Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “logistics”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Preparing research samples for safe arrival at centers and facilities: recipes for successful experiments

Preparation of biomacromolecules for structural biology studies is a complex and time-consuming process. The goal is to produce a highly concentrated, highly pure product that is often shipped to large facilities with tools to prepare the samples for crystallization trials or for measurements at synchrotrons and cryoEM centers. The aim of this article is to provide guidance and to discuss general considerations for shipping biomacromolecular samples. Details are also provided about shipping samples for specific experiment types, including solution- and cryogenic-based techniques. These guidelines are provided with the hope that the time and energy invested in sample preparation is not lost due to shipping logistics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Smart manufacturing maturity models and their applicability: a review

The purpose of this paper is to review existing smart manufacturing (SM) maturity models' dimensions and maturity levels to assess their applicability and drawbacks. There are many maturity models available but many of them have not been validated or do not provide a useful guide or tool for applications. This gap creates the need for a review of the existing maturity model's applicability. Nineteen peer-reviewed maturity models related to “Digital Transformation,” “Industry 4.0” or “Smart Manufacturing” were selected based on a systematic literature review and five consulting firm models were selected based on the author's industry knowledge. The chosen models were analyzed to determine 10 categories of dimensions. Then they are assessed on a 1–5 scale for how applicable they are in the 10 categories of dimensions. The five “consulting firm” models have a first-mover advantage, are more widely used in industry and are more applicable, but some require payment, and they lack published details and validation. The 19 “peer reviewed” models are not as widely used, lack awareness in the industry and are not as easy to apply because of no web tool for self-assessment, but they are improving. The categories defined to characterize the models and facilitate comparisons for users include “Information Technology (IT) and Cyber-Physical System (CPS) and Data,” “Strategy and Organization,” “Supply Chain and Logistics,” “Products and Services,” “Culture and Employees,” “Technology and Capabilities,” “Customer and Market,” “Cybersecurity and Risk,” “Leadership and Management” and “Governance and Compliance.” The analyzed maturity models were particularly weak in the areas of cybersecurity, leadership and governance. Researchers and practitioners can use this review with consideration of their specific needs to determine if a maturity model is applicable or if a new model needs to be developed. The review can also aid in the development of maturity models through the discussion of each of the dimension categories. Finally, compared to existing reviews of SM maturity models, this research determines comprehensive dimension categories and focuses on applicability and drawbacks.

42 ENGINEERING↗
DOE OSTI2026

A Physics-Aligned Multi-Domain Machine Learning Framework for Time-Localised Diagnosis of Power Electronics Faults

This paper presents a physics-aligned framework for fault diagnosis in multi-phase power-electronic systems using cycle-synchronous windowing and multi-domain features derived from Fourier, wavelet, and Hilbert–Huang representations. While both logistic regression and multilayer perceptron (MLP) models achieve perfect performance under standard evaluation, blind unseen testing reveals a critical failure in a baseline MLP. This is shown to arise from model selection based on validation accuracy. Using validation-loss-based selection restores correct unseen performance and improves confidence. Feature ablation shows that Fourier and wavelet features dominate, while computational analysis indicates that feature extraction, particularly HHT, governs runtime.

Kumar, Praveen [ORNL] (ORCID:0000000291877857)↗
DOE OSTI2022

Supply Chain Risk Management: Data Structuring

Supply chain risk management (SCRM) is an area of research that addresses both logistics concepts to maximize efficiency, reliability, and revenue as well as risk features, such as potential weak points, break points, and vulnerabilities within the supply chain. SCRM is used to find risks introduced at each node in a supply chain and how these risks can impact a company’s products, individuals, customers, and reputation. SCRM is a relatively new field, so standardized processes including data structuring are not fully documented. This paper explains the importance of a standard data structuring methodology and how it can enhance current SCRM efforts. Data ingest, structuring, and analysis are predominantly managed by humans. Automating some of the less complex steps can positively impact SCRM by allowing human analysts to focus on more strategic analyses. Types of data to be collected and structured are collected via publicly available information related to hardware, software, and corporate entities. After the data has been collected, the information is formatted in a specific manner, conforming to a schema, to allow for more effective and efficient ingest for further analysis. This paper outlines data structures used by Pacific Northwest National Laboratory for SCRM research and analysis purposes. These structures have been used for hundreds of analyses and have been successful in developing a common baseline. Data structuring is one of the first steps in data standardization, which will further mature and enhance the SCRM research area.

