SEARCH · Search NASA
Results for “load changes”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Evaluation of a Geotechnical Compaction Model to Estimate Sediment Deformation
When surface ruts from vehicle activity occur above archeological deposits, there is a potential for an impact to the archeological record to occur. While impacts to the soil surface are relatively straightforward to describe, sub-surface disturbances are more difficult to quantify. In particular, disturbances to buried archeological materials can be difficult to characterize. Materials can be damaged by surface disturbances, but they can also be impacted even if they stay intact. Interpretation of intact materials relocated within the sediment column can be problematic. Traditional characterization methods (e.g. excavation) can also disturb archaeological materials. Here, we present a geotechnical compaction model that can be used to assist with sub-surface characterization under vehicle ruts formed during off-road travel. The model is based on a Boussinesq analysis of stress increase below a surface load. The change in bulk density resulting from compaction is assumed to be proportional to the stress increase. The model results were compared against pilot scale tests conducted with homogeneous sediment and field scale in-situ tests. The results indicate that the model generally agreed with measurements to within 20%, although only a single soil type was evaluated in these studies, with a narrow range of initial bulk densities and rut depths. While application of the model across a broader matrix of soil types, bulk densities and rut depths would increase confidence in the model, it appears that the model may be suitable for use in archeological field investigations. Potential uses include both compliance and research based field applications.
Optimization of Process Parameters for the Axial Distortion and Distortion Range During Die Quenching of a Spiral Bevel Gear
The strength of high-duty gears can be improved by carburizing and quenching processes. Quenching distortion is inevitable during the process, and it has direct effects on the precision of the gear. For spiral bevel gear with big diameter and small thickness, die quenching process is applied in the manufacturing to control the distortion in axial direction. Die quenching parameters must be optimized to minimize the distortion while considering non-uniformity of chemical compositions and loads. In this paper, material models of gear material (22CrMoH) built based on the phase transformation kinetics, static mechanical properties and phase transformation plasticity equations. Finite element analysis (FEA) models are conducted to study the whole die quenching process based on the production process. Die quenching experiments and testing of microstructures and distortions are conducted to verify the accuracy of the FEA models. The effects of loads and bottom die obliquity on the final distortion are studied to set the appropriate range and control distortion. The distortion was very sensitive to the bottom die obliquity and some part of load ranges. The distortion changes little when the load of inner die was in the range of 1.38-2.07 MPa and the load of outer die was in the range of 2.07-2.76 MPa. The distortion range is fitted the production requirements by setting the inclination angle of bottom die between 0.12° and 0.29°.
Molecular Dynamics Simulation Study of Uniaxial Ratcheting Behaviors for Ultrafine-Grained Nanocrystalline Nickel
In this paper, molecular dynamics (MD) simulation-based study of deformation behavior of ultrafine-grained nanocrystalline nickel under asymmetric cyclic loading having stress ratios (R) such as − 0.2, − 0.4 and − 0.6 for different temperatures, viz. 100, 300 and 500 K, has been performed using embedded atom method potential. The predicted ratcheting strain by MD simulation for nanocrystalline Ni varies from 15 to 30%. A significant increase in ratcheting strain has been observed with the increase in temperature. It has been observed that the number of vacancies increases, and the number of clusters decreases with the increase in temperature. Slight reduction in crystallinity is identified at the middle of the each loading cycle from the performed cluster analysis. Zigzag pattern of dislocation density has been observed and leads to the decrease in dislocation density with the increase in temperature. Stress ratio does not show any significant effect on the number of vacancies, clusters and dislocation density on structural evolution during the asymmetric cyclic loading. Slight change in the grain rotation has been observed with the increase in temperature, and there is almost no change in the final texture evolved. From the post-tensile tests, ultimate tensile strength that remains same may be due to constant average dislocation density.
Dislocation evolution during tensile deformation in ferritic–martensitic steels revealed by high-energy X-rays
Deformation processes in Grade 91 (Fe–9%Cr–1%Mo–V,Nb) and Grade 92 (Fe–9%Cr–0.5%Mo–2%W–V,Nb) ferritic–martensitic steels were investigated at temperatures between 20 and 650 °C using high-energy synchrotron X-ray diffraction with in situ thermal–mechanical loading. The change of the dislocation density with strain was quantified by X-ray diffraction line profile analysis complemented by transmission electron microscopy measurements. The relationship between dislocation density and strain during uniform deformation was described by a dislocation model, and two critical materials parameters, namely dislocation mean free path and dynamic recovery coefficient, were determined as a function of temperature. Effects of alloy chemistry, thermal–mechanical treatment and temperature on the tensile deformation process in Grade 91 and Grade 92 steels can be well understood by the dislocation evolution behavior.
