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From Research to a Standard: The Hollow Specimen Technique for High-Pressure Hydrogen Gas Testing
No full textAs the world moves towards a decarbonised economy, the demand for sustainable and low-carbon alternatives is growing rapidly. Hydrogen has an important role to play in this transition, but in order to make the most of hydrogen as an energy carrier, a comprehensive understanding of its impact on the integrity of structural materials is necessary. Metallic materials, mainly steels, are the most commonly used structural materials in various components throughout the hydrogen supply chain. Ensuring their sustainable and safe use in hydrogen technologies is a crucial factor in the development of the hydrogen economy. This is particularly important since hydrogen can promote crack formation and ultimately lead to premature failure in these materials when combined with mechanical load. This process is commonly known as Hydrogen Embrittlement and can occur in almost all the steels used for the hydrogen technologies. Extensive materials qualification is required for the rapid implementation of hydrogen technologies, however, most accepted, and standardised test methods for determining the effect of gaseous hydrogen on metallic materials describe complex and costly procedures that are only available to a very limited extent worldwide.
The hollow specimen technique is a simple and economical method that has the potential to overcome the limitations of the current methods and complement them for qualifying metallic materials under high-pressure hydrogen gas. Unlike the conventional autoclave technique, this method requires significantly lower amounts of hydrogen, resulting in fewer safety measures and no need for complex equipment or a specialised laboratory. The technique's low investment and testing costs, simple operation, and shorter testing time make it an optimal for widespread use around the world, thereby increasing the output of results.
Initial studies have demonstrated the feasibility of assessing hydrogen effects in metallic materials using the hollow specimen technique. This led to the establishment of an ISO committee to standardise the method. However, there are open questions that currently prevent the technique from being standardised for hydrogen testing. These open questions relate to specimen geometry, inner hole surface and gas quality and are being addressed in the H2HohlZug project, of which the overall aim is to systematically close the gaps towards a standard. This contribution presents the structure and milestones of the project, followed by initial results
Exploring disjunctive programming formulations for optimal design of distillation columns
No full textAutomating process synthesis presents a formidable challenge in chemical engineering. Developing frameworks that are both general and accurate – while remaining computationally tractable – is particularly demanding. To further increase the solvable problem size, an advanced optimization framework is proposed, leveraging Generalized Disjunctive Programming (GDP) for process synthesis and design problems. This framework allows for multiple improvements over existing Mixed Integer Nonlinear Programming (MINLP) formulations, aiming to enhance feasibility and reduce solution time.
Process synthesis problems are typically posed as nonlinear optimization problems with continuous and discrete variables. Traditionally, these are formulated as MINLP problems, where discrete variables appear as integer or binary variables. These are usually relaxed to continuous variables to solve the MINLP.
In practice, the (in-)equality constraints are obtained starting from logical expressions, which are reformulated as algebraic constraints. Disjunctive expressions are typically converted into mixed integer constraints by using “big-M” constraints [1]. An alternative reformulation for treating disjunctions is the convex hull (chull) formulation, which achieves superior relaxation tightness but is rarely used due to its greater complexity and potential numerical difficulties [2].
Recent advances in GDP allow for automatic reformulation of optimization problems, e.g., by big-M or chull. Furthermore, dedicated GDP solution algorithms are now available [3]. Unlike conventional Branch and Bound or Outer Approximation algorithms, their logic-based counterparts can neglect inactive equations, reducing the size and complexity of subproblems. This is achieved by deactivating unused model equations during the solution procedure, as shown by Lee et al. [4].
This point is particularly interesting for chemical engineering, as a lot of time is spent computing NLP subproblems. Also, model formulation can have a large impact on the solution times of MINLP [1]. However, evaluating various model formulations tends to be rather tedious. Especially detailed model formulations that include rigorous thermodynamics and kinetics tend to increase the model size significantly. As a first step for further exploitation of GDP for process synthesis and process design, we developed a modeling environment and automatic code generation framework for GDP. This contribution aims at investigating different problem formulations for GDP in process design facilitated by the new framework.
