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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Fuel Composition Influences on Reciprocating Engine Performance

The introduction of new fuels into the market is a unique opportunity to take advantage of new fuel compositions to improve the efficiency and emissions of internal combustion reciprocating engines and alternative fuel feedstocks. However, there are numerous challenges that introductions of new fuels face before they can become first legal, then ubiquitous. This chapter reviews four different case studies related to changing fuel composition. In some circumstances, the fuel formulation was changed in seemingly minor ways, and resulted in the unanticipated consequences. In other cases, a fuel change was desired, but an unexpected barrier slowed the introduction of the fuel change. These case studies should be viewed as opportunities to understand the interdependencies that exist and factors that need to be considered when trying to change the fuel in the marketplace.

Szybist, James↗
DOE OSTI2020

Support for the 2019 IEEE Symposium on Fusion Engineering

This award provided financial support to help cover program-related expenses for the 2019 IEEE Symposium on Fusion Engineering (SOFE-2019), which was held 2-6 June 2019 near Jacksonville, FL. The SOFE is a biennial conference that enables fusion engineers from around the world to report on their work and stay current with the latest developments in fusion R&D, both physics and engineering. The symposium was sponsored by the Nuclear and Plasma Sciences Society (NPSS) of the Institute of Electrical and Electronics Engineers (IEEE), an international professional society whose members today span a wide range of disciplines. The IEEE Fusion Technology Standing Committee (FTC), most of whose members are involved in DOE Fusion Energy Sciences (FES) sponsored work, were responsible for organizing the SOFE. The SOFE has been the leading conference of choice for U.S. fusion engineers since its founding in 1965. In recent years, attendance has been growing as the conference has attracted greater participation of fusion researchers from other countries, especially from Asia but also from Europe and from the international ITER project, both central team and domestic agencies. This trend benefits U.S. participants by providing a forum to learn about the world’s most advanced fusion engineering activities and to strengthen collaborative ties with colleagues.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

RCT: Module 2.07 Respiratory Protection [Slides]

Internal dosimetry controls require the use of engineering controls to prevent the internal deposition of radioactive and non-radiological contaminants. However, when engineering and administrative controls are not available or feasible, respiratory protection may be necessary. The RCT should know and apply the considerations used in determining the respiratory protection equipment that is most appropriate for the job. The inappropriate use of or the use of the wrong respiratory protection equipment may result in undesirable health effects.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Digital Engineering & Data Acquisition

This is an Intern Poster submission regarding my research in the digital engineering team. My task was to create a software adapter to communicate between a data acquisition system (LabVIEW) and a data warehouse (Deep Lynx) as described in the poster.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Thermal Evolution of Internal Strain in Doped PbTe

Recent improvements in the efficiency of heat-to-electricity energy conversion in lead chalcogenide thermoelectrics involve reducing the thermal conductivity by incorporating large amounts of internal strain. The extent to which typical lead chalcogenide processing techniques (such as doping, ball milling, and densification) increase internal strain and dislocation density must be quantified to improve materials design. In this study, neutron powder diffraction is leveraged to evaluate the internal strain introduced by ball milling in doped and undoped powders. Doping with Na and/or Eu increases internal strain beyond ball milling alone, with the greatest increase from combining the two dopants. Strain recovery occurs in each powder above 400 K but can be suppressed by co-doping, indicating a strong dopant–dislocation interaction in this system. Therefore, high-temperature processing of PbTe powders should be avoided if high internal strain is desired. Low-temperature densification and/or rapid pressing techniques may be key to maintaining internal strain in the final pressed pellet. The diffraction peak asymmetry and correlated elastic softening measured in pressed PbTe pellets in past studies were not observed in the precursor powders measured here, suggesting that measurements of the Debye temperature on final pressed pellets are required to examine the influence of defect-induced elastic softening on thermal conductivity. This work provides key guidance for defect engineering to maximize internal strain and thermoelectric performance in PbTe thermoelectrics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Tunable Stochasticity in an Artificial Spin Network

Metamaterials present the possibility of artificially generating advanced functionalities through engineering of their internal structure. Artificial spin networks, in which a large number of nanoscale magnetic elements are coupled together, are promising metamaterial candidates that enable the control of collective magnetic behavior through tuning of the local interaction between elements. In this work, the motion of magnetic domain-walls in an artificial spin network leads to a tunable stochastic response of the metamaterial, which can be tailored through an external magnetic field and local lattice modifications. This type of tunable stochastic network produces a controllable random response exploiting intrinsic stochasticity within magnetic domain-wall motion at the nanoscale. An iconic demonstration used to illustrate the control of randomness is the Galton board. In this system, multiple balls fall into an array of pegs to generate a bell-shaped curve that can be modified via the array spacing or the tilt of the board. A nanoscale recreation of this experiment using an artificial spin network is employed to demonstrate tunable stochasticity. Furthermore, this type of tunable stochastic network opens new paths toward post-Von Neumann computing architectures such as Bayesian sensing or random neural networks, in which stochasticity is harnessed to efficiently perform complex computational tasks.

