility of DAC systems as a negative emission technology for addressing greenhouse effects.

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

Gel‐Derived Amorphous Bismuth–Nickel Alloy Promotes Electrocatalytic Nitrogen Fixation via Optimizing Nitrogen Adsorption and Activation

Abstract To achieve the electrochemical nitrogen reduction reaction (NRR) for efficient and sustainable NH 3 production, catalysts should exhibit high selectivity and activity with optimal adsorption energy. Herein we developed a three‐dimensional (3D) amorphous BiNi alloy toward a significantly enhanced NRR compared with its crystalline and metal counterparts. Ni alloying enables the chemisorption of nitrogen and the lower free‐energy change for the *NNH formation, and the 3D alloy electrocatalyst exhibits high catalytic activity for NH 3 production with a yield rate of 17.5 μg h −1 mg cat −1 and Faradaic efficiency of 13.8 %. The enhanced electron transfer and increased electrochemical surface area were revealed in the interconnected porous scaffold, affording it sufficiently efficient and stable activity for potential practical applications. This work offers new insights into optimizing the adsorption energy of reactants and intermediates combined with tuning the crystallinity of NRR electrocatalysts.

Fang, Zhiwei↗
DOE OSTI2020

Gel-Derived Amorphous Bismuth–Nickel Alloy Promotes Electrocatalytic Nitrogen Fixation via Optimizing Nitrogen Adsorption and Activation

To achieve the electrochemical nitrogen reduction reaction (NRR) for efficient and sustainable NH 3 production, catalysts should exhibit high selectivity and activity with optimal adsorption energy. Herein we developed a three-dimensional (3D) amorphous BiNi alloy toward a significantly enhanced NRR compared with its crystalline and metal counterparts. Ni alloying enables the chemisorption of nitrogen and the lower fre-energy change for the *NNH formation, and the 3D alloy electrocatalyst exhibits high catalytic activity for NH 3 production with a yield rate of 17.5 μg h -1 mg cat -1 and Faradaic efficiency of 13.8 %. The enhanced electron transfer and increased electrochemical surface area were revealed in the interconnected porous scaffold, affording it sufficiently efficient and stable activity for potential practical applications. Furthermore, this work offers new insights into optimizing the adsorption energy of reactants and intermediates combined with tuning the crystallinity of NRR electrocatalysts.

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

Solvent‐free enzymatic esterification of free fatty acids with glycerol for biodiesel application: Optimized using the Taguchi experimental method

Abstract Presence of free fatty acids along with glycerides poses a technical difficulty for biodiesel production. This work used a Taguchi L9 design to optimize the solvent‐free enzymatic process to result in the esterification of oleic acid with glycerol. Under optimal conditions the esterification reaction temperature of 60°C, enzyme dose of 5 wt%, glycerol: oleic acid molar ratio of 5:1, and reaction time of 3 h, a 75.235 ± 2.19% conversion of oleic acid to esters was achieved. With the addition of molecular sieves, the conversion increased to 86.73% ± 1.09%. However, using the parameters predicted by Taguchi design (60°C, 5 wt%, 5:1, and 4.5 h), 88.5% ± 1.11% of oleic acid could be converted to esters derivative. Diglycerides were the major product, and the reaction equilibrium was attained after 4 h. The immobilized enzyme could be used up to seven times with only a 10% reduction in the conversion. Thus, the process can efficiently reduce the free fatty acid content of oil to make it suitable for biodiesel production.

09 BIOMASS FUELS↗
DOE OSTI2023

Enzyme selection, optimization, and production toward biodegradation of post–consumer poly(ethylene terephthalate) at scale

Poly(ethylene terephthalate) (PET) is one of the world's most widely used polyester plastics. Due to its chemical stability, PET is extremely difficult to hydrolyze in a natural environment. Recent discoveries in new polyester hydrolases and breakthroughs in enzyme engineering strategies have inspired enormous research on biorecycling of PET. This study summarizes our research efforts toward large-scale, efficient, and economical biodegradation of post-consumer waste PET, including PET hydrolase selection and optimization, high-yield enzyme production, and high-capacity enzymatic degradation of post-consumer waste PET. First, genes encoding PETase and MHETase from Ideonella sakaiensis and the ICCG variant of leaf-branch compost cutinase (LCC ICCG ) were codon-optimized and expressed in Escherichia coli BL21(DE3) for high-yield production. To further lower the enzyme production cost, a pelB leader sequence was fused to LCC ICCG so that the enzyme can be secreted into the medium to facilitate recovery. To help bind the enzyme on the hydrophobic surface of PET, a substrate-binding module in a polyhydroxyalkanoate depolymerase from Alcaligenes faecalis (PBM) was fused to the C-terminus of LCC ICCG . The resulting four different LCC ICCG variants (LCC, PelB-LCC, LCC-PBM, and PelB-LCC-PBM), together with PETase and MHETase, were compared for PET degradation efficiency. A fed-batch fermentation process was developed to produce the target enzymes up to 1.2 g L –1 . Finally, the best enzyme, PelB-LCC, was selected and used for the efficient degradation of 200 g L –1 recycled PET in a well-controlled, stirred-tank reactor. Furthermore, the results will help develop an economical and scalable biorecycling process toward a circular PET economy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Reaction Optimization for Enzymatic Deconstruction of Industrially Relevant Nylon Composites

