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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Life Cycle Inventories and Life Cycle Assessments of Photovoltaic Systems

Life Cycle Assessment (LCA) is a structured, comprehensive method of quantifying material- and energyflows and their associated impacts in the life cycles of products (i.e., goods and services). One of the major goals of IEA PVPS Task 12 is to provide guidance on assuring consistency, balance, transparency and quality of LCA to enhance the credibility and reliability of the results. The current report presents the latest consensus LCA results among the authors, PV LCA experts in North America, Europe and Asia. At this time consensus is limited to five technologies for which there are well-established and up-to-date LCI data: mono- and multi-crystalline Si, CdTe CIGS, and high concentration PV (HCPV) using III/V cells. The LCA indicators shown herein include Energy Payback Times (EPBT), Greenhouse Gas emissions (GHG), criteria pollutant emissions, and heavy metal emissions. Life Cycle Inventories (LCIs) are necessary for LCA and the availability of such data is often the greatest barrier for conducting LCA. The Task 12 LCA experts have put great efforts in gathering and compiling the LCI data presented in this report. These include detailed inputs and outputs during manufacturing of cell, wafer, module, and balance-of-system (i.e., structural- and electrical- components) that were estimated from actual production and operation facilities. In addition to the LCI data that support the LCA results presented herein, data are presented to enable analyses of various types of PV installations; these include operational data of rooftop and ground-mount PV systems and country-specific PV-mixes. The LCI datasets presented in this report are the latest that are available to the public describing the status in 2011 for crystalline Si, 2010-2011 for CdTe, 2010 for CIGS, and 2010 for HCPV technology. This report provides an update of the life cycle inventory data in Section 5 of the previous report: V. Fthenakis, H. C. Kim, R. Frischknecht, M. Raugei, P. Sinha, M. Stucki , 2011, Life Cycle Inventories and Life Cycle Assessment of Photovoltaic Systems, International Energy Agency(IEA) PVPS Task 12, Report T12-02:2011. Updates are provided for the crystalline silicon PV global supply chain (Section 5.1), thin film PV module manufacturing (Sections 5.2-5.3), PV mounting structures (Section 5.5), and country-specific electricity grid mixes (Section 5.9). Other sections of this report are the same as in the previous report. Electronic versions of the updated tables in Section 5 are available at IEA PVPS (http://www.iea-pvps.org; select Task 12 under Archive) and treeze Ltd (http://treeze.ch; under Publications).

14 SOLAR ENERGY↗
DOE OSTI2016

Application of High Performance Computing for Simulating Cycle-to-Cycle Variation in Dual-Fuel Combustion Engines

Interest in operational cost reduction is driving engine manufacturers to consider lower-cost fuel substitution in heavy-duty diesel engines. These dual-fuel (DF) engines could be operated either in diesel-only mode or operated with premixed natural gas (NG) ignited by a pilot flame of compression-ignited direct-injected diesel fuel. One promising application is that of large-bore, medium-speed engines such as those used in locomotives. With realistic natural gas substitution levels in the fleet of locomotives currently in service, such fuel substitution could result in billions of dollars of savings annually in the US alone. However, under certain conditions, dual-fuel operation can result in increased cycle-to-cycle variability (CCV) during combustion, resulting in variations in cylinder pressure and work extraction. In certain situations, the CCV of dual-fuel operation can be notably higher than that of diesel-only combustion under similar operating conditions. Excessive CCV can limit the NG substitution rate and operating range of a dual-fuel engine by increasing emissions and reducing engine stability, reliability and fuel efficiency via incomplete natural-gas combustion. Running multiple engine cycles in series to simulate CCV can be quite time consuming. Hence innovative modelling techniques and large computing resources are needed to investigate the factors affecting CCV in dual-fuel engines. This paper discusses the use of the High Performance Computing resource Titan, at the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, to investigate cycle-to-cycle combustion variability of a dual-fuel engine. The CONVERGE CFD software was used to simulate multiple, parallel single cycles of dual-fuel combustion with perturbed operating parameters and boundary conditions. These perturbations are imposed according to a sparse grids sampling of the parameter space. The sampling scheme chosen is similar to a design of experiments method but uses functions designed to minimize the number of samples required to achieve a desired degree of accuracy. The perturbed input parameters were connected to engine performance output over a large parameter space. Additionally, the same methodology was used to perform a set of diesel-only simulations at similar operating conditions to assess the numerical capability of capturing relatively low combustion variation. Lastly, this technique is expected to be useful to understand and predict combustion stability limits (knock and misfire) which are important for designing dual-fuel engines with improved operability and reliability.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1973

The solar wind cycle, the sunspot cycle, and the corona.

