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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 991 records · Page 55

Chasing the Future: How Will Ocean Change Affect Marine Life?

Understanding the potential consequences of rising ocean carbon levels and related ocean changes for marine life and ecosystems is a high priority for the ocean research community and marine resource management. In the mid-1990s, two geoengineering proposals to mitigate global warming by carbon sequestration in the deep sea led to research measuring the effects of increased deep ocean carbon dioxide levels on marine animals. A few years later, ocean acidification and its effects on marine life became an international research priority. Here we provide an overview of several technical developments by scientists and engineers at the Monterey Bay Aquarium Research Institute (MBARI) that have enabled and enhanced deep-sea exploration and experiments to assess the effects of changing ocean conditions on benthic marine animals. Improvements in remotely operated vehicles (ROVs) have increased the efficiency of dive operations and enabled more complex measurements and experiments at great ocean depths. In situ respirometers and Free Ocean CO 2 Enrichment (FOCE) mesocosms have allowed measurement of physiological and behavioral responses of deep-sea animals to environmental change. A laboratory-based, gas-controlled aquarium system that regulates oxygen and pH in chilled waters was engineered to measure the physiological responses of deep-sea animals and biological communities to expected future environmental conditions. Recently, MBARI engineers and scientists developed an Upwelling Simulator, a lab-based aquarium control system that mimics ocean conditions during coastal upwelling. This system is programmable, allowing independent control of pH, oxygen, and temperature to enable experiments that examine the effects of present-day upwelling conditions, expected future conditions, or other changes in these environmental conditions. In sum, collaboration between the marine operations group, engineers, and scientists at MBARI has advanced methods to explore the ocean and understand the consequences of ocean change for marine organisms and ecosystems.

54 ENVIRONMENTAL SCIENCES↗

Model predictive control of mixing controlled compression ignition operation for low reactivity fuels

Using gasoline or other low reactivity fuels with a pilot injection or port fuel injection in a compression ignition engine has shown great potential in reducing NOx emissions while keeping high thermal efficiency compared to diesel. However, excessive combustion noise is caused by a high maximum pressure rise rate in the cylinder due to the higher fractions of premixed charge of the low-reactivity fuel. This noise can result in structural damage to engine components and as such, combustion noise limits the range of the operating parameters and makes the control of such engines challenging. In this study, a simulation environment was built up in MATLAB/Simulink leveraging a physics-based zero-dimension combustion model to capture the in-cylinder pressure time traces as well as metrics relevant to thermal efficiency and combustion noise. Here, in order to also facilitate the control of emissions, machine learning models were investigated to capture NOx emissions. A kernel-based extreme learning machine (K-ELM) performed best and had a coefficient of correlation (R-squared) of 0.998. The combustion and NOx emission models are valid for not only conventional gasoline fuel but also oxygenated alternative fuel blends at three different pilot injection strategies. In order to track key combustion metrics while keeping noise and emissions within constraints, a model predictive control (MPC) was applied for a compression ignition engine operating with a range of potential fuels and fuel injection strategies. The MPC is validated under different scenarios, including a load step change, fuel type change, and injection strategy change, with proportional–integral (PI) control as the baseline. The simulation results show that MPC reduces about 26% of ringing intensity in the transient process and 17% at the steady state for E30. Generally, MPC can optimize the overall performance through modifying the main injection timing, pilot fuel mass, and exhaust gas recirculation (EGR) fraction.

42 ENGINEERING↗

Effects of stratification and charge cooling on combustion in a gasoline direct-injection compression ignition (GDCI) engine