supply chain risk management, data structuring, re↗
DOE OSTI2024

Architecture Design for Remote Operation of Microreactors: Poster

The nuclear sector is pursuing a number of advanced reactor concepts, one of which is the microreactor, an advanced reactor characterized by a power output of less than 20 MWth. These reactors are intended for use in applications where traditional power solutions are economically or logistically impractical. One key feature required for the successful deployment of microreactors is a remote operation capability, which can dramatically cut staffing expenses by eliminating the need for licensed operators to be present at the site of each reactor and instead concentrate in a centrally located operations center. In moving to a remote operations framework for nuclear reactors, the number of potential attack surfaces for a cyber adversary looking to cause harm or disruption increases. Therefore, a robust cybersecurity architecture is required to mitigate these potential vulnerabilities. This paper explores the functional infrastructure, security, and communication requirements to adapt remote operations to nuclear applications. It then presents a reference architecture for remote operations of microreactors that applies best practices in cybersecurity and remote communications in the context of a digital twin remote operation system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Enhancing Automotive Intrusion Detection Through Multi-Modal Fusion: A CAN FD-LiDAR Approach

As vehicles become smarter and more autonomous, they increasingly depend on advanced sensors and communication technologies to operate securely. However, such growing dependence on technology—whether it’s CAN (Controller Area Network) for internal communication or LiDAR (Light Detection and Ranging) for sensing the world around them—also expands the attack surface for the types of cyber attacks. Traditional intrusion detection systems (IDS) typically monitor these systems in isolation, limiting their ability to detect sophisticated, crosssystem attacks. To address this, we propose a multi-modal fusion approach that combines real-world CAN FD signals (from the HCRL dataset) with LiDAR features (from the nuScenes dataset) to enhance attack detection. Our method employs a twostage ensemble approach. Calibrated XGBoost and LightGBM models initially process CAN FD (Fuzzing Data) and LiDAR data independently, detecting timing anomalies and space abnormalities. They are subsequently logarithmically combined with a logistic regression meta-model along with 17 engineered features capturing cross-modal behavior, prediction conflicts, and nonlinear interactions. This approach achieves an AUC of 0.87 and an F1-score of 0.82, surpassing single-modality baselines and early fusion methods, at merely 2 ms inference latency. Compared with deep learning competitors, it is 3 times more efficient, providing a lightweight, interpretable, and real time solution to automotive cybersecurity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Cryogenic Considerations for Superconducting Magnet System Design for the Material Plasma Exposure eXperiment (MPEX)

The Material Plasma Exposure eXperiment (MPEX) has been proposed as a facility to address plasma material interaction knowledge gaps to qualify and develop materials and technologies that surround plasma environments for future fusion reactors. Utilizing different radio-frequency (rf) heating technologies, MPEX is a linear plasma device that will generate fusion reactor–like plasmas with energies and particle fluxes at the target materials with electron temperatures of 1 to 15 eV, electron densities of 10 20 to 10 21 m -3 , and ion fluxes greater than 10 24 m -2 s -1 . Starting with the MPEX requirements with respect to magnetic fields between 0.1 and 2.5 T and warm bores of either 0.65 m or 1.56 m, conceptual designs for a superconducting magnet system have been developed that utilize multiple NbTi windings distributed across seven cryostats to accommodate rf heating, water cooling, and vacuum systems needed for MPEX. While the cryogenic and magnet technologies relative to the field and space requirements are mature, the integration of these technologies across multiple cryostats presents several technical and logistical challenges. An analysis of the preferred refrigeration approach, modular recondensing liquid helium cryocoolers, was performed. Utilizing a design margin of a factor of two, this approach is feasible within the current design requirements for MPEX with some considerations related to its implementation within the thermal shields and the magnet subsystem geometries.

