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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High-level co-production of 3-hydroxypropionic acid and 1,3-propanediol from glycerol: Metabolic engineering and process optimization

3-Hydroxypropionic acid (3-HP) and 1,3-propanediol (1,3-PDO) are value-added chemicals with versatile applications in the chemical, pharmaceutical, and food industries. Nevertheless, sustainable production of 3-HP and 1,3-PDO is often limited by the lack of efficient strains and suitable fermentation configurations. We report attempts have been made to improve the co-production of both metabolites through metabolic engineering of Escherichia coli and process optimization. First, the 3-HP and 1,3-PDO co-biosynthetic pathways were recruited and optimized in E. coli, followed by coupling the pathways to the transhydrogenase-mediated cofactor regeneration systems that increased cofactor availability and product synthesis. Next, pathway rebalancing and block of by-product formation significantly improved 3-HP and 1,3-PDO net titer. Subsequently, glycerol flux toward 3-HP and 1,3-PDO synthesis was maximized by removing metabolic repression and fine-tuning the glycerol oxidation pathway. Lastly, the combined fermentation process optimization and two-stage pH-controlled fed-batch fermentation co-produced 140.50 g/L 3-HP and 1,3-PDO, with 0.85 mol/mol net yield.

1,3-propanediol↗
DOE OSTI2022

Environmental, Economic, and Scalability Consideration of Selected Biomass-Derived Blendstocks for Mixing-Controlled Compression Ignition (MCCI) Engines

Co-Optimization of Fuels & Engines (Co-Optima) explores how simultaneous innovations in fuels and engines can boost fuel economy and vehicle performance while reducing emissions. The Analysis of Sustainability, Scale, Economics, Risk and Trade (ASSERT) team supports Co-Optima goals by evaluating environmental and economic drivers and the potential of bioblendstock candidates. The team assesses potential benefits or drawbacks of deploying co-optimized fuels and engines in the transportation sector. This presentation provides guidance into which Co-Optima bioblendstocks may be most economically and environmentally viable when scaled up.

biomass↗
DOE OSTI2020

What fuel properties enable higher thermal efficiency in spark-ignited engines?

The Co-Optimization of Fuels and Engines (Co-Optima) initiative from the US Department of Energy aims to co-develop fuels and engines in an effort to maximize energy efficiency and the utilization of renewable fuels. Many of these renewable fuel options have fuel chemistries that are different from those of petroleum-derived fuels. Because practical market fuels need to meet specific fuel-property requirements, a chemistry-agnostic approach to assessing the potential benefits of candidate fuels was developed using the Central Fuel Property Hypothesis (CFPH). The CFPH states that fuel properties are predictive of the performance of the fuel, regardless of the fuel's chemical composition. In order to use this hypothesis to assess the potential of fuel candidates to increase efficiency in spark-ignition (SI) engines, the individual contributions towards efficiency potential in an optimized engine must be quantified in a way that allows the individual fuel properties to be traded off for one another. This review article begins by providing an overview of the historical linkages between fuel properties and engine efficiency, including the two dominant pathways currently being used by vehicle manufacturers to reduce fuel consumption. Then, a thermodynamic-based assessment to quantify how six individual fuel properties can affect efficiency in SI engines is performed: research octane number, octane sensitivity, latent heat of vaporization, laminar flame speed, particulate matter index, and catalyst light-off temperature. The relative effects of each of these fuel properties is combined into a unified merit function that is capable of assessing the fuel property-based efficiency potential of fuels with conventional and unconventional compositions.

42 ENGINEERING↗
DOE OSTI2020

IEA Wind Task 32 and Task 37: Optimizing Wind Turbines with Lidar-Assisted Control Using Systems Engineering

Lidar-assisted control is a promising technology for reducing the levelized cost of energy from wind turbines, but quantifying its impact at the overall system level requires sophisticated systems engineering analysis and optimization frameworks. The joint workshop on Optimizing Wind Turbines with Lidar-Assisted Control Using Systems Engineering was held by the International Energy Agency Wind Task 32 (Lidar) and Task 37 (Systems Engineering) in October 2019 to address this challenge. This paper summarizes the outcome of the workshop and presents a road map for further research. The most promising applications of lidar-assisted control identified at the workshop and discussed here include 1) increasing annual energy production, 2) decreasing capital expenditure costs by reducing design loads, 3) extending turbine lifetime by reducing operating loads, and 4) enabling wind turbine class upgrades. For each application, we review the state of the art and highlight remaining research needs. Finally, we discuss strategies for addressing these research needs by conducting high-fidelity systems engineering optimizations.