With the development of low temperature engine combustion strategies, performance of gasoline-type fuels under compression ignition conditions has attracted extensive research interest. Meanwhile, for the sake of co-optimization of engines and fuels for future ground transportation, identification and evaluation of general fuel properties should be a core research priority instead of endless testing of specific fuels. In this study, the roles of fuel octane sensitivity in characterizing the ignition performance of gasoline surrogates have been systematically investigated under typical gasoline direct ignition compression ignition (GDCI) engine conditions using 3D combustion CFD simulation, especially considering the subsequent in-cylinder charge stratification and charge cooling. Two different operating conditions, high boost pressure low boost temperature (beyond-RON) case and low boost pressure high boost temperature (beyond-MON) case, were considered. By comparing with our previous zero-dimensional chemical kinetic study of gasoline surrogates in advanced compression ignition (ACI) engines, the effects of stratification and charge cooling on the combustion processes are investigated. It is found that different fuel octane sensitivities lead to slight difference in equivalence ratio stratification and charge cooling due to differences in volatility. However, fuel reactivity is still the more dominant factor than the stratification and charge cooling effects in determining combustion phasing. The present results help to justify the P-T domain framework for engine autoignition analysis of overlapping pressure-temperature trajectory with ignition delay iso-contour. Here, the results also provide useful guidance to the understanding of GCI combustion process, and to the evaluation of controlling fuel properties and the selection of alternative fuels in GCI engines.

33 ADVANCED PROPULSION SYSTEMS↗

AeroDyn V15.04: Design Tool for Wind and MHK Turbines

AeroDyn is a time-domain wind and MHK turbine aerodynamics module that can be coupled into the FAST version 8 multi-physics engineering tool to enable aero-elastic simulation of horizontal-axis wind turbines. AeroDyn V15.04 has been updated to include a cavitation check for MHK turbines, and can be driven as a standalone code to compute wind turbine aerodynamic response uncoupled from FAST. Note that while AeroDyn has been updated to v15.04, FAST v8.16 has not yet been updated and still uses AeroDyn v15.03.

16 TIDAL AND WAVE POWER↗

Fuel-air mixing in motored CFR engine at research octane number (RON) relevant condition

This paper presents a three-dimensional (3-D) computational fluid dynamics (CFD) study of a motored cooperative fuel research (CFR) engine at research octane number (RON) relevant condition. The boundary conditions for 3-D simulations were generated with a one-dimensional GT-Power model. For the first time in literature, a carburetor was added to a virtual CFR engine model with 3-D CFD. Therefore, the proposed setup can simulate the fuel and thermal stratifications inside the engine cylinder with realistic detail. The transient simulations in this work were performed within the Reynolds-averaged Navier-Stokes (RANS) framework with a Realizable k-ε turbulence model. Major conclusions from the present work are: (1) The in-cylinder flow of the CFR engine is swirl-dominated due to the existence of the intake valve shroud. (2) There is a significant amount of liquid droplets entering the cylinder during the intake stroke. The maximum instantaneous amount of liquid for 50% PRF 87 (containing 87% iso-octane and 13% n-heptane (v/v)) and 50% ethanol mixture is indicated to be around 26% of total injected fuel mass. (3) The heat of vaporization (HoV) of the fuel is responsible for creating both temperature and charge stratification inside the cylinder.

33 ADVANCED PROPULSION SYSTEMS↗

Crash simulation of UNS electric vehicle under frontal front impact

Sebelas Maret University has been developing an Electric Vehicle namely SmarT-EV UNS. The main structure of the car are chasis and body. The chasis is made from steel and the body is made from fiberglass composite. To ensure the safety of the car, both static and dynamic tests were carried out to these structures, including their materials, like: tensile test, bending test, and impact test. Another test needed by this vehicle is crashworthiness test. To perform the test, it is needed complex equipments and it is quite expensive. Another way to obtain vehicle crashworthiness behaviour is by simulate it. The purpose of this study was to simulate the response of the Smart-EV UNS electric vehicle main structure when crashing rigid barrier from the front. The crash simulation was done in according to the NHTSA (National Highway Traffic Safety Administration) within the speed of the vehicle of 35 mph. The UNS Electric Vehicle was modelled using SolidWorks software, and the simulation process was done by finite element method using ANSYS software. The simulation result showed that the most internal impact energy was absorbed by chassis part. It absorbed 76.2% of impact energy, then the base absorbed 11.3 %, while the front body absorbed 2.5 %, and the rest was absorbed by fender, hood, and other parts.