Recent theories of the solar cycle and of coronal heating strongly suggest that solar cycle variations of different quantities (i.e., sunspots, coronal green line, etc.) ought not to be expected to be in phase with one another. In agreement with this notion it is noted that the shape of the corona typical of a 'maximum' eclipse occurs 1.5 yr before sunspot maximum, compared with 2 yr as might be expected from Leighton's 'standard' model. Further, it is argued that the phase of the solar wind cycle can be determined from geomagnetic observations. Using this phase, a solar cycle variation of 100 km/sec in the solar wind velocity and 1 gamma in the magnetic field intensity becomes apparent. In general, the solar wind cycle lags behind the coronal-eclipse-form cycle by 3 yr, compared with the 2 yr that might be expected from model calculations.

Hirshberg, J.↗
NASA NTRS1976

High temperature, low-cycle fatigue of copper-base alloys for rocket nozzles. Part 2: Strainrange partitioning and low-cycle fatigue results at 538 deg C

Low-cycle fatigue tests of 1/2 Hard AMZIRC Copper and NARloy Z were performed in argon at 538 C to determine partitioned strain range versus life relationships. Strain-controlled low-cycle fatigue tests of a Zr-Cr-Mg copper-base alloy were also performed. Strain ranges, lower than those employed in previous tests, were imposed in order to extend the fatigue life curve out to approximately 400,000 cycles. An experimental copper alloy and an experimental silver alloy were also studied. Tensile tests were performed in air at room temperature and in argon at 538 C. Strain-controlled low-cycle fatigue tests were performed at 538 C in argon to define the fatigue life over the regime from 300 to 3,000 cycles. For the silver alloy, three additional heat treatments were introduced, and a limited evaluation of the short-term tensile and low-cycle fatigue behavior at 538 C was performed.

Conway, J. B.↗
NASA NTRS1990

A comparison of single-cycle versus multiple-cycle proof testing strategies

An evaluation of single-cycle and multiple-cycle proof testing (MCPT) strategies for SSME components is described. Data for initial sizes and shapes of actual SSME hardware defects are analyzed statistically. Closed-form estimates of the J-integral for surface flaws are derived with a modified reference stress method. The results of load- and displacement-controlled stable crack growth tests on thin IN-718 plates with deep surface flaws are summarized. A J-resistance curve for the surface-cracked configuration is developed and compared with data from thick compact tension specimens. The potential for further crack growth during large unload/reload cycles is discussed, highlighting conflicting data in the literature. A simple model for ductile crack growth during MCPT based on the J-resistance curve is used to study the potential effects of key variables. The projected changes in the crack size distribution during MCPT depend on the interactions between several key parameters, including the number of proof cycles, the nature of the resistance curve, the initial crack size distribution, the component boundary conditions (load vs. displacement control), and the magnitude of the applied load or displacement. The relative advantages of single-cycle and multiple-cycle proof testing appear to be specific, therefore, to individual component geometry, material, and loading.

Hudak, S. J., Jr.↗
NASA NTRS2000

Examination of Solar Cycle Statistical Model and New Prediction of Solar Cycle 23

Sunspot numbers in the current solar cycle 23 were estimated by using a statistical model with the accumulating cycle sunspot data based on the odd-even behavior of historical sunspot cycles from 1 to 22. Since cycle 23 has progressed and the accurate solar minimum occurrence has been defined, the statistical model is validated by comparing the previous prediction with the new measured sunspot number; the improved sunspot projection in short range of future time is made accordingly. The current cycle is expected to have a moderate level of activity. Errors of this model are shown to be self-correcting as cycle observations become available.

