Shutdown Period Research Articles

Quantitative Characterization of Compressed Air Systems

In industrial production plants, leaks in the compressed air system (CAS) lead to significant energy losses. In view of rising global energy costs, increasing the efficiency of such systems represents considerable costcutting potential. When analyzing CASs, it is often not possible to quantify the basic consumption and leakages from the available data. For companies seeking to evaluate potential savings and enhance energy efficiency, it is crucial to quantitatively assess the efficiency of CASs in terms of leakage rate. Determining the quality is typically done at a pressure of 1 bar, which is insufficient due to the volume influence of different compressor-units used in production plants. This work focuses on the algorithmic identification of pressure drop intervals as well as the determination and graphical representation of a quantitative trend visualization for industrial pressure drop intervals over a broad pressure range. For the study, the pressures time series of four production sites were recorded within one year. Shutdown periods were identified from the raw data using four machine-learning (ML) clustering and classification methods. By segmenting the pressure drop intervals, the shutdown periods were parameterized, from which quantitative trend visualization for the pressure drop periods due to compressor shutdown could be calculated and interpreted in the context of a graphical representation. The classification methods were compared as part of this work. With an F1-score of up to 0.99 being reached, only the variance differed among the algorithms. From the temporal observation of the calculated trend visualizations, rapidly falling curves suggest an increasing system leakage.

Application and Enlightenment of High Frequency Pressure Monitoring in Fracturing of Fengxi Block

Abstract Volumetric fracturing of large-scale horizontal Wells is one of the important techniques for unconventional oil and gas production. However, there are no effective quantitative methods to describe key parameters such as fracture size and formation flow parameters. The high frequency pressure monitoring technology is to install a high frequency pressure gauge at the wellhead, and obtain the fracture initiation position and the sealing property of the bridge plug through monitoring and analysis of the water hammer wave during the period of pump shutdown. At the same time, the fracturing scale, permeability and the original formation pressure are obtained by combining the analysis of seepage pressure. On the basis of quantitative monitoring results, the relationship between fracture parameters and geological and engineering factors is deeply analyzed, and the following conclusions are obtained. (1) The number of fractures opened is mainly affected by the physical properties of the reservoir, which in turn affects the amount of liquid intake and sand of the single cluster opened, and finally affects the length and height of fractures in the core area. (2) The fracture morphology is affected by the construction scale and reservoir mechanical properties of a single cluster, while the SRV of the core area and the overall SRV are more affected by the overall construction scale.

Rise in Autism Spectrum Disorder Diagnoses: A Comparative Analysis of Pre-pandemic and Pandemic Periods.

Introduction This study analyzes the rise in autism spectrum disorder (ASD) diagnoses by comparing pre-pandemic (2017-2019) and pandemic period (2021-2023) data from two private pediatric practices with different populations in New York. The year 2020 was out of the analysis to focus on the impact after the shutdowns on ASD, and there were disruptions in clinic operations during that year. Clinic I primarily served an African-American ethnic population, while Clinic II primarily served an Asian population. Methods A retrospective analysis was conducted using de-identified patient numbers from the electronic medical records (EMR) of two private clinics. Only the numbers of new patients diagnosed each year were included from January 1, 2017, to December 31, 2019 (pre-pandemic), and January 1, 2021, to December 31, 2023 (pandemic). Sixteen ASD International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) codes were included in this analysis. Descriptive and inferential statistics were used to find important patterns and determine statistical significance. Results The study included 537 patients, 182 from Clinic I and 355 from Clinic II. Clinic I demonstrated a significant increase in ASD diagnoses, from 63 (pre-pandemic) to 119 (pandemic) (χ2=17.23; p=0.000033). Clinic II demonstrated a significant increase in ASD diagnoses, from 149 (pre-pandemic) to 206 (pandemic) (χ2=9.15; p=0.00248). Conclusion The significant increase in ASD diagnoses in two private pediatric practices with different populations indicates a notable association with the shutdown periods and the pandemic. Factors such as disrupted routines, changes in access to healthcare services, and increased parental awareness may have contributed to this rise. Further longitudinal studies are needed to understand the long-term impacts of the pandemic on ASD diagnoses and care.

