Raytheon Technologies (Italy)

The ejection of a pilot from an aircraft is a complex, multi-step process where multiple forces such as seat ejection forces, aerodynamic forces, drogue chute forces and parachute forces act on the seat-manikin system. This paper describes a modeling approach to capture the response of the seat-manikin system under these forces. The goal of the modeling methodology is to provide a numerical framework to capture manikin response, specifically the articulation of the neck and spine to assess the potential for injury to a pilot. A model of a manikin, derived from the LSTC Hybrid III manikin, along with a simplified FE model of an ejection seat were used to simulate a seat-manikin ejection process. Accelerations from test data and also from numerical code were applied to simulate 0-0 ejection events. Neck-base accelerations from this model were applied to a head-neck model in a dynamics code IMBD to predict neck injury. Simultaneously, a fluid-structure interaction model with the manikin, including visor and helmet, ejection seat and safety features was developed. Models were validated with test data and results are described. This modeling framework will be developed further to increase the fidelity of the physics of ejection.

Redox flow batteries (RFBs) are a promising grid-scale energy storage platform, but broad deployment is stymied by technical and economic constraints.1 Within this context, the membrane serves a central role in determining battery performance, selectively facilitating the conduction of supporting salts to mitigate resistive losses while preventing the undesired crossover of active species.2 Tradeoffs between these two facets—namely, conductivity and permeability—requires a thorough understanding of how transport proceeds under the dynamic conditions observed in practical embodiments (e.g., varying compositions, alternating polarity). To this end, a wide range of experimental tools exists for characterizing membrane properties under controlled environments, but few methods directly assess crossover and resistive losses in conventional redox flow cells.3 In this presentation, we introduce a facile method for quantifying crossover rates in flow cells—compositionally unbalanced symmetric cell cycling (CUSCC). Using a previously developed zero-dimensional modeling framework,4 we first examine fundamental processes that give rise to a characteristic “capacity gain” profile. We then validate the technique experimentally using FeCl2 / FeCl3 and Nafion as a representative system, confirming theoretical predictions and establishing the efficacy of CUSCC for characterizing crossover. Finally, we apply the model to estimate transport parameters from experimental capacity gain profiles, yielding results that are consistent with conventional ex situ measurements. Overall, CUSCC is a robust method for quantifying crossover rates with standard flow cell hardware, potentially expanding the experimental toolkit for RFB development. Acknowledgments This work was supported as part of the Joint Center for Energy Storage Research, an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, contract number DE-AC02-06CH11357. B.J.N gratefully acknowledges the NSF Graduate Research Fellowship Program under Grant Number 2141064. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. References M. L. Perry and A. Z. Weber, J. Electrochem. Soc., 163, A5064–A5067 (2016).J. Yuan, Z.-Z. Pan, Y. Jin, Q. Qiu, C. Zhang, Y. Zhao, and Y. Li, Journal of Power Sources, 500, 229983 (2021).Y. A. Gandomi, D. S. Aaron, J. R. Houser, M. C. Daugherty, J. T. Clement, A. M. Pezeshki, T. Y. Ertugrul, D. P. Moseley, and M. M. Mench, J. Electrochem. Soc., 165, A970 (2018).B. J. Neyhouse, J. Lee, and F. R. Brushett, J. Electrochem. Soc., 169, 090503 (2022).

The performance degradation of materials exposed to corrosive atmospheric environments is a serious problem. Corrosion maintenance strategies and cycles are informed largely by historical trends in corrosivity dependent on the location of interest. Assessments are often made with sparse experimental data and are generalized to certain materials and baseline conditions observed in the past. The development of a predictive model which can better inform maintenance strategies and cycles offers opportunities for time savings and cost avoidance. Such a model would need to predict corrosion damage accumulation as a function of material of interest, location of interest, and atmospheric conditions during the period of exposure of interest.Machine learning has recently gained attention as a way to model complex systems and processes. Atmospheric corrosion is certainly a complex process as there are many parameters that influence it which are uncontrolled and vary in difficult to predict ways. A machine learning algorithm would require data to learn from which might include environmental conditions such as temperature, relative humidity, UV light exposure, time of wetness, salt concentration, applied loads, and electrochemical data for substrates, inhibitors, and coatings. The ability of machine learning to determine models and relationships from large data sets is well demonstrated; however, a Bayesian model approach can be more suitable when data is limited, expensive or time consuming to obtain, and expert knowledge can be leveraged to construct the relationships. While certain meteorological variables are well categorized and easy to retrieve other variables like corrosion damage or salt deposition are sparsely reported if measured at all. For this reason, a Bayesian Network Model has been developed to better predict the corrosion damage accumulation of C1010 steel and three different aerospace coating systems applied over AA2024-T351.The network map was constructed from parameters (nodes) which are known to influence corrosion. Mathematical relationships were used to develop relationships between nodes or to calculate new nodes as available. Finite element analysis was used to refine the network map and provide supplemental inputs for features that are difficult to measure experimentally. The model was initially trained from historic corrosion data gathered from collaborators across the DoD. Contemporary experimental data was also gathered in a 19-site survey of field exposure sites across the world. This dataset provided further inputs for training and also data for validation and testing. The methodology behind model construction will be highlighted and model predictions will be compared against experimental results. To date, there has been good correlation between model predictions and experimental results for C1010 steel mass loss, which provides a basis to further extend the model to aerospace coating systems. Figure 1

