Chalmers Research
Not a member yet
88095 research outputs found
Sort by
Transient torque reversals in indirect drive wind turblnes
Get PDFThe adverse effect of transient torque reversals (TTRs) оп wind turЬine gearboxes сап Ье severe due to their magnitude and rapid occurrence compared with other equipment. The primary damage is caused to the bearings as the bearing loaded zone rapidly changes its direction. Other components are also affected Ьу TTRs (such as gear tooth); however, its impact оп bearings is the largest. While the occurrence and severity of TTRs are acknowledged in the industry, there is а lack of academic litera\uadture оп their initiation, propagation and the associated risk of damage. Furthermore, in the wide range of operation modes of а wind turЬine, it is not known which modes сап lead to TTRs. Further, the interdependence of TTRs оп environmental loading like the wind is also not reported. This paper aims to address these unknowns Ьу expanding оп the understanding of TTRs using а high-fidelity numerical model of an indirect drive wind turЬine with а douЬly fed induction generator (DFIG). То this end, а multibody model of the drivetrain is developed in SIMPACK. The model of the drivetrain is explicitly coupled to state-of-the-art wind turЬine simulator OpenFAST and а grid-connected DFIG developed in MATLAB\uae\u27s Simulink\uae allowing а coupled analysis of the electromechanical system. А metric termed slip risk duration is pro\uadposed in this paper to quantify the risk associated with the TTRs. The paper first investigates а wide range of IEC design load cases to uncover which load cases сап lead to TTRs. lt was found that emergency stops and symmetric grid voltage drops сап lead to TTRs. Next, the dependence of the TTRs оп inflow wind parameters is investigated using а sensitivity analysis. lt was found that the instantaneous wind speed at the onset of the grid fault or emergency shutdown was the most influential factor in the slip risk duration. The investigation enaЫes the designer to predict the occurrence of TTRs and quantify the associated risk of damage. The paper concludes with recommendations for utility-scale wind turЬines and directions for future research
Magnetic Properties of Multifunctional 7LiFePO4under Hydrostatic Pressure
Get PDFLiFePO4 (LFPO) is an archetypical and well-known cathode material for rechargeable Li-ion batteries. However, its quasi-one-dimensional (Q1D) structure along with the Fe ions, LFPO also displays interesting low-temperature magnetic properties. Our team has previously utilized the muon spin rotation (μ +SR) technique to investigate both magnetic spin order as well as Li-ion diffusion in LFPO. In this initial study we extend our investigation and make use of high-pressure μ +SR to investigate effects on the low-T magnetic order. Contrary to theoretical predictions we find that the magnetic ordering temperature as well as the ordered magnetic moment increase at high pressure (compressive strain)
Statistical Modeling of Arctic Sea Ice Concentrations for Northern Sea Route Shipping
Get PDFThe safe and efficient navigation of ships traversing the Northern Sea Route demands accurate information regarding sea ice concentration. However, the sea ice concentration forecasts employed to support such navigation are often flawed. To address this challenge, this study advances a statistical interpolation method aimed at reducing errors arising from traditional interpolation approaches. Additionally, this study introduces an autoregressive integrated moving average model, derived from ERA5 reanalysis data, for short-term sea ice concentration forecasts along the Northern Sea Route. The validity of the model has been confirmed through comparison with ensemble experiments from the Coupling Model Intercomparison Project Phase 5, yielding reliable outcomes. The route availability is assessed on the basis of the sea ice concentration forecasts, indicating that the route will be available in the upcoming years. The proposed statistical models are also shown the capacity to facilitate effective management of Arctic shipping along the Northern Sea Route
Multi-omics signatures in new-onset diabetes predict metabolic response to dietary inulin: findings from an observational study followed by an interventional trial
Get PDFAIM: The metabolic performance of the gut microbiota contributes to the onset of type 2 diabetes. However, targeted dietary interventions are limited by the highly variable inter-individual response. We hypothesized (1) that the composition of the complex gut microbiome and metabolome (MIME) differ across metabolic spectra (lean-obese-diabetes); (2) that specific MIME patterns could explain the differential responses to dietary inulin; and (3) that the response can be predicted based on baseline MIME signature and clinical characteristics. METHOD: Forty-nine patients with newly diagnosed pre/diabetes (DM), 66 metabolically healthy overweight/obese (OB), and 32 healthy lean (LH) volunteers were compared in a cross-sectional case-control study integrating clinical variables, dietary intake, gut microbiome, and fecal/serum metabolomes (16 S rRNA sequencing, metabolomics profiling). Subsequently, 27 DM were recruited for a predictive study: 3 months of dietary inulin (10 g/day) intervention. RESULTS: MIME composition was different between groups. While the DM and LH groups represented opposite poles of the abundance spectrum, OB was closer to DM. Inulin supplementation was associated with an overall improvement in glycemic indices, though the response was very variable, with a shift in microbiome composition toward a more favorable profile and increased serum butyric and propionic acid concentrations. The improved glycemic outcomes of inulin treatment were dependent on better baseline glycemic status and variables related to the gut microbiota, including the abundance of certain bacterial taxa (i.e., Blautia, Eubacterium halii group, Lachnoclostridium, Ruminiclostridium, Dialister, or Phascolarctobacterium), serum concentrations of branched-chain amino acid derivatives and asparagine, and fecal concentrations of indole and several other volatile organic compounds. CONCLUSION: We demonstrated that obesity is a stronger determinant of different MIME patterns than impaired glucose metabolism. The large inter-individual variability in the metabolic effects of dietary inulin was explained by differences in baseline glycemic status and MIME signatures. These could be further validated to personalize nutritional interventions in patients with newly diagnosed diabetes