Artificial spin network↗
DOE OSTI2020

Exploiting ultra-large linear elasticity over a wide temperature range in nanocrystalline NiTi alloy

Many shape memory alloys can support large recoverable strains of a few percent by reversible stress-induced martensite transformation, yet they behave non-linear within a narrow operating temperature range. Developing the bulk metallic materials with ultra-large linear elasticity over a wide temperature range has proven to be difficult. In this work, a material design concept was proposed, that is true elastic deformation and reversible twinning-detwinning deformation run in parallel to overcome this challenge. By engineering the residual internal stress to realize the concurrency of true elastic deformation and twinning-detwinning deformation, a bulk nanocrystalline NiTi that possesses an ultra-large linear elastic strain up to 5.1 % and a high yield stress of 2.16 GPa over a wide temperature range of 270 °C was developed. This study offers a new avenue for developing the metallic materials with ultra-large linear elasticity over a wide temperature range of 270 °C (from 70 °C to -197 °C).

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. Additionally, this methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

AN AUTOMATED MACHINE LEARNING-GENETIC ALGORITHM FRAMEWORK WITH ACTIVE LEARNING FOR DESIGN OPTIMIZATION

The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

Owoyele, Opeoluwa↗
DOE OSTI2020

A novel active optimization approach for rapid and efficient design space exploration using ensemble machine learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using expensive function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface. The performance of the new active learning approach is compared with traditional global optimization methods, namely micro-genetic algorithm (pGA) and particle swarm optimization (PSO). It is demonstrated that the new optimizer is able to reach the global optimum much faster with a significantly fewer number of function evaluations. Subsequently, the new optimizer is also applied to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is again found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to particle swarm and genetic algorithm-based optimization techniques.

Machine learning, active learning, design optimiza↗
DOE OSTI2020

Engineered Barrier System Research Activities at LBNL (FY2020 Progress Report)

The design of a radioactive waste repository typically involves a multi-barrier system, including the natural barrier system (NBS), i.e., the host rock and its surrounding subsurface environment, and an engineered barrier system (EBS). The EBS is to be constructed from the man-made, engineered materials placed within a repository. The repository includes the waste form, waste canisters, buffer materials, backfill, and seals. The most common buffer material for EBS is compacted bentonite, which features low permeability and high retardation of radionuclides. Extensive studies concerning the behavior of bentonite backfill in crystalline and argillite/shale geologic environments for nuclear waste disposal have been conducted by means of laboratory experiments, numerical modeling, and large-scale in situ tests in Underground Research Laboratories (URLs) in Switzerland, France, Belgium, and Japan. This report includes the results of LBNL’s research activities conducted according to the objectives and scope of the work packages “SF-20LB01030802 Engineered Barrier System R&D - LBNL” and “SF-20LB01030806 Engineered Barrier System International Collaborations - LBNL” of the Spent Fuel and Waste Science and Technology (SFWST) (formerly called Used Fuel Disposal) Campaign of the Department of Energy’s (DOE) Office of Nuclear Energy. LBNL research studies included laboratory scale tests and modeling of the evolution of the EBS bentonite and associated coupled processes, and impacts of high temperature on parameters and processes relevant to the performance of crystalline and argillite repositories, including the evaluation of the technical basis for applying the maximum allowable temperature. The results of these studies are being addressing the technical elements necessary to evaluate the EBS design concepts. Emphasis is on the study of thermal, mechanical, and chemical processes that influence the performance of EBS, and the development of modeling capability for reliable assessment of these processes, and ultimately supporting the development of the GDSA model with detailed coupled THMC process models.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Spark-Assisted HCCI Residential CHP

A transformative small spark ignition (SI) internal combustion (IC) engine fueled with natural gas was developed for combined heat and power (CHP) applications using a combination of cycle simulations, computational fluid dynamics (CFD) modeling, and engine experiments. The resulting 1 kW CHP engine was demonstrated to achieve 36.1% brake thermal efficiency (BTE) while meeting aggressive exhaust emissions targets using a low-cost three-way catalyst. Starting from a “clean-sheet” design, modeling tools were used to select the optimal engine parameters and operating characteristics. The disruptive technologies developed for the CHP system have a target lifetime of at least 10 years. The engine cost at volume of 10,000 units was estimated to be $\$$1,050. A combination of experimental and simulation results were used to identify a path to the program target of 38.6% BTE. Base engine design parameters (i.e., displacement, speed, bore, stroke, valve timings, and compression ratio) were optimized to reduce friction, heat transfer, and exhaust losses. The base clean sheet prototype engine achieved 32% BTE. Optimization of the lube oil and coolant temperatures added 1.8% BTE. The addition of exhaust gas recirculation added 1.1% BTE. Novel thermal barrier coatings (TBCs) developed over the course of the project added 1.2% BTE. Further improvements in engine friction reduction (1.3% BTE) and improved combustion (1.2% BTE) are estimated to meet the program target 38.6% BTE, while also meeting the stringent criteria pollutant emissions targets.