42 ENGINEERING↗

Mitigating mechanical failure of crystalline silicon electrodes for lithium batteries by morphological design [Morphological design of silicon electrode with anisotropic interface reaction rate for lithium ion batteries]

Although crystalline silicon (c-Si) anodes promise very high energy densities in Li-ion batteries, their practical use is complicated by amorphization, large volume expansion and severe plastic deformation upon lithium insertion. Recent experiments have revealed the existence of a sharp interface between crystalline Si (c-Si) and the amorphous Li x Si alloy during lithiation, which propagates with a velocity that is orientation dependent; the resulting anisotropic swelling generates substantial strain concentrations that initiate cracks even in nanostructured Si. Here we describe a novel strategy to mitigate lithiation-induced fracture by using pristine c-Si structures with engineered anisometric morphologies that are deliberately designed to counteract the anisotropy in the crystalline/amorphous interface velocity. This produces a much more uniform volume expansion, significantly reducing strain concentration. Based on a new, validated methodology that improves previous models of anisotropic swelling of c-Si, we propose optimal morphological designs for c-Si pillars and particles. The advantages of the new morphologies are clearly demonstrated by mesoscale simulations and verified by experiments on engineered c-Si micropillars. The results of this study illustrate that morphological design is effective in improving the fracture resistance of micron-sized Si electrodes, which will facilitate their practical application in next-generation Li-ion batteries. In conclusion, the model and design approach present in this paper also have general implications for the study and mitigation of mechanical failure of electrode materials that undergo large anisotropic volume change upon ion insertion and extraction.

25 ENERGY STORAGE↗

DAKOTA (Design Analysis Kit for Optimization and Terascale) v.6.14

Sandia's Dakota software supports science and engineering transformation through advanced exploration of simulations. Specifically, Dakota manages and analyzes ensembles of simulations to provide broader and deeper perspective for analysts and decision makers. This enables them to: (1) enhance understanding of risk, (2) improve products, and (3) assess simulation credibility. In its simplest mode, Dakota can automate typical parameter variation studies through a generic interface to a computational model. Dakota also delivers advanced parametric analysis techniques enabling design exploration, optimization, model calibration, risk analysis, and quantification of margins and uncertainty with such models. It directly supports verification and validation activities. The algorithms implemented in Dakota aim to address challenges in performing these analyses with complex science and engineering models from desktop to high performance computers. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525 SAND2021-10468 O

Adams, BrianM.↗

DAKOTA (Design Analysis Kit for Optimization and Terascale) v.6.16

Sandia's Dakota software supports science and engineering transformation through advanced exploration of simulations. Specifically, Dakota manages and analyzes ensembles of simulations to provide broader and deeper perspective for analysts and decision makers. This enables them to: (1) enhance understanding of risk, (2) improve products, and (3) assess simulation credibility. In its simplest mode, Dakota can automate typical parameter variation studies through a generic interface to a computational model. Dakota also delivers advanced parametric analysis techniques enabling design exploration, optimization, model calibration, risk analysis, and quantification of margins and uncertainty with such models. It directly supports verification and validation activities. The algorithms implemented in Dakota aim to address challenges in performing these analyses with complex science and engineering models from desktop to high performance computers. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525 SAND2021-10468 O

Adams, BrianM.↗

Development of a single-shot-imaging thin film for an online Thomson parabola spectrometer

A single-shot-imaging thin scintillator film was developed for an online Thomson parabola (TP) spectrometer and the first analysis of laser accelerated ions, using the online TP spectrometer, was demonstrated at the JAEA-Kansai Advanced Relativistic Engineering Laser System (J-KAREN). An energy spectrum of {approx}4.0 MeV protons is obtained using only this imaging film without the need of a microchannel plate that is typically utilized in online ion analyses. A general-purpose Monte Carlo particle and heavy ion-transport code system, which consists of various quantum dynamics models, was used for the prediction of the luminescent properties of the scintillator. The simulation can reasonably predict not only the ion trajectories detected by the spectrometer, but also luminescence properties.