Kim, Myung-Hee Y.↗
DOE OSTI2018

Development of predictive capability of cycle-to-cycle variation in dual-fuel engines using supercomputing-based computational fluid dynamics

Cycle-to-cycle variation (CCV) in internal combustion engines is expected due to variabilities in operating conditions such as in-cylinder fuel stratification, mixing behavior, manifold air pressure, injection timing etc. Generally, engine experimental studies provide averaged quantities such as cylinder pressure, emissions, indicated mean effective pressure (IMEP) from multiple cycles and their CCV of IMEP, and CCV of cylinder pressure from consecutive cycles for a given test condition. Capturing 3D spatial distributions of relevant measures such as fuel-air mixing, temperature, turbulence levels and emissions from such experiments is challenging. Computational Fluid Dynamics (CFD) is an alternative to experiments, that can be used effectively to understand the spatial and temporal distributions of parameters of interest.Quantitative understanding of CCV in dual-fuel (DF) engines will accelerate the development and deployment of engines that provide fuel flexibility, economic advantage and emission compliance. Earlier work [1] performed to develop an understanding of the physics of CCV in large-bore, medium-speed, dual-fuel engines was partially successful. That effort entailed a sparse design of experiment (DOE) of full-cylinder geometry, closed-cycle, dual-fuel CFD simulations in which variabilities within some of the identified global parameters were exercised. The Coefficient of Variation (COV) of cylinder pressure was promising compared with experimental COV. This reinforced the understanding of which global parameters are significant contributors to CCV in DF combustion.The authors have used several high-performance supercomputing facilities in the process of developing predicting capability of CCV in large-bore engines, while operating in dual-fuel, diesel and natural gas (NG), combustion mode [1, 2]. In the current study CCV influencing parameters in a diesel-natural gas dual-fuel engine was studied using a 4th order DOE consisting of 137 concurrent cases. These full-geometry, closed-cycle, CFD simulations were run on the Mira supercomputing facility at the Argonne National Laboratory (ANL). This paper describes key learnings of that study along with a validation of the DOE. Further, the CCV prediction methodology, which is applicable to simulation-based design and testing, is described.

Gubba, Sreenivasa Rao↗
DOE OSTI2020

Large-eddy simulation study on cycle-to-cycle variation of knocking combustion in a spark-ignition engine

The cycle-to-cycle variation in the knock intensity is commonly encountered under abnormal combustion conditions. The severity of these abnormal combustion events can vary significantly, and the efficiency of engines at high loads is limited in practice by heavy knocking phenomena. Since, a thorough analysis of such recurrent but non-cyclic phenomena via experiments alone becomes highly cumbersome, in the present work, a multi-cycle large-eddy simulation study was performed to quantitatively predict cyclic variability in the combustion process and cyclic knock intensity variability in a direct injection spark-ignition engine. To account for the turbulence-chemistry interaction effects on flame propagation, the G-equation combustion model was used. Detailed chemistry was solved outside the flame front with a toluene primary reference fuel skeletal kinetic mechanism. For both the mild knock and heavy knock conditions, the numerical results were validated against experimental measurements. Based on the simulation results, a correlation analysis was performed considering combustion phasing, peak cylinder pressure and maximum amplitude of pressure oscillation. Furthermore, a detailed three-dimensional spatial analysis illustrated the evolution of auto-ignition kernel development and propagation of pressure waves during knocking combustion for three typical cycles with different knock intensities. In this process, it was found that an early occurrence of auto-ignition in the end gas was prone to high knock intensity. Although multiple auto-ignition kernels were observed in different cycles, the degree of coupling between chemical heat release and pressure waves varied, thereby leading to different maximum amplitude of pressure oscillation values.

42 ENGINEERING↗
DOE OSTI2012

Climate impacts of bioenergy: Inclusion of carbon cycle and albedo dynamics in life cycle impact assessment

