Impact of materials technology on the breeding blanket design – Recent progress and case studies in materials technology

A major part in the EUROfusion materials research program is dedicated to characterize and quantify nuclear fusion specific neutron damage in structural materials. While the majority of irradiation data gives a relatively clear view on the displacement damage, the effect of transmutation – i.e. especially hydrogen and helium production in steels – is not yet explored very well. However, few available results indicate that EUROFER-type steels will reach their operating limit as soon as the formation of helium bubbles reaches a critical amount or size. At that point, the material would fail due to embrittlement at the considered load. This paper presents a strategy for the mitigation of the before-mentioned problem using the following facts: • the neutron dose and related transmutation rate decreases quickly inside the first wall, that is, only a plasma-near area is extremely loaded • nanostructured oxide dispersion strengthened (ODS) steels may have an enormous trapping effect on helium and hydrogen, which would suppress the formation of large helium bubbles • compared to conventional steels, ODS steels show improved irradiation tensile ductility and creep strength In summary, producing the plasma facing, highly neutron and heat loaded part of blankets by an ODS steel, while using EUROFER97 for everything else, would allow a higher heat flux as well as a longer operating period. Consequently, we (1) developed and produced 14 % Cr ferritic ODS steel plates. (2) We fabricated a mockup with 5 cooling channels and a plated first wall of ODS steel, using the same production processes as for a real component. And finally, (3) we performed high heat flux tests in the HELOKA facility (Helium Loop Karlsruhe at KIT) applying short and up to 2 h long pulses, in which the operating temperature limit for EUROFER97 (i.e., 550 °C) was finally exceeded by 100 K. Thereafter, microstructure and defect analyses did not reveal defects or recognizable damage. Only a heat affected zone in the EUROFER/ODS steel interface could be detected. This demonstrates that the use of ODS steel could make a decisive difference in the future design and performance of breeding blankets

Fabrication routes for advanced first wall design alternatives

In future nuclear fusion reactors, plasma facing components have to sustain specific neutron damage. While the majority of irradiation data provides a relatively clear picture of the displacement damage, the effect of helium transmutation is not yet explored in detail. Nevertheless, available results from simulation experiments indicate that 9%-chromium steels will reach their operating limit as soon as the growing helium bubbles extent a critical size. At that point, the material would most probably fail due to grain boundary embrittlement. In this contribution, we present a strategy for the mitigation of the before-mentioned problem using the following facts. (1) The neutron dose and related transmutation rate decreases quickly inside the first wall of the breeding blankets, that is, only a plasma-near area is extremely loaded. (2) Nanostructured oxide dispersion strengthened (ODS) steels may have an enormous trapping effect on helium, which would suppress the formation of large helium bubbles for a much longer period. (3) Compared to conventional steels, ODS steels also provide improved irradiation tensile ductility and creep strength. Therefore, a design, based on the fabrication of the plasma facing and highly neutron and heat loaded parts of blankets by an ODS steel, while using EUROFER97 for everything else, would extend the operating time and enable a higher heat flux. Consequently, we (i) developed and produced 14%Cr ferritic ODS steel plates and (ii) optimized and demonstrated a scalable industrial production route. (iii) We fabricated a mock-up with five cooling channels and a plated first wall of ODS steel, using the same production processes as for a real component. (iv) Finally, we performed high heat flux tests in the Helium Loop Karlsruhe, applying a few hundred short and a few 2 h long pulses, in which the operating temperature limit for EUROFER97 (i.e. 550 ◦C) was finally exceeded by 100 K. (v) Thereafter, microstructure and defect analyses did not reveal critical defects or recognizable damage. Only a heat affected zone in the EUROFER/ODS steel interface could be detected. However, a solution to prohibit the formation of such heat affected zones is given. These research contributions demonstrate that the use of ODS steel is not only feasible and affordable but could make a decisive difference in the future design and performance of breeding blankets

Republicanism and the political economy of democracy

Europe is experiencing rapidly accelerating poverty and social exclusion, following half a decade of financial crisis and austerity politics. The key problem behind Europe's malaise, in our view, is the economic disenfranchisement of large parts of its population in the winner-takes-all-society. This article proposes that we examine the contribution of republican political theory as a distinctive approach that provides us with the conceptual and normative resources to reclaim what we call the political economy of democracy, the constellation of political and economic institutions aimed at promoting broad economic sovereignty and individuals' capacities to govern their own lives. This article identifies three key ideas that together constitute a distinctively republican approach to political economy: (1) establish an economic floor; (2) impose an economic ceiling to counter excess economic inequality; and (3) democratize the governance and regulation of the main economic institutions

Machine learning-based preoperative predictive analytics for lumbar spinal stenosis