Abstract C129: Adapting to disruption?: Changes in colorectal cancer screening modalities and disparities during the COVID-19 pandemic

Abstract Research Objective: At-home colorectal cancer screening methods, such as Fecal Immunochemical Tests (FIT), offered a safer, convenient alternative to in-clinic procedures like colonoscopies during the COVID-19 pandemic. Racial and ethnic minorities were more likely to use these tests prior to the pandemic, but it is unclear whether disparities in colorectal cancer screening changed because of the pandemic. We examined rates of colorectal cancer screening before and after shutdown periods across race/ethnicity, age, and income among health plan enrollees in the United States to inform screening catch-up strategies after COVID-19 disruptions. Study Design: We conducted a retrospective analysis of cancer screening claims from Optum Clinformatics Data Mart among enrollees from 2019-2021. Monthly screening volume was calculated for a three-year period, with January 2019-February 2020 as the pre-pandemic period, and July 2020-December 2021 as the post-pandemic period. We conducted an event study to calculate group-level monthly predicted probabilities of screening using a linear probability model with group interactions by month, controlling for demographic variables and comorbidities. Additionally, we used difference-in-difference (DD) regressions including group interacted with a post-pandemic dummy. The pandemic closure period months were excluded from our DD analysis. Eligible enrollees included those ages 40-75 with continuous enrollment 12 months prior to screening index month. Colorectal cancer screening claims included codes for colonoscopies and other endoscopic procedures as well as FIT and FOBT tests. Results: 2,576,760 health plan enrollees were included. Colonoscopy rates did not change significantly between the pre-COVID period and post-COVID period, however FIT test screening rates dropped from an average of 0.66% of age-eligible enrollees screened per month in the pre-pandemic period to 0.58%. Our difference-in-difference results suggested Hispanic-NH White disparities in FIT tests widened. For colonoscopy, Hispanic-NH White and Asian-White disparities widened, while Black-White disparities decreased. Conclusions: Despite the promise that in-home FIT testing would substitute for colonoscopy during the pandemic, we saw a much larger decline in FIT testing rates during the pandemic than for colonoscopy and increasing disparities. This study highlights the need for targeted strategies to address screening disparities exacerbated by the COVID-19 pandemic, particularly among Hispanic and NH-Asian populations. Citation Format: Cameron M. Kaplan, Willow Pastard, John Thacker, Todd Schneberk, Chun Nok Lam, Sarah Axeen, Jennifer Tsui. Adapting to disruption?: Changes in colorectal cancer screening modalities and disparities during the COVID-19 pandemic [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C129.

Stroke but no hospital admission: Lost opportunity for whom?

To counter the spread of COVID-19, the French government imposed several stringent social and political measures across its entire population. We hereto assess the impact of these political decisions on healthcare access in 2020, focusing on patients who suffered from an ischemic stroke. We divide our analysis into four distinct periods: the pre-COVID-19 pandemic period, the lockdown period, the "in-between" or transitional period, and the shutdown period. Our methodology involves utilizing a retrospective dataset spanning 2019-2020, an exhaustive French national hospital discharge diagnosis database for stroke inpatients, integrated with income information from the reference year of 2019. The results reveal that the most affluent were more likely to forgo medical care, particularly in heavily affected areas. Moreover, the most disadvantaged exhibited even greater reluctance to seek care, especially in the most severely impacted regions. The data suggest a loss of opportunity for less severely affected patients to benefit from healthcares during this lockdown period, regardless of demographic, location, and socioeconomic determinants. Furthermore, our analysis reveals a notable discrepancy in healthcare-seeking behavior, with less affluent patients and seniors (over 75 years old) experiencing slower rates of return to healthcare access compared to pre-pandemic levels. This highlights a persistent gap in healthcare accessibility, particularly among socioeconomically disadvantaged groups, despite the easing of COVID-19 restrictions.