Duckworth, Robert↗
DOE OSTI2021

Fault Detection Utilizing Convolution Neural Network on Timeseries Synchrophasor Data From Phasor Measurement Units

An end-to-end supervised learning method is proposed for fault detection in the electric grid using Big Data from multiple Phasor Measurement Units (PMUs). The approach consists of preprocessing steps aimed at reducing data noise and dimensionality, followed by utilization of six classification models considered for detecting faults. Three of the models were variants of Convolutional Neural Network (CNN) architectures that consider a single type of measurement (voltage, current or frequency) at all PMUs or all types together also at all PMUs. CNN based models were compared to traditional methods of Logistic Regression (LR), Multi-layer Perceptron (MLP) and Support Vector Machine (SVM). Evaluation was conducted on two-year data measured by PMUs at 37 locations in a large electric grid. Here, the response variable for classification were extracted from the grid-wide outage event log. Experiments show that CNN-based models outperformed traditional methods on one year out-of-sample outage detection over the entire grid.

42 ENGINEERING↗
DOE OSTI2022

Different CT slice thickness and contrast‐enhancement phase in radiomics models on the differential performance of lung adenocarcinoma

Abstract Background To investigate the effects of computed tomography (CT) reconstruction slice thickness and contrast‐enhancement phase on the differential diagnosis performance of radiomic signature in lung adenocarcinoma. Methods A total of 187 patients who had been pathologically confirmed with lung adenocarcinoma and nonadenocarcinoma were divided into a training cohort ( n = 149) and validation cohort ( n = 38). All the patients underwent contrast‐enhanced CT and the images were reconstructed with different slice thickness. The radiomic features were extracted from different slice thickness and scan phase. The logistic regression (LR) algorithm was used to build a machine learning model for each group. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve and DeLong test was used to evaluate its discriminating performance. Results Finally, 34 image features and five semantic features were selected to establish a radiomics model. Based on the three contrast‐enhanced CT phases and four reconstruction slice thickness, 12 groups of radiomics models showed good discrimination ability with the AUCs range from 0.9287 to 0.9631, sensitivity range from 0.8349 to 0.9083, specificity range from 0.825 to 0.925 in the training group. Similar results were observed in the validation group. However, there was no statistical significance between the different CT scan phase groups and different slice thickness ( p > 0.05). Conclusions The radiomic analysis of contrast‐enhanced CT can be used for the differential diagnosis of lung adenocarcinoma. Moreover, different slice thickness and contrast‐enhanced scan phase did not affect the discriminating ability in the radiomics models.

Wang, Yang↗
DOE OSTI2022

Using cloud radar to investigate the effect of rainfall on migratory insect flight

The fate of migrating insects that encounter rainfall in flight is a critical consideration when modelling insect movement, but few field observations of this common phenomenon have ever been collected due to the logistical challenges of witnessing these encounters. Operational cloud radars have been deployed around the world by meteorological agencies to study precipitation physics, and as a byproduct, provide a rich database of insect observations that is freely available to researchers. Although considered unwanted ‘clutter’ by the meteorologists who collect the data, the analysis method presented here enables ecologists to delineate co-occurring signals from insects and raindrops. We present a method that uses image processing techniques on cloud radar velocity spectra to examine the fate of migrating insects when they encounter precipitation. By analysing velocity spectra, we can distinguish flying insects from falling rain and compare the relative density of insects in flight before, during and after the rainfall. We demonstrate the method on a case of insect migration in Oklahoma, USA. Using this method, we show the first reconstructed images of migrating insect layers in flight during rainfall. Our analysis shows that mild to moderate rainfall diminishes the number of insects aloft but does not cause full termination of migratory flight, as has previously been suggested. We hope this technique will spur further investigations of how changing weather conditions impact insect migration, and enable some of the first of such studies in regions of the world that are underrepresented in the literature.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Factors associated with treatment limitations in two Swedish intensive care units: Prevalence and patient involvement

