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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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381 records · Page 22

Development of simplified model for injection rate prediction of diesel injectors during transient and steady operation

Determining the injection rate of the diesel injector during the transient short injection operations is a critical matter to enhance the combustion process by improving air/fuel mixture formation. In this regard, numerous injection models have been suggested in the literature to predict the injection rate accurately. However, such algorithms have not precisely predicted the rate of injection (ROI) in transient short injection events. In addition, most of those models require many input parameters to characterize hydraulic and mechanical subsystems in the injector, which can make them inconvenient to tune for different injector specifications, in other words not suitable for universal usage. Therefore, our study aims to develop a simple injection model that can predict injection rate precisely in the short and long injection durations considering relatively fewer input parameters. The model traces the pressure variation inside the sac based on the bulk modulus theory to predict nozzle exit velocity, thus the injection rate of the injector. In the algorithm, the bulk modulus of fuel (E) and discharge coefficients of the nozzle hole and needle seat (C d,H and C d,N ) have been considered to vary rather than set constant during the injection process since such flow parameters would have a critical impact on the flow development at the transient stage, thus affecting the injection rate of diesel injectors. Here with this modelling strategy, the accuracy of the new model on ROI prediction was confirmed with acceptable model error for both short and long injection durations by validating against experimental results. To understand the effect of the new model scheme on ROI prediction, ROI results with the new model have been discussed by comparing it with the ROI results predicted with constant flow parameters.

42 ENGINEERING↗

High-dimensional Data-driven Energy optimization for Multi-Modal Transit Agencies (HD-EMMA) (Final Technical Report)

Public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO 2 emission annually. Electric vehicles (EVs) can have a much lower environmental impact than comparable internal combustion engine vehicles (ICEVs), especially in urban areas. Unfortunately, EVs are also much more expensive than ICEVs. As a result, many public transit agencies can afford only mixed fleets of transit vehicles, consisting of EVs, hybrids (HEVs), and ICEVs. Transit agencies that operate such mixed fleets of vehicles face a challenging optimization problem: these agencies need to decide which vehicles are assigned to serving which transit trips. Since the advantage of EVs over ICEVs varies depending on the route and time of day (e.g., the benefit of EVs is higher in slower traffic with frequent stops and lower on highways), the assignment can have a significant effect on energy use and, hence, environmental impact. Through this project, we have developed reference data about energy collections and constructed a set of machine learning models that can accurately predict the energy consumption for the whole fleet at the level of each trip. We have used these models to develop a scheduling and assignment strategy that can rotate the different vehicle types across the transit agencies’ routes. The optimization algorithm ensures that the vehicles are matched to trips considering weather patterns, expected congestion, and road gradients to minimize the overall energy usage. We list the key observations from our project for other practitioners below. Details are available in the report, and the list of source code and our publications are included in the appendix. 1. We have demonstrated the feasibility of collecting, merging and analyzing large volumes of high-resolution real-world telemetry data from a mixed vehicle fleet. To mitigate the inherent noise of the recorded GPS points, the team developed an algorithm that filters data and maps the points onto a street. The algorithm considers previous and subsequent location measurements and different characteristics of nearby streets to determine how likely the vehicle travels on them. Then, the team segmented the time series into disjoint contiguous samples based on adjacent road segments and repeated the outlier detection and removal. For each data point, the team added features corresponding to elevation changes within the samples, weather features, such as temperature, and traffic data, such as speed ratio between actual speed and free-flow speed. 2. We have developed two forms of machine learning models that be used to understand and analyze the energy operations of a mixed vehicle transit fleet. The micro prediction model provides estimates of instantaneous energy prediction for all types of buses (diesel, hybrid, and electric). Such a model is important in evaluating the energy impacts of real-time bus operation strategies, but it is challenging due to diversified driving cycles of transit buses. The model can help the drivers understand the impact of their driving behaviors and short-term congestions. The macro prediction models estimate average energy consumption across the whole trip considering the features: distance traveled, various road-type features, elevation change, day of the week, time of day, various weather features (temperature, humidity, etc.), and traffic features (speed ratio and jam factor). 3. We have demonstrated that it is possible to transfer the machine learning models we have developed in this project to other teams and cities by using inductive transfer learning. We also showed that the performance of the macro energy prediction models can be improved using a multi-task learning approach where the learning parameters are shared between the models being developed for different vehicle types. The advantage of this approach is improved learning performance as the models can exploit common spatio-temporal and environmental characteristics. 4. Finally, we have developed trip and vehicle assignment and scheduling algorithms that use the energy prediction models and develop a trip to vehicle type (diesel, electric, hybrid) assignment for the whole operation to reduce overall emissions and cost. We have shown through simulations that the proposed algorithms can save $\$$ 48,910 in energy costs and 175 metric tons of CO 2 emission annually for CARTA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Physical, Chemical, and Mineralogical Characterizations of MSWI Ash Product and Recommendations for Downstream Processing