PEMFC AST Protocol for Air-Air Starts: Screening of Cathode Tolerances

The long-term durability of the cathode catalyst layer continues to be one of the main obstacles for the widespread commercialization of proton exchange membrane fuel cells (PEMFCs) for transportation applications. A major challenge is air/air-start events that occur after long shut-down periods where an H2/air gas front passes through the anode and causes polarization of the cathode electrode to ≈1.4-4.5 V. To screen catalyst materials for their air/air-start tolerance, several accelerated stress test (AST) protocols have been developed. The two most commonly used AST protocols have been recommended by the U.S. Department of Energy (DoE): (1) catalyst support protocol consisting of triangle sweep cycles: 500 mV/s between 1.0 V and 1.5 V (H2/N2, 80ׄ°C, 100% relative humidity (RH)) designed to trigger corrosion of the carbon support; (2) unmitigated start-up/shut-down durability protocol involving an H2/air front, whereby each cycle consists of a start-up event, intermittent fuel cell operation, an unmitigated shut-down event, and an idle period under relevant air/air-start conditions (35°C, 100% RH). While the second protocol is a closer representation of air/air-start events that occur in a PEMFC system, it requires specialized fuel cell testing equipment to allow for an H2/air front in the anode electrode. Conversely, the triangle sweep protocol can be used for catalyst benchmarking on any fuel cell test bench and even in model systems like rotating disc electrode (RDE), or gas diffusion electrode (GDE) setups.In this work, we compared H2/air front-based start-up/shut-down (SUSD) aging to the voltage-controlled carbon corrosion AST for four catalyst coated membranes (CCM). Using in-situ electrochemical and ex-situ characterization techniques (e.g., μXRD, SEM, and TEM), the degradation is compared with respect to the rate of loss in the electrochemically active surface area (ECSA). Particularly the correlation between voltage losses and ECSA, Pt particle size growth, catalyst layer thinning, and homogeneity of aging were investigated. Our results with the catalyst support AST confirms that this method exclusively triggers the carbon corrosion aging mechanism without a significant increase in the Pt particle size and is therefore a suitable screening protocol for support stability. However, the more application relevant SUSD protocol resulted in a combination of comparatively mild carbon corrosion and severe Pt dissolution/redeposition that caused significant Pt size growth and Pt band formation. Furthermore, SUSD resulted in inhomogeneous aging where the Pt size growth is more pronounced towards the anode inlet of the CCM. Due to the difference in the dominating aging mechanisms, the voltage loss response at nominally the same loss of ECSA varies greatly for the two protocols. Therefore, typical voltage-controlled AST (1-1.5V) is found to be unsuitable for predicting the air/air start tolerance of catalyst material. Instead, we propose to use an alternative voltage-controlled protocol that combines the two main degradation mechanisms of air/air starts: triangle sweep cycles at 500 mV/s between 0.7 V and 1.4 V (H2/N2, 45ׄ°C, 100% relative humidity).

The FastRICH ASIC for the LHCb RICH enhancements

The Ring-Imaging Cherenkov (RICH) detectors at LHCb have an excellent intrinsic time resolution due to the prompt Cherenkov radiation and focusing mirror optics. Novel RICH readout electronics, to be installed during the third Long Shutdown period (LS3) of the LHC, will introduce fast-timing information in the LHCb experiment. Central to the readout chain is a new application-specific integrated circuit (ASIC), called the FastRICH, with a unique set of features targeting operation in high-energy physics experiments and in particular the LHCb RICH detector.