Fe doped aluminoborate PKU-1 catalysts for the ketalization of glycerol to solketal: Unveiling the effects of iron composition and boron
An inexpensive Fe doped aluminoborate consisted of 18% Fe in PKU-1 material that exhibits high selectivity of 4-hydroxymethy-2,2-dimethyl-1,3-dioxolane (Solketal, 98.3%), considerable activity (TOF 51.7 h -1 ), and recyclable ability in the ketalization of glycerol to Solketal with acetone at 318 K has been developed. Here our study demonstrated that the structure of Fe (less agglomerated iron species vs. FeO x clusters) can be tuned by changing Fe loading in the PKU-1 material, which correlated well with experimental observations. Furthermore, the surface boron sites were promoted by iron loading and behaved as Lewis-acid sites to facilitate the reaction process of glycerol ketalization, while the Solketal selectivity was closely related with the structure of iron species in PKU-1, which was proved by kinetic studies, density function theory (DFT) calculations, and a series of spectroscopy studies. This investigation demonstrates that the surface B sites can play important roles in the reaction instead of being spectators.
Impact load-induced micro-structural damage and micro-structure associated mechanical response of concrete made with different surface roughness and porosity aggregates
The relationship between the nature of micro damage under impact loading and changes in mechanical behavior associated with different microstructures is studied for concretes made with two different coarse aggregates having significant differences mainly in roughness and porosity - sintered fly ash and uncrushed gravel. A range of techniques including X-ray diffraction, digital image analysis, mercury porosimetry, X-ray computed tomography, laser surface profilometry and scanning electron microscopy were used to characterize the aggregates and micro-structures. The concrete prepared with lightweight aggregates was stronger in compression than the gravel aggregate concrete due to enhanced hydration as a result of internal curing. In the lightweight concrete, it was deduced that an inhomogeneous micro-structure led to strain incompatibilities and consequent localized stress concentrations in the mix, leading to accelerated failure. The pore structure, compressibility, and surface texture of the aggregates are of paramount importance for the micro-cracking growth.
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.
Experimental and modeling study of C2–C4 alcohol autoignition at intermediate temperature conditions
C2–C4 alcohols are advantageous blendstocks identified by many research groups, including the U.S. Department of Energy Co-Optima Initiative, towards enabling efficient, boosted Spark-Ignition (SI) engines. Their use in advanced engine applications requires a comprehensive understanding of their intermediate-temperature autoignition behavior. This work reports an experimental and modeling study covering their fundamental autoignition characteristics in a twin-piston rapid compression machine at pressures of 20 and 40 bar, intermediate temperatures from 750 to 980 K, and two fuel loading conditions representative of boosted SI engines. Direct comparison between these alcohols is made, where the order of reactivity is established across different thermodynamic and fuel loading conditions. Changes in preliminary exothermicity (or intermediate-temperature heat release) displayed in single-stage autoignition across different alcohols and conditions are also quantified. This provides insight into fuel-to-fuel differences, and how these could affect advanced combustion concepts such as spark-assisted compression ignition. Kinetic models are used to simulate the experiments, and reasonable agreement is obtained. Furthermore, the sensitivity analysis results demonstrate the importance of accurately capturing the autoignition kinetics, particularly H-abstraction reactions on the parent fuels by OH and HO 2 , and the branching ratio associated with these.
Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks
With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. Here, this paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.
Wholesale Electricity Market Design with Increasing Levels of Renewable Generation: Incentivizing Flexibility in System Operations
This paper discusses the importance and challenges of incentivizing flexibility during short-term operations of the bulk power system due to the increasing variability and uncertainty from growing penetrations of variable generation (VG). Operational flexibility can refer to many aspects of a resource's capability to support the power system, such as the speed, range, and duration of power output, as well as the ability to autonomously respond to frequency or voltage changes. Inefficient utilization of existing flexibility, or unwillingness of resources to provide flexibility, can compromise system reliability by not meeting the changing net load, and it can also lead to higher costs when an inefficient use of flexibility resources occurs. There are many existing characteristics of market design that incentivize flexibility in some manner. How they incentivize the provision of flexibility as well as the level of flexibility is still debated. We explore some of these existing market designs, as well as new market mechanisms, such as pay-for-performance regulating reserve and flexible ramping products, that aim to explicitly incentivize the provision of more flexibility to the system, particularly as a result of increasing VG penetration levels.
