Modeling Multicomponent Fuel Droplet Vaporization with Finite Liquid Diffusivity Using Coupled Algebraic-Dqmom with Delumping

Multicomponent fuel droplet vaporization models for use in combustion CFD codes often prioritize computational efficiency over model complexity. This leads to oversimplifying assumptions such as single component droplets or infinite liquid diffusivity. The previously developed Direct Quadrature Method of Moments (DQMoM) with delumping model demonstrated a computationally efficient and accurate approach to solve for every discrete species in a well-mixed vaporizing multicomponent droplet. To expand the method to less restrictive cases, a new solution technique is presented called the Coupled Algebraic-Direct Quadrature Method of Moments (CA-DQMoM). In contrast to previous moment methods for droplet vaporization, CA-DQMoM solves for the evolution of two liquid distributions by coupling a monovariate, homogeneous DQMoM approach with additional algebraic moment equations, allowing for a more complex droplet vaporization model with finite rates of liquid diffusion to be solved with computational efficiency. To further decrease computational expense, an approximation that employs the same nodes for both distributions can be used in certain cases. Finally, a delumping technique is adapted to the finite diffusivity model to reconstruct discrete species information at minimal computational cost. The model is proven to be accurate relative to a full discrete component model for both a kerosene droplet comprised of 36 species and a multicomponent droplet of 200 species while maintaining the computational efficiency of continuous thermodynamics models. The combined accuracy and computational efficiency demonstrated by the CA-DQMoM with delumping model for a multicomponent fuel droplet with finite liquid diffusivity makes it ideal for incorporation into CFD models for complex combustion process

Computational Methods for Modeling Multicomponent Droplet Vaporization

Computational fluid dynamics (CFD) models for combustion of multicomponent hydrocarbon fuels must often prioritize computational efficiency over model complexity, leading to oversimplifying assumptions in the sub-models for droplet vaporization and chemical kinetics. Therefore, a computationally efficient hybrid droplet vaporization-chemical surrogate approach has been developed which emulates both the physical and chemical properties of a multicomponent fuel. For the droplet vaporization/physical portion of the hybrid, a new solution method is presented called the Coupled Algebraic-Direct Quadrature Method of Moments (CA-DQMoM) with delumping which accurately solves for the evolution of every discrete species in a vaporizing multicomponent fuel droplet with the computational efficiency of a continuous thermodynamics model. To link the vaporization model to the chemical surrogate portion of the hybrid, a Functional Group Matching (FGM) method is developed which creates an instantaneous surrogate composition to match the distribution of chemical functional groups in the vaporization flux of the full fuel. The result is a hybrid method which can accurately and efficiently predict time-dependent, distillation-resolved combustion properties of the vaporizing fuel and can be used to investigate the effects of preferential vaporization on combustion behavior

A Hybrid Droplet Vaporization-Chemical Surrogate Approach for Emulating Vaporization, Physical Properties, and Chemical Combustion Behavior of Multicomponent Fuels

The complex nature of multicomponent aviation fuels presents a daunting task for accurately simulating combustion behavior without incurring impractical computational costs. To reduce computation time, chemical fuel surrogates comprised of only a few species are used to emulate the combustion of complex pre-vaporized fuels. These surrogates are often unable to match the vaporization behavior and physical properties of the real fuel and fail to capture the effect of preferential vaporization on combustion behavior. Therefore, a computationally efficient, hybrid droplet vaporization-chemical surrogate approach has been developed which emulates both the physical and chemical properties of a multicomponent kerosene fuel. The droplet vaporization/physical portion of the hybrid uses the Coupled Algebraic–Direct Quadrature Method of Moments with delumping to accurately solve for the evolution of every discrete species in a vaporizing fuel droplet with the computational efficiency of a continuous thermodynamic model. The chemical surrogate portion of the hybrid is linked to the vaporization model using a functional group matching method, which creates an instantaneous surrogate composition to match the distribution of chemical functional groups (CH2, (CH2)n, CH3 and Benzyl-type) in the vaporization flux of the full fuel. The result is a hybrid method which can accurately and efficiently predict time-dependent, distillation-resolved combustion property targets of the vaporizing fuel and can be used to investigate the effects of preferential vaporization on combustion behavior

A Hybrid Droplet Vaporization-Adaptive Surrogate Model Using an Optimized Continuous Thermodynamics Distribution

Liquid transportation fuels are composed of hundreds of species, necessitating the use of surrogates in CFD simulations. Surrogates composed of a few species are often formulated to emulate the combustion properties targets (CPTs) of pre-vaporized fuels but fail to reproduce their vaporization behavior, implying that such surrogates cannot replicate the CPTs in the presence of preferential vaporization. The prevailing approach to this problem proposes a physical–chemical surrogate formulated to match the fuel’s distillation curve in addition to its CPTs. However, the physical–chemical surrogate approach requires more species, may not reproduce the instantaneous (distillation-resolved) CPTs, and is not well-suited to conditions in which non-surrogate species surround the droplets. A recent hybrid approach addresses these shortcomings by combining a continuous thermodynamic model (CTM) for droplet vaporization with an adaptive chemical surrogate formulated using functional group matching (FGM). Whereas the hybrid model previously required a delumping calculation to recover discrete fluxes prior to FGM, the approach is modified here to directly predict the fluxes of functional groups using the CTM, increasing its flexibility for high-pressure applications. To this end, a novel, purely mathematical distribution variable is proposed to correlate key functional groups, in addition to thermophysical and transport properties. The accuracy and flexibility of both hybrid approaches compare favorably with the physical–chemical surrogate method. While droplet vaporization rates are well-represented by both methods, functional group fluxes and instantaneous CPTs are predicted more accurately by the hybrid methods, illustrating their potential for improving the accuracy of Eulerian-phase solvers in the presence of preferential vaporization