36 MATERIALS SCIENCE↗

Design Analysis Kit for Optimization and Terascale Applications 6.0

Sandia's Dakota software (available at http://dakota.sandia.gov) supports science and engineering transformation through advanced exploration of simulations. Specifically it manages and analyzes ensembles of simulations to provide broader and deeper perspective for analysts and decision makers. This enables them to: (1) enhance understanding of risk, (2) improve products, and (3) assess simulation credibility. In its simplest mode, Dakota can automate typical parameter variation studies through a generic interface to a computational model. However, Dakota also delivers advanced parametric analysis techniques enabling design exploration, optimization, model calibration, risk analysis, and quantification of margins and uncertainty with such models. It directly supports verification and validation activities. The algorithms implemented in Dakota aim to address challenges in performing these analyses with complex science and engineering models from desktop to high performance computers.

Source record↗

Probabilistic R&D Portfolio Management Applied to Energy Storage

Here this paper proposes a methodology to analyze the potential of several alternative technologies in advancing a research and development portfolio. The methodology considers technology maturation, use case requirements, performance goals, cost projections, and existing research and development (R&D) efforts to predict the impact of various R&D investment tracks across several energy storage technologies. Technology maturation and cost projections are derived from a probabilistic Monte Carlo simulation, yielding results that incorporate uncertainty in modeling the impact of future R&D. This framework allows the researcher to perform a robust sensitivity analysis to uncover critical pathways and areas where investment will have a high impact.

25 ENERGY STORAGE↗

CFD modeling of near-wall combustion and unburned methane prediction in natural gas spark ignition engines

Natural gas-powered engines play a critical role in gas drilling, compression, and transmission sectors, but methane (CH 4 ) from engine combustion slip can be significant over their lifespan, contributing to atmospheric pollution and signaling reduced engine efficiency. Here, to address this challenge, computational fluid dynamics (CFD) simulations offer valuable insights into the in-cylinder combustion process, enabling the optimization of combustion strategies and engine designs to minimize unburned CH 4 slip. This study aims to evaluate and improve combustion models for simulating the combustion process and predicting unburned CH 4 concentrations in natural gas spark-ignition (SI) engines, including engines that are part of combined reformer-engine systems. Specifically, the performance of two flamelet-based combustion models—the Extended Coherent Flame Model (ECFM) and the G-equation model—was assessed using experimental engine data collected under varying excess-air ratio (λ) conditions and fuel compositions, including natural gas and syngas blends. In addition, to enhance the predictive capabilities of the G-equation model, a flame-wall interaction (FWI) sub-model was integrated into its framework. The effects of its model parameters, such as quenching and influence distance, on combustion behavior and unburned methane predictions were analyzed in detail. The ECFM tended to predict delayed combustion phasing under diluted mixture conditions, resulting in overprediction of unburned CH 4 concentrations. In contrast, the G-equation model provided reasonable predictions of combustion pressure, while representing higher the CH 4 reduction rate across the operating condition compared to experimental data. Incorporating the FWI sub-model—with the quenching distance calculated based on a pressure-dependent relation (P -0.48 ) and a fixed influence distance of 1.5 mm—further improved the G-equation model’s accuracy in predicting CH 4 reduction rates without compromising its ability to simulate the combustion process.

Combustion model↗

WE-AB-209-01: A Monte Carlo-Based Method to Include Random Errors in Robust Optimization

