Ignition of hydrogen-fluorinated oxidizers Final report, 30 Jun. - 30 Nov. 1966
Ignition of hydrogen fluorinated oxidizers
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Ignition of hydrogen fluorinated oxidizers
Applicability of low temperature ignition method for determining carbon in alkali metals
Feasibility study of oxygen/hydrogen powdered metal ignition
Resonance-tube igniter for hydrogen-oxygen rocket engines
Vacuum-ignition phenomena in Apollo rocket engine when oriented in upward-firing attitude
Experiments, or 'shots', conducted at the National Ignition Facility (NIF) are discrete events that occur over a very short time frame (tens of nanoseconds) separated by many hours. Each shot is part of a larger campaign of shots to advance scientific understanding in high-energy-density physics. In one campaign, scientists use energy from the 192-beam, 1.8-Megajoule pulsed laser in the NIF system to symmetrically implode a hydrogen-filled target, thereby creating conditions similar to the interior of stars in a demonstration of controlled fusion. Each NIF shot generates gigabytes of data from over 30 diagnostics that measure optical, x-ray, and nuclear phenomena from the imploding target. We have developed systems to manage all aspects of the shot cycle. Other papers will discuss the control of the lasers and targets, while this paper focuses on the setup and management of campaigns and diagnostics. Because of the low duty cycle of shots, and the thousands of adjustments for each shot (target type, composition, shape; laser beams used, their power profiles, pointing; diagnostic systems used, their configuration, calibration, settings) it is imperative that we accurately define all equipment prior to the shot. Following the shot, and capture of the data by the automatic control system, it is equally imperative that we archive, analyze and visualize the results within the required 30 minutes post-shot. Results must be securely archived, approved, web-visible and downloadable in order to facilitate subsequent publication. To-date NIF has successfully fired over 2,500 system shots, as well as thousands of test firings and dry-runs. We will present an overview of the highly-flexible and scalable campaign management systems and tools employed at NIF that control experiment configuration of the facility all the way through presentation of analyzed results.
We utilize the Powertrain Systems Analysis Toolkit (PSAT) combined with transient engine and aftertreatment component models to simulate the impact of premixed charge compression ignition (PCCI) on the fuel economy and emissions of light-duty (LD) diesel-powered conventional and hybrid electric vehicles (HEVs). Our simulated aftertreatment train consists of a diesel oxidation catalyst (DOC), lean NOx trap (LNT), and catalyzed diesel particulate filter (DPF). The results indicate that utilizing PCCI combustion significantly reduces fuel consumption and tailpipe emissions for the conventional diesel-powered vehicle with NOx and particulate emissions controls. These benefits result from a favorable engine speed-load distribution over the cycle combined with a corresponding reduction in the need to regenerate the LNT and DPF. However, the current PCCI technology appears to offer less potential benefit for diesel HEVs equipped with similar emissions controls. This is because PCCI can only be activated over a relatively small part of the drive cycle. Thus we conclude that future utilization of PCCI in diesel HEVs will require significant extension of the available speed-load range for PCCI and revision of current HEV engine management strategies before significant benefits can be realized.
The south pole bang-time (SPBT) diagnostic views National Ignition Facility (NIF) implosions through the lower hohlraum laser entrance hole to measure the time of peak x-ray emission (peak compression) in indirect drive implosions. Five chemical-vapor-deposition (CVD) diamond photoconductive detectors (PCD's) with different filtrations and sensitivities record the time-varying x rays emitted by the target. Wavelength-selecting highly oriented pyrolytic graphite (HOPG) crystal mirror monochromators increase the x-ray signal-to-background ratio by filtering for 11-keV emission. Diagnostic timing and the in-situ temporal instrument response function are determined from laser impulse shots on the NIF. After signal deconvolution and background removal, the bang time is determined to 45-ps accuracy. The x-ray 'yield' (mJ/sr/keV at 11 keV) is determined from the total area under the peak.
The convergent ablator experiments at the National Ignition Facility (NIF) are designed to measure the peak velocity and remaining ablator mass of an indirectly driven imploding capsule. Such a measurement can be performed using an x-ray source to backlight the capsule and an x-ray streak camera to record the capsule as it implodes. The ultimate goal of this experiment is to achieve an accuracy of 2% in the velocity measurement, which translates to a {+-}2 ps temporal accuracy over any 300 ps interval for the streak camera. In order to achieve this, a 4-{omega} (263nm) temporal fiducial system has been implemented for the x-ray streak camera at NIF. Aluminum, Titanium, Gold and Silver photocathode materials have been tested. Aluminum showed the highest quantum efficiency, with five times more peak signal counts per fiducial pulse when compared to Gold. The fiducial pulse data was analyzed to determine the centroiding a statistical accuracy for incident laser pulse energies of 1 and 10 nJ, showing an accuracy of {+-}1.6 ps and {+-}0.7 ps respectively.