Supplementary Cementitious Materials in Building Blocks—Diagnosing Opportunities in Sub-Saharan Africa
Get PDFSustainable building should at least be affordable and carbon neutral. Sub-Saharan Africa (SSA) is a region struggling with housing affordability. Residential buildings are often constructed using block-based materials. These are increasingly produced using ordinary Portland cement (PC), which has a high carbon footprint. Using alternative Supplementary Cementitious Materials (SCMs) for block production might reduce the footprint and price. The purpose is to assess the level of information for SCM use in blocks in SSA and to use this information for Diagnosing the improvement potential as part of an Opportunity Study. Results from the scoping review show that aggregated information on SCMs and the quantities available is limited. Diagnosing the theoretical improvement potential in using cassava peel ash, rice husk ash, corn cob ash, volcanic ash and calcined clays, indicates that SCMs could represent a yearly value of approximately USD 400 million, which could be transferred from buying cement to local production. The use of SCMs could save 1.7 million tonnes of CO2 per year and create some 50,000 jobs. About 5% of the PC used for block production could be substituted, indicating that, in addition to using SCMs, other solutions are needed to secure production of sustainable blocks
Effects on Serum Hormone Concentrations after a Dietary Phytoestrogen Intervention in Patients with Prostate Cancer: A Randomized Controlled Trial
Get PDFPhytoestrogens have been suggested to have an anti-proliferative role in prostate cancer, potentially by acting through estrogen receptor beta (ERβ) and modulating several hormones. We primarily aimed to investigate the effect of a phytoestrogen intervention on hormone concentrations in blood depending on the ERβ genotype. Patients with low and intermediate-risk prostate cancer, scheduled for radical prostatectomy, were randomized to an intervention group provided with soybeans and flaxseeds (∼200 mg phytoestrogens/d) added to their diet until their surgery, or a control group that was not provided with any food items. Both groups received official dietary recommendations. Blood samples were collected at baseline and endpoint and blood concentrations of different hormones and phytoestrogens were analyzed. The phytoestrogen-rich diet did not affect serum concentrations of testosterone, insulin-like growth factor 1, or sex hormone-binding globulin (SHBG). However, we found a trend of decreased risk of increased serum concentration of estradiol in the intervention group compared to the control group but only in a specific genotype of ERβ (p = 0.058). In conclusion, a high daily intake of phytoestrogen-rich foods has no major effect on hormone concentrations but may lower the concentration of estradiol in patients with prostate cancer with a specific genetic upset of ERβ
Regulating the Solvation Structure of Electrolyte via Dual–Salt Combination for Stable Potassium Metal Batteries
Get PDFBatteries using potassium metal (K-metal) anode are considered a new type of low-cost and high-energy storage device. However, the thermodynamic instability of the K-metal anode in organic electrolyte solutions causes uncontrolled dendritic growth and parasitic reactions, leading to rapid capacity loss and low Coulombic efficiency of K-metal batteries. Herein, an advanced electrolyte comprising 1\ua0M potassium bis(fluorosulfonyl)imide (KFSI) + 0.05\ua0M potassium hexafluorophosphate (KPF6) dissolved in dimethoxyethane (DME) is introduced as a simple and effective strategy of regulated solvation chemistry, showing an enhanced interfacial stability of the K-metal anode. Incorporating 0.05\ua0M KPF6 into the 1\ua0M KFSI in DME electrolyte solution decreases the number of solvent molecules surrounding the K ion and simultaneously leads to facile K+ de-solvation. During the electrodeposition process, these unique features can lower the exchange current density between the electrolyte and K-metal anode, thereby improving the uniformity of K electrodeposition, as well as potentially suppressing dendritic growth. Even under a high current density of 4\ua0mA cm−2, the K-metal anode in 0.05\ua0M KPF6-containing electrolyte ensures high areal capacity and an unprecedented lifespan with stable Coulombic efficiency in both symmetrical half-cells and full-cells employing a sulfurized polyacrylonitrile cathode
Application of aerobic granular sludge for municipal wastewater treatment - Process performance and microbial community dynamics under fluctuating conditions