For maximum flexibility and independence from any given programming language, the modeling and problem formulation is implemented within MOSAICmodeling [5], a platform that allows for model formulation at the documentation level. Users formulate equations in LaTeX, which are then translated into MathML/XML preserving all relevant information while remaining as general as possible. The first step hereby consists in the definition of a suitable notation. This defines all possibly occurring variables and their indices and sets the basis of all MathML/XML operations. To include GDP into this workflow, already existing variable definitions were extended to also include logical / Boolean variables. Equations are created based on those extended notations: Basic logical operators – and (∧), or (∨), not (¬), implication (⇒), and equivalence (⇔) – were added to include logical expressions. Furthermore, disjunctions can be created by linking Boolean variables with single equations or sets of equations. The resulting systems defined in MathML/XML can be exported to any programming language using MOSAICmodeling’s UDLS feature [6], which has been extended tocapture connections between disjunctive, logical variables, and their associated equations, allowing for code export to GAMS, Julia, and Pyomo.
Four different MINLP/GDP formulations were implemented in MathML/XML in MOSAICmodeling, exported, and evaluated within pyomo regarding their benefits in optimizing thermal separation problems. For this case study, the MINLP formulation of Kraemer et al. [7] is evaluated (pureMINLP), which determines the optimal column height to achieve desired product specifications while minimizing costs for a multicomponent distillation column. The MINLP formulation varies locations of feed, reflux, and boilup streams. Each separation stage is modeled rigorously applying thermodynamics of varying complexity.
An alternative, GDP formulation (pureGDP) specifies disjunctions for each separation stage as in [QGrossmann2000rig]. The disjunctive variables decide which stages are active or not. For active stages, the stage formulation is the same as above. For inactive stages, a passthrough of liquid and vapor streams without thermodynamic calculations is applied. The feed location is modeled as a nested disjunction for the active stages.
Two additional formulations, in between pureMINLP and pureGDP, apply different levels of relaxation: Feed relaxed GDP (fr-GDP) employs the disjunctive formulation for the stages, while applying pureMINLP’s formulation for the feed location. Decision variable GDP (dv-GDP) on the other hand, applies a formulation that translates directly into the pureMINLP formulation if the dv-GDP is relaxed by BigM and the M is chosen accordingly.
The four different MINLP/GDP formulations are combined with two different implementations for thermodynamics calls: (1) explicit formulation using the Antoine equation for vapor pressures, linearized heats of evaporation, and constant specific heat capacities; (2) external thermodynamic function calls using a CAPE-OPEN interface with TEA as thermodynamics engine [8]. Note that the same thermodynamic models are used for explicit and external thermodynamics implementations to ensure comparability of the solutions. However, the interface supports any CAPE-OPEN compliant thermodynamics engine, allowing for even highly complex equations of state, such as, PC-SAFT. Future work will therefore also include non-idealities, which is not in scope of this contribution.
To evaluate the performance of available GDP solvers, the formulations in MathML/XML are exported to pyomo. Exports to julia and GAMS were also developed. However, they currently lack support for dedicated GDP solvers.
Three different GDP formulations (rGDP, cGDP and bGDP) were investigated with respect to runtime until an optimal solution was found. These formulations were benchmarked against a commonly used pMINLP formulation. The maximum number of stages for all columns were 32, of which 30 were choosable by the optimizer.
We discovered that all GDP formulations perform worse or equal to the pMINLP formulation. However, some formulations outperform others. It was shown, that the inclusion of mass and energy balances of the separation stages into the global constraints is absolutely nescessary to robustly find the optimal stage number. Their inclusion into the disjuncts leads to a degradation of the outer approximation linearization and therefore hinders the solution process. It was also shown, that the bGDP formulation can greatly improve the solution time, by structuring the active stages into binary encoded blocks. This reduces the required binary variables, leading to improved solver Performance. Lastly, we were able to show, that the bGDP achieves pairity in runtime with the pMINLP benchmark, showing strong indications that bGDP can surpass the pMINLP formulation in future, more advanced optimization Problems.
This study also shows that simply transforming an MINLP formulation into a GDP does not necessarily yield benefits. Runtime strongly varies across the three GDP formulations. To this end, further investigations for efficient exploitation of GDP for process synthesis is required. Our modeling framework in MathML/XML now supports fast formulation of highly complex GDPs and evaluation in a variety of supporting platforms (pyomo, julia, GAMS), speeding up the process of tailoring problem formulations.