Life cycle assessment (LCA) can be an invaluable tool for the structured environmental impact assessment of bioenergy product systems. However, the methodology's static temporal and spatial scope combined with its restriction to emission-based metrics in life cycle impact assessment (LCIA) inhibits its effectiveness at assessing climate change impacts that stem from dynamic land surface-atmosphere interactions inherent to all biomass-based product systems. In this paper, we focus on two dynamic issues related to anthropogenic land use that can significantly influence the climate impacts of bioenergy systems: i) temporary changes to the terrestrial carbon cycle; and ii) temporary changes in land surface albedo-and illustrate how they can be integrated within the LCA framework. In the context of active land use management for bioenergy, we discuss these dynamics and their relevancy and outline the methodological steps that would be required to derive case-specific biogenic CO{sub 2} and albedo change characterization factors for inclusion in LCIA. We demonstrate our concepts and metrics with application to a case study of transportation biofuel sourced from managed boreal forest biomass in northern Europe. We derive GWP indices for three land management cases of varying site productivities to illustrate the importance and need to consider case- or region-specific characterization factors for bioenergy product systems. Uncertainties and limitations of the proposed metrics are discussed. - Highlights: Black-Right-Pointing-Pointer A method for including temporary surface albedo and carbon cycle changes in Life Cycle Impact Assessment (LCIA) is elaborated. Black-Right-Pointing-Pointer Concepts are applied to a single bioenergy case whereby a range of feedstock productivities are shown to influence results. Black-Right-Pointing-Pointer Results imply that case- and site-specific characterization factors can be essential for a more informed impact assessment. Black-Right-Pointing-Pointer Uncertainties and limitations of the proposed methodologies are elaborated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗
DOE OSTI2020

Laminar-to-turbulent flame transition and cycle-to-cycle variations in large eddy simulation of spark-ignition engines

This paper investigates the effect of laminar-to-turbulent flame transition modeling on the prediction of cycle-to-cycle variations (CCVs) in large eddy simulation (LES) of spark-ignition (SI) engines. A laminar-to-turbulent flame transition model that describes the non-equilibrium sub-filter flame speed evolution during an early stage of flame kernel growth is developed. In the present model, the flame transition is characterized by the flame kernel size at which the flame transition ends, defined here as the flame transition scale. The proposed model captures the effects that variations in a turbulent flow field have on the evolution of early-stage burning rates, through variations in the flame transition scale. The proposed flame transition model is combined with the front propagation formulation (FPF) method and a spark-ignition model to predict CCVs in a gasoline direct injection SI engine. It is found that multi-cycle LES with the proposed flame transition model reproduces experimentally-observed CCVs satisfactorily. When the transition model is not considered or when variations in the transition process are neglected, CCVs are significantly under-predicted for the case considered here. These results indicate the importance of modeling the laminar-to-turbulent flame transition and the effect of turbulence on the transition process, when predicting CCVs, under certain engine conditions. The LES results are also used to analyze sources for variations in the flame transition. It is found, for the present engine case, that the most important source is the cycle-to-cycle variation in the turbulence dissipation rate, which is used to measure the strength of turbulence in the proposed model, near a spark plug. The large-scale velocity field and the variations of the laminar flame speed due to the mixture composition and thermal stratification are also found to be important factors to contribute to the variations in the flame transition.

42 ENGINEERING↗
DOE OSTI2012

Assessment of transition fuel cycle performance with and without a modified-open fuel cycle

The impacts of a modified-open fuel cycle (MOC) option as a transition step from the current once-through cycle (OTC) to a full-recycle fuel cycle (FRC) were assessed using the nuclear systems analysis code DANESS. The MOC of interest for this study was mono-recycling of plutonium in light water reactors (LWR-MOX). Two fuel cycle scenarios were evaluated with and without the MOC option: a 2-stage scenario with a direct path from the current fleet to the final FRC, and a 3-stage scenario with the MOC option as a transition step. The FRC reactor (fast reactor) was assumed to deploy in 2050 for both scenarios, and the MOC reactor in the 3-stage scenario was assumed to deploy in 2025. The last LWRs (using either UOX or MOX fuels) come online in 2050 and are decommissioned by 2110. Thus, the FRC is achieved after 2110. The reprocessing facilities were assumed to be available 2 years prior to the deployment of the MOC and FRC reactors with maximum reprocessing capacities of 2000 tHM/yr and 500 tHM/t for LWR-UOX and LWR-MOX used nuclear fuels (UNFs), respectively. Under a 1% nuclear energy demand growth assumption, both scenarios were able to sustain a full transition to the FRC without delay. For the 3-stage scenario, the share of LWR-MOX reactors reaches a peak of 15% of installed capacity, which resulted in 10% lower cumulative uranium consumption and SWU requirements compared to the 2-stage scenario during the transition period. The peak UNF storage requirement decreases by 50% in the 3-stage scenario, largely due to the earlier deployment of the reprocessing plants to support the MOC fuel cycle. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

