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  Item type:Öğe, Erişim durumu: Açık Erişim ,

  Calibration and optimization of rans turbulence models for various cases

  (ITU Graduate School, 2026) Turutoğlu, Cem; Çadırcı, Sertaç; 503182032; Mechanical Engineering

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  Reynolds-Averaged Navier-Stokes turbulence models dominate both engineering applications and academic research due to their low computational cost and ability to deliver accurate predictions across a wide range of turbulent flows. Although high-fidelity approaches such as large-eddy simulations and direct numerical simulations can resolve a wide range of turbulent scales, their computational cost makes them impractical for most real-world problem investigations. Even though they offer intriguing alternatives, hybrid approaches like wall-modeled large-eddy simulations and Reynolds-Averaged Navier-Stokes/Large Eddy Simulation coupling still depend on turbulence modeling. Improving engineering design procedures' prediction performance in turbulent flows remains pivotal aspect today because they naturally advance from low-fidelity to high-fidelity methodologies within allowable computational resources. From this perspective, the modeling capabilities of widely-used Reynolds-Averaged Navier-Stokes turbulence models were enhanced for canonical problems in engineering applications and academic research. These improvements were achieved through optimization of their closure coefficients, replacement with defined functions, or coupling different turbulence models. The studies conducted within the scope of thesis have been disseminated to the international literature through one conference paper and three articles. In the first part of the study, round free jet was modeled with standard k − ε turbulence model and the results were compared with experimental measurements. The aim of the first study was to improve the prediction capability of standard k − ε turbulence model, which was known as unsuccessful in modeling flows involving anisotropic Reynolds stresses such as turbulent jets. It was aimed to investigate the effect of model constants such as Cε1, the coefficient of turbulent production; Cε2, the coefficient of turbulent kinetic energy dissipation and Cµ, used to calculate turbulence viscosity. Steady-state and three-dimensional computational fluid dynamics analyses were performed by systematically changing these constants and the results were evaluated based on dimensionless velocity and turbulence profiles, jet half-width, centerline velocity and turbulence intensity along the jet centerline. It was observed that the adjusted new constants achieve more successful results than the default coefficients. By calibrating the model coefficients that alters from the default model coefficients by 16% for Cε1, 17% for Cε2, and 1.2% for Cµ, it is revealed that jet centerline velocity distribution and jet half-width distribution along the jet center axis can be estimated better. The new closure coefficients of the standard k − ε were also validated for different Reynolds numbers. The numerical simulations revealed that the velocity profile change with the calibrated model coefficients and the numerical results aligned well with the experimental observations. By this way, the computation errors due to uncertainty of turbulence modeling encountered in numerical investigations were reduced. In the second part, the investigation of turbulent jet flows was extended. Since Reynolds Stress Models directly solve the Reynolds stress equations rather than relying on modeling approximations, they were anticipated to provide more accurate numerical results for flows characterized by strong anisotropy. Nevertheless, it was observed that the calculated jet half-width, velocity decay, and spreading rate differed from experimental results due to uncertainties inherent in the turbulence model. Consequently, closure coefficients of the Reynolds Stress Model were calibrated using a variant of the Multi-Objective Genetic Algorithm based on jet half-width data obtained experimentally in the near-field region of the jet. With the use of appropriate discretization scheme and computational grid, adjusted coefficient combination for the turbulence model showed improved accuracy in predicting jet half-width at Reynolds numbers of 10000 and 20000, reducing the errors of calculated decay constant and spreading rate approximately from 2% to 1% and from 16% to 5%, respectively. A detailed examination of the turbulence budget along the longitudinal axis