The investigated problem sizes are small, chosen as a proof of concept for formulation, code generation, and solution of GDPs. Given the runtime of the investigated problems, increasing system size is feasible. Future work will aim to increase the total system size and move towards more general process synthesis, potentially putting GDP problem runtime below that of relaxed MINLP
From Micromechanics to Macro Validation: A New Approach for Aerospace Structures
No full textGiven the high experimental effort required to verify fiber‑reinforced polymer composites (FRP) in aviation, the PROVING project (Production, Optimization, and Virtual Verification for Generative Manufacturing Processes) aims to establish a streamlined and robust verification methodology based on analytical and numerical approaches. As a foundation for this virtual verification process, BAM contributed extensive material testing for parameter identification, feeding directly into the material models and probabilistic methods.
Beyond the experiments, an analytical‑numerical model was developed to more accurately determine the in‑situ stress state within the composite matrix. Since damage initiation in FRP is largely driven by inter-fiber failure, the three‑dimensional matrix stress state is essential for structural verification. In addition to external loading, thermomechanical residual stresses arising from the mismatched thermal expansion of fibers and matrix contribute to the stress state.
Within PROVING, a calculation method was developed that incorporates the thermomechanical behavior of carbon‑fiber‑reinforced polymers (CFRP) into the verification process with minimal experimental effort. The method determines matrix thermal residual stresses using micromechanical modeling combined with finite element analysis
NFDI-MatWerk/IUC02 data schema for elevated temperature tensile data of Ni-based superalloys
No full textStrength characteristics, such as proof strength and tensile strength, of metallic materials are usually measured at room temperature. However, for high-temperature materials, the values at operational temperatures are equally important for analyzing the mechanical behavior of components or for designing purposes. Elevated temperature tensile tests (often referred to as hot tensile tests) generally enable the measurement of the same material parameters as those obtained at room temperature. Typically, tensile and proof strength, elongation after fracture, and reduction of area are analyzed, with the focus often being on proof strength.
The attached data schema for elevated temperature tensile tests was developed within the German NFDI-MatWerk initiative (https://nfdi-matwerk.de/). It builds on a previously published creep data schema and follows a reference data methodology that has also been previously outlined.
The presented schema constitutes a structured approach for collecting all relevant information on an elevated temperature tensile test experiment. Overall, this development, as defined in the previously published data schema for creep data, aims to provide a comprehensive hierarchical data description that can be implemented in data management platforms (such as electronic laboratory notebooks), facilitate consistent quality assessment across different users and data providers, and promote the alignment of datasets to the FAIR principles by providing easy interoperability and full reusability.
The presented schema was initially developed for datasets of Ni-based high-temperature alloys. However, thanks to its modular structure, it can also be applied to tensile tests of various metallic and other materials. Although the focus is on elevated temperature tensile testing, the data schema also offers a solid foundation for documenting room temperature tensile tests. In such cases, an adjustment of the requirement profile might be necessary, and categories or entries specific to mechanical testing at elevated temperatures, such as “temperature-measuring system”, “specified temperature”, or “soaking time”, become irrelevant.
Overall, a detailed approach was followed to ensure the collection of all relevant information, including both metadata and test results. This includes, for example, comprehensive descriptions of the material’s manufacturing history and of the laboratory equipment, and basic strength and deformation characteristic values. The resulting data schema aims to support the description and identification of high-quality datasets, which may qualify as reference data of materials. Our current definition of reference data of materials has been published elsewhere. It should be noted, however, that not all research datasets —depending on their origin and purpose— require this full level of detail. Nevertheless, the presented data schema can support data providers in evaluating the completeness and value of their datasets.
This version of the data schema covers elevated temperature tensile tests on both single- and polycrystalline specimen materials. The terminology is aligned with DIN EN ISO 6892-1 and DIN EN ISO 6892-2. It is designed to record the use of temperature measurement using thermocouples and the use of contacting extensometer systems.