Impact of Urban Pollution on Organic-Mediated New-Particle Formation and Particle Number Concentration in the Amazon Rainforest
A major challenge in assessing the impact of aerosols on climate change is to understand how human activities change aerosol loading and properties relative to the pristine-preindustrial baseline. Here we combine chemical transport simulation and field measurements to investigate the effect of anthropogenic pollution from an isolated metropolis on particle number concentration over the preindustrial-like Amazon rainforest through various new-particle formation (NPF) mechanisms and primary particle emissions. To represent organic-mediated NPF, we employ a state-of-the-art model that systematically simulates the formation chemistry and thermodynamics of extremely low volatility organic compounds as well as their roles in NPF processes, and further update the model to improve organic NPF simulations under human-influenced conditions. Results show that urban pollution from the metropolis increases particle number concentration by a factor of 5–25 over the downwind region (within 200 km from city center) compared to background conditions. Overall, our model indicates that NPF contributes over 70% of the total particle number in the downwind region except immediately adjacent to the sources. Among different NPF mechanisms, the ternary NPF involving organics and sulfuric acid overwhelmingly dominates. The improved understanding of particle formation mechanisms will help better quantify anthropogenic aerosol forcing from pristine-preindustrial times to present day.
Global Patterns and Controls of Nutrient Immobilization on Decomposing Cellulose in Riverine Ecosystems
Microbes play a critical role in plant litter decomposition and influence the fate of carbon in rivers and riparian zones. When decomposing low-nutrient plant litter, microbes acquire nitrogen (N) and phosphorus (P) from the environment (i.e., nutrient immobilization), and this process is potentially sensitive to nutrient loading and changing climate. Nonetheless, environmental controls on immobilization are poorly understood because rates are also influenced by plant litter chemistry, which is coupled to the same environmental factors. Here we used a standardized, low-nutrient organic matter substrate (cotton strips) to quantify nutrient immobilization at 100 paired stream and riparian sites representing 11 biomes worldwide. Immobilization rates varied by three orders of magnitude, were greater in rivers than riparian zones, and were strongly correlated to decomposition rates. In rivers, P immobilization rates were controlled by surface water phosphate concentrations, but N immobilization rates were not related to inorganic N. The N:P of immobilized nutrients was tightly constrained to a molar ratio of 10:1 despite wide variation in surface water N:P. Immobilization rates were temperature-dependent in riparian zones but not related to temperature in rivers. However, in rivers nutrient supply ultimately controlled whether microbes could achieve the maximum expected decomposition rate at a given temperature. Collectively, we demonstrated that exogenous nutrient supply and immobilization are critical control points for decomposition of organic matter.
Colloidal quantum dots for optoelectronics
Colloidal quantum dots (QDs) are semiconductor nanocrystals that have unique size-tunable optoelectronic properties and are suitable for wet processing. QD research aims to answer fundamental questions about the chemical and physical properties of nanoscale materials and use these tools for technological applications ranging from bio-imaging to quantum optics. At the core of this field is a set of synthetic, processing and analytical methods designed to produce QDs in uniform ensembles that meet the highest performance standards. Here, this Primer reviews QD fabrication methods with a focus on the applications of QDs in printed optoelectronics and quantum optics. After outlining the current state-of-the-art QD syntheses, the experimental and computational analysis of QDs is discussed. These topics are then connected to the methodologies, processes and concepts required for developing QD-based photodetectors, light-emitting devices and quantum optics applications. Special attention is paid to challenges in reproducibility and current limitations of the field, such as the need to balance non-restricted material composition with high performing technology while achieving long-term stability in QD devices under operating conditions. Finally, the ongoing advancement in QD synthesis, precise atomic-level analysis and computational methodologies are highlighted as key drivers towards rational QD design, particularly in understanding how structural changes under loading impact QD properties.
Quantification of storage required for preserving frequency security in wind‐integrated systems
Abstract The penetration of wind power generation into the power grid has been accelerated in recent times due to the aggressive emission targets set by governments and other regulatory authorities. Although wind power has the advantage of being environment‐friendly, wind as a resource is intermittent in nature. In addition, wind power contributes little inertia to the system as most wind turbines are connected to the grid via power electronic converters. These negative aspects of wind power pose serious challenges to the frequency security of power systems as penetration increases. In this work, an approach is proposed where an energy storage system (ESS) is used to mitigate frequency security issues of wind‐integrated systems. ESSs are well equipped to supply virtual inertia to the grid due to their fast‐acting nature, thus replenishing some of the energy storage capability of displaced inertial generation. In this work, a probabilistic approach is proposed to estimate the amount of inertia required by a system to ensure frequency security. Reduction in total system inertia due to the displacement of conventional synchronous generation by wind power generation is considered in this approach, while also taking into account the loss of inertia due to forced outages of conventional units. Monte Carlo simulation is employed for implementing the probabilistic estimation of system inertia. An ESS is then sized appropriately, using the system swing equation, to compensate for the lost inertia. The uncertainty associated with wind energy is modeled into the framework using an autoregressive moving average technique. Effects of increasing the system peak load and changing the wind profile on the expected system inertia are studied to illustrate various factors that might affect system frequency security. The proposed method is validated using the IEEE 39‐bus test system.