With the ever increasing power demand for next generation aircraft, hybrid propulsion and more electrical systems, fault detection and protection are crucial to flight safety and reliable operations, especially for DC distribution network. Solid State Circuit Breakers (SSCB) using state of the art (SOA) wide bandgap (WBG) semiconductor devices are potential candidates to meet the fast action, high power density and high efficiency requirements for aerospace electrical systems. The objective of this paper is to introduce test methods to evaluate the operation and performance of these fast-acting protection devices. A literature review of the current SSCB technology is included in the introduction, followed by the design phase test criteria. Test circuits and experimental setup for both the dynamic breaking and steady state efficiency evaluations are described in detailed. Fault current interruption capability and to verify efficiency at nominal operating conditions of a medium voltage DC (MVDC) SSCB were demonstrated and verified through the test results.

View Video Presentation: https://doi.org/10.2514/6.2022-0876.vid Gas turbine hot section components leverage cooling air, usually in an impinging jet configuration, for thermal management. The heat transfer in impinging jets can be substantially impacted by the roughness characteristics of the hot surface. Surface roughness is aggravated by modern manufacturing techniques and materials, such as additive and ceramic matrix composites. The broader scope of this work aims to develop a first-principles based simulation framework for predicting flow and heat transfer characteristics of impinging jet flow including surface roughness effects and with specific focus on conditions relevant for gas turbine combustor liners. The purview of this report focuses on fully resolved simulations of impinging jets at $Re=10000$ for smooth wall with conjugate heat transfer (CHT) and non-CHT configuration, using high-order spectral element method based, GPU accelerated, code - NekRS. Initial benchmark study with a non-CHT setup and constant heat flux boundary condition is presented herein, validating NekRS simulation against prior results. Primary and secondary peaks in Nusselt number distribution along the wall are captured by the simulations, reported in prior experimental and DNS literature, the latter being owed to azimuthal distortions of primary and secondary vortices near the stagnation region, responsible for the second local maxima. CHT simulation shows a similar radial map for the Nusselt number, however the radial heat flux distribution at the solid-fluid interface is reorganized. The results from the simulations herein provide a benchmark for the proposed rough wall CHT simulation, currently underway.