The requirement profile refers to the highest quality class of reference data, taken from calibrated instruments, and which shall enable the following usages:
1. Checking one's own elevated temperature tensile test results on nominally similar material
2. Verification of own testing set-up (e.g., by testing the same or a similar material)
3. Using the data as input data for simulations in the context of design or alloy developmen
A unique authenticator for additively manufactured parts derived from their microstructure
No full textComponents produced using additive manufacturing can be marked for unique identification and secure authentication [1,2]. Serial numbers and machine-readable codes can be used to identify the component, and link digital product-related data (i.e., a digital product passport) to the actual components. The most prevailing solution consists of local process manipulation, such as printing a quick response (QR) code [3] or a set of blind holes on the surface of the internal cavity of hollow components. However, local manipulation of components may alter the properties, and external tagging features can be altered or even removed by post-processing treatments. This work therefore aims to provide a new methodology for identification, authentication, and traceability of additively manufactured (AM) components using microstructural features that are unique to each part. X-ray computed tomography (XCT) was employed to image the microstructural features of AlSi10Mg parts. Based on size and geometry, the most prominent features were selected to create a unique digital authenticator. We implemented a framework in Python using open-access modules that can successfully create a digital object authenticator using the segmented microstructure information from XCT. The authenticator is stored as a QR code, along with the 3D information of the selected features
Comparative residual stress analysis on a DED-Arc manufactured high-strength steel component using the contour method and XRD
No full textLightweight construction is a vital approach for reducing CO₂ emissions. It contributes to the development of more energy-efficient structures and supports the overall goal of achieving carbon neutrality in the transition to sustainable manufacturing. Thus, a topology-optimized design often leads to complex geometries. Additive manufacturing (AM) processes such as direct energy deposition with arc (DED-Arc) offers a great design freedom due to the build-up of components in layers. Furthermore, they enable efficient production due to the high deposition rate, process reliability and good automation capability. Further efficiency can be achieved through weight optimization, enabled by high-strength steels. However, a major challenge is the process induced residual stresses (RS) in the component. High tensile residual stresses are detrimental as they increase the risk of cold cracking. Knowledge of residual stress distribution is crucial for predicting service life of the component and structural integrity assessment, especially for safety critical applications. Therefore, this study focuses on the use of Contour method (CM) to analyse the full field longitudinal residual stresses in an open hollow cuboid component (dimensions: 120 x 50 x 30 mm3) manufactured by DED-Arc (yield strength > 730 MPa). In the contour method, the component is cut along a desired plane of interest and the contour of deformed cut surface is measured. A finite element model is used to reconstruct the residual stresses field in the 2-dimensional plane of cut. In this paper a modified cutting strategy was employed, post cutting the deformed cut surfaces were measured utilizing two surface measurement techniques i.e., coordinate measuring machine (CMM) and a 3D Scanner. The accuracy of the contour method was validated against surface stresses measured using X-ray diffraction. Additionally, a comparison of neutron diffraction experiments was conducted. The residual stresses were further correlated with hardness measurements. The results from surface measurement techniques showed good agreement regarding the measured displacement contours and the contour method results revealed peak stresses in the DED-Arc walls, bending deformation in substrate induces tensile stresses at the bottom of the substrate plate and compressive stresses in the middle top region. The residual stresses obtained from diffraction and contour method showed a good agreement and correlated qualitatively with the hardness measurements
Bericht zum Fortschritt des laufenden Projekts: „Praktikable Prüfung geschweißter Ferngasleitungen aus niedrig-legierten Stählen für den sicheren Transport von Wasserstoff“
