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In this paper, the high-temperature constitutive behaviour of an additively manufactured ductile nickel-based superalloy is investigated and modelled, with application to thermomechanical fatigue, low-cycle fatigue and creep conditions at temperatures up to 800∘800∘C. Thermomechanical fatigue tests have been performed on smooth specimens in both in-phase and out-of-phase conditions at a temperature range of 100−800∘100−800∘C, and creep tests at 625∘625∘C, 700∘700∘C, 750∘750∘C and 800∘800∘C. Additionally, low-cycle fatigue tests at different strain ranges and load ratios have been performed at 700∘700, Funding Agencies|Linkoping University and Siemens Energy AB

For heat resistant alloys, long-term structural stability at high temperatures is a critical issue for alloy design and applications. In this paper, the long-term creep behaviours and structural stabilities of six heat resistant high Ni alloys and austenitic stainless steels have been studied. The longest creep rupture life is up to 359 283 hours. High Ni and Cr alloys show a good combination of high creep and oxidation resistances. Precipitation of nano MX particles with a very low growth rate improves long-term creep resistance at high temperatures. Long-term stable multiple nanoprecipitates of MX, Cu-rich, Laves and M23C6 phases can greatly contribute to the creep strength. Low Ni austenitic stainless steels show comparatively low oxidation and creep resistances. It was first found that at 800 & DEG;C, Cr2N could form in the low Ni steel with a long-term crept by the absorption of nitrogen from the air into the matrix.

Increased demand for actionable knowledge in operations- and supply chain management has fuelled the interest in collaborative, action-oriented research design as well as modes of theorising that generate adaptable and actionable frameworks. Whilst action research (AR) design as well as middle-range theories (MRT) offer guiding principles herein, they are researcher centric in nature. It is taken for granted that practitioners that enter such an endeavour have a certain level of knowledge or experience prior to the initial stages of formalising the research problem. Practitioners in non-academic, operations management-intensive industries or craftsmanship-based industries, such as construction or carpeting (often in the SME range) are often neither prepared nor equipped with the principles necessary to convey their managerial challenges into collaborative research design. This risk limiting or even hindering altogether such participation. This paper elaborates on combining the logic of AR and MRT. By conceptualising a preparatory phase for initiating practitioner engagement, complementing the conventional AR cycle, a four-step approach is presented: (1) Identifying a joint interest; (2) Teaching - Awakening interest in the topic through MRT frameworks; (3) Accepting buy-in to the AR cycle and determining the problem; and (4) Proposing MRT frameworks for analysis and entering the traditional AR cycle.

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]