Abstract The aim was to study the prevalence, documentation, and patient involvement in treatment limitations (TLs) in two Swedish intensive care units (ICUs). All patients admitted to the ICUs of two Swedish regional hospitals in 2019 were screened for inclusion. Exclusion criteria included postanesthesia care <24 h. Patients were identified using the Swedish Intensive Care Registry (SIR) and data were extracted from SIR and hospital charts. Uni‐ and multivariable logistic analysis was performed to investigate associations with the presence of TLs. A total of 3090 patients were admitted to the two ICUs in 2019. After exclusion, 1019 patients were included in the study. 45.5% were women and the mean age was 62.9 years. 26.5% of the patients had one or several TLs. Age (OR 1.04 per one year increase 95% confidence interval (CI) 1.02–1.05), SAPS3‐score (OR 1.08 per one unit increase 95% CI 1.06–1.09) and ICU length of stay (OR 1.11 per one day increase 95% CI 1.05–1.17) were independently associated with an increased likelihood of receiving a TL. 17% of the patients were involved in the decision‐making process and in >30% of cases neither the patient nor next‐of‐kin were informed. Women were to a larger extent involved in the decision process than men (24.5 vs. 12.5% p < .05). When the intensivist documented why a TL was established, patient autonomy was four times more commonly stated as the motivation for the TL among women compared to men (15.5% vs. 3.8% p < .05). TLs were common in two Swedish ICUs but a substantial number of patients and next‐of‐kin were not involved in the decision‐making process or informed of the decision. Women were more often than men engaged in the decision to establish a TL.

Jönsson, Nino↗
DOE OSTI2021

A regional inter-disciplinary partnership focusing on the development of a carinata-centered bioeconomy

Brassica carinata or Ethiopian mustard, a non-edible oilseed brassica, is a low carbon, purpose-grown, and none-to-low indirect land-use change bioenergy feedstock for the production of drop-in sustainable aviation fuel, biodiesel, renewable diesel, and a suite of value-added coproducts. Carinata oil converted to drop-in fuel using an American Society for Testing and Materials approved Catalytic Hydrothermolysis process has been successfully tested in commercial and military aviation. Carinata meal, the residue after oil extraction, is a high-protein feed supplement for livestock, poultry, and swine, and can also yield specialty products. The Southeast Partnership for Advanced Renewables from Carinata (SPARC) is a public–private partnership formed with a twofold mission: (1) Removing physical, environmental, social, and economic constraints that prevent regional intensification of carinata production as a low-carbon feedstock for renewable fuel and coproducts and (2) demonstrating enhanced value across the entire value chain by mitigating risk to farmers and other stakeholders. The partnership's goal is to energize the US bioeconomy through sustainable agriculture and thus contribute to energy security and economic diversification. SPARC relies on a combination of cutting-edge multidisciplinary research and active industry engagement to facilitate adoption of the crop. This involves informing stakeholders along the entire supply chain, from producers to end-users, policymakers, influencers, and the public, about the opportunities and best practices related to carinata. This article provides context and background concerning carinata commercialization as a winter cash crop in the Southeast US for renewable fuels and bioproducts. The advances made to date in the areas of feedstock development, fuel and coproduct development, meal valorization, supply chain logistics, and stakeholder engagement are outlined.

09 BIOMASS FUELS↗
DOE OSTI2020

Clusters of COVID ‐19 in long‐term care hospitals and facilities in Japan from 16 January to 9 May 2020

