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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Subsidence Risk Mitigation in LLW Final Covers: A Case History - 18286

Typical waste packaging and disposal practices at low-level radioactive waste (LLW) disposal facilities, while adequate for safe transportation and facility operations, introduce significant subsidence risk for the facilities' final cover systems. Subsidence is often caused by the collapse of voids in the waste packaging and surrounding cover soils that eventually propagate to the ground surface. This subsidence could result in the loss of final cover system integrity, stormwater ponding, concentrated flow of surface water and/or gas, increased leachate generation, transport of contaminants from the landfill, and potential adverse chemical reactions within the waste mass. For new LLW facilities, steps can be taken to minimize the subsidence potential of newly placed waste. However, there is a significant inventory of existing disposal facilities (trenches, pits, shafts, vaults, etc.), tank farms, and historic, subsurface burial grounds that require final covers - all of which present real subsidence risks. Final covers for these existing LLW disposal sites can be engineered to mitigate these risks. A case history of a 60-acre LLW final cover system design and construction project illustrates an innovative approach to this problem that offers superior risk mitigation compared to typical final cover design practice. This approach combines engineering simulations of the projected final cover mechanical performance, specific probabilistic performance targets, and targeted cover system improvements to manage the subsidence risk in the final design. The result is a successful final cover project that meets the needs of the community, state and federal regulators, and facility owner. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

actuated signal control↗
DOE OSTI2015

Large eddy simulation of a reacting spray flame with multiple realizations under compression ignition engine conditions

An n-dodecane spray flame (Spray A from Engine Combustion Network) was simulated using a detailed combustion model along with a dynamic structure LES model to evaluate its performance at engine-relevant conditions and understand the transient behavior of this turbulent flame. The liquid spray was treated with a traditional Lagrangian method and the gas-phase reaction was modeled using a detailed combustion model. A 103-species skeletal mechanism was used for the n-dodecane chemical kinetic model. Significantly different flame structures and ignition processes are observed for the LES compared to those of RANS predictions. The LES data suggests that the first ignition initiates in lean mixture and propagates to rich mixture, and the main ignition happens in rich mixture, preferable less than 0.14 in mixture fraction space. LES was observed to have multiple ignition spots in the mixing layer simultaneously while the main ignition initiates in a clearly asymmetric fashion. The temporal flame development also indicates the flame stabilization mechanism is auto-ignition controlled and modulated by flame propagation. Soot predictions by LES present much better agreement with experiments compared to RANS both qualitatively and quantitatively. Multiple realizations for LES were performed to understand the realization to realization variation and to establish best practices for ensemble-averaging diesel spray flames. The relevance index analysis suggests that an average of 2 and 5 realizations can reach 99\% of similarity to the target average of 16 realizations on the temperature and mixture fraction fields, respectively. However, more realizations are necessary for OH and soot mass fraction due to their high fluctuations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Predicting Cycle-to-Cycle Variation With Concurrent Cycles in a Gasoline Direct Injected Engine With Large Eddy Simulations

High cycle-to-cycle variation (CCV) is detrimental to engine performance, as it leads to poor combustion and high noise and vibration. In this work, CCV in a gasoline engine is studied using large eddy simulation (LES). The engine chosen as the basis of this work is a single-cylinder gasoline direct injection (GDI) research engine. Two stoichiometric part-load engine operating points (6 BMEP, 2000 RPM) were evaluated: a non-dilute (0% EGR) case and a dilute (18% EGR) case. The experimental data for both operating conditions had 500 cycles. The measured CCV in IMEP was 1.40% for the non-dilute case and 7.78% for the dilute case. To estimate CCV from simulation, perturbed concurrent cycles of engine simulations were compared to consecutively obtained engine cycles. The motivation behind this is that running consecutive cycles to estimate CCV is quite time-consuming. For example, running 100 consecutive cycles requires 2–3 months (on a typical cluster), however, by running concurrently one can potentially run all 100 cycles at the same time and reduce the overall turnaround time for 100 cycles to the time taken for a single cycle (2 days). The goal of this paper is to statistically determine if concurrent cycles, with a perturbation applied to each individual cycle at the start, can be representative of consecutively obtained cycles and accurately estimate CCV. 100 cycles were run for each case to obtain statistically valid results. The concurrent cycles began at different timings before the combustion event, with the motivation to identify the closest time before spark to minimize the run time. Only a single combustion cycle was run for each concurrent case. The calculated standard deviation of peak pressure and coefficient of variance (COV) of indicated mean effective pressure (IMEP) were compared between the consecutive and concurrent methods to quantify CCV. It was found that the concurrent method could be used to predict CCV with either a velocity or numerical perturbation. Here, a large and small velocity perturbation were compared and both produced correct predictions, implying that the type of perturbation is not important to yield a valid realization. Starting the simulation too close to the combustion event, at intake valve close (IVC) or at spark timing, under-predicted the CCV. When concurrent simulations were initiated during or before the intake even, at start of injection (SOI) or earlier, distinct and valid realizations were obtained to accurately predict CCV for both operating points. By simulating CCV with concurrent cycles, the required wall clock time can be reduced from 2–3 months to 1–2 days. Additionally, the required core-hours can be reduced up to 41%, since only a portion of each cycle needs to be simulated.

