Person: BULUT, EMRE
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BULUT
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EMRE
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Publication The investigation of the effects of spray parameters on the thermal and mechanical properties of 22MnB5 steel during hybrid quenching process(Begell House, 2021-01-01) Sevilgen, Gökhan; Ertan, Rukiye; Bulut, Emre; Öztürk, Ferruh; Eşiyok, Ferdi; Abi, Tuğçe Turan; Alyay, İlhan; SEVİLGEN, GÖKHAN; ERTAN, RUKİYE; BULUT, EMRE; ÖZTÜRK, FERRUH; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü.; 0000-0002-7746-2014; 0000-0001-9159-5000; ABG-3444-2020; KIH-2391-2024; AAG-8907-2021; JIW-7185-2023In this paper, the investigation of the effects of spray parameters on the thermal and mechanical properties of 22MnB5 steel during the hybrid quenching (HQ) process was performed. The HQ method presented in this study includes early removal of hot-stamped parts from the die and transfer to an external multinozzle spray cooling device. The proposed method was developed due to the need for improving disadvantages of the traditional hot stamping process such as complexity of controlling the cooling rate during die quenching by using cooling channels and of providing the reduced tool contact surface temperature after a certain cycle of the hot stamping process. This paper focuses on the temperature distribution and mechanical characteristics of high-strength 22MnB5 steel during the HQ process. Moreover, the methodology developed in this paper can be used to get tailored parts where the cooling rates are locally chosen to achieve structures with graded properties, i.e., to allow local modification of final mechanical properties in order to provide high energy absorption to enhance the crashworthiness of the whole component and thus to improve the vehicle safety performance. The three-dimensional numerical model of spray cooling was also developed by using the computational fluid dynamics (CFD) method to get the suitable process parameters such as spray height and initial blank temperature and to present the detailed heat transfer analysis of hot-stamped parts during the hybrid quenching process.Publication A new approach for battery thermal management system design based on grey relational analysis and latin hypercube sampling(Elsevier, 2021-09-16) Bulut, Emre; Albak, Emre İsa; Sevilgen, Gökhan; Öztürk, Ferruh; BULUT, EMRE; ALBAK, EMRE İSA; SEVİLGEN, GÖKHAN; ÖZTÜRK, FERRUH; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü; Bursa Uludağ Üniversitesi/Gemlik Asım Kocabıyık Meslek Yüksekokulu/Hibrit ve Elektrikli Araç Teknolojisi; 0000-0001-9159-5000; 0000-0001-9215-0775; 0000-0002-7746-2014; ABG-3444-2020; AAG-8907-2021; I-9483-2017; JCO-2416-2023; FRD-1816-2022A liquid cooling system is an effective type of battery cooling system on which many studies are presented nowadays. In this research, the effects of the mass flow rate and number of channels on the maximum temperature and pressure drop are investigated for multi-channel serpentine cooling plates. A new approach with LHS and GRA is used to obtain the optimum ranges of design parameters to minimize the pressure drop, maximum temperature and to maximize the convective heat transfer coefficient. In this study, the values of the parameters for the numerical modeling are obtained by the experiments. The width and height of the serpentine channel and mass flow rate are chosen as input parameters and the pressure drop, convective heat transfer coefficient and maximum temperature are selected as output parameters. Comparing with the base design, the optimized design provided up to 40.3% decrease in the pressure drop with a penalty of 11.3% decrease in the convective heat transfer coefficient with a slight decrease in the maximum temperature. The proposed approach can be used to design better cooling plates to keep the batteries in safe temperature ranges and to reduce the power consumption by optimizing the pressure drop and maximum temperature values.Publication Estimation of energy management strategy using neural-network-based surrogate model for range extended vehicle(Mdpi, 2022-12-14) Türker, Erkan; Bulut, Emre; Kahraman, Arda; Çakıcı, Mehmet; Öztürk, Ferruh; Türker, Erkan; BULUT, EMRE; ÖZTÜRK, FERRUH; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü.; 0000-0001-9159-5000; 0000-0003-0150-8052; JCO-2416-2023; AAG-8907-2021; CDI-5654-2022; JIW-7185-2023In this paper, an energy-management strategy based on fuel economy is presented to achieve a further range increase for range-extended light commercial vehicles. Estimation of the energy-management strategy was carried out using a neural-network-based surrogate model for an range-extended vehicle. Surrogate-based optimization plays an important role in optimization problems, which are based on complex structures with uncertainties in data sets due to various conditions. Neural networks have advantages in creating surrogate-based models in cases of complex problems with uncertainties in data sets to evaluate the process and estimate the outputs. This study discusses additional power-unit applications and vehicle integration for a light commercial electric vehicle. It provides preliminary design work and techniques for identifying NVH problems in particular. SIMULINK and neural-network-based surrogate models are established, and the changeable parameters of the vehicle, such as mass, battery/fuel-tank capacity, internal combustion engine power and electric motor power units are simulated in different dynamic and static conditions to determine an energy-management strategy for a range-extended vehicle based on fuel economy under various conditions. It was seen that APU parameters and an energy-management strategy significantly affected the fuel consumption of REX. A neural-network-based surrogate-model approach gave high-precision results in predicting the operating strategy according to different loading conditions to reduce fuel consumption. In some cases, it can be required to determine the fuel consumption results in various conditions with the variables, which may be out-of-boundary conditions. It was seen that the proposed neural-network-model also offers higher prediction ability in cases of unexpected results in data sets of various conditions compared to regression analysis. The results show that estimation and optimization of energy management using a neural-network-based surrogate model can be achieved by adapting the operating strategy according to different loading conditions to reduce fuel consumption. This study presents an approach for future new vehicle projects by transforming a prototype light commercial electric vehicle to REX. The proposed approach was developed to design the most efficient range-extended vehicle by changing all variables without costly computations and time-consuming analysis. It is possible to generate variable data sets and to have reference knowledge for future vehicle projects.