The Impact of COVID-19 on the Cypriot Stock Market Dynamics

This paper examines the effects of coronavirus disease 2019 (COVID-19) pandemic outbreak on the Cypriot Stock Exchange (CSE), which has encountered substantial turmoil. For this purpose, daily stock market returns were used over the period of September 3, 2019 - July 10, 2020, for the Cypriot economy. The study applied Granger causality models to explore whether the CSE is impacted by the crisis generated by novel coronavirus. Hsiao’s approach to Granger causality was employed to investigate the causalities among COVID-19 and stock market returns, as well as between pandemic measures and several commodities. The analyses uncover intricate dynamics and contributing factors, shedding light on the observed volatility. The findings demonstrate significant volatility throughout the pandemic, with notable shifts in market conditions. Nevertheless, the stock market showcased resilience and recovery during the initial shutdown period. These insights contribute to understanding the pandemic’s impact on the CSE, offering crucial guidance for investors, policymakers, and market participants.

Building caring cities: From disaster relief to community-based infrastructure for unauthorized and low-paid immigrant workers

ABSTRACT This paper asks “who cares” in the precarious city when the state fails. In Los Angeles, one of the largest immigrant-based economies in the United States, the COVID-19 pandemic exposed the extreme vulnerability of low-paid immigrant workers who were unable to do paid work during government-mandated shutdown periods. Unauthorized workers and immigrants in mixed-status households, who are the focus of our study, faced additional challenges, contending with hunger, eviction, and lack of access to essential medicine and public health care due to their exclusion from federal relief assistance. The role of civil society organizations in addressing the gaps and fissures of the neoliberal state is heavily criticized, we argue that scholars—as well as policymakers—need to pay closer attention to exactly how organizational actors are rebuilding state-society relations guided by principles of relational care, strategic responsiveness, and infrastructural efficacy, rather than neglect, incompetence, and criminality. As we face a future of ongoing epidemiological and ecological crises, our public institutions have much to learn from immigrant workers and social justice organizations that are working in concert to build caring cities in precarious and carceral times.

Summary of caesium evaporation and deposition during SPIDER first campaign

SPIDER (Source for the Production of Ions of Deuterium Extracted from a Radio frequency plasma) is currently in a major shutdown period dedicated to the upgrade of several components in order to enhance its performances and guarantee their agreement with the ITER requirements. During this phase, an in-depth inspection of the beam source components is fundamental to understand SPIDER behaviour during the previous experimental campaigns, and in particular caesium evaporation inside the source by means of Cs ovens. Caesium evaporation and its deposition on the plasma grid (PG) is fundamental to minimize the work function of PG surface causing an increased generation of negative ions and a reduction of the electron density in front of the PG, which means less co-extracted electrons. This work presents the analysis of the caesium ovens performances and of their status after removal. Inductively Coupled Plasma Mass Spectrometry (ICP-MS) analyses were carried out on samples of plasma source walls, and the experimental data was compared to the results obtained from other investigations: numerical models for the caesium evaporation and deposition, chemical surface analyses (performed in strategical locations of the source walls and extraction grid), and Laser Absorption Spectroscopy (LAS) measurements. The non-uniformity of Cs distribution has been taken into account since it has repercussions on negative ion extraction efficiency and uniformity.

Towards unsupervised fault detection for offshore wind turbine cable protection systems using contrastive learning

As offshore wind power expands globally, it is essential to ensure the reliable operation of components of such critical infrastructures. A less explored instance of such components, which are though essential in terms of operation, is found in subsea turbine cables and their protection systems, whose failure can incur prolonged shutdown periods and costly repairs. We propose a novel unsupervised machine learning approach exploiting use of Distributed Acoustic Sensing (DAS) data and contrastive learning for monitoring offshore wind turbine Cable Protection Systems (CPSs). A Transformer neural network adapted for time-series ingests the high-frequency, noisy DAS CPS time-series measurements, and is trained to learn a coherent representation of the data using a contrastive learning scheme that enforces temporal and positional consistency in the latent space. This latent representation can then be used to perform anomaly detection in an unsupervised manner, alleviating the need for costly labeled offshore anomaly data. We demonstrate that a coherent representation of the data is learnt by the model, which we then use to detect synthetic anomalies and an actual CPS stabilization event.

The number needed to biopsy for cutaneous melanoma in academic dermatology clinics.

