| 1. |
- Zhang, F. P., et al.
(författare)
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Lack of androgen receptor SUMOylation results in male infertility due to epididymal dysfunction
- 2019
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Androgen receptor (AR) is regulated by SUMOylation at its transactivation domain. In vitro, the SUMOylation is linked to transcriptional repression and/or target gene-selective regulation. Here, we generated a mouse model (ArKl) in which the conserved SUMO acceptor lysines of AR are permanently abolished (Ar-K381R, (K500R)) ArKl males develop normally, without apparent defects in their systemic androgen action in reproductive tissues. However, the ArKl males are infertile. Their spermatogenesis appears unaffected, but their epididymal sperm maturation is defective, shown by severely compromised motility and fertilization capacity of the sperm. Fittingly, their epididymal AR chromatin-binding and gene expression associated with sperm maturation and function are misregulated. AR is SUMOylated in the wild-type epididymis but not in the testis, which could explain the tissue-specific response to the lack of AR SUMOylation. Our studies thus indicate that epididymal AR SUMOylation is essential for the post-testicular sperm maturation and normal reproductive capability of male mice.
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| 2. |
- Franken, T., et al.
(författare)
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Advanced Predictive Diesel Combustion Simulation Using Turbulence Model and Stochastic Reactor Model
- 2017
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Ingår i: SAE Technical Papers. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International. - 0148-7191 .- 2688-3627. ; 2017-March:March
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Tidskriftsartikel (refereegranskat)abstract
- Today numerical models are a major part of the diesel engine development. They are applied during several stages of the development process to perform extensive parameter studies and to investigate flow and combustion phenomena in detail. The models are divided by complexity and computational costs since one has to decide what the best choice for the task is. 0D models are suitable for problems with large parameter spaces and multiple operating points, e.g. engine map simulation and parameter sweeps. Therefore, it is necessary to incorporate physical models to improve the predictive capability of these models. This work focuses on turbulence and mixing modeling within a 0D direct injection stochastic reactor model. The model is based on a probability density function approach and incorporates submodels for direct fuel injection, vaporization, heat transfer, turbulent mixing and detailed chemistry. The advantage of the probability density function approach compared to mean value models is its capability to account for temperature and mixture inhomogeneities. Therefore, notional particles are introduced each with its own temperature and composition. The particle condition is changed by mixing, injection, vaporization, chemical reaction and heat transfer. Mixing is modeled using the one-dimensional Euclidean minimum spanning tree mixing model, which requires the scalar mixing frequency as input. Therefore, a turbulence model is proposed to calculate the mixing time depending on turbulent kinetic energy and its dissipation. The turbulence model accounts for density, swirl, squish and injection effects on turbulent kinetic energy within the combustion chamber. Finally, the 0D stochastic reactor model is tested for 40 different operating points distributed over the whole engine map. The results show a close match of experimental heat release rate and NOx emissions. The trends of measured CO and HC concentrations are captured qualitatively. Additionally, the 0D simulation results are compared to more detailed 3D CFD combustion simulation results for three operating points differing in engine speed and load. The comparison shows that the 0D stochastic reactor model is able to capture turbulence effects on local temperature and mixture distribution, which in turn affect NOx, CO and HC emission formation. Overall, the 0D stochastic reactor model has proven its predictive capability for the investigated diesel engine and can be assigned to tasks concerning engine map simulation and parameter sweeps.
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| 3. |
- Franken, T., et al.
(författare)
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Multi-Objective Optimization of Fuel Consumption and NOx Emissions with Reliability Analysis Using a Stochastic Reactor Model
- 2019
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Ingår i: SAE Technical Papers. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International. - 0148-7191 .- 2688-3627. ; 2019-April:April
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Tidskriftsartikel (refereegranskat)abstract
- The introduction of a physics-based zero-dimensional stochastic reactor model combined with tabulated chemistry enables the simulation-supported development of future compression-ignited engines. The stochastic reactor model mimics mixture and temperature inhomogeneities induced by turbulence, direct injection and heat transfer. Thus, it is possible to improve the prediction of NOx emissions compared to common mean-value models. To reduce the number of designs to be evaluated during the simulation-based multi-objective optimization, genetic algorithms are proven to be an effective tool. Based on an initial set of designs, the algorithm aims to evolve the designs to find the best parameters for the given constraints and objectives. The extension by response surface models improves the prediction of the best possible Pareto Front, while the time of optimization is kept low. This work presents a novel methodology to couple the stochastic reactor model and the Non-dominated Sorting Genetic Algorithm. First, the stochastic reactor model is calibrated for 10 low, medium and high load operating points at various engine speeds. Second, each operating point is optimized to find the lowest fuel consumption and specific NOx emissions. The optimization input parameters are the temperature at intake valve closure, the compression ratio, the start of injection, the injection pressure and exhaust gas recirculation rate. Additionally, it is ensured that the maximum peak cylinder pressure and turbine inlet temperature are not exceeded. This enables a safe operation of the engine and exhaust aftertreatment system under the optimized conditions. Subsequently, a reliability analysis is performed to estimate the effect of off-nominal conditions on the objectives and constraints. The novel multi-objective optimization methodology has proven to deliver reasonable results. The zero-dimensional stochastic reactor model with tabulated chemistry is a fast running physics-based model that allow to run large optimization problems in a short amount of time. The combination with the reliability analysis also strengthens the confidence in the simulation-based optimized engine operation parameters.