Aim To clarify the association of cluster number and size of coronavirus disease 2019 (COVID‐19) in long‐term care (LTC) hospitals/facilities, general medical/welfare facilities and non‐medical/welfare facilities with morbidity and mortality in 47 prefectures during 16 January to 9 May 2020 in Japan. Methods Information on COVID‐19 clusters ( n ≥2), and morbidity and mortality of COVID‐19 was collected. Results A total of 381 clusters with 3786 infected cases were collected, accounting for 23.9% of 15 852 cumulated cases on 9 May 2020. Although the cluster number (/10 7 subjects) in LTC hospitals/facilities was significantly smaller compared with those in the other two groups, the cluster size in LTC hospitals/facilities was significantly larger than that in non‐medical/welfare facilities. Cluster numbers in general medical/welfare facilities and in non‐medical/welfare facilities were significantly positively correlated with morbidity (/10 5 ), indicating relatively early identification of clusters in these facilities. Unlike in these facilities, cluster size in LTC hospitals/facilities was significantly positively correlated with morbidity, indicating that clusters in LTC hospitals/facilities were finally identified after already having grown to a large size in areas where infection was prevalent. Multivariate logistic regression analysis showed that both cluster number and cluster size only in LTC hospitals/facilities were independently associated with higher mortality (≥median 0.64/10 5 subjects) after adjustment. Conclusions Preventive efforts against COVID‐19 outbreaks even at the early phase of the epidemic are critically important in LTC hospitals/facilities, as both the larger number and size of clusters only in LTC hospitals/facilities were independently linked to higher mortality in prefectures in Japan. Geriatr Gerontol Int 2020; 20: 715–719 .

Iritani, Osamu↗
DOE OSTI2021

Congenital malformation and hemoglobin A1c in the first trimester among Japanese women with pregestational diabetes

Abstract Aim To investigate the incidence of major congenital malformations in Japanese women with pregestational diabetes, and to determine the cutoff value of hemoglobin A1c (HbA1c) in the first trimester associated with congenital malformations. Methods This retrospective cohort study included singleton pregnancies in Japanese women with pregestational diabetes, including type 1 and type 2 diabetes, and specific types of diabetes due to other causes. The primary outcome was the incidence of major congenital malformations. The secondary outcome was the incidence of all congenital malformations. The cutoff value of HbA1c for congenital malformations was calculated using receiver operating characteristic curve analysis. The adjusted odds ratios (aOR) of major congenital malformations were calculated using multiple logistic regression analyses. Results This study enrolled 292 patients, including 132 (45.2%) with type 1 diabetes, 156 (53.4%) with type 2 diabetes, and 4 (1.4%) with other specific types. The incidence rates of major congenital malformations and all congenital malformations were 7.2% (21/292) and 12.7% (37/292), respectively. The cutoff value of HbA1c in the first trimester for major malformations and for all congenital malformations was 6.5%. HbA1c ≥ 6.5% was significantly associated with major malformations (aOR 3.5; 95% confidence interval: 1.2–12.6; p = 0.018). Conclusion The incidence of major congenital malformations significantly increased in pregnant Japanese women with HbA1c values of 6.5% or higher. The recommended HbA1c value during the first trimester used in other countries can be applied to pregnant Japanese women.

Nakanishi, Kentaro↗
DOE OSTI2024

DEVELOPMENT AND DEMONSTRATION TESTBED FOR THE REMOTE OPERATIONS AND MONITORING OF MICROREACTORS

The nuclear industry is rapidly developing many advanced-reactor concepts for near-term deployment in both traditional and non-traditional nuclear-powered applications. One such category of advanced reactor is the microreactor, a class of reactor with less than 20MWth power output, intended for applications where the economics or logistics of traditional power sources are difficult. This includes applications such as remote communities, mining sites, defense installations, or humanitarian and disaster-relief missions. One key enabling feature for the successful deployment of microreactors is a remote operations capability. Remote operations provide monitoring and control capabilities which can significantly reduce staffing costs by eliminating the need for licensed operators at each reactor facility and improve the economic viability for microreactor deployment. A remote concept of operations is not currently an established capability in the nuclear industry. In addition, no demonstration microreactor is expected to complete construction or go critical until at least 2026. This leaves two major capability gaps: the successful demonstration of a remote concept of operations for microreactors and a test bed suitable for said demonstration. Both gaps must be addressed in order to advance the remote concepts of nuclear operation and, more broadly, microreactors themselves from paper to reality. This paper aims to fill these gaps and describes a test bed that would support development and deployment of a remote concept of nuclear operations, initial experimental results from that test bed, and the application of the test bed and experimental results for a digital-twin-based remote concept of operations underdevelopment at Idaho National Laboratory (INL). The platform chosen as a remote concept of nuclear operations test bed is the Single Primary Heat Extraction and Removal Emulator, known as SPHERE, located at INL. SPHERE is a small-scale non-nuclear test bed that emulates thermal behavior of a microreactor. The small-scale and non-nuclear nature of SHPERE limit safety concerns associated with remote operations while still providing the physical response representative of a microreactor. A network connection was added to SPHERE that enables remote-monitoring capability. This allows for real-time data streaming to networked workstations, data historians, and human-machine interfaces (HMIs). These are all critical components in a remote concept of operations, thus providing a robust development and demonstration platform. An initial experiment was performed using the SPHERE remote operations testbed. This included running a comprehensively instrumented SPHERE through a series of steady-state and transient operating scenarios in both normal and abnormal operating conditions, all while streaming live test data to a remote HMI and data warehouse. This initial experiment served three purposes: (1) characterizing the response of SPHERE, (2) demonstrating the remote connection to SPHERE, and (3) providing a baseline data set for development of a digital-twin-based remote concept of operations that is under development at INL.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Power Take-Off Design Study for a Small-Scale Oscillating Surge Wave Energy Converter for Powering the Blue Economy Applications