in the self-similar region revealed that the new model coefficients enhanced the modeling of diffusion term but compromised the advection term. As a result of the altered advection term, increased error margins were observed in turbulence intensity and velocity distribution along the jet centerline, although dissipation along the axis was improved. Hence, the modeling error in jet half-width calculations using the numerical method was decreased, enhancing performance of the Reynolds Stress Model compared to default coefficients. For the third part of the study, a modified Renormalization Group k − ε turbulence model for axial compressor cascade flows was introduced, developed to improve the prediction of total pressure loss coefficient and wake profiles at various operating conditions. Built on the theoretical foundation of Renormalization Group theory of turbulence, the model incorporates a locally adaptive η0 coefficient, enabling it to better capture flow physics across varying near-wall resolutions and wall treatment approaches. The model was named as RNG Fc and validated using experimental data of The Advisory Group for Aerospace Research and Development Working Group 18, focusing on the V2 and V103 compressor cascades under on design and off-design conditions. Comparative analyses versus original renormalization group k − ε and Shear Stress Transport k − ω models demonstrate that the presented model achieves superior accuracy in total pressure loss calculations and wake region predictions at various inlet Mach numbers. The model's performance remains consistent across different computational grids, including low y+ and high y+ structures, and is compatible with both scalable wall functions and enhanced wall treatments. Notably, the modified model exhibits computational cost efficiency and numerical stability, making it a promising tool for engineering applications. This work highlighted the potential of the RNG Fc model as a robust and cost-effective alternative for turbulence modeling in axial compressor cascade investigations, offering significant improvements over traditional Reynolds Averaged Navier-Stokes models. To overcome another recognized limitation of the Reynolds-Averaged Navier-Stokes framework, particularly in prediction of reattachment mechanisms for flows separating from continuous surfaces, the final part of this study aimed to overcome this deficiency with the widely-used Shear Stress Transport k − ω turbulence model. Although the Shear Stress Transport model performs well for a wide range of flows, its predictive capability deteriorates in wake regions dominated by complex turbulent structures. To enhance its accuracy, a modified model incorporating the Renormalization Group theory of turbulence is proposed. The model correction is introduced as an additional source term into the dissipation equation and limited to the reattachment region through a blending function to ensure computational efficiency and numerical stability. The model is validated using several canonical problems, including the curved backward-facing step, vertical and inclined backward-facing steps, and periodic hills case at various Reynolds numbers. Effects of the inflow boundary condition are carefully examined to isolate the intrinsic performance of the modified model. Comparisons with benchmark Large Eddy Simulation studies and experimental data showed that the RNG-SST model substantially improves the prediction of mean velocity, turbulence kinetic energy, and wall quantities such as pressure coefficient, wall shear stress, and boundary layer parameters. In particular, prediction of the reattachment position is improved, yielding reduced relative errors by nearly half of the default Shear Stress Transport model. These findings demonstrate that integrating Renormalization Group theory provides a computationally cost-effective, numerically robust, and physically consistent improvement to the default Shear Stress Transport turbulence model for separated flows from a continuous surface. Collectively, the thesis demonstrates that targeted calibration, physics-informed coefficient adjustment, and selective coupling can meaningfully reduce turbulence model induced uncertainty in Reynolds Averaged Navier-Stokes closures across jets, compressor cascades, and separated flows. The proposed models retain the low computational cost and robustness required in engineering while delivering accuracy gains typically associated with more complex approaches, thereby advancing the reliability of turbulence modeling within practical numerical investigations.