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| 4. |
- Franken, T., et al.
(författare)
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Multi-objective optimization of water injection in spark-ignition engines using the stochastic reactor model with tabulated chemistry
- 2019
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Ingår i: International Journal of Engine Research. - : SAGE Publications. - 1468-0874 .- 2041-3149. ; 20:10, s. 1089-1100
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Tidskriftsartikel (refereegranskat)abstract
- Water injection is investigated for turbocharged spark-ignition engines to reduce knock probability and enable higher engine efficiency. The novel approach of this work is the development of a simulation-based optimization process combining the advantages of detailed chemistry, the stochastic reactor model and genetic optimization to assess water injection. The fast running quasi-dimensional stochastic reactor model with tabulated chemistry accounts for water effects on laminar flame speed and combustion chemistry. The stochastic reactor model is coupled with the Non-dominated Sorting Genetic Algorithm to find an optimum set of operating conditions for high engine efficiency. Subsequently, the feasibility of the simulation-based optimization process is tested for a three-dimensional computational fluid dynamic numerical test case. The newly proposed optimization method predicts a trade-off between fuel efficiency and low knock probability, which highlights the present target conflict for spark-ignition engine development. Overall, the optimization shows that water injection is beneficial to decrease fuel consumption and knock probability at the same time. The application of the fast running quasi-dimensional stochastic reactor model allows to run large optimization problems with low computational costs. The incorporation with the Non-dominated Sorting Genetic Algorithm shows a well-performing multi-objective optimization and an optimized set of engine operating parameters with water injection and high compression ratio is found.
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| 5. |
- Hosia, A., et al.
(författare)
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Experimental feeding rates of gelatinous predators Aurelia aurita and Mnemiopsis leidyi at low northern Baltic Sea salinity
- 2012
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Ingår i: Boreal Environment Research. - 1239-6095. ; 17:6, s. 473-483
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Tidskriftsartikel (refereegranskat)abstract
- The effect of the northern Baltic Sea's low salinity on feeding rates of a native scyphozoan Aurelia aurita and a recent invader to southern Baltic Sea, ctenophore Mnemiopsis was investigated experimentally. Incubations with Acartia spp. prey (4.19-25.16 indiv. l(-1)) were used to estimate clearance rates for both predators. Mnentiopsis leidyi digestion times were measured for several natural prey items. Wet weight (ww):length/diameter relationships as well as clearance rates (0.49 +/- 0.15 1 g(ww)(-1) h(-1) [mean +/- SE] for M. leidyi [mean oral-aboral length +/- SD = 9.6 +/- 1.5 mm]; and 0.18 +/- 0.07 1 g(ww)(-1) h(-1) [mean +/- SE] for A. aurita [mean bell diameter SD = 37.3 +/- 6.9 mm]) and digestion times at salinity 5.7 were within the ranges reported from higher salinities. These preliminary results suggest that the low salinity does not significantly depress the feeding rates or potential predatory impact of these gelatinous predators.
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| 6. |
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| 7. |
- Matrisciano, Andrea, 1986, et al.
(författare)
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Development of a Computationally Efficient Progress Variable Approach for a Direct Injection Stochastic Reactor Model
- 2017
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Ingår i: SAE Technical Papers. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International. - 0148-7191 .- 2688-3627. ; 2017-March:March
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Tidskriftsartikel (refereegranskat)abstract
- A novel 0-D Probability Density Function (PDF) based approach for the modelling of Diesel combustion using tabulated chemistry is presented. The Direct Injection Stochastic Reactor Model (DI-SRM) by Pasternak et al. has been extended with a progress variable based framework allowing the use of a pre-calculated auto-ignition table. Auto-ignition is tabulated through adiabatic constant pressure reactor calculations. The tabulated chemistry based implementation has been assessed against the previously presented DI-SRM version by Pasternak et al. where chemical reactions are solved online. The chemical mechanism used in this work for both, online chemistry run and table generation, is an extended version of the scheme presented by Nawdial et al. The main fuel species are n-decane, α-methylnaphthalene and methyl-decanoate giving a size of 463 species and 7600 reactions. A single-injection part-load heavy-duty Diesel engine case with 28 % EGR fueled with regular Diesel is investigated with both tabulated and online chemistry. Comparisons between the two approaches are presented by means of overall engine performance and engine-out emission predictions and in equivalence ratio-temperature space. The new implementation delivers reasonably good agreement with the online chemistry one. The methodology presented in this paper allows for the use of detailed chemistry in the DI-SRM with high computational efficiency and thus facilitates the use of the DI-SRM in the engine development process.
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