As part of stockpile stewardship, the Inertial Confinement Fusion program has focused on exploring fusion burn at the National Ignition Facility. The process attempt to use lasers, rather than nuclear fission, to generate x-rays, implode, and fuse deuterium (D) tritium (T) in a small capsule. Laser light is directed into a gold or uranium cylinder (a "hohlraum") producing x-rays that penetrate, heat, and explode the exterior of a spherical capsule.
A design optimization campaign was conducted to search for improved combustion profiles that enhance gasoline compression ignition in a heavy-duty diesel engine with a geometric compression ratio of 17.3. A large-scale design of experiments approach was used for the optimization, employing three-dimensional computational fluid dynamics simulations. The main parameters explored include geometric features, injector specifications, and swirl motion. Both stepped-lip and re-entrant bowls were included in order to assess their respective performance implications. A total of 256 design candidates were prepared using the software package CAESES for automated and simultaneous geometry generation and combustion recipe perturbation. The design optimization was conducted for three engine loads representing light to medium load conditions. The design candidates were evaluated for fuel efficiency, emissions, fuel-air mixing, and global combustion behavior. Simulation results showed that the optimum designs were all stepped-lip bowls, due to improvements in fuel-air mixing, as well as reduced heat loss and emissions formation. Improvements in indicated specific fuel consumption of up to 3.2% were achieved while meeting engine-out NOx emission targets of 1-1.5 g/kW center dot h. Re-entrant bowls performed worse compared to the baseline design, and significant performance variations occurred across the load points. Specifically, the re-entrant bowls were on par with the stepped-lip bowls under light load conditions, but significant deteriorations occurred under higher load conditions. As a final task, selected optimized designs were then evaluated under full-load conditions.
N/A This is a presentation for the Ignite off Competition so there is no abstract for the specific project.
The light-off of the CO oxidation is simultaneous on all Pt crystal surfaces vicinal to the (111) plane, regardless of the reaction conditions, and in contrast with the structural dependence of Pd. In this work, using ambient-pressure XPS we find that, immediately prior to ignition, atomic oxygen incorporates to the subsurface plane, leading to buckling of the topmost CO-Pt layer, and effectively equaling the CO desorption temperature at terraces and steps.
Here, one of the biggest challenges in high-energy physics is to analyze a complex mix of experimental and simulation data to gain new insights into the underlying physics. Currently, this analysis relies primarily on the intuition of trained experts often using nothing more sophisticated than default scatter plots. Many advanced analysis techniques are not easily accessible to scientists and not flexible enough to explore the potentially interesting hypotheses in an intuitive manner. Furthermore, results from individual techniques are often difficult to integrate, leading to a confusing patchwork of analysis snippets too cumbersome for data exploration. This paper presents a case study on how a combination of techniques from statistics, machine learning, topology, and visualization can have a significant impact in the field of inertial confinement fusion. We present the $\mathrm{ND}^2\mathrm{AV}$: N-dimensional data analysis and visualization framework, a user-friendly tool aimed at exploiting the intuition and current workflow of the target users. The system integrates traditional analysis approaches such as dimension reduction and clustering with state-of-the-art techniques such as neighborhood graphs and topological analysis, and custom capabilities such as defining combined metrics on the fly. All components are linked into an interactive environment that enables an intuitive exploration of a wide variety of hypotheses while relating the results to concepts familiar to the users, such as scatter plots. $\mathrm{ND}^2\mathrm{AV}$ uses a modular design providing easy extensibility and customization for different applications. $\mathrm{ND}^2\mathrm{AV}$ is being actively used in the National Ignition Campaign and has already led to a number of unexpected discoveries.