Plastics such as polyamides (PAs) possess unique physicochemical properties that make them indispensable in modern society. However, their energy‐intensive production and challenging end‐of‐life management highlight the urgent need for efficient recycling or remanufacturing solutions. Enzymatic depolymerization offers a promising route toward circular recycling, yet remains constrained by limited enzyme characterization, lack of validation under industrially relevant conditions and substrates, and overall performance. Here, we optimized the reaction conditions for three recently discovered nylon‐degrading enzymes. One of them, Nyl12, achieved product titers with PA6 and PA66 that exceed previously reported values, without enzyme engineering or substrate pretreatment. We further demonstrated the scalability of the process and its application to complex PA‐based materials used in microelectronic components. Analysis of substrate features, including surface area and particle size, revealed key parameters governing enzymatic activity and provided a framework for future pretreatment and process optimization efforts. In combination, these efforts provide a new benchmark for enzymatic nylon recycling.

nylon↗
DOE OSTI2020

Optimization of the autoclave preparation process for improving resistant starch content in rice grains

Abstract The autoclave preparation process to increase the content of resistant starch (RS) in rice grains was optimized, and the results showed that the optimal preparation process was obtained with a water content of 41.63%, a pH of 5.95, an autoclave time of 60.96 min, and a refrigeration time of 17.11 hr. Under these conditions, the theoretical value of RS content in rice grains reached 17.57%. After autoclaving, the estimated glycemic index (EGI) in rice grains was reduced from 78.35 to 66.08 measured after cooking, suggesting that autoclaving was capable of increasing the RS content in rice grains and reducing its EGI value. These results may help spark new concepts and methods for the development of specialized foods for specific populations, such as people with diabetes.

Zheng, Yunzhan↗
DOE OSTI2022

Data–driven and constrained optimization of semi–local exchange and nonlocal correlation functionals for materials and surface chemistry

Reliable predictions of surface chemical reaction energetics require an accurate description of both chemisorption and physisorption. Herein, we present an empirical approach to simultaneously optimize semi-local exchange and nonlocal correlation of a density functional approximation to improve these energetics. A combination of reference data for solid bulk, surface, and gas-phase chemistry and physical exchange-correlation model constraints leads to the VCML-rVV10 exchange-correlation functional. Owing to the variety of training data, the applicability of VCML-rVV10 extends beyond surface chemistry simulations. It provides optimized gas phase reaction energetics and an accurate description of bulk lattice constants and elastic properties.

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

Lagrangian–Eulerian multidensity topology optimization with the material point method

Abstract In this paper, a hybrid Lagrangian–Eulerian topology optimization (LETO) method is proposed to solve the elastic force equilibrium with the Material Point Method (MPM). LETO transfers density information from freely movable Lagrangian carrier particles to a fixed set of Eulerian quadrature points. This transfer is based on a smooth radial kernel involved in the compliance objective to avoid the artificial checkerboard pattern. The quadrature points act as MPM particles embedded in a lower‐resolution grid and enable a subcell multidensity resolution of intricate structures with a reduced computational cost. A quadrature‐level connectivity graph‐based method is adopted to avoid the artificial checkerboard issues commonly existing in multiresolution topology optimization methods. Numerical experiments are provided to demonstrate the efficacy of the proposed approach.

Li, Yue↗
DOE OSTI2021

A moving morphable component‐based topology optimization approach considering transient structural dynamic responses

Abstract A novel approach is developed under the moving morphable component (MMC) framework to solve topology optimization problems considering transient structural dynamic responses, where the structure for optimization is constructed with a collection of structural components, and the structural layout can be described explicitly via only a small number of design variables characterizing the positions and geometries of the components. The transient dynamic response of the structure and corresponding sensitivities are computed via step‐by‐step numerical integration and mode reduction technique is also employed to enhance the computational efficiency. Compared with existing works, the numerical instabilities induced by localized vibration modes can be substantially alleviated and the computational effort for direct sensitivity analysis is significantly reduced in the proposed approach. Two and three‐dimensional numerical examples considering the cases of nonproportional damping mechanism and time‐varying mass are also provided to manifest the validity of the proposed approach.

Li, Jialin↗

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