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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.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Temporary Arterial Embolization of Liver Parenchyma with Degradable Starch Microspheres (EmboCept{sup ®}S) in a Swine Model

BackgroundThis study aimed to evaluate the embolic properties, time to reperfusion, and histologic changes in temporary embolization of liver tissue with degradable starch microspheres (DSM) in a swine model.MethodsIn four adult minipigs, DSMs were injected into the right or left hepatic artery on the lobar level until complete stasis of the blood flow was detectable angiographically. The time required to complete angiographically determined reperfusion was noted. The animals were killed 3 h after complete reperfusion, and samples were taken from the liver. Histologic examinations of the embolized liver parenchyma and untreated tissue were performed.ResultsHepatic arterial embolization using DSMs was technically successful in all cases, with complete blood flow stasis shown by control angiography. A single vial of DSMs (450 mg/7.5 ml) was sufficient to embolize a whole liver lobe in all cases. Angiography showed complete reconstitution of hepatic arterial perfusion after a mean time to reperfusion of 32 ± 6.1 min (range, 26–39 min). Hematoxylin and eosin staining showed no histologically detectable differences between untreated tissue and parenchyma embolized with DSMs except for mild sinusoidal congestion in one case. Indirect in situ DNA nick end labeling staining (TUNEL) showed only single positive hepatocytes, indicating apoptosis.ConclusionTemporary embolization of the hepatic artery using DSMs is feasible with complete reperfusion after 30 min in pigs. Even after complete arterial blood flow stasis, no extensive tissue damage to the embolized liver parenchyma was observed at histologic examinations in this short-term study.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Female Pelvic Vein Embolization: Indications, Techniques, and Outcomes

Until recently, the main indication for pelvic vein embolization (PVE) in women was to treat pelvic venous congestion syndrome (PVC) but increasingly, patients with refluxing pelvic veins associated with leg varicosities are also being treated. A more unusual reason for PVE is to treat pelvic venous malformations, although such lesions may be treated with sclerotherapy alone. Embolotherapy for treating PVC has been performed for many years with several published studies included in this review, whilst an emerging indication for PVE is to treat lower limb varicosities associated with pelvic vein reflux. Neither group, however, has been subjected to an adequate randomized, controlled trial. Consequently, some of the information presented in this review should be considered anecdotal (level III evidence) at this stage, and a satisfactory ‘proof’ of clinical efficacy remains deficient until higher-level evidence is presented. Furthermore, a wide range of techniques not accepted by all are used, and some standardization will be required based on future mandatory prospective studies. Large studies have also clearly shown an unacceptably high recurrence rate of leg varicose veins following venous surgery. Furthermore, minimally or non-invasive imaging is now revealing that there is a refluxing pelvic venous source in a significant percentage of women with de novo leg varicose veins, and many more with recurrent varicosities. Considering that just over half the world’s population is female and a significant number of women not only have pelvic venous reflux, but also have associated leg varicosities, minimally invasive treatment of pelvic venous incompetence will become a common procedure.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Capacitated p -hub approach for park-and-ride facility location problem under nested logit demand function: polyhedral approaches

By generalizing the unconstrained p-hub approach for the park-and-ride (P&R) facility location problem under the multinomial logit demand function, the capacitated p-hub approach for the problem under the nested logit demand function captures a broader range of real-world cases. To solve this problem optimally, we introduce a mixed-integer linear program and accelerate its solution by enhancing the branch-and-cut procedure. To address the problem at a large scale, we introduce two other polyhedral approaches: variable neighborhood search (VNS) and adaptive randomized rounding (ARR). Downtown areas in Seoul have a high modal share of public transportation and congested road traffic, yet P&R has not been widely implemented. Therefore, we apply the ARR procedure to solve a real-world problem using traffic and geographic data from the Seoul metropolitan area. ARR performs better than VNS and addresses real-world cases. The solutions obtained by ARR present a phased expansion plan that encourages policymakers to start installing a small number of P&Rs immediately.