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  Raylı sistemlerde dijital ikiz teknolojisi ile bim, gis ve iot tabanlı gerçek zamanlı izleme altyapısının geliştirilmesi

  (Lisansüstü Eğitim Enstitüsü, 2026-06-16) Bozkurt, Tolga; Duran, Zaide; 501231652; Geomatik Mühendisliği

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  Raylı sistemler; yüksek yolcu kapasitesi, kesintisiz işletme gerekliliği ve güvenlik odaklı yapısı nedeniyle sürekli izleme, bakım ve karar destek süreçlerinin etkin biçimde yönetilmesini gerektiren kritik ulaşım altyapılarıdır. Özellikle metro istasyonları; yürüyen merdivenler, asansörler, havalandırma sistemleri, teknik hacimler ve yolcu alanları gibi çok sayıda fiziksel bileşenin bir arada çalıştığı karmaşık yapılardır. Bu yapılarda çevresel koşulların, ekipman davranışlarının ve mekânsal verilerin bütünleşik biçimde izlenmesi, işletme sürekliliğinin sağlanması, bakım süreçlerinin iyileştirilmesi ve operasyonel görünürlüğün artırılması açısından önemli bir ihtiyaç oluşturmaktadır. Bu tez çalışmasında, raylı sistemlerde dijital ikiz teknolojisi bağlamında BIM, GIS ve IoT tabanlı gerçek zamanlı izleme altyapısının geliştirilmesi amaçlanmıştır. Çalışma kapsamında Yapı Bilgi Modellemesi, Coğrafi Bilgi Sistemleri ve Nesnelerin İnterneti sensörlerinden elde edilen veriler ortak bir dijital ortamda bütünleştirilmiş; metro istasyonu ölçeğinde gerçek zamanlı izleme, mekânsal yorumlama ve operasyonel görünürlük sağlayan izleme odaklı bir dijital ikiz yaklaşımı ortaya konulmuştur. Bu yaklaşım, farklı veri kaynaklarının tek bir platformda ilişkilendirilmesini ve fiziksel varlıkların dijital ortamda izlenebilir hale getirilmesini hedeflemektedir. Önerilen altyapıda, metro istasyonuna ait BIM modeli üç boyutlu mekânsal temsil için kullanılmış, GIS verileri istasyonun raylı sistem ağı içindeki konumunu ve coğrafi bağlamını sağlamış, IoT sensörleri ise çevresel ve ekipman temelli ölçümlerin sisteme aktarılmasına olanak tanımıştır. Çevresel izleme kapsamında sıcaklık, nem, karbondioksit, partikül madde, uçucu organik bileşenler ve ortam gürültüsü gibi parametreler; ekipman izleme kapsamında ise titreşim, yüzey sıcaklığı, manyetik akı ve akustik ölçüm verileri değerlendirilmiştir. Bu veriler, ilgili fiziksel varlıklar ve sensör konumları ile ilişkilendirilerek web tabanlı üç boyutlu bir izleme arayüzünde görselleştirilmiştir. Çalışma, Metro İstanbul tarafından işletilen M7 hattındaki Çırçır metro istasyonu üzerinde gerçekleştirilmiştir. Uygulama sürecinde sensör verilerinin toplanması, BIM modelinin web ortamına uygun hale getirilmesi, GIS verileri ile mekânsal ilişkinin kurulması ve tüm veri katmanlarının ortak bir dijital ikiz altyapısında birleştirilmesi ele alınmıştır. Böylece farklı kaynaklardan elde edilen verilerin tek bir izleme platformunda nasıl bütünleştirilebileceği ve bu bütünleşik yapının istasyon yönetimi açısından nasıl kullanılabileceği değerlendirilmiştir. Elde edilen bulgular, BIM, GIS ve IoT entegrasyonuna dayalı dijital ikiz altyapısının metro istasyonlarında çevresel koşulların ve ekipman durumlarının daha anlaşılır, izlenebilir ve mekânsal bağlam içinde değerlendirilebilir hale gelmesini sağladığını göstermektedir. Çevresel sensör verileri, istasyon içinde farklı bölgelerde ölçüm değerlerinin değişebildiğini ortaya koymuş; ekipman sensörleri ise yürüyen merdiven ve asansör gibi bileşenlerin çalışma davranışlarının sensör tabanlı ölçümlerle izlenebilir hale getirilebildiğini göstermiştir. Geliştirilen sistem, sensör verilerinin fiziksel konumlarıyla birlikte yorumlanmasına olanak tanıyarak operasyonel görünürlüğü artırmıştır. Sonuç olarak bu tez, raylı sistemlerde dijital ikiz teknolojisinin metro istasyonu ölçeğinde uygulanabilir olduğunu göstermektedir. Geliştirilen yapı, tam otonom veya yapay zekâ destekli bir dijital ikiz sistemi olmamakla birlikte, gerçek saha verilerinin BIM ve GIS tabanlı mekânsal verilerle ilişkilendirilmesine dayalı uygulanabilir bir izleme altyapısı sunmaktadır. Bu altyapı, ilerleyen aşamalarda kestirimci bakım, anomali tespiti, enerji verimliliği, sürdürülebilirlik değerlendirmesi ve karar destek sistemleri için kullanılabilecek temel bir veri entegrasyon zemini oluşturmaktadır.

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  Ulaştırma altyapı projelerinde bütünleşik risk yönetimi: Bir risk kayıt tablosu yaklaşımı

  (Lisansüstü Eğitim Enstitüsü, 2026-06-16) Oğul, Osman Berk; Tokdemir, Onur Behzat; 501231102; Yapı İşletmesi