Linear and nonlinear instability in vertical counter-current laminar gas-liquid flows
We consider the genesis and dynamics of interfacial instability in vertical gas-liquid flows, using as a model the two-dimensional channel flow of a thin falling film sheared by counter-current gas. The methodology is linear stability theory (Orr-Sommerfeld analysis) together with direct numerical simulation of the two-phase flow in the case of nonlinear disturbances. We investigate the influence of two main flow parameters on the interfacial dynamics, namely the film thickness and pressure drop applied to drive the gas stream. To make contact with existing studies in the literature, the effect of various density contrasts is also examined. Energy budget analyses based on the Orr-Sommerfeld theory reveal various coexisting unstable modes (interfacial, shear, internal) in the case of high density contrasts, which results in mode coalescence and mode competition, but only one dynamically relevant unstable interfacial mode for low density contrast. A study of absolute and convective instability for low density contrast shows that the system is absolutely unstable for all but two narrow regions of the investigated parameter space. Direct numerical simulations of the same system (low density contrast) show that linear theory holds up remarkably well upon the onset of large-amplitude waves as well as the existence of weakly nonlinear waves. For high density contrasts, corresponding more closely to an air-water-type system, linear stability theory is also successful at determining the most-dominant features in the interfacial wave dynamics at early-to-intermediate times. Nevertheless, the short waves selected by the linear theory undergo secondary instability and the wave train is no longer regular but rather exhibits chaotic motion. The same linear stability theory predicts when the direction of travel of the waves changes — from downwards to upwards. We outline the practical implications of this change in terms of loading and flooding. The change in direction of the wave propagation is represented graphically in terms of a flow map based on the liquid and gas flow rates and the prediction carries over to the nonlinear regime with only a small deviation.
Design and Operation of a Sensible Heat Peaking Unit for Small Modular Reactors
Approximately 19% of the electricity produced in the United States comes from nuclear power plants. Traditionally, nuclear power plants, as well as larger coal-fired plants, operate in a baseload manner at or near steady-state for prolonged periods of time. Smaller, more maneuverable plants, such as gas-fired plants, are dispatched to match electricity supply and demand above the capacity of the baseload plants. However, air quality concerns and CO2 emission standards has made the burning of fossil fuels less desirable, despite the current low cost of natural gas. Wind and solar photovoltaic (PV) power generation are attractive options due to their lack of carbon footprint and falling capital costs. Yet, these renewable energy sources suffer from inherent intermittency. This inherent intermittency can strain electric grids, forcing carbon-based and nuclear sources of energy to operate in a load follow mode. For nuclear reactors, load follow operation can be undesirable due to the associated thermal and mechanical stresses placed on the fuel and other reactor components. Various methods of Thermal Energy Storage (TES) can be coupled to nuclear (or renewable) power sources to help absorb grid variability caused by daily load demand changes and renewable intermittency. Our previous research has shown that coupling a sensible heat TES system to a Small Modular Reactor (SMR) allows the reactor to run at effectively nominal full power during periods of variable electric demand by bypassing steam to the TES system during periods of excess capacity. In this study we demonstrate that this stored thermal energy can be recovered, allowing the TES system to act as a peaking unit during periods of high electric demand, or used to produce steam for ancillary applications such as desalination. For both applications the reactor is capable of operating continuously at approximately 100% power.
Model-centric distribution automation: Capacity, reliability, and efficiency
A series of analyses along with field validations that evaluate efficiency, reliability, and capacity improvements of model-centric distribution automation are presented. With model-centric distribution automation, the same model is used from design to real-time control calculations. A 14-feeder system with 7 substations is considered. The analyses involve hourly time-varying loads and annual load growth factors. Phase balancing and capacitor redesign modifications are used to better prepare the system for distribution automation, where the designs are performed considering time-varying loads. Coordinated control of load tap changing transformers, line regulators, and switched capacitor banks is considered. In evaluating distribution automation versus traditional system design and operation, quasi-steady-state power flow analysis is used. In evaluating distribution automation performance for substation transformer failures, reconfiguration for restoration analysis is performed. In evaluating distribution automation for storm conditions, Monte Carlo simulations coupled with reconfiguration for restoration calculations are used. As a result, the evaluations demonstrate that model-centric distribution automation has positive effects on system efficiency, capacity, and reliability.