No full textDas Vorhaben untersucht, wie geschweißte Ferngasleitungen aus niedriglegierten und höherfesten Stählen sicher für den Transport von Wasserstoff genutzt werden können. Im Fokus steht, in welchem Maß Wasserstoff unter verschiedenen Druck-, Temperatur- und Gaszusammensetzungen in Werkstoffe und Schweißverbindungen eindringt, deren Duktilität mindert und sprödbrüchiges Versagen verursachen kann. Dafür wird die Prüftechnik der Hohlzugproben in Kombination mit Slow-Strain-Rate-Tests weiterentwickelt, um reale Beanspruchungsbedingungen abzubilden und wirtschaftliche, praxisnahe Prüfungen zu ermöglichen. Untersucht werden insbesondere Schweißnahtgefüge, deren Interaktion, zyklische Belastungen sowie die Übertragbarkeit der Laborergebnisse auf reale Komponenten. Ziel ist ein praktikables Bewertungs- und Qualifizierungskonzept, das die H2-Readiness bestehender und neuer Rohrleitungen beschleunigt und in Normung und Industrieanwendung überführt werden kann
Large-scale thermo-mechanical simulation of laser beam welding using high-performance computing: A qualitative reproduction of experimental results
No full textLaser beam welding (LBW) is a non-contact joining technique that has gained significant importance in modern industrial manufacturing. One potential problem, however, is the formation of solidification cracks, which particularly affects alloys with a pronounced melting range. The aim of the present work is the development of computational methods and software tools to numerically simulate LBW. In order to obtain a sufficiently accurate solution, a large number of finite elements has to be used. Therefore, a highly parallel scalable solver framework, based on the software library PETSc, was used to solve this computationally challenging problem on a high-performance computing architecture. Finally, the experimental results and the numerical simulations are compared. They are found to be in good qualitative agreement, which confirms the validity of the numerical simulations and allows for a better interpretation of the experimentally observed strain distribution
Improving the chipping resistance of pre-sintered zirconia white-bodies
No full textObjectives: The high occurrence of fractures, cracking and chipping of zirconia pre-sintered blanks and blocks during machining decreases their yield and can transfer lifetime-limiting racks to the final sintered restoration. This study has the objective of characterizing the mechanical and fracture properties of two zirconia compositions while varying temperature and time of pre-sintering, in order to assess the space for possible improvement.
Methods: We selected two typical granular powders with 3 mol% (3YSZ, Zpex®, Tosoh) or 5 mol% (5YSZ, Zpex Smile®, Tosoh) yttria-stabilized zirconia and two pre-sintered commercial analogs (IPS e.max® ZirCAD MO, Ivoclar and Katana™ STML, Kuraray). The debinding and pre-sintering stages of the experimental powders were characterized using thermal analyses (differential scanning calorimetry and thermogravimetry), and the crystal phase composition was quantified using X-ray diffraction (XRD). Physical and mechanical properties such as density, hardness, flexural modulus, biaxial flexural strength and fracture toughness were measured for two pre-sintering temperatures (1000 ◦C, 1100 ◦C) and increasing holding times at those temperatures (2 h, 4 h, 6 h). The chipping resistance for those conditions was quantified using the edge chipping test using a Vickers diamond indenter.
Results: Thermal analyses revealed that both powders show comparable debinding behavior and contained approx. 3.8 mass % organic binder, which burns-out completely between 300 and 400 ◦C. The crystallographic phase changes occurring during the 2–6 h at 1000 ◦C and 1100 ◦C was not detectable in the DSC signal, but quantifiable by XRD. Namely, a major content of monoclinic phase in both powders transforms completely into the two tetragonal phases, starting below 1000 ◦C and concluding above 1100 ◦C. All physical and mechanical properties increased with holding time for both temperatures, though more steeply for pre-sintering at 1100◦C. Edge chipping resistance response was well aligned with other fracture properties, with a more marked improvement for 3YSZ pre-sintered at 1100 ◦C. For all properties, the 3YSZ zirconia showed statistically-higher values for the same temperature-time conditions, in agreement with the values obtained for the commercial materials as well.
Significance: The results demonstrate the weakness of pre-sintered zirconia products concerning fracture properties, but also the potential for improvement as related to type of zirconia and pre-sintering conditions. This study outlines the use of a set of mechanical tests that can characterize chipping resistance and guide future research engaging in optimizing the machining resistance of pre-sintered zirconia products
Corundum Particles as Trypsin Carrier for Efficient Protein Digestion
No full textThis dataset contains raw LC–MS/MS files of trypsin-digested NISTmAb acquired on a SCIEX TripleTOF 6600 mass spectrometer in DIA (SWATH) mode for antibody quantification. In addition, it includes MALDI-TOF MS peptide mass fingerprints of trypsin-digested Herceptin as well as reference spectra that can be used for antibody identification using the open-source software ABID 2.0 (https://bam.de/ABID).
The data were generated within a study demonstrating the applicability of corundum-immobilized trypsin for antibody digestion and its suitability for peptide-based LC–MS/MS quantification and MALDI-TOF MS fingerprinting–based antibody identification