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  Ulaştırma altyapı projeleri, ölçekleri, çok paydaşlı yapıları ve uzun uygulama süreleri nedeniyle yüksek düzeyde belirsizlik ve risk içeren proje türleri arasında yer almaktadır. Bu tür projelerde risklerin yalnızca tanımlanması ve listelenmesi yeterli olmamakta; risklerin sistematik bir yapı içerisinde sınıflandırılması, analiz edilmesi ve karar süreçlerine etkin biçimde entegre edilmesi gerekmektedir. Mevcut çalışmaların önemli bir bölümü bu süreçleri bütünleşik bir yönetim sisteminin bileşenleri olarak değil, birbirinden bağımsız süreçler olarak ele almaktadır. Bunun yanı sıra nitel ve nicel analiz yöntemleri çoğu zaman birbirinden bağımsız uygulanmakta; nitel değerlendirmeler risklerin gerçek finansal sonuçlarını ortaya koymakta yetersiz kalırken, nicel yöntemler genellikle sınırlı sayıda risk veya önceden tanımlanmış senaryolara uygulanmaktadır. Bu çerçevede çalışmamız; risk nedenlerinin Risk Kırılım Yapısı ile hiyerarşik olarak sınıflandırılması, risklerin kurumsal bir döngü içinde yönetilmesi ve tüm bu çıktıların Risk Kayıt Tablosu'nda bütünleşik bir karar destek sistemine dönüştürülmesi olmak üzere birbirini besleyen üç temel bileşen üzerine kurgulanmıştır. Proje genelinde tanımlanan risk nedenleri, her kategorinin ortak köken ve etki mekanizması taşıması ilkesiyle proje verilerinden tümevarımsal olarak geliştirilen bir Risk Kırılım Yapısı çerçevesinde; Finansal Riskler, Operasyonel Riskler, Yasal ve Sözleşmeden Kaynaklanan Riskler, Ülke Riskleri ve Mücbir Sebep Riskleri olmak üzere beş ana kategori altında sınıflandırılmıştır. Risk yönetim süreci ise, yapım ekibinin saha gözlemlerinden teknik ofislerin sistematik değerlendirmelerine, oradan proje yönetim ve risk yönetim ofislerine uzanan çok katmanlı bir kurumsal yapı içerisinde işlenmektedir. Riskler, proje yaşam döngüsünün erken safhalarından itibaren periyodik döngüler halinde belirlenmekte, finansal etki ve olasılık değerlendirmeleriyle analiz edilmekte ve yanıt stratejilerine bağlanarak izlenmektedir. Geliştirilen risk kayıt tablosu, risklerin sınıflandırıldığı, finansal etki temelli nicel analizlerle değerlendirilip gruplandırıldığı, yanıt stratejilerine bağlandığı ve periyodik olarak güncellendiği dinamik bir karar destek sistemi olarak kurgulanmış; geleneksel yaklaşımlardaki statik liste anlayışının ötesine geçilmiştir. Bu yapı sayesinde riskler; kaçınma, azaltma ve kabul etme stratejileri çerçevesinde proje yönetimi sürecine entegre edilebilir aksiyon planlarına dönüştürülmüş ve karar vericilere riskleri önceliklendirme, karşılaştırma ve yorumlama imkânı sunulmuştur.

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  Catalytic role of low-cost natural minerals in sewage sludge gasification using different gasifying agents for hydrogen-enriched syngas production

  (ITU Graduate School, 2026) Alper, Dilek; Zengin Balcı, Gülsüm Emel; Sarıoğlan, Alper; 501182802; Environmental Biotechnology