Purpose: To develop an efficient method to implement random set-up errors and organ motion in robust optimization for proton therapy treatment planning. Methods: The plans were created with an in-house robust optimizer, coupled with a super-fast Monte Carlo (MC) engine (less than 1 minute for final dose). MC simulates random errors by shifting randomly the starting point of each particle, according to their probability distribution. Such strategy assumes a sufficient number of treatment fractions. Two strategies are presented: 1) Full robust optimization with beamlets that already include the effect of random errors and 2) Mixed robust optimization, where the nominal beamlets are involved but a correction term C modifies the prescription. Starting from C=0, the method alternates optimization of the spot weights with the nominal beamlets and updates of C, with C=Drandom-Dnominal and where Drandom results from a regular MC computation (without pre-computed beamlets) that simulates random errors. Updates of C can be triggered as often as necessary by running the MC engine with the last corrected values for the spot weights as input. The method was applied to lung and prostate cases. For both patients the range error was set to 3%, systematic setup error to 5mm and standard deviation for random errors to 5 mm. Comparison between full robust optimization and the mixed strategy (with 3 updates of C) is presented. Results: Target coverage was far below the clinical constraints (D{sub 95} > 95% of the prescribed dose) for plans where random errors were not simulated, especially for lung case. However, by using the proposed strategies the plans achieved a target coverage above clinical constraints. Conclusion: Full robust optimization gives better results than the mixed strategy, but the latter can be useful in cases where a MC engine is not available or too computationally intensive for beamlets calculation.

60 APPLIED LIFE SCIENCES↗

Numerical Simulation of Gas and Liquid Two-Phase Flow in the RH Process

A mathematical model has been established to simulate the gas–liquid two-phase flow in the RH process. The effects of interphase forces and the bubble-induced turbulence on the fluid flow and motion of gas bubbles were investigated. The interphase forces include the drag force, the virtual mass force, the lift force, and the pressure gradient force, respectively. The prediction results of the liquid velocity and the recirculation rate were compared with the experimental data measured with a Particle Image Velocimetry technique. The results indicated that the drag force and the virtual mass force dominated the gas plume shape in the up-leg snorkel, and significantly influenced the liquid velocity and the distribution of the gas phase. The lift force mainly affected the spreading of gas bubbles in the radial direction of the snorkel. However, the pressure gradient force could be neglected because it had no effect on the liquid velocity and the gas volume fraction. The increase of the bubble-induced turbulence decreased the liquid velocity, and by adjusting the lift force, it widened the shape of the individual gas plumes. To accurately simulate the gas–liquid flow behavior in the RH degasser, these appropriate parameters, such as the drag coefficient, the virtual mass coefficient, the lift coefficient, and the bubble-induced turbulence coefficient, were determined by comparing the predicted results and the measured data.

42 ENGINEERING↗

Computational Design of Alloys for Energy Technologies

Advanced materials that maintain their mechanical performance under elevated temperatures, corrosive environments, and a range of static and evolving stresses are needed to improve the efficiency and reduce the environmental impact of a wide spectrum of energy technologies. For instance, cost-efficient alloys that can withstand high temperatures (e.g., 700 °C) have a critical role in improving the efficiency and economics of power generation to support decarbonization of the energy sector; such is true of both the nuclear and fossil energy sectors. Considering both the threats of the energy crisis, namely soaring costs of greenhouse gas emission-producing energy and climate change, it is essential to increase the pace of material discovery and enable rapid paths for material qualification to advance clean energy technologies. Conventionally, alloy development has followed a slow Edisonian process that uses repeated cycles of making, characterizing, and modifying to arrive at optimum composition and processing conditions to achieve the desired component performance. This optimization is followed by the necessary stepwise materials qualification. Furthermore, the increasing adoption of sound data management and physics-informed machine learning represents the next step in the acceleration of materials design and development. In the integrated computational materials engineering (ICME) approach, computational modeling and simulation data from different length and time scales can be combined with complex microstructural details from multimodal experimental characterization and selective property testing to close the design loop for rapid alloy development.

Computational Design Of Materials↗

Hollow-core microstructured ‘revolver’ fibre for the UV spectral range

We report the first silica-based hollow-core microstructured fibre for the UV spectral range, with a reflective cladding formed by a single ring of capillaries. The optical loss in the fibre at a wavelength of 250 nm is 3 dB m{sup −1}. We demonstrate distinctions between the propagation of high-order modes and the fundamental mode of the hollow core of a ‘revolver’ fibre with noncontacting cladding capillaries and examine the effect of multimode excitation on the transmission bandwidth of the fibre. Based on experimental data and numerical simulation results, we formulate recommendations how to reduce the level of losses and extend transmission bands in a hollow-core revolver fibre in the UV spectral range. (paper)