17 WIND ENERGY↗
DOE OSTI2023

An adaptive sampling augmented Lagrangian method for stochastic optimization with deterministic constraints

The primary goal of this paper is to provide an efficient solution algorithm based on the augmented Lagrangian framework for optimization problems with a stochastic objective function and deterministic constraints. Our main contribution is combining the augmented Lagrangian framework with adaptive sampling, resulting in an efficient optimization methodology validated with practical examples. To achieve the presented efficiency, here we consider inexact solutions for the augmented Lagrangian subproblems, and through an adaptive sampling mechanism, we control the variance in the gradient estimates. Furthermore, we analyze the theoretical performance of the proposed scheme by showing equivalence to a gradient descent algorithm on a Moreau envelope function, and we prove sublinear convergence for convex objectives and linear convergence for strongly convex objectives with affine equality constraints. The worst-case sample complexity of the resulting algorithm, for an arbitrary choice of penalty parameter in the augmented Lagrangian function, is $\mathscr{O}$(ϵ -3-δ ) , where ϵ > 0 is the expected error of the solution and δ > 0 is a user-defined parameter. If the penalty parameter is chosen to be $\mathscr{O}$(ϵ -1 ), we demonstrate that the result can be improved to $\mathscr{O}$(ϵ -2 ) , which is competitive with the other methods employed in the literature. Moreover, if the objective function is strongly convex with affine equality constraints, we obtain $\mathscr{O}$(ϵ -1 log(1/ϵ)) complexity. Finally, we empirically verify the performance of our adaptive sampling augmented Lagrangian framework in machine learning optimization and engineering design problems, including topology optimization of a heat sink with environmental uncertainty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Description and Use of SCALE Sampler Parametric Capability for Engineering Analysis and Optimization

The Sampler sequence was introduced into the SCALE nuclear modeling and simulation suite in SCALE 6.2 to perform uncertainty quantification via random sampling of nuclear data, material number densities, and dimensions. Sampler was expanded with the introduction of a parametric capability in SCALE 6.2.2. This paper discusses input for the Sampler parametric sequence and presents two case studies of analyses performed using the sequence. These case studies include preconceptual design of a package for transporting high assay low-enriched uranium (HALEU) oxide and scoping calculations to support subcritical limit development for a future update of the ANSI/ANS-8.1 (ANS-8.1) standard. The parametric capability within Sampler provides many benefits to analysts. For instance, parametric sweeps are frequently used to identify optimum parameter values as part of safety analysis or system design, but such sweeps can require substantial engineering time or may rely on custom-written scripts or scripts such as Write One, Run Many (or WORM) developed outside of any software quality assurance program. With the parametric capabilities in Sampler, however, a large number of inputs can be generated automatically without recourse to scripting by individual analysts. The parametric capability can also be used in lieu of the CSAS5S search sequence to identify optimum parameters more simply with straightforward inputs and outputs. Sampler can also be used to calculate input parameters from engineering specifications. For example, diameters can be converted to radii, or masses can be used to calculate number densities. Overall, the Sampler parametric capability provides a robust feature within SCALE, eliminating the need for user-developed scripting.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Enabling On-Demand Aerospace Component Manufacturing: Topology Optimization of GE Engine Bracket and Fabrication Using Metal FFF

Additive Manufacturing (AM) offers advantages over conventional manufacturing processes, particularly by reducing the number of parts produced through multistage combined technologies, but these often result in low manufacturing yields or require post-processing. AM facilitates the production of complex geometries with fine features, overhangs, and lattice structures. For instance, Laser Powder Bed Fusion (LPBF) technology enables the fabrication of intricate parts that can be easily post-processed by removing residual powder. Laser powder bed AM technologies are widely discussed in the literature due to their design freedom in creating complex geometries, with and without the need for support generation. However, rapid solidification due to a thermal gradient in the build direction, which leads to the formation of columnar grains and warpage, is one of the challenges. To address this challenge, we propose layer-by-layer metal FFF technology, followed by the debinding and sintering process, as an alternative to powder- and laser-based approaches. Furthermore, design for additive manufacturing (DfAM) principles are discussed to minimize the need for support generation and enable easy post-processing, thereby improving surface finish to meet high tolerances in fabricating components for aerospace and healthcare applications.

