Blend Prediction Model for Freezing Point of Jet Fuel Range Hydrocarbons

Sustainable aviation fuels are a near-term solution for aviation greenhouse gas emission reduction. To become a sustainable aviation fuel, a synthetic fuel derived from a renewable source must have specifications written into ASTM D7566 as an annex to regulate its quality. However, before a sustainable aviation fuel can be added, it must be thoroughly evaluated and approved by all stakeholders through an time and volume intensive, as well as expensive process described in ASTM D4054. For this reason, the prescreening process is being developed. Prescreening is a process to measure or predict, from very small sample volumes, key fuel properties that are crucial for operability of an aircraft. The intention of the prescreening process is to inform suppliers of possible risks to passing the evaluations of ASTM D4054. Freezing point is one of the critical safety stipulations that require fuel to remain in liquid state under severe weather conditions. Methods to predict the freezing point of hydrocarbon blends are scarce in current literature. These pre-existing blend prediction models are either not validated within the typical temperature range for jet fuel standards, or they contain an interaction coefficient which is only obtained experimentally. The goal of this study is to develop a blending rule to accurately predict the freezing point of combinations of jet fuel range hydrocarbons. To do so, blends of hydrocarbons with freezing points varying from one another were tested. Binary and ternary blends containing bicyclohexyl, cis-1,2-dimethylcyclohexane, and an alternative jet fuel (POSF 12968) were tested along with separate tests including binary and ternary blends of tridecane, cis-1,2-dimethylcyclohexane, and trans-decahydronaphthalene. The experimental values obtained were compared with linear predictive blending model results. A new model based on Gibbs free energy is reliable for neat molecules, however, is currently being developed to predict the freezing point of hydrocarbon blends.https://ecommons.udayton.edu/stander_posters/3755/thumbnail.jp

Determination of a Freeze Point Blending Rule for Jet Fuel Range Hydrocarbons

Sustainable aviation fuels are the near term solution for greenhouse gas emission reduction associated with the aviation sector. There are extensive safety requirements established by an ASTM committee that the alternative aviation fuel must meet in order to achieve approval. Freeze point is one of the safety requirements that allow fuel to remain in liquid state under severe weather conditions. Methods and models to predict the freeze point of hydrocarbon blends are scarce in current literature. In the model that is currently being used, the validated temperature range for freeze point prediction is higher than the typical range for the jet fuel hydrocarbons. For other existing prediction models, an interaction coefficient determined by an experimental result is needed in the calculation to improve the accuracy of the prediction. The goal of this study is to develop an accurate freeze point blending rule for the jet fuel range hydrocarbons to evaluate eligibility for sustainable aviation fuel purposes. Here, a wide range of hydrocarbons with various freeze points were tested. Binary and ternary blends containing Bicyclohexyl, cis1-2 Dimethylcyclohexane, and an alternative jet fuel were tested. The experimental values obtained from varying compositions of each component for the binary and ternary blends were compared with linearly predicted values by volume percent and mole percent. While the linear prediction was comparable to the experimental values, there is still an aspect hindering more accurate predictions. The speculated missing aspect is the molecular structure. From other sources, it is known that molecules with the same chemical composition but varying structure can exhibit starkly different freezing points. Due to this, further testing is being conducted on molecules with these traits.https://ecommons.udayton.edu/stander_posters/3373/thumbnail.jp

La\u3csub\u3e0.7\u3c/sub\u3eSr\u3csub\u3e0.3\u3c/sub\u3eFe\u3csub\u3e0.7\u3c/sub\u3eGa\u3csub\u3e0.3\u3c/sub\u3eO\u3csub\u3e3-δ\u3c/sub\u3e as Electrode Material for a Symmetrical Solid Oxide Fuel Cell

In this research, La0.7Sr0.3Fe0.7Ga0.3O3−δ (LSFG) perovskite oxide was successfully prepared using a microwave-assisted combustion method, and employed as both anode and cathode in symmetrical solid oxide fuel cells. A maximum power density of 489 mW cm−2 was achieved at 800 °C with wet H2 as the fuel and ambient air as the oxidant in a single cell with the configuration LSFG|La0.8Sr0.2Ga0.83Mg0.17O3−δ|LSFG. Furthermore, the cells demonstrated good stability in H2 and acceptable sulfur tolerance