42 ENGINEERING↗

Investigation of powertrain system decarbonization using electrically assisted turbocharging and hybridization in off-road vehicles

In recent years, the causes, effects, and existential threat of a global anthropogenic climate shift have drawn significant attention and stimulated mitigation efforts from all genres of scientific, political, and industrial bodies. The greenhouse effect and the detrimental environmental impact of excess greenhouse gas (GHG) emissions, like carbon dioxide, is well studied and targeted as the primary culprit for aversive action. However, some fields exist where reducing GHG emissions is met by considerable challenges. One such field is the production and operations of off-road vehicles. Applications of these vehicles are highly diverse and are often characterized by rugged, especially transient, and power intensive duty cycles that make one-fits-all vehicle configuration solutions impractical. This research surveys existing literature to identify the modern technologies packages and challenges that face development and configuration of powertrain systems which currently navigate the regulated emissions and unique duty cycle requirements of this market space. A novel powertrain concept is proposed and evaluated with respect to the pinnacle objectives of load performance improvement, criteria pollutant reduction, and reducing the life cycle GHG intensity of its operation. A sure-fire path to vehicular GHG reduction is through improving the fuel efficiency of internal combustion engines (ICEs), an entrenched component of the off-road vehicle sector. Unfortunately, this is more easily said than done. An approach that has proven successful in this endeavor is engine downsizing and turbocharging, where a larger engine is replaced with a smaller one with added air system boosting via turbocharger to reduce frictional and pumping losses while also enabling access to additional fuel energy. However, practical realization of these potential benefits is often impeded by the transient response capability of the smaller engine across the operating space. For this reason, downsized ICE powertrains have turned to electrified forced induction systems (EFISs) for a decoupling of exhaust energy and engine speed from boost capability. Platformed on 48V hybrid technology, these systems introduce the need for more sophisticated controls around the engine gas-exchange process for the management of boost performance and exhaust gas emissions. On this account, simulation studies utilizing a GT-POWER model of a turbocharged 4.5 L engine outfit with an EFIS using an electrically driven compressor (eBooster®) are conducted to provide insight into the performance of this air handling architecture on an off-road engine. The results show that improvement in transient torque response time in sync with reduction in engine-out soot and NOx emissions are possible with an engine recalibration that leverages the transient air-fuel ratio authority of the eBooster®. Benefits are further demonstrated when duty cycle simulations of the 48V mild-hybrid engine concept are exercised, showing an acute decrease in cumulative fuel usage and soot production. Powertrain hybridization is another technological pathway achieving pronounced GHG reduction successes in modern on-road vehicle applications through integration of Li-ion battery technology. In the off-road vehicle segment, a review of available literature concludes that hybridized architectures are present but generally lack the depth of technologies that have both high specific energy and power capabilities, and thus are limited in their inclusion of Li-ion batteries for ICE assistance and enhanced energy storage capability. Therefore, building on the mild-hybrid engine results, the downsized and eBoosted engine concept was integrated into a larger high-voltage battery-hybrid series-electric powertrain system. Hybrid powertrain parameter sensitivity studies were carried out in a numerical charge-sustaining framework, providing novel insights into power flows between the battery and the engine and how their respective capabilities and operation contribute to GHG and criteria pollutant emissions of diverse duty cycles. Application of supervisory power management introduced robustness into the power sourcing and battery SOC control process and showed that optimum specification of battery properties can yield synergies between GHG emissions and battery energy capacity. Furthermore, examination of recent literature on Li-ion batteries has shown that pack manufacturing is a highly energy intensive process, thereby producing considerable quantities of GHGs that scale with energy storage capacity. Also scaling with a battery’s energy storage capacity is its investment cost. In consideration of these factors, an inclusive technoeconomic and GHG life cycle analysis is conducted. This analysis systematically compares the carbon footprint and total cost of ownership associated with the proposed hybrid powertrain concept to reference and alternative powertrain configurations, facilitating a thorough evaluation of the decarbonization effectiveness and economic viability.

99 GENERAL AND MISCELLANEOUS↗