OBJECTIVE Patient-reported outcome measures (PROMs) following decompression surgery for lumbar spinal stenosis (LSS) demonstrate considerable heterogeneity. Individualized prediction tools can provide valuable insights for shared decision-making. The authors aim to evaluate the feasibility of predicting short- and long-term PROMs, reoperations, and perioperative parameters by machine learning (ML) methods. METHODS Data were derived from a prospective registry. All patients had undergone single- or multilevel mini-open facet-sparing decompression for LSS. The prediction models were trained using various ML-based algorithms to predict the endpoints of interest. Models were selected by area under the receiver operating characteristic curve (AUC). The endpoints were dichotomized by minimum clinically important difference (MCID) and included 6-week and 12-month numeric rating scales for back pain (NRS-BP) and leg pain (NRS-LP) severity and the Oswestry Disability Index (ODI), as well as prolonged surgery (> 45 minutes), extended length of hospital stay ( > 28 hours), and reoperations. RESULTS A total of 635 patients were included. The average age was 62 ± 10 years, and 333 patients (52%) were male. At 6 weeks, MCID was seen in 63%, 76%, and 61% of patients for ODI, NRS-LP, and NRS-BP, respectively. At internal validation, the models predicted MCID in these variables with accuracies of 69%, 76%, and 85%, and with AUCs of 0.75, 0.79, and 0.92. At 12 months, 66%, 63%, and 51% of patients reported MCID; the observed accuracies were 62%, 74%, and 66%, with AUCs of 0.68, 0.72, and 0.79. Reoperations occurred in 60 patients (9.5%), of which 27 (4.3%) occurred at the index level. Overall and index-level reoperations were predicted with 69% and 63% accuracy, respectively, and with AUCs of 0.66 and 0.61. In 15%, a length of surgery greater than 45 minutes was observed and predicted with 78% accuracy and AUC of 0.54. Only 15% of patients were admitted to the hospital for longer than 28 hours. The developed ML-based model enabled prediction of extended hospital stay with an accuracy of 77% and AUC of 0.58. CONCLUSIONS Preoperative prediction of a range of clinically relevant endpoints in decompression surgery for LSS using ML is feasible, and may enable enhanced informed patient consent and personalized shared decision-making. Access to individualized preoperative predictive analytics for outcome and treatment risks may represent a further step in the evolution of surgical care for patients with LSS

Deep learning-based preoperative predictive analytics for patient-reported outcomes following lumbar diskectomy: feasibility of center-specific modeling

BACKGROUND CONTEXT: There is considerable variability in patient-reported outcome measures following surgery for lumbar disk herniation. Individualized prediction tools that are derived from center- or even surgeon-specific data could provide valuable insights for shared decision-making. PURPOSE: To evaluate the feasibility of deriving robust deep learning-based predictive analytics from single-center, single-surgeon data. STUDY DESIGN: Derivation of predictive models from a prospective registry. PATIENT SAMPLE: Patients who underwent single-level tubular microdiskectomy for lumbar disk herniation. OUTCOME MEASURES: Numeric rating scales for leg and back pain severity and Oswestry Disability Index scores at 12 months postoperatively. METHODS: Data were derived from a prospective registry. We trained deep neural network-based and logistic regression-based prediction models for patient-reported outcome measures. The primary endpoint was achievement of the minimum clinically important difference (MCID) in numeric rating scales and Oswestry Disability Index, defined as a 30% or greater improvement from baseline. Univariate predictors of MCID were also identified using conventional statistics. RESULTS: A total of 422 patients were included (mean [SD] age: 48.5 [11.5] years; 207 [49%] female). After 1 year, 337 (80%), 219 (52%), and 337 (80%) patients reported a clinically relevant improvement in leg pain, back pain, and functional disability, respectively. The deep learning models predicted MCID with high area-under-the-curve of 0.87, 0.90, and 0.84, as well as accuracy of 85%, 87%, and 75%. The regression models provided inferior performance measures for each of the outcomes. CONCLUSIONS: Our study demonstrates that generating personalized and robust deep learning-based analytics for outcome prediction is feasible even with limited amounts of center-specific data. With prospective validation, the ability to preoperatively and reliably inform patients about the likelihood of symptom improvement could prove useful in patient counselling and shared decision-making

Deep learning-based preoperative predictive analytics for patient-reported outcomes following lumbar diskectomy: feasibility of center-specific modeling

Background Context: There is considerable variability in patient-reported outcome measures following surgery for lumbar disk herniation. Individualized prediction tools that are derived from center- or even surgeon-specific data could provide valuable insights for shared decision-making. Purpose: To evaluate the feasibility of deriving robust deep learning-based predictive analytics from single-center, single-surgeon data. Study Design: Derivation of predictive models from a prospective registry. Patient Sample: Patients who underwent single-level tubular microdiskectomy for lumbar disk herniation. Outcome Measures: Numeric rating scales for leg and back pain severity and Oswestry Disability Index scores at 12 months postoperatively. Methods: Data were derived from a prospective registry. We trained deep neural network-based and logistic regression-based prediction models for patient-reported outcome measures. The primary endpoint was achievement of the minimum clinically important difference (MCID) in numeric rating scales and Oswestry Disability Index, defined as a 30% or greater improvement from baseline. Univariate predictors of MCID were also identified using conventional statistics. Results: A total of 422 patients were included (mean [SD] age: 48.5 [11.5] years; 207 [49%] female). After 1 year, 337 (80%), 219 (52%), and 337 (80%) patients reported a clinically relevant improvement in leg pain, back pain, and functional disability, respectively. The deep learning models predicted MCID with high area-under-the-curve of 0.87, 0.90, and 0.84, as well as accuracy of 85%, 87%, and 75%. The regression models provided inferior performance measures for each of the outcomes. Conclusions: Our study demonstrates that generating personalized and robust deep learning-based analytics for outcome prediction is feasible even with limited amounts of center-specific data. With prospective validation, the ability to preoperatively and reliably inform patients about the likelihood of symptom improvement could prove useful in patient counselling and shared decision-making