The need for a characteristics-based approach to radioactive waste classification as informed by advanced nuclear fuel cycles using the fuel-cycle integration and tradeoffs (FIT) model

This study explores the impact of wastes generated from potential future fuel cycles and the issues presented by classifying these under current classification criteria, and discusses the possibility of a comprehensive and consistent characteristics-based classification framework based on new waste streams created from advanced fuel cycles. A static mass flow model, Fuel-Cycle Integration and Tradeoffs (FIT), was used to calculate the composition of waste streams resulting from different nuclear fuel cycle choices. Because heat generation is generally the most important factor limiting geological repository areal loading, this analysis focuses on the impact of waste form heat load on waste classification practices, although classifying by metrics of radiotoxicity, mass, and volume is also possible. Waste streams generated in different fuel cycles and their possible classification based on the current U.S. framework and international standards are discussed. It is shown that the effects of separating waste streams are neglected under a source-based radioactive waste classification system. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Cycle length dependence of stellar magnetic activity and solar cycle 23

Solar cycle (SC) 23 was extraordinarily long with remarkably low magnetic activity. We have investigated whether this is a common behavior of solar-type stars. From the Ca ii H and K line intensities of 111 stars observed at Mount Wilson Observatory from 1966 to 1991, we have retrieved data of all 23 G-type stars and recalculated their cycle lengths using the damped least-squares method for the chromospheric activity index S as a function of time. A regression analysis was performed to find relations between the derived cycle length, P{sub avg}, and the index for excess chromospheric emission, R{sub HK}{sup ′}. As a noteworthy result, we found a segregation between young and old solar-type stars in the cycle length-activity correlation. We incorporated the relation for the solar-type stars into the previously known rule for stellar chromospheric activity and brightness to estimate the variation of solar brightness from SC 22 to SC 23 as (0.12 ± 0.06)%, much higher than the actual variation of total solar irradiance (TSI) ≤0.02%. We have then examined solar spectral irradiance (SSI) to find a good phase correlation with a sunspot number in the wavelength range of 170–260 nm, which is close to the spectral range effective in heating the Earth’s atmosphere. Therefore, it appears that SSI rather than TSI is a good indicator of the chromospheric activity, and its cycle length dependent variation would be more relevant to the possible role of the Sun in the cyclic variation of the Earth’s atmosphere.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SOLAR CYCLE 25: ANOTHER MODERATE CYCLE?

Surface flux transport simulations for the descending phase of Cycle 24 using random sources (emerging bipolar magnetic regions) with empirically determined scatter of their properties provide a prediction of the axial dipole moment during the upcoming activity minimum together with a realistic uncertainty range. The expectation value for the dipole moment around 2020 (2.5 ± 1.1 G) is comparable to that observed at the end of Cycle 23 (about 2 G). The empirical correlation between the dipole moment during solar minimum and the strength of the subsequent cycle thus suggests that Cycle 25 will be of moderate amplitude, not much higher than that of the current cycle. However, the intrinsic uncertainty of such predictions resulting from the random scatter of the source properties is considerable and fundamentally limits the reliability with which such predictions can be made before activity minimum is reached.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Waste Classification based on Waste Form Heat Generation in Advanced Nuclear Fuel Cycles Using the Fuel-Cycle Integration and Tradeoffs (FIT) Model

This study explores the impact of wastes generated from potential future fuel cycles and the issues presented by classifying these under current classification criteria, and discusses the possibility of a comprehensive and consistent characteristics-based classification framework based on new waste streams created from advanced fuel cycles. A static mass flow model, Fuel-Cycle Integration and Tradeoffs (FIT), was used to calculate the composition of waste streams resulting from different nuclear fuel cycle choices. This analysis focuses on the impact of waste form heat load on waste classification practices, although classifying by metrics of radiotoxicity, mass, and volume is also possible. The value of separation of heat-generating fission products and actinides in different fuel cycles is discussed. It was shown that the benefits of reducing the short-term fission-product heat load of waste destined for geologic disposal are neglected under the current source-based radioactive waste classification system , and that it is useful to classify waste streams based on how favorable the impact of interim storage is in increasing repository capacity.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