Capacitated p-hub approach↗
DOE OSTI2025

Exploring urban typologies using comprehensive analysis of transportation dynamics

Abstract As urban areas continue to expand and develop, categorizing cities into typologies offers a valuable framework for understanding metropolitan dynamics and fostering inter-city collaboration. However, existing typologies related to urban mobility have limitations, failing to consider cities within a single large urban region and often overlooking crucial dimensions such as trip demand and traffic flow. In this paper, we introduce a transportation-focused characterization for cities within a large urban region, specifically the San Francisco Bay Area, California. We incorporate over 40 metrics across five transportation dimensions: trip demand, road network, multi-modal network, traffic flow, and land use. Specifically, for the trip demand dimension, we include metrics capturing residents’ trip characteristics, such as mode share, intra-city trips, and inter-city trips. Additionally, we analyze the purpose of trips entering the city to gain a deeper understanding of incoming trip patterns. In the traffic flow dimension, we examine metrics like vehicle miles traveled, delay, and congestion to assess the traffic conditions on the street network. These, combined with other dimensions, provide a comprehensive view of a city’s transportation dynamics. Using unsupervised machine learning clustering methods, we identified eight distinct typologies for the Bay Area: Live Work Cities; Job and Activity Magnet Cities; Anchor Cities; Multi-modal Cities; Hyper-connected Cities; Low-density Residential Cities; Medium-density Residential Cities; and Mixed-use Residential Cities. Our findings show that many clusters are strongly influenced by trip demand and traffic flow metrics. Finally, we examine the practicality of this typology and its potential to guide collaborative transportation management strategies. The typologies provide a foundation for dialogue among Bay Area cities, focusing on evaluating shared characteristics and leveraging successes or challenges to develop unified strategies for transportation management.

Kuncheria, Anu↗
DOE OSTI2017

Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: A case study of an urban storm in Beijing

Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters have occurred almost annually in the urban area of Beijing, the capital of China. Based on a self-organizing map (SOM) artificial neural network (ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product (GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANN is suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors, producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. As a result, the points that were assigned risk grades of IV or V were located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Current Role of the Total Artificial Heart in the Management of Advanced Heart Failure

The total artificial heart (TAH) is a form of mechanical circulatory support that involves resection of the native ventricles followed by placement of a device that can restore total pulmonary and systemic flow. Given the increasing burden of congestive heart failure and cardiovascular disease, the number of people in need of cardiac replacement therapy will continue to grow. Despite aggressive efforts to expand the donor pool, the number of heart transplants in the United States (US) has plateaued at less than 3000 per year. In addition, there is increasing recognition of the long-term complications of current generation left ventricular assist devices such as progressive aortic insufficiency, complications related to blood trauma, and both early and delayed right ventricular failure. These factors may serve to expand the role of the TAH in the treatment of patients with end-stage heart failure particularly if new generation devices are developed that are durable, have an improved safety profile, and are totally implantable. Purpose of Review: To review the role and current evidence of the use of the TAH in the management of advanced heart failure and discuss development of recent TAHs that may have an impact on the field in the near future. Recent Findings: Many patients that receive a heart transplant are bridged with a mechanical support device, most commonly a left ventricular assist device (LVAD). However, there is a small subset of patients with profound biventricular (BV) failure or structural abnormalities that preclude LVAD placement, who will require support with a biventricular assist device (BiVAD) or TAH. There are numerous studies showing the efficacy of the TAH in bridging to transplantation. Also, recent studies have shown equal rates of bridging to transplantation between patients receiving a TAH compared to a BiVAD. However, BiVAD support has a higher incidence of stroke in addition to complications related to the native heart such as arrhythmias and valve dysfunction. Currently, there are multiple new generation artificial hearts in both preclinical development and in clinical trials for both bridge to transplant and destination therapy. Summary: TAH have been shown to be effective for circulatory support in select patients with end-stage heart failure. Current LVADs are associated with significant long-term complications related to retention of the native heart and pump design. Many of these complications may be addressed by the increased use of cardiac replacement therapy, i.e., total artificial hearts. Multiple generations of both pulsatile and advanced design continuous flow TAH are under development which have the potential to expand the role of TAHs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Microvascular dysfunction in infiltrative cardiomyopathies