No full textPressures of growing cities, competition for use of urban areas and higher influent loads, are pushing for innovative technologies for wastewater treatment with low demands for land footprint and costs. Furthermore, wastewater treatment is needed to move towards a circular economy by harvest of valuable resources such as nutrients and energy. Aerobic granular sludge (AGS) is a biofilm process without a carrier material for wastewater treatment, exhibiting efficient treatment performance, excellent settleability, high biomass retention, tolerance to toxicity and high loads of organic matter. In this thesis, the first implementation of the AGS process in the Nordic countries was studied to assess the treatment performance, microbial community structure, energy usage, land footprint, and volume needs. The results in this project suggested that selective sludge withdrawal, retaining long solids retention time, sufficient substrate availability, and operational flexibility are important factors for granulation. Both the AGS and parallel conventional activated sludge (CAS) process achieved stable organic matter, nitrogen, and phosphorus removal with low average effluent concentrations. Seasonal variations and environmental factors were identified as important for microbial community succession. The granular biofilm demonstrated higher biomass concentration, diversity, and lower seasonal fluctuations in community composition than the flocculent sludge. A one-year energy comparison resulted in lower specific energy usages (kWh m-3 and kWh reduced P.E.-1) and land footprint for the AGS compared to the CAS process. However, a potential for decreased energy usage was recognised for both systems, leading to the conclusion that operational optimisation and process design might be as important as the type of technology. Additionally, the influence of decreasing temperature on AGS was studied in lab-scale reactors, revealing different responses of the functional groups in the microbial community, and even various response of ASVs at the genus level. In conclusion, the AGS technology for municipal wastewater treatment under fluctuating conditions achieved low average effluent concentrations, was more compact and energy efficient compared to the CAS
Evaluating Surprise Adequacy for Deep Learning System Testing
No full textThe rapid adoption of Deep Learning (DL) systems in safety critical domains such as medical imaging and autonomous driving urgently calls for ways to test their correctness and robustness. Borrowing from the concept of test adequacy in traditional software testing, existing work on testing of DL systems initially investigated DL systems from structural point of view, leading to a number of coverage metrics. Our lack of understanding of the internal mechanism of Deep Neural Networks (DNNs), however, means that coverage metrics defined on the Boolean dichotomy of coverage are hard to intuitively interpret and understand. We propose the degree of out-of-distribution-ness of a given input as its adequacy for testing: the more surprising a given input is to the DNN under test, the more likely the system will show unexpected behavior for the input. We develop the concept of surprise into a test adequacy criterion, called Surprise Adequacy (SA). Intuitively, SA measures the difference in the behavior of the DNN for the given input and its behavior for the training data. We posit that a good test input should be sufficiently, but not overtly, surprising compared to the training dataset. This article evaluates SA using a range of DL systems from simple image classifiers to autonomous driving car platforms, as well as both small and large data benchmarks ranging from MNIST to ImageNet. The results show that the SA value of an input can be a reliable predictor of the correctness of the mode behavior. We also show that SA can be used to detect adversarial examples, and also be efficiently computed against large training dataset such as ImageNet using sampling
Understanding and Managing Non-functional Requirements for Machine Learning Systems
Get PDFBackground: Machine Learning (ML) systems learn using big data and solve a wide range of prediction and decision making problems that would be difficult to solve with traditional systems. However, increasing use of ML in complex and safety-critical systems has raised concerns about quality requirements, which are defined as Non-Functional requirements (NFRs). Many NFRs, such as fairness, transparency, explainability, and safety are critical in ensuring the success and acceptance of ML systems. However, many NFRs for ML systems are not well understood (e.g., maintainability), some known NFRs may become more important (e.g., fairness), while some may become irrelevant in the ML context (e.g., modularity), some new NFRs may come into play (e.g., retrainability), and the scope of defining and measuring NFRs in ML systems is also a challenging task.Objective: The research project focuses on addressing and managing issues related to NFRs for ML systems. The objective of the research is to identify current practices and challenges related to NFRs in an ML context, and to develop solutions to manage NFRs for ML systems.Method: We are using design science as a base of the research method. We carried out different empirical methodologies–including interviews, survey, and a part of systematic mapping study to collect data, and to explore the problem space. To get in-depth insights on collected data, we performed thematic analysis on qualitative data and used descriptive statistics to analyze qualitative data. We are working towards proposing a quality framework as an artifact to identify, define, specify, and manage NFRs for ML systems.Findings: We found that NFRs are crucial and play an important role for the success of the ML systems. However, there is a research gap in this area, and managing NFRs for ML systems is challenging. To address the research objectives, we have identified important NFRs for ML systems, and NFR and NFR measurement-related challenges. We also identified preliminary NFR definition and measurement scope and RE-related challenges in different example contexts.Conclusion: Although NFRs are very important for ML systems, it is complex and difficult to define, allocate, specify, and measure NFRs for ML systems. Currently the industry and research is does not have specific and well organized solutions for managing NFRs for ML systems because of unintended bias, the non-deterministic behavior of ML, and expensive and time-consuming exhaustive testing. Currently, we are working on the development of a quality framework to manage (e.g., identify important NFRs, scoping and measuring NFRs) NFRs in the ML systems development process