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  Sewage sludge, a by-product of wastewater treatment plant operations, presents a growing environmental challenge due to increasing urbanization, industrial activities and population growth. The urgent demand for cost-effective, energy-efficient and sustainable management strategies for sewage sludge has driven the search for alternative treatment technologies. Among them, thermal conversion methods offer promising solutions, especially gasification emerges as the most advantageous thermochemical process compared to incineration and pyrolysis. The valorization of sewage sludge through gasification plays a critical role in sustainable waste management. Converting sludge into high value-added products not only reduces landfill dependency and greenhouse gas emissions but also aligns with circular economy principles by enabling resource recovery and energy generation. This study focuses on the gasification of sewage sludge for syngas production, specifically investigating the catalytic effects of natural minerals (dolomite, olivine and limonite) and nickel-impregnated biochar. Gasification experiments were conducted in a fixed-bed tubular reactor at laboratory scale and a fluidized-bed reactor at pilot scale. Six different gasifying agents (air, steam, CO2, steam-air, steam-CO2 and CO2-air) were tested in the fixed-bed reactor, while fluidized-bed experiments were performed under air and steam-air conditions. Additionally, the effect of incorporating hazelnut shell into sewage sludge at varying ratios was examined under air gasification in the fluidized-bed reactor to identify an optimal blending ratio. Sewage sludge samples, collected from municipal wastewater treatment plants, were characterized using analytical techniques, including proximate and ultimate analysis, calorimetry, ash fusion tests, X-ray fluorescence, thermogravimetric analysis and fourier transform infrared spectroscopy analysis. Based on the characterization results, sewage sludge was identified as a suitable feedstock for gasification processes due to its high organic content and calorific value. In the fixed bed gasification, the best results regarding the carbon conversion of 88% were obtained in steam-air condition whereas carbon conversion exceeded 99% in the presence of catalytic minerals. The low calorific value of the char samples in 0.1-0.2 MJ/kg and high syngas yields of 15.4-17.0% in total upon catalytic gasification supported the improved efficiencies in comparison to the noncatalytic situation with 1.9 MJ/kg and 14.4%, respectively. CH4 and C2-C5 compounds in the product gas have been reduced in the presence of catalytic minerals as an indication of the increased tar reforming activity favoring hydrogen production. Gasification efficiency can be also assessed by the percentage of syngas consisting of H2, CO, CO2, CH4 and C2-C5s in the total gas yield. The highest syngas percentage of 19.95% was observed under CO2-air condition, while CO2 alone resulted in the lowest value of 11.00%. The addition of steam to CO2 improved this value to 14.50%, which is consistent with the higher reactivity of the char-H2O(g) reaction compared to char-CO2. This study demonstrated that the incorporation of catalysts into sewage sludge enhances syngas quality and gasification performance in the fluidized-bed reactor, while also effectively decreasing bed agglomeration and associated operational challenges. Under air gasification conditions, all catalysts improved process performance to varying degrees. The Ni-based catalyst addition provided the highest H2 content of 13.03% and H2/CO ratio of 1.57, whereas olivine achieved the highest CH4 formation (6.39%) and resulted in the highest heating value of 6.73 MJ/m3 and cold gas efficiency (CGE) of 71% , indicating its strong contribution to overall energy recovery. The introduction of steam to air improved gasification performance in terms of syngas quality, consistent with the enhancement of reforming and water-gas shift reactions. Steam addition inceased the H2 production from 8.36% to 19.28%, H2/CO from 0.99 to 2.14 and higher heating value (HHV) of syngas from 5.44 MJ/m3 to 8.31 MJ/m3. Under steam-air gasification, dolomite assisted gasification yielded the highest H2 content of 21.72%, while the olivine-catalyzed gasification produced the highest CH4 fraction of 8.87%. In terms of gasification efficiency, the olivine-mixed gasification exhibited the most favorable performance under steam-air conditions, achieving an HHV of 7.08 MJ/Nm3 (13.41 MJ/Nm3, N2-free), a CGE of 85% and a CCE of 79.25%. These results highlight the critical role of catalyst selection and gasifying atmosphere in optimizing sewage sludge gasification performance in fluidized-bed systems. In terms of chemical composition of fly ash and bottom ash from sewage sludge gasification, the major components were SiO2, P2O5, CaO, and Al2O3 of under air and steam-air atmospheres. Considering the proximate analysis results of the ash samples obtained in both gasification atmospheres, among the catalysts tested, olivine demonstrated the most efficient performance by achieving the lowest volatile and fixed carbon contents in both of bottom and fly ashes, suggesting superior carbon conversion. In contrast, the Ni-based catalyst resulted in higher volatile and fixed carbon contents. Regarding tar analysis, among all catalysts, olivine enabled the most efficient tar transformation, exhibiting the minimal overall GC-MS peak area and indicating its strong ability for degrading and reforming tar compounds. Among all the catalysts evaluated in the fluidized gasification, olivine proved to be the most effective catalyst in promoting tar reforming, resulting in the highest syngas yield and improved overall gasification efficiency. In the fluidized bed gasification, blending sewage sludge with hazelnut shell influenced gasification performance. The gasification of blended sewage sludge and hazelnut shell was investigated at varying blending ratios 100:0, 75:25, 50:50, 25:75 and 0:100. The 75:25 SS:HS ratio yielded the best results in syngas quality in terms of the highest H2 (9.17%), CH4 (4.84%) and CO (9.91%) contents and energy efficiency (CGE:67.7 % and HHV:5.88 Mj/m3). In the comparison of the two reactor types, sludge gasification yielded syngas with a greater combustible gas content and higher higher heating value in the fluidized-bed reactor than in the fixed-bed reactor, attributable to enhanced gas-solid interaction and superior heat and mass transport. Steam addition markedly enhanced hydrogen production in the fluidized-bed reactor, while its impact was negligible in the fixed-bed system, likely due to steam-induced catalyst deactivation under continuous exposure.