Infiltrative heart diseases are characterized by myocardial tissue alterations leading to mechanical dysfunction which in turn develops into bi-ventricular congestive heart failure. Also the coronary microvasculature undergoes significant remodeling and dysfunction. The effects of the unbalance of the mechanical cross-talk between cardiac muscle and vessels and of the impairment of vasodilatory function can be measured non-invasively by means of positron emission tomography and cardiac magnetic resonance.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Impacts of high resolution data on traveler compliance levels in emergency evacuation simulations

In this article, we conducted a comparison study of evacuation assignment based on Traffic Analysis Zones (TAZ) and high resolution LandScan USA Population Cells (LPC) with detailed real world roads network. A platform for evacuation modeling built on high resolution population distribution data and activity-based microscopic traffic simulation was proposed. This platform can be extended to any cities in the world. The results indicated that evacuee compliance behavior affects evacuation efficiency with traditional TAZ assignment, but it did not significantly compromise the performance with high resolution LPC assignment. The TAZ assignment also underestimated the real travel time during evacuation. This suggests that high data resolution can improve the accuracy of traffic modeling and simulation. The evacuation manager should consider more diverse assignment during emergency evacuation to avoid congestions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Insufficiency Fractures After Radiation Therapy: An Update

Purpose of Review: Radio therapy, alone or in combination with surgery and/or chemotherapy, is commonly used to treat a variety of neoplasms, resulting in effective local tumor control. Unfortunately radiation therapy can also change the structure of the skeletal system, leading to edema, vascular congestion, areas of hemorrhage, loss of cellularity, focal bone marrow changes, fatty transformation, osteitis, hematopoietic marrow regeneration, osteolysis of symphysis pubis or sacroiliac joints, avascular necrosis, insufficiency fractures, osteoradionecrosis, and radiation-induced neoplasms. Recent Findings: The various radiotherapy effects can be studied and diagnosed by analyzing MR signal intensity, CT attenuation, and FDG-PET activity. Summary: In this review, we report a complete analysis of side effects of radiotherapy on bones, focusing on the spine, chest wall, and pelvis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

A Bayesian modeling framework for crash severity effects of active traffic management systems

Transportation agencies utilize Active traffic management (ATM) systems to dynamically manage recurrent and non-recurrent congestion based on real-time conditions. While these systems have been shown to have some safety benefits, their impact on injury severity outcomes is currently uncertain. In this paper, we used full Bayesian mixed logit models to quantify the impact that ATM deployment had on crash severities. The estimation results revealed lower severities with ATM deployment. Marginal effects for ATM deployments that featured hard shoulder running (HSR) revealed lower likelihoods for severe and moderate injury crashes of 15.9 % and for minor injury crashes of 10.1 %. The likelihood of severe and moderate injury crashes and minor injury crashes reduced by 12.4 % and 8.33 % with ATM without HSR. The models were observed to be temporally transferable and had forecast error of 0.301 and 0.304 for the two models, revealing better performance with validation data. These results have implications for improving freeway crash risk at critical locations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Analysis and prediction of intersection traffic violations using automated enforcement system data

We report that the automated enforcement system (AES) is an effective way of supplementing traditional traffic enforcement, and the traffic violation data from AES can also be effectively used for safety research. In this study, traffic violation data were used to analyze the influencing factors associated with traffic violations and to predict the probability of violations at intersections. The potential factors influencing violations include 24 independent factors related to time, space, traffic and weather. Results from a logistic model showed that the midday period, weekends, residential districts, collector roads, congested traffic conditions, high traffic flow, lower wind speed and low temperature would increase the probability of traffic violations. The probability of violations was predicted by the random forest algorithm, which was proven to be the best traffic violation prediction model among logistic regression, Gaussian naive Bayes, and support vector machine. Moreover, the proximity weighted synthetic oversampling technique (ProWSyn) method was applied to reduce the impact of the imbalance ratio (IR) and improve the model’s prediction performance. The receiver operating characteristics (ROC) curves and Precision-Recall (PR) curves illustrated that the random forest algorithm using oversampling data had the best classifier prediction performance than undersampling data. The area under curve (AUC) and out-of-bag (OOB) error with IR = 1 reached 0.914 and 0.0787, which showed the better performance of the random forest algorithm using ProWSyn in dealing with imbalanced traffic violation data.