A standard metric for melanoma detection is the number needed to biopsy (NNB). This metric has been used to evaluate practicing dermatologists, dermatology advanced practice professionals, and primary care providers. This metric, however, has rarely been applied to residency clinics. We aimed to determine the NNB at the University of Colorado residency clinics. Moreover, we sought to determine the impact of the coronavirus disease 2019 (COVID-19) pandemic on NNB. This study is a retrospective analysis of biopsies performed from 2016 to 2022 at the Denver Health Medical Center and the Rocky Mountain Regional Veteran Affairs dermatology clinics. Differential diagnosis at the time of biopsy was searched for keywords including melanoma, melanoma in situ, and lentigo maligna. Skin biopsies that included re-excisions were excluded. The NNB was subsequently generated by dividing the number of biopsied lesions with suspected melanoma by the number of histologically confirmed melanomas. The data was further separated by pre-COVID-19 (2016-February 2020), COVID-19 shutdown period (March 2020-July 2020), and post-COVID-19 (March 2020-present). Demographic data, including age, sex, race, and Fitzpatrick type, were collected. There were 2230 biopsies with suspected melanoma in the differential diagnosis at both clinic sites from 2016 to 2022. Of these, 362 were histologically confirmed melanoma. Total NNB was 6.16. The pre-COVID-19 NNB was 5.86, and the post-COVID-19 NNB was 6.91. Residency clinics have NNB similar to published values of practicing dermatologists. Furthermore, within these clinics, the impact of the COVID-19 pandemic was appreciated by a relative, although statistically insignificant, increase in NNB.

Destabilization Mechanism and Stability Study of Collapsible Loess Canal Slopes in Cold and Arid Regions

The combination of seasonal shutdowns, water conveyance, cold, and drought can easily lead to the deterioration of the anti-seepage system and loess foundation of the canal, which contributes to the destruction of the slope. To reveal the failure mechanism of the collapsible loess canal slope, this paper is based on the results of laboratory tests and adopts numerical simulations to analyze the stability of the canal slope under different conditions. The results show that the shear strength indexes and elastic modulus E of loess decrease following an exponential pattern with the increase in wetting-drying and freezing-thawing (WD-FT) cycles. The height of the seepage overflow point yields little effect on the water level behind the impermeable membrane, whereas the height of the water level has a significant effect. In the operation period, the slope under any working conditions is in a relatively stable state. However, the slope with a water level of 4.5 m behind the impermeable membrane tends to be unstable after three WD-FT cycles during the shutdown period. By replacing the surface-degraded loess with sand gravel and picking a depth of 0.9–1.2 m, the slope will maintain a long-term stable state.

Legal Analysis of Rising Divorce Cases: Impact of COVID-19 in Bandar Lampung, Indonesia

Objective: This study aim to analyse the legal implications of increase in the divorce rate as a result of the Covid-19 pandemic at the Tanjungkarang Religious Court in Bandar Lampung, Indonesia. Additionally, the study seeks to identify the underlying factors that contributed to the occurrence of divorce at the Tanjungkarang Religious Court during the pandemic. Theoretical framework: Literature in this study was based on the phenomena that several regions in Indonesia have experienced an increase in the divorce rate as a result of the Covid-19 pandemic; the Tanjungkarang Religious Court in Bandar Lampung, Indonesia, is one example. From January to June 2020, the Tanjungkarang Religious Court in Bandar Lampung, Indonesia, documented 699 divorces amid the COVID-19 pandemic. Method: This study employs a qualitative methodology and conducts field research. In the interim, both primary and secondary data, acquired through field observation, documentation, and direct interviews, are included in the collected information. In order to facilitate the process of verifying the data, the researcher employed the triangulation method to ascertain its validity. Results and Conclusion: this study found that During the Covid-19 pandemic, the Tanjungkarang Bandar Lampung-Indonesia Religious Court witnessed a surge in divorce petitions. June brings the highest volume of divorce cases. Divorce filings experienced a surge during the months of April, May, and June, as well as throughout the shutdown period. Second, divorce cases during the Covid-19 pandemic were precipitated by a variety of factors, with economic factors ranking highest. According to report data from the Tanjungkarang Religious Court, the factors of physical disability, imprisonment sentence, and coerced marriage account for the smallest number of divorce cases. Implications of the research: In order to promote resilience and well-being among families amidst the uncertainties of the current pandemic, it is possible for policymakers, legal practitioners, and community stakeholders to acknowledge the difficulties presented by the disease and devise focused interventions to assist individuals navigating the process of marital dissolution. Originality/value: Studying how COVID-19 has affected divorce rates in particular areas, such as Bandar Lampung, Indonesia, could shed light on the ways in which the pandemic has impacted socioeconomic variables, legal frameworks, and family dynamics there. Policymakers, attorneys, and social service providers can be more informed about the difficulties that families may encounter in times of crisis by having a better understanding of these effects. This knowledge can also help to shape the creation of support networks and laws that deal with these problems. Such studies can also add to the body of knowledge in academia regarding how significant crises affect legal systems and family structures.