The power take-off (PTO) is an integral part of wave energy conversion, and the design process is nontrivial. Better PTOs, and better processes for selecting and designing PTO architectures for various applications, would benefit devices that assist in powering the blue economy by decreasing time and money spent on PTO design and increasing the overall energy capture performance of these devices. This paper chronicles the selection process of a PTO for a small-scale surge-type wave energy converter (WEC) for the purpose of informing future PTO selection processes. Three PTO architectures are evaluated in WEC-Sim: a hydraulic check valve PTO, a hydraulic active valving PTO, and a directly electrified PTO. Simple models of each PTO are constructed. Because a model for the small-scale device was initially unavailable, the PTOs are simulated on a large-scale device. The results are scaled down using Froude scaling and compared to results from directly simulating a small-scale model. Strong assumptions are made because this work is early in the design stages, and a coarse look at PTO options was desired. Specifically, the effectiveness of controls is investigated, along with the efficiency of energy conversion. However, energy capture is only part of the consideration; there are also logistic concerns to be considered when selecting a PTO. For example, components for large-scale WECs are so large and expensive that it may make sense to custom-build PTO components, but small-scale WECs would benefit from off-the-shelf availability because the cost of customization would be a significant portion of the total capital cost of deployment at a small scale. Submersible, off-the-shelf components are much easier to source for hydraulic PTOs. Because of highly effective controls, efficient energy conversion, and availability of marine-grade components, an active valving hydraulic PTO is selected for this small-scale surge-type WEC.

energy conversion/systems↗
DOE OSTI2021

Evaluation of U-net-based Image Segmentation Model to Digital Mammography

Detecting suspicious lesions in medical imaging is the important first step in computer-aided detection (CAD) systems. However, detecting abnormalities in breast tissue is difficult due to the lesion's varying size, shape, margin, and contrast with the background tissue. We focused on mass segmentation, a method that provides notable morphological features by outlining contours of masses. Accurate segmentation is crucial for correct diagnosis. Recent advancements in deep learning have improved object detection and segmentation, and these techniques are also being applied to medical imaging studies. We focused on U-net, which is a recently developed mass segmentation algorithm based on a fully convolutional network. The U-net architecture consists of (1) a contracting path to increase the resolution of the output and (2) a symmetric expanding path to better locate the region of interest. The performance of a U-net model was tested with 63 digital mammograms from INbreast, a publicly available database. We trained the model with images resized to 40x40 pixels and conducted 10-fold cross-validation to prevent overfitting. The model's performance with respect to breast density and the lesion's BI-RADS rating was also investigated. Dice coefficients (DC) were used as a performance measure to compare the predicted segmentation of the model with the ground truth. Logistic regression and an analysis of variance were performed to determine the significance of the DCs with regards to breast density and lesion behavior and to calculate the 95% confidence interval. The average DC was 0.80. The difference between DCs for BI-RADS 2 and 4c and for BI-RADS 2 and 5 were significant, suggesting that the model has more difficulty in segmenting benign lesions.

Cho, Priscilla↗