42 ENGINEERING↗
DOE OSTI2022

A data-centric weak supervised learning for highway traffic incident detection

Using the data from loop detector sensors for near-real-time detection of traffic incidents on highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leveraging human-labeled incident data, the false alarm rate is often too high to be used in practice. Specifically, the inconsistency in the human labeling of the incidents significantly affects the performance of supervised learning models. To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of traffic incident detection on highways. We develop a weak supervised learning workflow to generate high-quality training labels for the incident data without the ground truth labels, and we use those generated labels in the supervised learning setup for final detection. This approach comprises three stages. First, we introduce a data preprocessing and curation pipeline that processes traffic sensor data to generate high-quality training data through leveraging labeling functions, which can be domain knowledge-related or simple heuristic rules. Second, we evaluate the training data generated by weak supervision using three supervised learning models-random forest, k-nearest neighbors, and a support vector machine ensemble-and long short-term memory classifiers. The results show that the accuracy of all of the models improves significantly after using the training data generated by weak supervision. Third, we develop an online real-time incident detection approach that leverages the model ensemble and the uncertainty quantification while detecting incidents. Finally, we show that our proposed weak supervised learning workflow achieves a high incident detection rate (0.90) and low false alarm rate (0.08).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Plentiful electricity turns wholesale prices negative

In 2020, average wholesale electricity prices in the United States fell to $21/MWh, their lowest level since the beginning of the 21st century. Low natural gas prices and the proliferation of low marginal cost resources like wind and solar had already established a trend toward lower wholesale prices, and this trend was exacerbated by declining electricity demand due to the Covid-19 pandemic in 2020. Negative real-time hourly wholesale prices occurred in about 4% of all hours and wholesale market nodes across the United States, but these were not distributed evenly. Regional clusters emerged, for example, in the Permian Basin in western Texas, and in Kansas and western Oklahoma in the Southwest Power Pool (SPP), negative prices accounted for more than 25% of all hours. Negative electricity prices result either from local congestion of the transmission system leading supply to exceed demand locally or due to system-wide oversupply. Looking at the latter condition in SPP, we find that all major generator types contribute to this excess supply, because of limited ramping flexibility or self-scheduled out-of-market unit commitments. Additional monetary production incentives such as renewable energy credits or tax credits also enable negative bids; indeed, negative prices predominantly occur when demand levels are low and wind production levels are high. Frequent negative prices can inform the value of additional renewable energy investments at specific locations, the need for transmission and storage development, and opportunities load growth or adaptation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Modeling and simulation of consumer response to dynamic pricing.

Assessing the impacts of dynamic-pricing under the smart grid concept is becoming extremely important for deciding its full deployment. In this paper, we develop a model that represents the response of consumers to dynamic pricing. In the model, consumers use forecasted day-ahead prices to shift daily energy consumption from hours when the price is expected to be high to hours when the price is expected to be low while maintaining the total energy consumption as unchanged. We integrate the consumer response model into the Electricity Market Complex Adaptive System (EMCAS). EMCAS is an agent-based model that simulates restructured electricity markets. We explore the impacts of dynamic-pricing on price spikes, peak demand, consumer energy bills, power supplier profits, and congestion costs. A simulation of an 11-node test network that includes eight generation companies and five aggregated consumers is performed for a period of 1 month. In addition, we simulate the Korean power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Transactive control of fast-acting demand response based on thermostatic loads in real-time retail electricity markets