Studies on the Experimental Measurement of the Low-Frequency Aerodynamic Noise of Large Wind Turbines

With the continuous warming of the global climate, expanding the use of renewable energy has become one of the main social responsibilities. However, as the number of installed wind turbines and their physical dimensions continue to increase, the issue of generated noise has become increasingly significant in influencing the acceptance and endorsement of wind power projects by neighboring communities. In this paper, we investigated the noise generated by two wind turbine units with rated powers of 1.5 MW and 4.5 MW and analyzed the variations in low-frequency noise during their operation and shutdown periods. This research shows that the power of a single unit has a significant impact on the low-frequency noise emitted into the environment. Compared with 1.5 MW wind turbines, 4.5 MW wind turbines generate more low-frequency noise when operating at the same wind speed. Further analysis of the narrowband frequency spectra and one-third octave spectra of the measured noise indicates that the low-frequency noise from the 4.5 MW wind turbine increases significantly in the range of approximately 80 Hz to 300 Hz, with more pronounced variations below 250 Hz corresponding to changes in wind speed. However, the overall variations in low-frequency noise with wind speed are not as notable as those observed for the 1.5 MW wind turbine. Due to the relatively weak attenuation of low-frequency noise in the atmosphere, the higher low-frequency content of large wind turbines may cause more distress to residents near wind farms. The result of this study emphasizes that in the planning and design of wind power projects, in addition to considering the efficiency of single-unit power generation and the contribution of renewable energy, it is also necessary to pay full attention to noise emission issues to ensure that the project is widely supported and accepted in the community.

Mothers, Household Bubbles, and Social Support During the First Wave of the COVID-19 Pandemic

Parents of young children experienced many stressors due to stay-at-home directives in the first wave of the COVID-19 pandemic. Bubbles were implemented by some governments, allowing households to connect with another household while minimizing contagion risk, but little is known about their effectiveness. We explored the social support experiences of Canadian mothers living in Nova Scotia during this first wave, focusing on whether they perceived household bubbles to be helpful in reducing parenting stress. In-depth interviews were completed with 18 mothers (aged 21–49) who had at least one child under the age of 12 during the first shutdown. Interviews focused on how they coped during the initial shutdown period, the immediate time after they paired up with another household, and what was happening for them currently (approximately eight to ten months later). Data were analyzed using qualitative description and content analysis through application of topic, descriptive, and analytical coding; memo writing; and matrix analysis. Deciding who to bubble with typically focused on direct support for parents or having playmates for children. Having a bubble arrangement reduced the pressure of the situation, and perceptions of future emergency backup support also reduced anxiety levels. Support from family members who lived far away, however, was still key for some families. Household bubbles play an important role in reducing stress levels during a pandemic through received and perceived support. As the COVID-19 pandemic evolves, policy directives and support interventions need to enhance social support for parents and peer interactions for young children.

