Material-based process-chain optimization in metal forming

The characteristics of a manufacturing product are influenced by a variety of different factors, such as the material properties of the base product. The prediction of properties that give optimal results in metal forming applications is a complex task but of high interest for the manufacturer. To realize such a prediction scheme, the process chain is split up into individual process steps and for each of them an inverse modeling is required. The specific aim of this work is to present an approach for the inverse problem formulation of a process step and to solve it using methods of machine learning. Moreover, the challenges that often arise due to the ill-posed nature of inverse problems will be discussed. The main focus is on the crystallographic texture of metals, which strongly affects the deformation behavior during a process step and highly influences the characteristics of the final product. (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Multi-objective Geometry Optimization of a Gas Cyclone Using Triple-Fidelity Co-Kriging Surrogate Models

Cyclone separators are widely used in a variety of industrial applications. A low-mass loading gas cyclone is characterized by two performance parameters, namely the Euler and Stokes numbers. These parameters are highly sensitive to the geometrical design parameters defining the cyclone. Optimizing the cyclone geometry therefore is a complex problem. Testing a large number of cyclone geometries is impractical due to time constraints. Experimental data and even computational fluid dynamics simulations are time-consuming to perform, with a single simulation or experiment taking several weeks. Simpler analytical models are therefore often used to expedite the design process. However, this comes at the cost of model accuracy. Existing techniques used for cyclone shape optimization in literature do not take multiple fidelities into account. This work combines cheap-to-evaluate well-known mathematical models of cyclones, available data from computational fluid dynamics simulations and experimental data to build a triple-fidelity recursive co-Kriging model. This model can be used as a surrogate with a multi-objective optimization algorithm to identify a Pareto set of a finite number of solutions. The proposed scheme is applied to optimize the cyclone geometry, parametrized by seven design variables.     

Brownian Paths Homogeneously Distributed in Space: Percolation Phase Transition and Uniqueness of the Unbounded Cluster

We consider a continuum percolation model on \(\mathbb {R}^d\), \(d\ge 1\). For \(t,\lambda \in (0,\infty )\) and \(d\in \{1,2,3\}\), the occupied set is given by the union of independent Brownian paths running up to time t whose initial points form a Poisson point process with intensity \(\lambda >0\). When \(d\ge 4\), the Brownian paths are replaced by Wiener sausages with radius \(r>0\). We establish that, for \(d=1\) and all choices of t, no percolation occurs, whereas for \(d\ge 2\), there is a non-trivial percolation transition in t, provided \(\lambda \) and r are chosen properly. The last statement means that \(\lambda \) has to be chosen to be strictly smaller than the critical percolation parameter for the occupied set at time zero (which is infinite when \(d\in \{2,3\}\), but finite and dependent on r when \(d\ge 4\)). We further show that for all \(d\ge 2\), the unbounded cluster in the supercritical phase is unique. Along the way a finite box criterion for non-percolation in the Boolean model is extended to radius distributions with an exponential tail. This may be of independent interest. The present paper settles the basic properties of the model and should be viewed as a springboard for finer results.     

Numerical Investigation of a Bluff-Body Stabilised Nonpremixed Flame with Differential Reynolds-Stress Models

A numerical investigation of a bluff-body stabilised nonpremixedflame, and the corresponding nonreacting flow, has been performed withdifferential Reynolds-stress models (DRSMs). The equilibrium chemistry model is employed and an assumed-shape beta function PDFapproach is used to represent the interaction between turbulence andchemistry. The Reynolds flux of the mixture fraction is obtained from atransport equation, hence a full second moment closure is used. Toclarify the applicability of the existing DRSMs in this complex flame,several models, including LRR-IP model, JM model, SSG model as well as amodified LRR-IP model, have been applied and evaluated. The existingmodels, with default values of the coefficients, cannot provide overallsatisfactory predictions for this challenging test case. The standardLRR-IP model over predicts the centreline velocity decay rate, andtherefore does not perform satisfactory. The modified LRR-IP model, withmodel constant C _∈1 = 1.6 instead of the standard value1.44 (here named BM-M1), gives better results for the mean velocity.However in the nonreacting case this does not lead to improvement inpredicting rms fluctuating velocities especially downstream of therecirculation zone. Motivated by the need to improve the prediction, anew modification of the LRR-IP model is proposed (BM-M2), with modelconstant C ₂ = 0.7in the pressure strain correlation rather thanthe standard value 0.6. With the new modified model, a verysignificant improvement of the prediction of flow field is obtained inthe nonreacting case, whereas in the reacting case the prediction ofthe flow field is of the same overall quality as with BM-M1. This showsthat some DRSMs have different behaviour in the nonreacting case andthe reacting case. In the reacting case also the mean and variance ofmixture fraction are considered and it is found that the best resultsare obtained with the BM-M1 model, with SSG as second best. Combiningthe results for flow field and mixture fraction field it is concludedthat the BM-M1 model is recommended for further studies of thisbluff-body stabilised flame. Grid independence of the result isdemonstrated.     

Multiparametric diagnostics of cardiomyopathies by microRNA signatures

The diagnosis of cardiomyopathies by endomyocardial biopsy analysis is the gold standard for confirmation of causative reasons but is failing if a sample does not contain the area of interest due to focal pathology. Biopsies are revealing an extract of the current situation of the heart muscle only, and the need for global organ-specific or systemic markers is obvious in order to minimize sampling errors. Global markers like specific gene expression signatures in myocardial tissue may therefore reflect the focal situation or condition of the whole myocardium. Besides gene expression profiles, microRNAs (miRNAs) represent a new group of stable biomarkers that are detectable both in tissue and body fluids. Such miRNAs may serve as cardiological biomarkers to characterize inflammatory processes, to confirm viral infections, and to differentiate various forms of infection. The predictive power of single miRNAs for diagnosis of complex diseases may be further increased if several distinctly deregulated candidates are combined to form a specific miRNA signature. Diagnostic systems that generate disease-related miRNA profiles are based on microarrays, bead-based oligo sorbent assays, or on assays based on real-time polymerase chain reactions and placed on microfluidic cards or nanowell plates. Multiparametric diagnostic systems that can measure differentially expressed miRNAs may become the diagnostic tool of the future due to their predictive value with respect to clinical course, therapeutic decisions, and therapy monitoring. We discuss here specific merits, limitations and the potential of currently available analytical platforms for diagnostics of heart muscle diseases based on miRNA profiling. Contains 34 references. Figure

Modern diagnostics of cardiomyopathies will include multiparametric analysis of microRNA profiles in endomyocardial biopsies by real-time PCR or bead-based OLISA techniques. In contrast to high-throughput screening technologies diagnostic systems are realized by down-scaling sample volumes and simultaneous measurement of a limited number of stable disease related parameters.     

A convenient [2+2] cycloaddition-cycloreversion reaction for the synthesis of 1,1-dicyanobuta-1,3-diene-scaffolded peptides as new imaging chromophores

We report on the chemoselective coupling between colorless peptide fragments functionalized with a mutually reactive electron-rich N^α-(4-ethynylphenyl)-N^α-(methyl)-glycyl- and an electron-deficient [4-(2,2-dicyanovinyl)]benzoyl moiety. The resulting donor-substituted 1,1-dicyanobuta-1,3-dienes represent a new class of orange-red colored (λ_max = 450-500 nm, with molar extinction coefficients (ε) above 5,000 mol⁻¹ dm³ cm⁻¹) peptide-based imaging chromophores.