Coordinated operation of distributed thermostatic loads such as heat pumps and air conditioners can reduce energy costs and prevents grid congestion, while maintaining room temperatures in the comfort range set by consumers. This paper furthers efforts towards enabling thermostatically controlled loads (TCLs) to participate in real-time retail electricity markets under a transactive control paradigm. An agent-based approach is used to develop an effective and low complexity demand response control scheme for TCLs. The proposed scheme adjusts aggregated thermostatic loads according to real-time grid conditions under both heating and cooling modes. Here, a case study is presented showing the method reduces consumer electricity costs by over 10% compared to uncoordinated operation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

An enhanced SOCP-based method for feeder load balancing using the multi-terminal soft open point in active distribution networks

The integration of distributed generators (DGs) exacerbates the feeder power flow fluctuation and load unbalanced condition in active distribution networks (ADNs). The unbalanced feeder load causes inefficient use of network assets and network congestion during system operation. The flexible interconnection based on the multi-terminal soft open point (SOP) significantly benefits the operation of ADNs. The multi-terminal SOP, which is a controllable power electronic device installed to replace the normally open point, provides accurate active and reactive power flow control to enable the flexible connection of feeders. An enhanced SOCP-based method for feeder load balancing using the multi-terminal SOP is proposed in this paper. Furthermore, by regulating the operation of the multi-terminal SOP, the proposed method can mitigate the unbalanced condition of feeder load and simultaneously reduce the power losses of ADNs. Then, the original non-convex model is converted into a second-order cone programming (SOCP) model using convex relaxation. In order to tighten the SOCP relaxation and improve the computation efficiency, an enhanced SOCP-based approach is developed to solve the proposed model. Finally, case studies are performed on the modified IEEE 33-node system to verify the effectiveness and efficiency of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Power-traffic coordinated operation for bi-peak shaving and bi-ramp smoothing – A hierarchical data-driven approach

The power distribution system and urban transportation system are two networked system bare their own operation constraints, such peak load in power systems and traffic congestion in transportation system. With the increasing number of electrical vehicles and charging/discharging stations, two systems are become tightly coupled. However, to optimize the two systems target using electrical vehicles as decision control variables cannot be easily solved using a uniformed optimization frame work. Thus we propose a hierarchical optimization approach to address this problem, which consists of a higher and a lower level. In the higher level, the power distribution system and urban transportation system are treated together to minimize the social cost. Meanwhile, the electrical vehicles and the charging/discharging stations are treated as customers to minimize their own expenditures. Then, an equilibrium is designed to determine the optimal charging/discharging price. In the lower level, the models of power distribution system and urban transportation system are developed to provide a detailed analysis. Specifically, in power distribution system, the three-phase unbalanced optimal power flow problem is relaxed with the semidefinite relaxation programming, and solved with alternating direction method of multiplier. A dynamic user equilibrium problem is formulated for the urban transportation system. For electrical vehicles, the state of charge is considered to optimize the charging/discharging schedule and reduce the impacts of power distribution systems. We conducted the simulation and numerical analysis using the IEEE 8500-bus distribution system and the Sioux Falls system with about 10,000 cars. The results demonstrate the feasibility and effectiveness of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

An ERCOT test system for market design studies

An open source test system is developed that permits the dynamic modeling of centrally-managed wholesale power markets operating over highvoltage transmission grids. In default mode, the test system models basic operations in the Electric Reliability Council of Texas (ERCOT): namely, centrally-managed day-ahead and real-time markets operating over successive days, with congestion handled by locational marginal pricing. These basic operational features characterize all seven U.S. energy regions organized as centrally-managed wholesale power markets. Modeled participants include dispatchable generators, load-serving entities, and non-dispatchable generation such as unrmed wind and solar power. Users can congure a broad variety of parameters to study basic market and grid features under alternative system conditions. Users can also easily extend the test system's Java/Python software classes to study modied or newly envisioned market and grid features. Finally, the test system is integrated with a high-level simulation framework that permits it to function as a software component within larger systems, such as multi-country systems or integrated transmission and distribution systems. Finally, detailed test cases with 8-bus and 200-bus transmission grids are reported to illustrate these test system capabilities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