The RADTRAD analysis methodology for the Fuel Handling Accident during the long-term shutdown period of Chinshan Nuclear Power Plant

Abstract This study presents a methodology for the Fuel Handling Accident (FHA) analysis during the long-term shutdown period with the fuels still residing in the reactor core for the Chinshan BWR/4 Nuclear Power Plant. This methodology can be used to calculate the radiation dose received by the control room personnel, assuming failure in safety systems, facilities or structures. The computer code RADTRAD is employed to perform the transport and release of radioactive inventories first, and then calculate the control room dose based on appropriate dose conversion factor for the FHA. The results show that even failure in safety systems, facilities or structures, the whole body dose, thyroid dose and beta-skin dose are well below the acceptance limits specified in the Standard Review Plan.

Integrated model "reservoir-well" based on D-CRMP

The active use of integrated modeling in reservoir engineering of oil fields has led to research to overcome the problems of its practical application. Typically, a 3D hydrodynamic model is used as a component characterizing the fluid flow in the reservoir. The downsides of the model are high computational complexity and low reliability of the initial data. Therefore, the objectives of the study are to construct an integrated reservoir-well model that does not have these drawbacks and to verify its adequacy. The article presents an integrated model for fast decision-making in the development of oil fields, as well as for analysis of the characteristics of the inter-well space, which doesn't require prior information about the reservoir. The integrated model is based on a representation of the analytical capacitance-resistance model (CRM), which considers shutdown periods of production wells. Oil flow rates are calculated using the Koval method. The pressure distribution in wells is calculated using a correlation similar to that used in the tNavigator software. A comparison was made between the calculation results obtained using the integrated model based on D-CRMP and the calculations in the tNavigator.

RUL prediction for two-phase degrading systems considering physical damage observations

This paper focuses on a specific type of two-phase degrading system commonly encountered in industrial practice. The first phase is moderate with a low degradation rate while the second is rapid with a high rate. Current studies usually rely solely on sensor measurements to divide phases and predict the remaining useful life (RUL), ignoring the utilization of actual physical damage observations, such as wear depth and crack length. These observations, available during system shutdown periods, directly reflect system states and phase changes. To this end, we propose a novel RUL prediction framework consisting of offline training and online prediction processes. In the offline training process, the physical damage observations and sensor measurements are utilized to estimate the parameters of a two-phase Wiener process and a bijective function matrix. In the online prediction process, real-time sensor measurements are transformed into virtual damage observations for RUL prediction. To enhance the accuracy of phase change point detection, a change point detection algorithm is proposed for both processes. The effectiveness is demonstrated using a simulation and a real case study.

Solution of the Heat and Mass Transfer Problem for Soil Radiant Heating Conditions Using the Method of Finite Integral Fourier Transform

To achieve high agricultural yields, it is essential to predict the soil temperature and moisture regime, considering the heating technology employed. The research object is soil heated by a ceiling-mounted infrared emitter. The research subject encompasses one-dimensional unsteady fields of soil moisture content and temperature. The research goal is to forecast the soil temperature and moisture regime under radiant heating conditions. The research methods involve the analytical solution of heat and mass transfer differential equations using the method of finite integral Fourier transforms. Research results indicate that the top layer of milled peat, with an initial moisture content of 3.7 kg/kg, will reach a final moisture content of 1.0 kg/kg in approximately 6 hours during infrared drying. As a result of radiant heating, the soil temperature will rise from an initial 5 ℃ to a final 22.6 ℃ in approximately 3 hours. The analytical solution of the mass transfer differential equation can be utilized for theoretical studies of drying capillary-porous materials, such as determining the drying period or the thickness of the material layer that will dry to a specified final moisture content. The analytical solution of the heat transfer differential equation, accounting for both thermal conductivity and the Dufour effect, can be employed to manage the operation of the infrared radiation source, such as determining its operational and shutdown periods when the soil surface temperature reaches its maximum (critical) value. The mathematical solutions discussed in the article do not consider thermodiffusion processes in the soil layer (Soret effect), which presents a promising direction for further scientific research.