In recent years, as engine control strategies have grown increasingly sophisticated in a continued drive for increasing efficiency and reducing emissions, engine operation has been pushed into regimes that are limited by combustion instabilities. These instabilities produce undesirable abnormal combustion events, which pose barriers to further improvement in engine efficiency. Many of the phenomena involved are difficult to model or control using traditional, purely physics-based models and reactive control approaches. Artificial intelligence (AI) techniques offer some promise for achieving more effective combustion stability control, especially when appropriately applied within a physics-informed framework. This chapter illustrates the current state-of-the-art in applying AI to combustion stability control and examines three case studies with application to the dilute stability limit in spark-ignition engines to illustrate the utility and limitations of AI in these applications.
The Department of Energy “Co-Optimization of Fuels and Engines” initiative aims to simultaneously develop novel high-performance fuels with advanced engine designs to reduce petroleum consumption. To achieve commercialization, advanced engines running on alternative fuels still must meet emissions regulations. Warm three-way catalysts (TWC) are very effective at meeting the stringent emissions regulations on pollutants such as nitrogen oxides (NOx), non-methane organic gases (NMOG) and carbon monoxide (CO) from gasoline-fueled spark-ignition (SI) engines operating under stoichiometric conditions; thus, most SI engine emissions occur during cold-start, when the TWC has not yet achieved light-off. In the current study, the light-off behavior of novel high-performance fuel candidates has been investigated on a hydrothermally-aged commercial TWC using a synthetic engine-exhaust flow reactor system according to industry guidelines. Over 30 potential fuel components were examined in this study, including alkanes, alkenes, alcohols, ketones, esters, aromatic ethers, and non-oxygenated aromatic hydrocarbons. Short-chain acyclic oxygenates, including alcohols, ketones, and esters, tended to light off at relatively low temperatures, while alkenes, aromatics, and cyclic oxygenates tended to light off at relatively high temperatures. The light-off behavior of alkanes and alkenes depended strongly on their size and structure. In terms of the influence on CO light-off on the TWC, the fuels fell into two distinct categories: (i) non-inhibiting species including C 2 -C 3 alcohols, alkanes, acyclic ketones, and esters; and (ii) inhibiting species including alkenes, aromatic hydrocarbons, cyclic oxygenates, and C 4 alcohols.
This paper describes the experimental results for an energy tuning assembly created to modify the National Ignition Facility deuterium–tritium fusion neutron source into a notional thermonuclear and prompt fission neutron spectrum, which has applications in integral measurements, nuclear data benchmarks, and radiation effects on microelectronics. The Monte Carlo neutron transport utilized MCNP5 to estimate the ETA-modified fluence using the ENDF-B/VIII.0 and IRDFF-II continuous energy nuclear data libraries, and SCALE Sampler was used to estimate the systematic nuclear data covariance using ENDF-B/VII.1 and IRDFF-II in a 252-group structure. The experiment fielded eight activation foils and a highly enriched uranium sample. This provided fifteen reaction channels that were used in a forward-fit comparison to the modeled results and to unfold the neutron spectrum using STAYSL. Gamma-ray spectrometry was performed on the activation and highly enriched uranium foils, and the reduced Χ 2 between the modeled and experimental values was 1.21. The results from the STAYSL unfold, reduced Χ 2 = 1.62, indicated that the modeled neutron spectrum was achieved and the systematic nuclear data uncertainty associated with the neutron transport and activation product cross sections was representative of the experiment. Integral cumulative fission product yield data were collected for 37 mass chains with a combination of gamma-ray spectrometry and radiochemical analysis. Fission product analysis was generally in agreement with two models using a semi-empirical fit and the General Observables of Fission code, with the exception of mass chains 88, 109, 111, 112, 113, 129, 139, 142, 144, 151, and 156.
Gasoline Compression Ignition (GCI) engines have the potential to achieve high fuel efficiency and to significantly reduce both NOx and particulate matter (PM) emissions by operating under dilute partially-premixed conditions. This low temperature combustion strategy is dependent upon direct-injection of gasoline during the compression stroke and potentially near top dead center (TDC). The timing and duration of the in-cylinder injections can be tailored based on speed and load to create optimized conditions that result in a stable combustion. We present the results of advanced aerosol analysis methods that have been used for detailed real-time characterization of PM emitted from a single-cylinder GCI engine operated at different speed, load, timing, and number and duration of near-TDC fuel injections. PM characterization included 28 measurements of size and composition of individual particles sampled directly from the exhaust and after mass and/or mobility classification. We use these data to calculate particle effective density, fractal dimension, dynamic shape factors in free-molecular and transition flow regimes, average diameter of primary spherules, number of spherules, and void fraction of soot agglomerates.