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At least 1,531 records · Page 85

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DOE OSTI2014

Improving microbial biogasoline production in Escherichia coli using tolerance engineering

Engineering microbial hosts for the production of fungible fuels requires mitigation of limitations posed on the production capacity. One such limitation arises from the inherent toxicity of solvent-like biofuel compounds to production strains, such as Escherichia coli . Here we show the importance of host engineering for the production of short-chain alcohols by studying the overexpression of genes upregulated in response to exogenous isopentenol. Using systems biology data, we selected 40 genes that were upregulated following isopentenol exposure and subsequently overexpressed them in E. coli . Overexpression of several of these candidates improved tolerance to exogenously added isopentenol. Genes conferring isopentenol tolerance phenotypes belonged to diverse functional groups, such as oxidative stress response ( soxS , fpr , and nrdH ), general stress response ( metR , yqhD , and gidB ), heat shock-related response ( ibpA ), and transport ( mdlB ). To determine if these genes could also improve isopentenol production, we coexpressed the tolerance-enhancing genes individually with an isopentenol production pathway. Our data show that expression of 6 of the 8 candidates improved the production of isopentenol in E. coli , with the methionine biosynthesis regulator MetR improving the titer for isopentenol production by 55%. Additionally, expression of MdlB, an ABC transporter, facilitated a 12% improvement in isopentenol production. To our knowledge, MdlB is the first example of a transporter that can be used to improve production of a short-chain alcohol and provides a valuable new avenue for host engineering in biogasoline production.

09 BIOMASS FUELS↗

DOE OSTI2019

Understanding the engines and progenitors of gamma-ray bursts

Our understanding of the engines and progenitors of gamma-ray bursts has expanded through the ages as a broader set of diagnostics has allowed us to test our understanding of these objects. In this work, we review the history of the growth in our understanding, focusing on 3 leading engines and 9 potential progenitors. The first gravitational wave detection of a short burst is the latest in a series of breakthrough observations shaping this understanding and we study the importance of multi-diagnostic, multi-messenger observations on these engines and their progenitors. Our understanding based on a detailed study of nearby bursts can be applied to make predictions for the trends expected as we begin to observe high redshift bursts and we discuss these trends.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Data Science in Chemical Engineering: Applications to Molecular Science

Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science—the application tools for molecular discovery and property optimization at the atomic scale. Here, we summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

A software package for plasma facing component analysis and design: the Heat flux Engineering Analysis Toolkit (HEAT)

The engineering limits of plasma facing components (PFCs) constrain the allowable operational space of tokamaks. Poorly managed heat fluxes that push the PFCs beyond their limits not only degrade core plasma performance via elevated impurities, but can also result in PFC failure due to thermal stresses or melting. Simple axisymmetric assumptions fail to capture the complex interaction between 3D PFC geometry and 2D or 3D plasmas. This results in fusion systems that must either operate with increased risk or reduce PFC loads, potentially through lower core plasma performance, to maintain a nominal safety factor. High precision 3D heat flux predictions are necessary to accurately ascertain the state of a PFC given the evolution of the magnetic equilibrium. A new code, the Heat flux Engineering Analysis Toolkit (HEAT), has been developed to provide high precision 3D predictions and analysis for PFCs. HEAT couples many otherwise disparate computational tools together into a single open source python package. Magnetic equilibrium, engineering CAD, finite volume solvers, scrape off layer plasma physics, visualization, high performace computing, and more, are connected in a single web-based user interface. Linux users may use HEAT without any software prerequisites via an appImage. This manuscript introduces HEAT, discusses the software architecture, presents first HEAT results, and outlines physics modules in development.

divertor physics↗

DOE OSTI2016

WISDEM® (Wind-Plant Integrated System Design and Engineering Model) [SWR-14-05]