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  Benchmarking somatic wes pipelines: A systematic evaluation of computational parameters and their clinical implications

  (Graduate School, 2026-05-21) Aydemir, Gül Eda; Baysan, Mehmet; 504231565; Computer Engineering

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  Next-generation sequencing (NGS) technologies have transformed biomedical research and clinical diagnostics by enabling rapid, high-throughput profiling of genomic material at a fraction of the cost of earlier sequencing methods. Among the various applications of NGS, whole-exome sequencing (WES) has become particularly important for cancer genomics. WES captures the protein-coding regions of the genome---where the majority of disease-causing mutations reside---and is substantially more cost-effective than whole-genome sequencing (WGS). In the context of oncology, somatic variant calling---the computational process of identifying mutations that arise specifically in tumor tissue and are absent from the matched normal tissue of the same patient---serves as the foundation for precision medicine workflows that guide treatment selection, prognostication, and biomarker discovery. The bioinformatics pipelines used to perform somatic variant calling are composed of multiple sequential steps, and at each step researchers must select from a range of competing algorithms and configure their associated parameters. These steps typically include quality-control trimming of raw sequencing reads, alignment (mapping) of reads to a reference genome, handling of PCR duplicate reads, base quality score recalibration (BQSR), and the variant calling step itself. Although a substantial body of literature has examined how the choice of variant calling algorithm affects detection accuracy, far less attention has been paid to the upstream pre-processing steps or to the interactions between different stages of the pipeline. This thesis presents a comprehensive benchmarking study designed to address these gaps. A total of 480 pipeline configurations were constructed by combining six key factors: two mapping algorithms (BWA-MEM and Bowtie2), three variant callers (Mutect2, Strelka2, and SomaticSniper), two trimming options, two base recalibration options, two duplicate handling strategies, and two high-performance computing environments. These configurations were applied to paired tumor--normal WES datasets from five independent sequencing centers provided by the Sequencing Quality Control Phase~2 (SEQC2) consortium. Pipeline outputs were evaluated against the SEQC2 high-confidence variant set containing 1,161 validated single-nucleotide polymorphisms (SNPs). The results reveal substantial heterogeneity in variant calling outcomes driven primarily by the choice of variant caller, which accounted for 57.27\% of the total variance in F1-scores according to a Type~III ANOVA analysis. The sequencing center---which captures inter-center differences primarily driven by variations in read depth and library preparation---was the second largest contributor at 20.59\%. Adapter trimming was found to significantly improve recall, raising it from 0.717 to 0.800, without a statistically significant reduction in precision (FDR-corrected $p = 0.3318$). Base recalibration improved precision from 0.636 to 0.699 while reducing recall by less than 1\%. When both pre-processing steps were applied together, the overall F1-score reached 0.733, compared to 0.651 for the baseline. Importantly, trimming did not introduce meaningful computational overhead, whereas base recalibration increased execution times substantially. Among individual pipeline configurations, Bowtie combined with Mutect achieved the highest F1-score (0.922). A consensus approach combining 34 pipelines achieved the highest F1-score of 0.94. An ensemble voting strategy using as few as two optimally selected pipelines attained an F1-score of 0.926, falling within 1.48\% of the consensus peak while dramatically reducing computational requirements. The clinical implications of pipeline choice were examined through tumor mutational burden (TMB) analysis and the detection of cancer- and drug-associated gene variants, demonstrating that computational decisions directly influence therapeutic recommendations. Specifically, different mapper--caller combinations produced markedly different TMB estimates, and the application of trimming and base recalibration reduced TMB variability. In conclusion, the principal outcome of this thesis is that different somatic WES pipeline configurations yield heterogeneous variant lists from identical input data, raising serious consistency concerns for NGS-based clinical genomics. The choice of variant calling algorithm is the dominant driver of this heterogeneity, while pre-processing steps---particularly adapter trimming and base recalibration---introduce measurable trade-offs that propagate to clinically relevant endpoints such as TMB and actionable mutation detection. Based on these findings, when computational resources are limited, BWA-MEM combined with Mutect2 with both trimming and base recalibration enabled provides the best balance of accuracy and speed; when maximum accuracy is targeted, an ensemble approach consisting of at least two complementary pipelines should be used. Continuous benchmarking and transparent reporting of pipeline configurations are essential to mitigate this heterogeneity and ensure the reliability of NGS-based clinical decision-making.

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