Rare FGFR Oncogenic Alterations in Sequenced Pediatric Solid and Brain Tumors Suggest FGFR Is a Relevant Molecular Target in Childhood Cancer

PURPOSE: Multiple FGFR inhibitors are currently in clinical trials enrolling adults with different solid tumors, while very few enroll pediatric patients. We determined the types and frequency of alterations () in pediatric cancers to inform future clinical trial design. METHODS: Tumors with alterations were identified from two large cohorts of pediatric solid tumors subjected to targeted DNA sequencing: The Dana-Farber/Boston Children\u27s Profile Study (n = 888) and the multi-institution GAIN/iCAT2 (Genomic Assessment Improves Novel Therapy) Study (n = 571). Data from the combined patient population of 1,395 cases (64 patients were enrolled in both studies) were reviewed and cases in which an alteration was identified by OncoPanel sequencing were further assessed. RESULTS: We identified 41 patients with tumors harboring an oncogenic alteration. Median age at diagnosis was 8 years (range, 6 months-26 years). Diagnoses included 11 rhabdomyosarcomas, nine low-grade gliomas, and 17 other tumor types. Alterations included gain-of-function sequence variants (n = 19), amplifications (n = 10), oncogenic fusions (:: [n = 3], :: [n = 1], :: [n = 1], :: [n = 1], and :: [n = 1]), pathogenic-leaning variants of uncertain significance (n = 4), and amplification in combination with a pathogenic-leaning variant of uncertain significance (n = 1). Two novel fusions in two different patients were identified in this cohort, one of whom showed a response to an FGFR inhibitor. CONCLUSION: In summary, activating alterations were found in approximately 3% (41/1,395) of pediatric solid tumors, identifying a population of children with cancer who may be eligible and good candidates for trials evaluating FGFR-targeted therapy. Importantly, the genomic and clinical data from this study can help inform drug development in accordance with the Research to Accelerate Cures and Equity for Children Act

Implementation of Germline Testing for Prostate Cancer: Philadelphia Prostate Cancer Consensus Conference 2019

PURPOSE: Germline testing (GT) is a central feature of prostate cancer (PCA) treatment, management, and hereditary cancer assessment. Critical needs include optimized multigene testing strategies that incorporate evolving genetic data, consistency in GT indications and management, and alternate genetic evaluation models that address the rising demand for genetic services. METHODS: A multidisciplinary consensus conference that included experts, stakeholders, and national organization leaders was convened in response to current practice challenges and to develop a genetic implementation framework. Evidence review informed questions using the modified Delphi model. The final framework included criteria with strong (\u3e 75%) agreement (Recommend) or moderate (50% to 74%) agreement (Consider). RESULTS: Large germline panels and somatic testing were recommended for metastatic PCA. Reflex testing-initial testing of priority genes followed by expanded testing-was suggested for multiple scenarios. Metastatic disease or family history suggestive of hereditary PCA was recommended for GT. Additional family history and pathologic criteria garnered moderate consensus. Priority genes to test for metastatic disease treatment included BRCA2, BRCA1, and mismatch repair genes, with broader testing, such as ATM, for clinical trial eligibility. BRCA2 was recommended for active surveillance discussions. Screening starting at age 40 years or 10 years before the youngest PCA diagnosis in a family was recommended for BRCA2 carriers, with consideration in HOXB13, BRCA1, ATM, and mismatch repair carriers. Collaborative (point-of-care) evaluation models between health care and genetic providers was endorsed to address the genetic counseling shortage. The genetic evaluation framework included optimal pretest informed consent, post-test discussion, cascade testing, and technology-based approaches. CONCLUSION: This multidisciplinary, consensus-driven PCA genetic implementation framework provides novel guidance to clinicians and patients tailored to the precision era. Multiple research, education, and policy needs remain of importance

Implementation of Germline Testing for Prostate Cancer: Philadelphia Prostate Cancer Consensus Conference 2019.

PurposeGermline testing (GT) is a central feature of prostate cancer (PCA) treatment, management, and hereditary cancer assessment. Critical needs include optimized multigene testing strategies that incorporate evolving genetic data, consistency in GT indications and management, and alternate genetic evaluation models that address the rising demand for genetic services.MethodsA multidisciplinary consensus conference that included experts, stakeholders, and national organization leaders was convened in response to current practice challenges and to develop a genetic implementation framework. Evidence review informed questions using the modified Delphi model. The final framework included criteria with strong (> 75%) agreement (Recommend) or moderate (50% to 74%) agreement (Consider).ResultsLarge germline panels and somatic testing were recommended for metastatic PCA. Reflex testing-initial testing of priority genes followed by expanded testing-was suggested for multiple scenarios. Metastatic disease or family history suggestive of hereditary PCA was recommended for GT. Additional family history and pathologic criteria garnered moderate consensus. Priority genes to test for metastatic disease treatment included BRCA2, BRCA1, and mismatch repair genes, with broader testing, such as ATM, for clinical trial eligibility. BRCA2 was recommended for active surveillance discussions. Screening starting at age 40 years or 10 years before the youngest PCA diagnosis in a family was recommended for BRCA2 carriers, with consideration in HOXB13, BRCA1, ATM, and mismatch repair carriers. Collaborative (point-of-care) evaluation models between health care and genetic providers was endorsed to address the genetic counseling shortage. The genetic evaluation framework included optimal pretest informed consent, post-test discussion, cascade testing, and technology-based approaches.ConclusionThis multidisciplinary, consensus-driven PCA genetic implementation framework provides novel guidance to clinicians and patients tailored to the precision era. Multiple research, education, and policy needs remain of importance