The Wind-Plant Integrated System Design and Engineering Model (WISDEM®) is a set of models for assessing overall wind plant cost of energy (COE). The models use wind turbine and plant cost and energy production as well as financial models to estimate COE and other wind plant system attributes. WISDEM® is accessed through Python, is built using OpenMDAO, https://openmdao.org/, and uses several sub-models that are also implemented within OpenMDAO. These sub-models can be used independently but they are required to use the overall WISDEM® turbine design capability. Please install all of the pre-requisites prior to installing WISDEM® by following the directions below. For additional information about the NWTC effort in systems engineering that supports WISDEM® development, please visit the official NREL systems engineering for wind energy website. https://www.nlr.gov/wind/systems-engineering

Ning, S.Andrew↗

DOE OSTI2021

WISDEM® v3.4.1(Wind Plant Integrated System Design and Engineering Model) [SWR-14-05]

The Wind Plant Integrated System Design and Engineering Model (WISDEM®) is a set of models for assessing overall wind plant cost of energy (COE). The models use wind turbine and plant cost and energy production as well as financial models to estimate COE and other wind plant system attributes. WISDEM® is accessed through Python, is built using OpenMDAO, and uses several sub-models that are also implemented within OpenMDAO. These sub-models can be used independently but they are required to use the overall WISDEM® turbine design capability. Please install all of the pre-requisites prior to installing WISDEM® by following the directions below. For additional information about the NWTC effort in systems engineering that supports WISDEM® development, please visit the official NREL systems engineering for wind energy website. https://www.nlr.gov/wind/systems-engineering

Dykes, Katherine↗

DOE OSTI2022

Inference-Engine v0.1.0

Given a pre-trained neural network, Inference-Engine performs maps network inputs to outputs by executing the forward pass through the provided network. Although the predominant programming language for machine-learning is Python, most high-performance computing (HPC) applications are written in Fortran, C, or C++. Inference-Engine aims to support HPC programs and is written in Fortran, a language with a large feature set supporting interoperability with C. This software exposes concurrency in a portable way by using standard language features that some modern Fortran compilers can exploit with various optimizations, including offloading computation to a Graphics Processing Unit (GPU). In particular, this software makes extensive use of Fortran's "do concurrent" parallel loop construct, implicitly parallel array statements, and pure procedures that can be invoked inside "do concurrent" blocks. Inference-Engine also supports dynamic choice of inference methods at runtime. Two current options include one method that uses Fortran's "dot_product" intrinsic function inside "do concurrent" blocks and another method that instead uses Fortran' "matmul" array intrinsic function. We plan to investigate automatic compiler offloading of "do concurrent" calculations to GPUs and compile-time substitution of optimized libraries such as the Basic Linear Algebra Library (BLAS) for "matmul" invocations. We also envision the potential for the choice of which method to use could happen at program launch based on in situ performance measurements on any given platform.

Rouson, Damian↗

DOE OSTI2024

WISDEM® v3.15.2 2024 (Wind-Plant Integrated System Design and Engineering Model) [SWR-14-05]

The Wind-Plant Integrated System Design and Engineering Model (WISDEM®) is a set of models for assessing overall wind plant cost of energy (COE). The models use wind turbine and plant cost and energy production as well as financial models to estimate COE and other wind plant system attributes. WISDEM® is accessed through Python, is built using OpenMDAO, and uses several sub-models that are also implemented within OpenMDAO. These sub-models can be used independently but they are required to use the overall WISDEM® turbine design capability. Please install all of the pre-requisites prior to installing WISDEM® by following the directions below. For additional information about the NWTC effort in systems engineering that supports WISDEM® development, please visit the official NREL systems engineering for wind energy website. https://www.nrel.gov/wind/systems-engineering.html

Dykes, Katherine↗

DOE OSTI2025

Software For Advanced Large-scale Analysis Of Magnetic Confinement For Numerical Design, Engineering & Research (salamander)