42 ENGINEERING↗
DOE OSTI2026

Cummins PEM Fuel Cell System for Heavy Duty Applications

The motivation for this project was to develop a high-pressure 100+ kW fuel cell stack that can be used in a modular fashion to power HD applications up to 1 MW. One of the issues that was identified during fuel cell system integration was the need for extra space for heat rejection systems(radiators) as well as additional fans and associated energy consumption to compensate for lower operating temperatures of fuel cells compared to internal combustion (IC) engines. To maximize the heat rejection capability of existing water-based coolant systems that are prevalent on IC engine-based systems, fuel cell stack design was developed to withstand and operate at IC engine coolant temperatures or higher, which in turn helps to reduce the radiator size requirement.

08 HYDROGEN↗
DOE OSTI2022

Development of A Multi-Spectral Pyrometry Sensor for High-Speed Transient Surface-Temperature Measurements in Combustion-Relevant Harsh Environments

Accurate and high-speed transient surface-temperature measurements of combustion devices including internal combustion (IC) engines, gas turbines, etc., provide validation targets and boundary conditions for computational fluid dynamics models, and are broadly relevant to technology advancements such as performance improvement and emissions reduction. Development and demonstration of a multi-infrared-channel pyrometry-based optical instrument for high-speed surface-temperature measurement is described. The measurement principle is based on multi-spectral radiation thermometry (MRT) and uses surface thermal radiation at four discrete spectral regions and a corresponding emissivity model to obtain surface temperature via non-linear least squares (NLLS) optimization. Rules of thumb for specifying the spectral regions and considerations to avoid interference with common combustion products are developed; the impact of these along with linear and non-linear MRT analysis are assessed as a function of temperature and signal-to-noise ratio. A multi-start method to determine the MRT-solution global optimum is described and demonstrated. The resulting multi-channel transient pyrometry instrument is described along with practical considerations including optical-alignment drift, matching intra-channel transient response, and solution-confidence indicators. The instrument demonstrated excellent >97% accuracy and >99% 2-sigma precision over the 400–800 °C range, with ~20 µs (50 kHz, equivalent to 0.2 cad at 2000 RPM IC-engine operation) transient response in the bench validation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Effects of non-thermal termolecular chemistry on detonation development in hydrogen (H2) / methane (CH4) - air mixtures

In combustion simulations, it is usually assumed that highly-energized collision complexes (either radical or stable species) formed in exothermic reactions are always under thermal equilibrium as they evolve through competing networks of reactions. However, in practical flames, non-trivial amounts of reactive radicals such as H, O and OH are present apart from O2. As a result, collisions of these reactive species with the energized collision complexes have been shown in recent studies to induce non-thermal reactivity. These studies have also demonstrated that such non-thermal reactions can be suitably represented in macroscopic kinetics models as chemically termolecular reactions. The present work was focused on identifying and quantifying the effects of including such reactions on the evolution of an initial deflagration front to a developing detonation in H2/CH4-air mixtures under boosted internal combustion (IC) engine conditions. Specifically, fully resolved simulations, with and without non-thermal reactivity, were performed for a constant volume reactor containing stoichiometric H2/CH4-air mixture. It was found that inclusion of termolecular chemistry resulted in a delayed onset of end-gas auto-ignition. Concurrently, the developing detonation intensity was observed to be significantly higher. Chemical explosive mode analysis (CEMA) was performed to identify the dominant species/reactions responsible for the observed variation in the results.

Detonation↗
DOE OSTI2022

Novel Solver Algorithms for Nearly Singular Linear Systems Arising in Combustion Modelling

Direct Numerical Simulations of realistic combustion devices are extremely challenging due to the wide separation of scales in the simulation, for example an internal combustion (IC) engine chamber, and the flame thickness of a high-pressure flame. The PeleLMeX solver uses adaptive mesh refinement (AMR) to evolve multi-species reacting flows in the low Mach number limit at the Exascale and relies on an embedded boundary (EB) approach to represent complex geometries. In that framework, the EB geometries often give rise to very small cut-cells along the boundary, which translate into extreme ill-conditioning of the pressure-projection, with eigenvalues that span 15-16 orders of magnitude. In this talk, we focus on the case of a typical IC piston bowl geometry for which we present on a novel approach towards solving these nearly singular linear systems with ILU-based, C-AMG smoothers on massively parallel architectures. In particular, we use scaling and equilibration algorithms to handle the non-normality of the upper triangular factors. This enables us to approximate the highly sequential triangular solve algorithm, embedded in the AMG smoothing-solve phase, with Jacobi iterations. This approximation can be written as a convergent Neumann series whose terms are composed of highly parallel sparse matrix vector multiplications. The result is an algorithm that substantially decreases setup and solve time, compared to state-of-the-art, for these challenging linear systems.

combustion modelling↗