The primary objectives of this project are to (1) systematically characterize MSWI ash, and (2) based on characterization findings, design preliminary flowsheets for downstream processing. To achieve these objectives, a total of ten tasks were completed, including sample collection, physical separation tests, liberation tests, synthetic MSWI ash preparation, elemental composition analysis, sequential chemical extraction, mineralogical characterization, pozzolanic activity characterization, thermal stability characterization, processing flowsheet design, TEA and T2M, and project performance reporting. Many useful findings and conclusions were obtained from the exhaustive efforts of this project from several different aspects, including: a) Valuable Metals in MSWI Ash: MSWI ash contains a diverse array of valuable metals. Based on potential recoverable values, the most valuable metals present in MSWI ash include Fe, Ti, Mn, Cu, Zn, V, Co, Ni, Sr, Sn, Ag, Mo, and Sc. Some of these metals have been identified as critical minerals by DOE and DOI, suggesting that MSWI is a promising feedstock for critical mineral recovery. Noticeable graphical and seasonable variations in the valuable metal content of MSWI ash were observed. Nevertheless, it was challenging to discern any clear, definitive patterns for conclusions from those observations. Compared with bottom ash, fly ash contains more volatile metals, such as Zn and Sn, but less nonvolatile metals, such as Fe, Mn, Cu, Zn, Co, and Ni. Mineralogical analyses showed that MSWI ash contains a substantial amount of calcium minerals, such as portlandite, lime, gypsum, and calcite. In addition, it was found that different types of valuable metals often exist in the same particles. b) Physical Separation of MSWI Ash: Both dry sieving and wet sieving were performed on MSWI ash. A notable disparity in the size distribution of the same material was observed when using the two different sieving methods. The disparity is due to the agglomeration of small particles. For the valuable metals investigated, no significant enrichment in a specific size fraction was observed, suggesting that it is challenging to preconcentrate the valuable metals through size fractionation. Due to the presence of ferromagnetic materials, such as Fe, most of the materials reported to the magnetic products obtained by dry magnetic separation. However, the enrichment effect is minimal due to the existence of particle agglomerates. Density separation at a cut-off density of 2.7 SG or higher led to noticeable enrichment of selected valuable metals, particularly Ti. The unburned carbon present in MSWI ash was effectively removed by flotation using diesel as the collector. A novel reagent scheme, Na2S plus cationic collectors, that can efficiently beneficiate nonferrous metals plus Co was developed. c) Liberation Tests: The particle size of MSWI ash was effectively reduced by grinding, and as a result, the encapsulated valuable metal particles (if any) were liberated to a certain degree. However, particle size reductions did not noticeably enhance the beneficiation performance using the physical separation methods, primarily due to the inefficiency of these methods in processing fine particles and/or a possibility that insufficient liberation is not a limiting factor for achieving satisfactory physical separation performance. Valuable metals were classified into water leachable, ion-exchangeable, acid soluble, reducible, oxidable, and insoluble forms. It was found that the distributions in the different categories, i.e., the occurrence modes of the valuable metals, were not affected by the particle size. d) Leaching Characteristics of Metals from MSWI Ash: Most of the valuable metals were extracted from the fly ash samples when using 1 M HCl or HNO3 as the lixiviant. The leaching reaction is a very fast process, which can reach equilibrium within the first 5 min. The releasing of Co, Ni and Ag are sensitive to leaching