Platinum alloys catalyze oxygen reduction in conventional polymer-electrolyte fuel cells [1]. The platinum is deposited on carbon, and the nature of the carbon support affects cell polarization. Two commonly used carbon blacks are Vulcan and Ketjenblack. Vulcan (V) has a relatively low surface area of (228 m2/g [2]) and ~95% of the Pt resides on the external surface [3]. Ketjenblack (KB) has a higher surface area, 891 m2/g [2], and ~60% of the Pt resides in micropores in the carbon [3]. Pt/KB displays higher catalytic activity, while Pt/V exhibits better performance at high current densities. Pt inside micropores is not in direct contact with ionomer and, consequently, is not poisoned by adsorbed anions. However, Pt inside KB micropores is relatively inaccessible to oxygen and protons, which detrimentally affects performance at high current densities [4].The area-specific impedance of catalyst layers is frequently measured by operating cells with nitrogen at the working electrode, to create a blocking electrode, and hydrogen at the counter electrode [5, 6, 7, 8]. The area-specific impedance of the catalyst layer can be determined from the difference between the maximum value of the real part of the impedance at low frequency and the intercept of the impedance with the real axis at high frequency. The area-specific resistance of a catalyst layer determined in this way is R = l⸱(3κ)-1, where l is the thickness of the catalyst layer and k is the effective conductivity of ionomer.Recently, Harzer et al. deposited platinum on Ketjenblack by different techniques [4]. They fabricated two electrodes with platinum preferentially on the external surface of carbon black, and one electrode with platinum preferentially inside micropores like commercial Pt/KB. They measured higher resistance to oxygen transport, higher values of R, and larger polarization on oxygen at high current densities on catalyst layers having platinum predominantly in micropores. They postulated that higher R was caused by a poorer distribution of ionomer. A different hypothesis is considered in this work: that ohmic losses in micropores contribute significantly to R. Firstly, a formula is developed to describe the impedance of a blocking electrode with substantial resistance inside micropores. Secondly, the model is used to interpret the experiments of Harzer et al. and estimate the conductivity of the pore solution that would fit their results. Acknowledgements: This work was funded under the Fuel Cell Performance and Durability Consortium (FC-PAD), by the Fuel Cell Technologies Office (FCTO), Office of Energy Efficiency and Renewable Energy (EERE), of the U.S. Department of Energy under contract number DE-EE0007652. This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Transport of redox active molecules across the separator in a flow battery represents an important source of inefficiency and electrolyte-imbalance-induced capacity fade [1]. This transport can occur by diffusion, migration, and convection, with driving forces that change as cells are charged and discharged. The negative electrolyte in a vanadium redox flow battery, for example, contains V2+ and V3+ in proportions that depend on state of charge, but these species are absent from the positive side, leading to ever present concentration driving forces for diffusion across the separator. Migration alternately enhances and diminishes crossover since the electric field in the membrane orients in opposite directions during charge and discharge. Finally, hydraulic and osmotic pressure differences between the positive and negative electrolytes cause solvent flow that carries redox ions.Rigorous theoretical treatment of transport across membranes in flow batteries with concentrated-solution theory is complicated by the large number of species. For example, Nafion in a vanadium redox flow battery may contain: V2+, V3+, VO2+, VO2 +, H+, HSO4 -, SO4 2-, H2O, and fixed SO3 - anions (8). The number of multicomponent diffusion coefficients required to characterize a systems is , where n is the number of species present (after accounting for species coupled by rapid chemical equilibrium). The n multicomponent diffusion coefficients define one conductivity, transference numbers or ratios, , and diffusion coefficients of neutral combinations of species. The experimental and theoretical complexities associated with the multicomponent framework when many species are present motivates exploration of the applicability of the simpler, but less generally valid dilute-solution framework. In particular, we seek to understand how well do the relationships between diffusion coefficients, transference numbers, and conductivity predicted by dilute solution theory hold in practical membranes exposed to application-relevant electrolyte solutions? Do transference numbers predicted from conductivity and permeability measurements agree with independently measured values?Figure 1 shows measured transference numbers for the four vanadium ions relevant to flow batteries in N211 and N212 as blue and red squares, respectively. The measurements were made using a cell with three membranes and four flow compartments [2, 3]. Good agreement between the two data sets is apparent. The averages of the N211 and N212 values are plotted as crosses and reported in boxes to the right of the symbols. Transference numbers for V(IV) and V(V) are approximately an order of magnitude lower than transference numbers for V(II) and V(III). The grey squares with blue and red perimeters are the transference numbers calculated from the formula using measured conductivities and permeabilities. The average calculated values are given to the left of the data symbols, to facilitate comparison with the measured values. The measured and calculated transference numbers agree in magnitude, and no systemic differences are evident. The calculated transference number for V(III) appears to be off the experimental trend with one exception: a relatively large transference number is calculated for V(III) because it has a charge number of 3. The disagreement between the measured and calculated transference numbers for V(III) could arise from ion pairing effectively lowering the charge number. The calculated transference numbers are higher for V(II) and V(III) than V(IV) and V(V) because their permeabilities are higher while their conductivity is lower. All these results indicate the value of the study: dilute solution theory accurately predicts transference numbers for all species except perhaps V(III) in Nafion, and this deviation from the model for V(III) may inform the solvated behavior of V(III) ions.AcknowledgementsThis work was supported as part of the Joint Center for Energy Storage Research, an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences. The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357.

In this paper we present a model-based methodology and a tool-chain supporting pseudo-automated code generation for different Timer Units, which represent a new approach in this field. Programmable Timer Units are timing co-processors used to elaborate complex high-resolution timing functions subject to hard real-time constraints. Verification at the different design stages, as required per safety standards’ certification, is becoming a major concern for Timer Units code development life-cycle. Enabling correct-by-construction code generation, our methodology supports code development, integration and testing across all design phases. We show how high-level functional models derived from functional requirements can be mapped onto the target architecture and how architecture-specific code can be generated. Our methodology is then applied to an automotive reference example.