Singh, Abhishek [University of Michigan, Ann Arbor↗
DOE OSTI2024

Development of integrated neutronics and thermal analysis capabilities to support design and optimization of fusion engineering demonstration facility systems and blanket design (Final Scientific/Technical Report)

A fusion engineering demonstration facility would be the first step in the commercial fusion plant developmental pathway that aims to be an engineering demonstration of tritium self-sufficiency. One of the primary requirements for tritium self-sufficiency, as well as to ensure low plant tritium release to the external environment, is to minimize the tritium loss through the internal components, which requires accurate predictions of the tritium behavior for a wide range of materials and system conditions. This grant addressed the lack of a comprehensive model which accounts for particular conditions, such as the significance of temperature fields and neutronics information, and the systematic uncertainty quantification of associated material properties that impact tritium generation, utilization, and loss mechanisms in the blanket.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Development of integrated neutronics and thermal analysis capabilities to support design and optimization of fusion engineering demonstration facility systems and blanket design (Final Scientific/Technical Report)

A fusion engineering demonstration facility would be the first step in the commercial fusion plant developmental pathway that aims to be an engineering demonstration of tritium self- sufficiency. One of the primary requirements for tritium self-sufficiency, as well as to ensure low plant tritium release to the external environment, is to minimize the tritium loss through the internal components, which requires accurate predictions of the tritium behavior for a wide range of materials and system conditions. This grant addressed the lack of a comprehensive model which accounts for particular conditions, such as the significance of temperature fields and neutronics information, and the systematic uncertainty quantification of associated material properties that impact tritium generation, utilization, and loss mechanisms in the blanket.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Robust Parameter Design on Dual Stochastic Response Models With Constrained Bayesian Optimization

In engineering system design, minimizing the variations of the quality measurements while guaranteeing their overall quality up to certain levels, namely the robust parameter design (RPD), is crucial. Recent works have dealt with the design of a system whose response-control variables relationship is a deterministic function with a complex shape and function evaluation is expensive. In this work, we propose a Bayesian optimization method for the RPD of stochastic functions. Dual stochastic response models are carefully designed for stochastic functions. The heterogeneous variance of the sample mean is addressed by the predictive mean of the log variance surrogate model in a two-step approach. We establish an acquisition function that favors exploration across the feasible and optimality-improvable regions to effectively and efficiently solve the stochastic constrained optimization problem. Further, the performance of our proposed method is demonstrated by the extensive numerical and case studies. Note to Practitioners-Many manufacturing processes involve undesirable variations, which create variations in the final products. For example, many emerging manufacturing processes, such as nanomanufacturing, involve complex physical and chemical dynamics and transformation, creating variations in the manufacturing output. In such processes, it is crucial to design the manufacturing processes or products so that they have minimum variations in their quality. Meanwhile, it is also important to maintain the overall quality of the designed processes or products. Furthermore, acquiring data from many advanced manufacturing processes is often very costly, especially in the designing stage. In this work, we propose a data-driven method that automatically finds the best setting of manufacturing processes or products with the minimum variations of quality and a given constraint on the average quality satisfied. Our proposed method is used before conducting every experiment; It analyzes the historical data from previous experiments and provides a setting to be used in the next experiment. Our proposed method efficiently utilizes the historical data, and thus finds the best robust setting by conducting only a small number of experiments.

42 ENGINEERING↗
DOE OSTI2022

Building and Executing Aggressive Research Plans in a Large National Laboratory Consortium: Insights from the Co-Optimization of Fuels and Engines Initiative

This report describes lessons learned in the establishment, execution and termination of a large, multi-institutional consortium, derived from the Co-Optimization of Fuels and Engines experience. The decision to form a consortium comes with benefits (in advancing challenging multidisciplinary research) and costs (in time and additional management funds). Once the decision is made, key elements to a strong start include establishing a shared vision and goals; engaging an experienced project manager early; instituting feedback and oversight mechanisms to ensure relevance, strong performance, and situational awareness. Once a consortium is up and running, DOE and leadership should strike the right balance between competition and collaboration; foster an environment that builds trust; and adjust the organizational structure as needed to maintain collaboration. Finally, DOE and the labs can plan effectively for a smooth transition as a consortium winds down. This report provides some additional lessons and details on these lessons that we hope future DOE and lab leaders will find useful as they contemplate standing up new consortia.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