42 ENGINEERING↗
DOE OSTI2024

In-cylinder spray evolution in a motored central-injection gasoline engine: Imaging and simulating the effects of flash-boiling and intake crossflow

Accurate predictions of fuel spray behavior and mixture formation in simulations of direct-injection spark-ignition (DISI) engines are fundamental to ensure proper description of all subsequent processes including ignition, combustion, and emissions. In this work, the spray evolution in a single-cylinder optical DISI engine was studied experimentally and numerically with the goal of enabling predictive computational fluid dynamics (CFD) modeling of in-cylinder sprays. The authors explored a wide range of operating conditions characterized by several fuel injection temperatures and engine speeds, using a well-characterized nine-component gasoline surrogate known as PACE-20. The effect of flash boiling and intake crossflow on the spray is discussed, with a focus on evaluating the ability of the spray models to capture highly transient spray behavior. In the experiments, the fuel temperature was varied between 20°C and 80°C, allowing for non-flash- to flash-boiling transition to emerge with enhanced flashing intensity at the highest temperatures. Spray collapse resulted in vapor-rich regions, owing to the locally lower inertia of the fluid. Varying the engine speed from 650 to 1950 rpm promoted increasingly more turbulent in-cylinder crossflow which interacted with the spray during the injection event and resulted in enhanced spray dispersion. The CFD model was able to capture the spray morphology transition at different fuel temperatures and engine speeds adequately. Further, it is shown that the spray breakup model could capture the transitional spray behavior induced by flash boiling atomization and intake flow via proper initialization of the spray cone angle and calibration of the spray models’ constants.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

High-Fidelity Space-Time Adaptive Multiphysics Simulations in Nuclear Engineering

We delivered a series of fundamentally new computational technologies that have the potential to significantly advance the state-of-the-art of computer simulations of transient multiphysics nuclear reactor processes. These methods were implemented in the form of a C++ library, and applied to a number of multiphysics coupled problems relevant to nuclear reactor simulations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
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 OSTI2025

Simulating many-engine spacecraft: Exceeding 1 quadrillion degrees of freedom via information geometric regularization

We present an optimized implementation of the recently proposed information geometric regularization (IGR) for unprecedented scale simulation of compressible fluid flows applied to multi-engine spacecraft boosters. We improve upon state-of-the-art computational fluid dynamics (CFD) techniques in terms of computational cost, memory footprint, and energy-to-solution metrics. Unified memory on coupled CPU–GPU or APU platforms increases problem size with negligible overhead. Mixed half/single-precision storage and computation are used on well-conditioned numerics. We simulate flow at 200 trillion grid points and 1 quadrillion degrees of freedom, exceeding the current record by a factor of 20. A factor of 4 wall-time speedup is achieved over optimized baselines. Ideal weak scaling is observed on OLCF Frontier, LLNL El Capitan, and CSCS Alps using the full systems. Strong scaling is near ideal at extreme conditions, including 80% efficiency on CSCS Alps with an 8 node baseline and stretching to the full system.

Wilfong, Benjamin [Georgia Institute of Technology↗
DOE OSTI2016

Evaluation of fuel-borne sodium effects on a DOC-DPF-SCR heavy-duty engine emission control system: Simulation of full-useful life

Here we report that for renewable fuels to displace petroleum, they must be compatible with emissions control devices. Pure biodiesel contains up to 5 ppm Na + K and 5 ppm Ca + Mg metals, which have the potential to degrade diesel emissions control systems. This study aims to address these concerns, identify deactivation mechanisms, and determine if a lower limit is needed. Accelerated aging of a production exhaust system was conducted on an engine test stand over 1001 h using 20% biodiesel blended into ultra-low sulfur diesel (B20) doped with 14 ppm Na. This Na level is equivalent to exposure to Na at the uppermost expected B100 value in a B20 blend for the system full-useful life. During the study, NOx emissions exceeded the engine certification limit of 0.33 g/bhp-hr before the 435,000-mile requirement. Replacing aged diesel oxidation catalyst (DOC), diesel particulate filter (DPF), and selective catalytic reduction (SCR) devices with new degreened parts showed that each device contributed equally to the NOx increase. Following this systems-based evaluation, a detailed investigation of the individual components was completed. Na was determined to have minimal impact on DOC activity. For this system, it is estimated that B20-Na resulted in 50% more ash into the DPF. However, the Na did not diffuse into the cordierite DPF nor degrade its mechanical properties. The SCR degradation was found to be caused by a small amount of precious group metals (PGM) contamination that increased NH3 oxidation, and lowered NOx reduction. Therefore, we determined that the primary effect of Na in this study is through increased ash in the DPF rather than deactivation of the catalytic activity.

