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@PHDTHESIS{Blum:1028362,
      author       = {Blum, Christopher},
      othercontributors = {Steinseifer, Ulrich and Behr, Marek},
      title        = {{U}ncertainty-aware modeling of hemolysis in rotary blood
                      pumps using reduced-order models and {B}ayesian parameter
                      estimation},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2026-01581},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2026},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2026},
      abstract     = {In extracorporeal membrane oxygenation (ECMO), a
                      life-support therapy used in patients with severe cardiac or
                      respiratory failure, blood is circulated outside the body
                      through an artificial circuit that includes a membrane
                      oxygenator and a rotary blood pump (RBPs). While ECMO can be
                      life-saving, it exposes blood to artificial surfaces and
                      elevated mechanical stresses, particularly in RBPs, which
                      operate under supraphysiological shear conditions and are a
                      primary source of red blood cell (RBC) damage. A key
                      complication is shear-induced hemolysis, the mechanical
                      destruction of RBCs in high-shear blood flows. This damage
                      releases hemoglobin and contributes to adverse events such
                      as thrombosis, inflammation, and organ dysfunction. To
                      better understand and reduce hemolysis, numerical models
                      have become an essential tool in the design and evaluation
                      of blood-contacting devices. However, current in-silico
                      approaches face major limitations. They are computationally
                      expensive, often lack validation against clinical data, and
                      typically neglect biological and experimental variability.
                      As a result, their ability to reliably predict hemolysis
                      across realistic clinical conditions remains limited. The
                      overarching aim of this dissertation is to develop a
                      computationally efficient, uncertainty-aware in-silico
                      framework that can predict hemolysis across the full
                      clinical operating range of RBPs and explicitly account for
                      key sources of uncertainty. To make large-scale predictions
                      feasible, a reduced-order model was developed based on
                      non-intrusive polynomial chaos expansion. This approach
                      enables rapid yet accurate simulation of flow conditions
                      across the entire clinical operating space. Building on
                      this, stress-based hemolysis predictions were validated to
                      in-vivo data from 580 ECMO patients. While the results
                      showed good agreement with cohort-level trends, they also
                      revealed substantial inter- and intra-patient variability,
                      pointing to the limits of purely mechanical models. To
                      investigate the source of this variability, a controlled
                      experimental study was conducted using a custom-built
                      shearing device. Repeated hemolysis measurements under
                      identical conditions showed unexpectedly high intra-donor
                      variability, challenging assumptions underlying standard
                      calibration practices and highlighting inherent uncertainty
                      in experimental data. To address this, a Bayesian parameter
                      estimation framework was introduced that incorporates this
                      variability directly into model calibration. The resulting
                      probabilistic model provides not only hemolysis predictions
                      but also quantified uncertainty, increasing robustness,
                      interpretability, and credibility in both clinical and
                      regulatory contexts. Together, these contributions help to
                      establish a more comprehensive framework for hemolysis
                      modeling by combining computational efficiency, clinical
                      validation, and uncertainty quantification. Building on this
                      foundation, future applications such as in-silico clinical
                      trials or patient-specific modeling across diverse patient
                      conditions may become more feasible.},
      cin          = {811001-4 ; 923710 / 416010},
      ddc          = {620},
      cid          = {$I:(DE-82)811001-4_20140620$ / $I:(DE-82)416010_20140620$},
      pnm          = {DFG project G:(GEPRIS)467133626 - Strömungsinduzierte
                      Hämolyse in blutführenden Medizinprodukten – ein
                      datenbasierter mechanistischer Ansatz (467133626)},
      pid          = {G:(GEPRIS)467133626},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2026-01581},
      url          = {https://publications.rwth-aachen.de/record/1028362},
}