temperature, a higher recovery value could be obtained when using relatively higher leaching temperatures. The leachability of the valuable metals present in MSWI bottom ash is relatively lower than that of fly ash. Leaching recoveries increased with elevations in the acid concentration. Relatively high leaching recoveries were obtained for REEs, Mn, Co, Ni, Cu, and Zn using 1 M HCl or HNO3 as the lixiviant. Elevations in the reaction temperature noticeably increased the leachability of the valuable metals, whereas the leachability was barely influenced by oxidizing and reducing agents. Similar to fly ash, leaching valuable metals from bottom ash is a rapid process, with most of the leaching reaction completed within the first 5 minutes. e) Combusted iPhones: The original structure of iPhones was remained after treating at 400 ºC and 600 ºC, while after being treated at 800℃, the screen bent, and the back cover of iPhone melted. Increasing the combustion temperature to 1000℃, the screen scattered, and most of the components turned into ashes. Combustion enhanced the leachability of REEs, while the leachability of the other valuable metals, except for Zn, was barely affected. Most of the REEs present in the original iPhones occurred as oxidizable forms. With elevations in the combustion temperature up to 600 ºC, the oxidizable REEs were transformed to acid soluble forms. However, further elevations in temperature resulted in decreases in the acid soluble fraction and corresponding increases in the reducible and oxidizable forms. Additionally, combustion temperature also significantly altered the occurrence modes of other metals present in the iPhones. f) Synthetic MSWI Ash: It was found that in the absence of hydrogen peroxide, all the elements except for Si were leached to certain degrees. It is noteworthy that approximately 80% of Zn was leached with 1.2 M HCl. When hydrogen peroxide was added to the reaction system, noticeable increases in the leaching recovery of Fe, Mn, Co, Ni, and Cu were observed. The leaching recovery of Al and Si was barely affected by adding hydrogen peroxide. These results suggested that the majority of Zn in the synthetic MSWI ash existed as metal oxide, a portion of Fe, Mn, Co, Ni, and Cu existed as metal oxide, and Al and Si are associated with glasses which are difficult to leach. Additionally, the remaining Fe, Mn, Co, Ni, and Cu in the metallic form were efficiently oxidized in the presence of hydrogen peroxide. g) Pozzolanic Activity and Thermal Stability of MSWI Ash: MSWI fly ash has higher pozzolanic activity compared to the bottom ash sample, which indicates that the fly ash sample consumed more portlandite because of its smaller particle size as reactivity fundamentally relates to reaction surface area. However, after the recovery of valuable elements, the pozzolanic activity of both the valuable elements fraction and the less valuable elements-rich products decreased significantly, which means that the valuable elements recovery lowers the Ca(OH)2 consumption, thus leading to the low activity of SCM. h) Flowsheet Design for Metal Recovery from MSWI Ash: Based on the results of the comprehensive physical separation and acid leaching tests, circuits that enable the beneficiation of the valuable metals were developed. In these circuits, the valuable metals are recovered into nonferrous, ferrous, and other valuable metal concentrates, which are processed separately in the acid leaching step. The subsequent separation and purification steps are simplified due to the physical beneficiation step. In addition, the overall recovery cost is reduced since physical beneficiation is much cheaper compared with chemical processing. Using different technologies, such as selective precipitation and solvent extraction, a comprehensive hydrometallurgical circuit was designed, and compounds of Cu, Zn, Mn, Co, and Ni with a purity close to or even higher than 95% were successfully generated.

36 MATERIALS SCIENCE↗