As magnetic confinement fusion energy gains traction internationally to enable abundant energy production, designing components for fusion systems is a pressing challenge. During the planned lifetime of a fusion device, components evolve in extreme environments and must withstand large, repeated thermal loads and bombardment by 14 MeV neutrons, plasma ions, and neutral particles (deuterium, tritium, and helium), corrosive conditions, etc. All these physical processes take place simultaneously, interact in intricate ways, and impose important constraints that can affect performance. Experimental data is rare and costly to obtain, making design particularly challenging. Predictive computational frameworks must be an integral part of an accelerated and cost-effective design process by modeling fusion system performance in simulated environments. To better understand component degradation and operational impacts on their performance, the Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER) is designed as an open-source, fully integrated, multiphysics, multiscale, NQA-1 compliant framework facilitating 3D, high-fidelity fusion system modeling. To that end, SALAMANDER is a MOOSE-based framework, and therefore leverages MOOSE upstream libraries such as PETSc and libMesh to deliver sophisticated finite element, finite volume, and nonlinear solver technology for fusion energy simulations. SALAMANDER couples MOOSE physics module capabilities—such as thermal hydraulics, heat conduction, Navier-Stokes, and thermomechanics—with tritium transport via TMAP8, neutronics via Cardinal, and nascent particle-in-cell capabilities. Direct simulation Monte Carlo methods will be used to address neutral transport near the walls. By coupling all these physics in an integrated application, SALAMANDER will enable high-fidelity modeling of irradiation levels and plasma exposure conditions of plasma facing components and their impact on heat and tritium distributions, as well as the resulting mechanical constraints experienced by the plasma facing components and performance of blanket systems. Furthermore, SALAMANDER will be particularly suited for engineering studies thanks to the stochastic tool module readily available in MOOSE, allowing for extended uncertainty quantification and risk analysis studies. It is also able to use computer-aided design (CAD) meshes to model complex geometries, which is indispensable for fusion systems. SALAMANDER therefore supports design, safety, engineering, and research projects for magnetic confinement fusion systems

Simon, Pierre-Clement [Idaho National Laboratory (↗

DOE OSTI2016

Detailed characterization of particulate matter emitted by lean-burn gasoline direct injection engine

This study presents detailed characterization of the chemical and physical properties of PM emitted by a 2.0L BMW lean-burn turbocharged GDI engine operated under a number of combustion strategies that include lean homogeneous, lean stratified, stoichiometric, and fuel rich conditions. We characterized PM number concentrations, size distributions, and the size, mass, compositions, and effective density of fractal and compact individual exhaust particles. For the fractal particles, these measurements yielded fractal dimension, average diameter of primary spherules, and number of spherules, void fraction, and dynamic shape factors as function of particle size. Overall, the PM properties were shown to vary significantly with engine operation condition. Lean stratified operation yielded the most diesel-like size distribution and the largest PM number and mass concentrations, with nearly all particles being fractal agglomerates composed of elemental carbon with small amounts of ash and organics. In contrast, stoichiometric operation yielded a larger fraction of ash particles, especially at low speed and low load. Three distinct forms of ash particles were observed, with their fractions strongly dependent on engine operating conditions: sub-50 nm ash particles, abundant at low speed and low load, ash-containing fractal particles, and large compact ash particles that significantly contribute to PM mass loadings

Environmental Molecular Sciences Laboratory↗

DOE OSTI2019

Experimental investigation of the relationship between thermal barrier coating structured porosity and homogeneous charge compression ignition engine combustion