Towards a verified compiler prototype for the synchronous language SIGNAL

International audienceSIGNAL belongs to the synchronous languages family which are widely used in the design of safety-critical real-time systems such as avionics, space systems, and nuclear power plants. This paper reports a compiler prototype for SIGNAL. Compared with the existing SIGNAL compiler, we propose a new intermediate representation (named S-CGA, a variant of clocked guarded actions), to integrate more synchronous programs into our compiler prototype in the future. The front-end of the compiler, i.e., the translation from SIGNAL to S-CGA, is presented. As well, the proof of semantics preservation is mechanized in the theorem prover Coq. Moreover, we present the back-end of the compiler, including sequential code generation and multithreaded code generation with time-predictable properties. With the rising importance of multi-core processors in safety-critical embedded systems or cyber-physical systems (CPS), there is a growing need for model-driven generation of multithreaded code and thus mapping on multi-core. We propose a time-predictable multi-core architecture model in architecture analysis and design language (AADL), and map the multi-threaded code to this model

Ni-Doped Sr\u3csub\u3e2\u3c/sub\u3eFe\u3csub\u3e1.5\u3c/sub\u3eMo\u3csub\u3e0.5\u3c/sub\u3eO\u3csub\u3e6-δ\u3c/sub\u3e as Anode Materials for Solid Oxide Fuel Cells

10% Ni-doped Sr2Fe1.5Mo0.5O6-δ with A-site deficiency is prepared to induce in situ precipitation of B-site metals under anode conditions in solid oxide fuel cells. XRD, SEM and TEM results show that a significant amount of nano-sized Ni-Fe alloy metal phase has precipitated out from Sr1.9Fe1.4Ni0.1Mo0.5O6-δ upon reduction at 800◦C in H2. The conductivity of the reduced composite reaches 29 S cm−1 at 800◦C in H2. Furthermore, fuel cell performance of the composite anode Sr1.9Fe1.4Ni0.1Mo0.5O6-δ-SDC is investigated using H2 as fuel and ambient air as oxidant with La0.8Sr0.2Ga0.87Mg0.13O3 electrolyte and La0.6Sr0.4Co0.2Fe0.8O3 cathode. The cell peak power density reaches 968 mW cm−2 at 800◦C and the voltage is relatively stable under a constant current load of 0.54 A cm−2. After 5 redox cycles of the anode at 800◦C, the fuel cell performance doesn’t suffer any degradation, indicating good redox stability of Sr1.9Fe1.4Ni0.1Mo0.5O6-δ. Peak power density of 227 mW cm−2 was also obtained when propane is used as fuel. These results indicate that a self-generated metal-ceramic composite can been successfully derived from Sr2Fe1.5Mo0.5O6-δ by compositional modifications and Sr1.9Fe1.4Ni0.1Mo0.5O6-δ is a very promising solid oxide fuel cell anode material with enhanced catalytic activity and inherited good redox stability from the parent ceramic material

La\u3csub\u3e0.6\u3c/sub\u3eSr\u3csub\u3e1.4\u3c/sub\u3eMnO\u3csub\u3e4+δ\u3c/sub\u3e Layered Perovskite Oxide: Enhanced catalytic Activity for the Oxygen Reduction Reaction

Efficient electrocatalysts for the oxygen reduction reaction (ORR) is a critical factor to influence the performance of lithium–oxygen batteries. In this study, La0.6Sr1.4MnO4+δ layered perovskite oxide as a highly active electrocatalyst for the ORR has been prepared, and a carbon-coating layer with thickness \u3c5 nm has been successfully introduced to enhance the electronic conductivity of the as-prepared oxide. XRD, XPS, Raman, SEM and TEM measurements were carried out to characterize the crystalline structure and morphology of these samples. Rotating ring-disk electrode (RRDE) technique has been used to study catalytic activities of the as-prepared catalysts for the ORR in 0.1 M KOH media. RRDE results reveal that carbon-coated La0.6Sr1.4MnO4+δ exhibits better catalytic activity for the ORR. For the carbon-coated La0.6Sr1.4MnO4+δ, the ORR proceeds predominately via a direct four electron process, and a maximum cathodic current density of 6.70 mA cm−2 at 2500 rpm has been obtained, which is close to that of commercial Pt/C electrocatalyst under the same testing conditions