42 ENGINEERING↗
DOE OSTI2021

Linear and Nonlinear Solvers for Simulating Multiphase Flow within Large-Scale Engineered Subsurface Systems

Simulation of multiphase flow in the subsurface is well-known to be computationally challenging. While there have been many studies that have explored approaches to overcoming these challenges, they often utilize relatively simple case studies. In this paper, we focus on the unique numerical challenges posed by modeling large-scale engineered subsurface systems, characterized by discrete features embedded in a heterogeneous natural subsurface setting. The man-made features such as shafts, tunnels, and barriers often cause multiple challenges in modeling the domain for multiphase porous media flow. This flow scenario can have a wide range of applications such as nuclear waste repositories, enhanced recovery of a petroleum reservoir, geothermal engineering, and carbon sequestration. An example of these severe numerical challenges is the case of performance assessment (PA) for Waste Isolation Pilot Plant (WIPP), the only operating deep geological repository in the US, which simulates extreme material properties of bedded salt rock formation and extreme contrast due to open excavation next to the formation. The models have extremes not only of permeability and porosity but also of the constitutive models needed for multiphase flow; additionally, they have process models like salt creep closure reducing porosity over time, fracturing in clay and anhydrite interbeds of the bedded salt, gas generation from the waste materials, and unintentional human borehole intrusions in some scenarios. Numerical simulations require the solution of coupled systems of nonlinear PDEs; in our work, we use the open-source simulator PFLOTRAN which is based on Finite Volume discretization. The solution of the nonlinear equations requires use of the Newton-Raphson iteration at each time step, which entails the solution of the linearized Jacobian system at each iteration. The effects of all the processes (i.e., large number of unknowns, highly nonlinear constitutive relations, large contrasts in material properties in short distances) lead to an ill-conditioned Jacobian matrix that severely challenges traditional linear solver, i.e., stabilized biconjugate gradient with block Jacobi incomplete LU preconditioner (BCGS-ILU) leading to non-convergence for traditional Newton-Raphson nonlinear solver causing unacceptably long computation time for each model. This paper presents linear solvers such as constrained pressure residual (CPR) two-stage preconditioner with alternate-block-factorization (ABF) and quasi- implicit pressure and explicit saturation (QIMPES) decouplers and flexible generalized residual solver (FGMRES). The new general-purpose nonlinear solver, Newton trust-region dogleg Cauchy (NTRDC), is also introduced to resolve extreme nonlinearities in the models. We demonstrate the effectiveness of each method relative to the default BCGS-Newton solver. The two best cases had nearly 50 times speed-up and achieved completion of a simulation in 14 hours that never completed due to non-convergence with the default solver. We also investigate the strong scalability of each method and discuss some of the deficiencies found for Block Jacobi preconditioner using parallel domain decomposition, and node packing effects of modern processor architecture.

Preconditioner, Nonlinear, Porous media, Multiphas↗
DOE OSTI2024

NuQKD: A Modular Quantum Key Distribution Simulation Framework for Engineering Applications

Abstract An experimental Quantum Key Distribution (QKD) implementation requires advanced costly hardware, unavailable in most research environments, making protocol testing and performance evaluation complicated. This has been a major motivation for the development of QKD simulation frameworks, to allow researchers to obtain insight before proceeding into practical implementations. Several simulators have been introduced over the recent years. However, only four are publicly available, only one of which models equipment imperfections. Currently, no open‐source simulator includes all following capabilities: channel attenuation modelling, equipment imperfections and effects on key rates, estimation of elapsed time during classical and quantum‐ channel processes, use of truly random binary sequences for qubits and measurement bases, shared ‐bit fraction customization. In this paper, we present NuQKD, an open‐ source modular, intuitive simulator, featuring all the above capabilities. NuQKD establishes communication between two computer terminals, accepts custom user inputs (iterations, raw key size, attacker interception rate etc.) and evaluates the sifted key, Quantum Bit Error Rate (QBER), elapsed communication time, and more. NuQKD capabilities include optical fiber and free ‐space simulation, modeling of equipment/channel imperfections, bitstrings from True Random Number Generators, decoy‐state protocol support, and automated evaluation of performance metrics. We expect NuQKD to enable convenient and accurate representation of actual experimental conditions.