Heat transfer has a monumental influence on homogeneous charge compression ignition combustion. When a thermal barrier coating is applied to the combustion chamber, the insulating effect magnifies the wall temperature swing, decreasing heat transfer during combustion. This enables improvements in both thermal and combustion efficiency without the detrimental impacts of intake charge heating. Increasing the temperature swing requires coatings with lower thermal conductivity and heat capacity. An enticing avenue for simultaneously decreasing both thermal conductivity and capacity is to increase the porosity fraction. A proprietary solution precursor plasma spray process enables discrete organization of the porosity structure, called inter-pass boundaries, which in turn produces a step-reduction in thermal conductivity for a given porosity level. In this investigation, yttria-stabilized zirconia is used to create four different thermal barrier coatings to study the potential of structured porosity as means of improving the “temperature swing” behavior in a homogeneous charge compression ignition engine. The baseline coating is “dense YSZ,” applied using a standard air-plasma spray process. Furthermore, significant reductions of the thermal conductivity are achieved by utilizing the solution precursor plasma spray process to create inter-pass boundaries with a moderate overall porosity. Performance, efficiency, and emissions are compared against both a baseline configuration with a metal piston and an air-plasma spray “dense YSZ” coating. Experiments are carried out in a single-cylinder gasoline homogeneous charge compression ignition engine with exhaust re-induction. Experiments indicate that incorporating structured porosity into thermal barrier coatings produces tangible gains in combustion and thermal efficiencies. However, there is an upper limit to porosity levels acceptable for homogeneous charge compression ignition engine application because an elevated porosity fraction leads to excessive surface roughness and undesirable fuel interactions. Comparison of the coatings showed the best results with coating thickness of up to 150 µm. Thicker coatings led to slower surface temperature response and attenuated swing temperature magnitude.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

A control-oriented combustion model framework for compression ignition engines operating on low-reactivity fuel

This work focuses on zero-dimensional modeling of the heat release rate in a compression ignition engine operating on gasoline-like fuels. Due to the properties of gasoline, such as high volatility and longer ignition delay than diesel, the injection strategies can vary significantly from the operation with conventional diesel fuel. Different injection strategies are commonly used to achieve varying degrees of in-cylinder stratification in order to shape the combustion event and maximize efficiency. The proposed zero-dimensional combustion model was developed to account for the different stages in combustion caused by the fuel stratification. As the ignition delay model is an integral part of the entire combustion process and significantly affects the prediction accuracy, special attention has been paid to local phenomena influencing ignition delay. A one-dimensional spray model by Musculus and Kattke was employed in conjunction with a Lagrangian tracking approach in order to estimate the local air–fuel ratio within the spray tip, as a proxy for reactivity. The local air–fuel ratio, in-cylinder temperature and pressure were used in an integral fashion to estimate the ignition delay. Heat release rates were modeled using first-order non-linear differential equations. The proposed combustion model was validated against experimental data of a heavy-duty compression ignition engine with up to three injection events at mostly 1038 r/min and 14 bar brake mean effective pressure. Further validation of the model was carried out at other engine loads and speeds. Model prediction errors in CA50 of less than 1 °CA across all conditions were found. Modeling results of other combustion metrics such as combustion duration and indicated mean effective pressure are also highly satisfactory. In addition, the model has been shown to be capable of estimating the ringing intensity for most conditions.

Pamminger, Michael↗

DOE OSTI2021

Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization

In recent years, the use of machine learning-based surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising technique for reducing the computational cost associated with engine design optimization. However, such methods still suffer from drawbacks. One main disadvantage is that the default machine learning (ML) hyperparameters are often severely suboptimal for a given problem. This has often been addressed by manually trying out different hyperparameter settings, but this solution is ineffective in case of a high-dimensional hyperparameter space. Besides this problem, the amount of data needed for training is also not known a priori. In response to these issues that need to be addressed, the present work describes and validates an automated active learning approach, AutoML-GA, for surrogate-based optimization of internal combustion engines. In this approach, a Bayesian optimization technique is used to find the best machine learning hyperparameters based on an initial dataset obtained from a small number of CFD simulations. Subsequently, a genetic algorithm is employed to locate the design optimum on the ML surrogate surface. In the vicinity of the design optimum, the solution is refined by repeatedly running CFD simulations at the projected optima and adding the newly obtained data to the training dataset. It is demonstrated that AutoML-GA leads to a better optimum with a lower number of CFD simulations, compared to the use of default hyperparameters. The proposed framework offers the advantage of being a more hands-off approach that can be readily utilized by researchers and engineers in industry who do not have extensive machine learning expertise.