AADLib, A Library of Reusable AADL Models

The SAE Architecture Analysis and Design Language is now a well-established language for the description of critical embedded systems, but also cyber-physical ones. A wide range of analysis tools is already available, either as part of the OSATE tool chain, or separate ones. A key missing elements of AADL is a set of reusable building blocks to help learning AADL concepts, but also experiment already existing tool chains on validated real-life examples. In this paper, we present AADLib, a library of reusable model elements. AADLib is build on two pillars: 1/ a set of ready-to- use examples so that practitioners can learn more about the AADL language itself, but also experiment with existing tools. Each example comes with a full description of available analysis and expected results. This helps reducing the learning curve of the language. 2/ a set of reusable model elements that cover typical building blocks of critical systems: processors, networks, devices with a high level of fidelity so that the cost to start a new project is reduced. AADLib is distributed under a Free/Open Source License to further disseminate the AADL language. As such, AADLib provides a convenient way to discover AADL concepts and tool chains, and learn about its features

High Performance Low Temperature Solid Oxide Fuel Cells with Novel Electrode Architecture

In this study, we have fabricated high performance low temperature solid oxide fuel cells (LT-SOFCs) with both acicular anodes and cathodes with thin Gd-doped ceria (GDC) electrolyte film. The acicular Ni-Gd0.1Ce0.9O2−δ (Ni-GDC) anode was prepared using freeze drying tape casting, while the hierarchically porous cathode with nano-size Sm0.5Sr0.5CoO3 (SSC) particles covering an acicular GDC skeleton was prepared by a combination of freeze drying tape casting and self-rising approaches. The acicular electrodes with 5–200 μm pores/channels enhance mass transport, while SSC particles of about 50 nm in the cathode promote electrochemical reactions. Cells which have this novel electrode architecture show a significantly high power output of 1.44 W cm−2 and an extremely low cell polarization resistance of 0.0379 Ω cm2 at 600 °C

Experimental Validation of Low Temperature Viscosity Predictions for Sustainable Aviation Fuel Blends

With the rise of focus and funding in sustainable initiatives, the transportation sector has identified Sustainable Aviation Fuels (SAFs) as a response to reduce carbon dioxide and other greenhouse gas outputs into the atmosphere. Before SAFs can be used by airlines, they have to pass an approval process to make sure fuels operate within industry standards. The approval processes is very time and material expensive. To lower overall costs to this process, a pre-screening process has been developed to predict physical and chemical properties of the prospective fuels. Viscosity has been identified as one of the key properties as it lends itself to is ignition probability prediction.The focus of this study is to validate different viscosity extrapolation and blending models at low temperatures. The blends tested are ternary blends of current fuels and key molecules found within approved SAFs. Four different sets of blends were tested to see how other physical or chemical properties affect the viscosity when blended and measured at -40°C and -20°C. Of the six models tested, the Arrhenius Blending Model results in the least amount of error compared to experimental values. As molecules were introduced into the blend sets, errors increased. Overall low error suggests the utility of this blend model in property prediction. To further lower error, future work can investigate the effects of molecular size and interactions within blends.https://ecommons.udayton.edu/stander_posters/3375/thumbnail.jp

Exploring AADL verification tool through model transformation

International audienceArchitecture Analysis and Design Language (AADL) is often used to model safety-critical real-time systems. Model transformation is widely used to extract a formal specification so that AADL models can be verified and analyzed by existing tools. Timed Abstract State Machine (TASM) is a formalism not only able to specify behavior and communication but also timing and resource aspects of the system. To verify functional and nonfunctional properties of AADL models, this paper presents a methodology for translating AADL to TASM. Our main contribution is to formally define the translation rules from an adequate subset of AADL (including thread component, port communication, behavior annex and mode change) into TASM. Based on these rules, a tool called AADL2TASM is implemented using Atlas Transformation Language (ATL). Finally, a case study from an actual data processing unit of a satellite is provided to validate the transformation and illustrate the practicality of the approach