Gkouliaras, Konstantinos↗
DOE OSTI2021

Impact of Materials Properties on Higher-Temperature Engine Operation

Here, we examine the effects on materials temperatures and engine efficiency via simulations of engines operating at temperatures which exceed the thermal limits of today’s materials. Potential focus areas include high-speed, high-load operation (in the fuel-enrichment zone) as well as conditions of selective cooling at lower speeds and loads. We focus on a light-duty DISI and a heavy-duty CI engine using GT-Power. Temperature distributions within the head, block, piston, and valves were obtained from 3D FEA simulations coupled with 1D GT-Power representations of the engine’s gas flow and combustion regions. We use experimentally measured thermal properties of current commercial alloys for specific engine components, as well as candidate developmental alloys with improved temperature tolerance, to gauge the effects of materials properties on engine performance, particularly focusing on operating areas where materials which can withstand higher temperatures can enable intensified combustion conditions and increased specific output. The outcome of this work is guidance on materials selection targets and benefits as OEMs meet engine-performance needs in the coming decades, to include Class 8 line-haul freight vehicles with reduced emissions, as well as design of future near net zero-carbon light-duty range extenders for more rapid electrification of medium- and heavy-duty vehicles.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An Investigation into the Effects of Swirl on the Performance and Emissions of an Opposed-Piston Two-Stroke Engine using Large Eddy Simulations

Opposed-piston two-stroke (OP-2S) engines have the potential to achieve higher thermal efficiency than a conventional four-stroke diesel engine. However, the uniflow scavenging process is difficult to control over a wider range of speed and loads due to its sensitivity to pressure dynamics, port timings, and port design. Specifically, the angle of the intake ports can be used to generate swirl which has implications for open and closed cycle effects. This study proposes an analysis of the effects of port angle on the in-cylinder flow distribution and combustion performance of an OP-2S using computational fluid dynamics engine. Large Eddy Simulation (LES) was used to model turbulence given its ability to predict in-cylinder mixing and cyclic variability. A three-cylinder model was validated to experimental data collected by Achates Power and the grid was verified using an LES quality approach from the literature. The model was used to simulate port angles from 12 to 29 degrees at constant pressure and temperature boundary conditions. Results indicated that the higher bulk swirl ratio generated by larger port angles tends to trap more internal residuals. This effect on the scavenging performance, combined with the larger trapped swirl ratio, also has a significant impact on the combustion performance in a two-stroke engine. It was concluded that there exists a tradeoff of efficiency and emissions that must be considered when increasing the port angle in a uniflow two-stroke engine. 1. Introduction Two-stroke engines were developed in the 1800s and are commonly used in practice today for lightweight power applications such as motorcycles or handheld power tools, as well as large-bore low speed engines for stationary power and marine applications. Two-stroke engines provide the distinct advantage of higher torque/power output but present challenges due to its coupled intake and exhaust process. Four-stroke engines have the benefit of using two full piston strokes to induct intake charge and to expel exhaust gases. Controlling the trapped charge composition in a twostroke engine via a scavenging process is crucial for reliable and efficient engine operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Validation of (not-historical) large-event near-fault ground-motion simulations for use in civil engineering applications

Ground-motion simulations generated from physics-based wave propagation models are gaining increasing interest in the engineering community for their potential to inform the performance-based design and assessment of infrastructure residing in active seismic areas. A key prerequisite before the ground-motion simulations can be used with confidence for application in engineering domains is their comprehensive and rigorous investigation and validation. This article provides a four-step methodology and acceptance criteria to assess the reliability of simulated ground motions of not historical events, which includes (1) the selection of a population of real records consistent with the simulated scenarios, (2) the comparison of the distribution of Intensity Measures (IMs) from the simulated records, real records, and Ground-Motion Prediction Equations (GMPEs), (3) the comparison of the distribution of simple proxies for building response, and (4) the comparison of the distribution of Engineering Demand Parameters (EDPs) for a realistic model of a structure. Specific focus is laid on near-field ground motions (<10km) from large earthquakes (M w 7), for which the database of real records for potential use in engineering applications is severely limited. The methodology is demonstrated through comparison of (2490) near-field synthetic records with 5 Hz resolution generated from the Pitarka et al (2019) kinematic rupture model with a population of (38) pulse-like near-field real records from multiple events and, when applicable, with NGA-W2 GMPEs. Lastly, the proposed procedure provides an effective method for informing and advancing the science needed to generate realistic ground-motion simulations, and for building confidence in their use in engineering domains.

42 ENGINEERING↗