Owoyele, Opeoluwa↗

DOE OSTI2023

Assessment of design and location of an active prechamber igniter to enable mixing-controlled combustion of ethanol in heavy-duty engines

Here, this numerical study focuses on assessing the key design parameters of interest for use of an active prechamber igniter as an ignition assistance device to enable mixing-controlled combustion (MCC) of ethanol (E100) in a heavy-duty Caterpillar C9.3B engine. Computational fluid dynamic (CFD) simulations of a baseline diesel and prechamber retrofitted C9.3B at a gross indicated mean effective pressure (IMEPg) of 5 bar and 1800 rpm are carried out using CONVERGE. In particular, the sizing of the prechamber volume, sizing of total orifice cross sectional area (orifice diameter), and location of the prechamber relative to a centrally mounted common rail direct injector are varied to discern the appropriate operational and design characteristics to achieve robust ignition assistance at the selected conditions. Simulation results indicate that use of an active prechamber igniter with E100 as a standalone fuel source can replicate ignition delays and thermal efficiencies similar to diesel combustion at the same engine boundary conditions, thus not requiring any changes to the engine’s air handling system. Igniter mounting location, orifice sizing, and jet targeting were found to have the strongest influence on ignition assistance with preference toward larger orifice diameters that appropriately located heating contributions in near vicinity to the direct injector.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Optimization of spray breakup model parameters for predicting fuel spray and film characteristics in gasoline direct injection engines

This study investigated the behavior of gasoline direct injection (GDI) sprays using computational fluid dynamics (CFD). The authors developed an approach to identify optimal spray breakup model parameters by evaluating an error function across numerous simulations, with the goal of minimizing discrepancies from experimental data. Using the optimal setup, the simulated spray matched well with projected liquid volume distributions, liquid penetration, and spray width measured in a constant-pressure continuous-flow chamber. To further validate the approach, the same setup was tested across various fuels, injectors, and operating conditions. Subsequently, the optimal setup, along with a recently developed spray-wall interaction model, were applied to a direct-injection spark-ignited engine under late-injection conditions to predict and evaluate fuel film formation and evolution at varying engine coolant temperatures. Here, with the centrally mounted injector directing the spray toward the piston, simulations indicated that the spray tends to impinge on the piston surface. The proposed simulation framework also accurately captured the aggregate film area on the piston surface, aligning with previously published experimental results. Moreover, simulations showed that increasing the coolant temperature from cold start conditions (333 K) to warm conditions (363 K) reduced the fuel mass deposited on the piston by roughly 50%. Furthermore, for the spray-guided engine configuration studied in this work, the CFD model predicted minimal film deposition on the spark plug electrodes regardless of the coolant temperatures due to a relatively weak in-cylinder flow during the compression phase.

Computational fluid dynamics (CFD)↗

DOE OSTI2014

A gene stacking approach leads to engineered plants with highly increased galactan levels in Arabidopsis

Background: Engineering of plants with a composition of lignocellulosic biomass that is more suitable for downstream processing is of high interest for next-generation biofuel production. Lignocellulosic biomass contains a high proportion of pentose residues, which are more difficult to convert into fuels than hexoses. Therefore, increasing the hexose/pentose ratio in biomass is one approach for biomass improvement. A genetic engineering approach was used to investigate whether the amount of pectic galactan can be specifically increased in cell walls of Arabidopsis fiber cells, which in turn could provide a potential source of readily fermentable galactose. Results: First it was tested if overexpression of various plant UDP-glucose 4-epimerases (UGEs) could increase the availability of UDP-galactose and thereby increase the biosynthesis of galactan. Constitutive and tissue-specific expression of a poplar UGE and three Arabidopsis UGEs in Arabidopsis plants could not significantly increase the amount of cell wall bound galactose. We then investigated co-overexpression of At UGE2 together with the β-1,4-galactan synthase GalS1. Co-overexpression of At UGE2 and GalS1 led to over 80% increase in cell wall galactose levels in Arabidopsis stems, providing evidence that these proteins work synergistically. Furthermore, At UGE2 and GalS1 overexpression in combination with overexpression of the NST1 master regulator for secondary cell wall biosynthesis resulted in increased thickness of fiber cell walls in addition to the high cell wall galactose levels. Immunofluorescence microscopy confirmed that the increased galactose was present as β-1,4-galactan in secondary cell walls. Conclusions: This approach clearly indicates that simultaneous overexpression of At UGE2 and GalS1 increases the cell wall galactose to much higher levels than can be achieved by overexpressing either one of these proteins alone. Moreover, the increased galactan content in fiber cells while improving the biomass composition had no impact on plant growth and development and hence on the overall biomass amount. Thus, we could show that the gene stacking approach described here is a promising method to engineer advanced feedstocks for biofuel production.

09 BIOMASS FUELS↗

DOE OSTI2022

Engineering isoprenoids production in metabolically versatile microbial host Pseudomonas putida

Abstract With the increasing need for microbial bioproduction to replace petrochemicals, it is critical to develop a new industrial microbial workhorse that improves the conversion of lignocellulosic carbon to biofuels and bioproducts in an economically feasible manner. Pseudomonas putida KT2440 is a promising microbial host due to its capability to grow on a broad range of carbon sources and its high tolerance to xenobiotics. In this study, we engineered P. putida KT2440 to produce isoprenoids, a vast category of compounds that provide routes to many petrochemical replacements. A heterologous mevalonate (MVA) pathway was engineered to produce potential biofuels isoprenol (C 5 ) and epi-isozizaene (C 15 ) for the first time in P. putida . We compared the difference between three different isoprenoid pathways in P. putida on isoprenol production and achieved 104 mg/L of isoprenol production in a batch flask experiment through optimization of the strain. As P. putida can natively consume isoprenol, we investigated how to prevent this self-consumption. We discovered that supplementing l -glutamate in the medium can effectively prevent isoprenol consumption in P. putida and metabolomics analysis showed an insufficient energy availability and an imbalanced redox status during isoprenol degradation. We also showed that the engineered P. putida strain can produce isoprenol using aromatic substrates such as p -coumarate as the sole carbon source, and this result demonstrates that P. putida is a valuable microbial chassis for isoprenoids to achieve sustainable biofuel production from lignocellulosic biomass. Graphical Abstract

09 BIOMASS FUELS↗

DOE OSTI2022

Evaluation of engineered low-lignin poplar for conversion into advanced bioproducts

Lignocellulosic resources are promising feedstocks for the manufacture of bio-based products and bioenergy. However, the inherent recalcitrance of biomass to conversion into simple sugars currently hinders the deployment of advanced bioproducts at large scale. Lignin is a primary contributor to biomass recalcitrance as it protects cell wall polysaccharides from degradation and can inhibit hydrolytic enzymes via non-productive adsorption. Several engineering strategies have been designed to reduce lignin or modify its monomeric composition. For example, expression of bacterial 3-dehydroshikimate dehydratase (QsuB) in poplar trees resulted in a reduction in lignin due to redirection of metabolic flux toward 3,4-dihydroxybenzoate at the expense of lignin. This reduction was accompanied with remarkable changes in the pools of aromatic compounds that accumulate in the biomass. The impact of these modifications on downstream biomass deconstruction and conversion into advanced bioproducts was evaluated in the current study. Using ionic liquid pretreatment followed by enzymatic saccharification, biomass from engineered trees released more glucose and xylose compared to wild-type control trees under optimum conditions. Fermentation of the resulting hydrolysates using Rhodosporidium toruloides strains engineered to produce α-bisabolene, epi-isozizaene, and fatty alcohols showed no negative impact on cell growth and yielded higher titers of bioproducts (as much as + 58%) in the case of QsuB transgenics trees. Our data show that low-recalcitrant poplar biomass obtained with the QsuB technology has the potential to improve the production of advanced bioproducts.

09 BIOMASS FUELS↗