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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Data Driven Fuel Efficient Driving Behavior Feedback for Fleet Vehicles

Dependency of the transport sector on fossil fuels is encouraging a significant amount of research in to improving fuel efficiency in vehicles. Three primary techniques are identified for vehicle fuel efficiency improvement: 1) vehicle technology improvements such as drivetrain improvements, 2) traffic infrastructure improvements such as traffic flow management and route selection, and 3) driver behavior changes such as acceleration and deceleration profiles. Out of the 3 techniques, driver behavior changing has the least implementation cost and is able to provide immediate results. Thus, this paper presents a fuel efficient driving behavior identification and feedback architecture that is specific to fleet vehicles. The presented method utilizes historical data from fleet drivers on specific routes and generates fuel optimal velocity profiles that do not affect travel time. The identified velocity profile is the prompted to the driver via a low-cost plug-and-play style un-obstructive display. The display uses an intuitive and easily understandable visualization to prompt drivers on fuel efficient velocity. The presented architecture was tested on the Idaho National Laboratory (INL) bus fleet in real-world driving conditions and was shown to be able to increase the fuel economy by 9% and 20% in two different driving scenarios.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2013

Natural equilibria in steady-state neutron diffusion with temperature feedback

The critical diffusion equation with feedback is investigated within the context of steady-state multiphysics. It is proposed that for critical configurations there is no need to include the multiplication factor k in the formulation of the diffusion equation. This is notable because exclusion of k from the coupled system of equations precludes the mathematically tenuous notion of a nonlinear eigenvalue problem. On the other hand, it is shown that if the factor k is retained in the diffusion equation, as is currently common practice, then the resulting problem is equivalent to the constrained minimization of a functional representing the critical equilibrium of neutron and temperature distributions. The unconstrained solution corresponding to k = 1 represents the natural equilibrium of a critical system at steady-state. Computational methods for solving the constrained problem (with k) are briefly reviewed from the literature and a method for the unconstrained problem (without k) is outlined. A numerical example is studied to examine the effects of the constraint in the nonlinear system. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Linear permeability evolution of expanding conduits due to feedback between flow and fast phase change: Linear Permeability Evolution of Conduit

Conduits are ubiquitous and critical pathways for many fluids relevant for geophysical processes such as magma, water, and gases. Predicting flow through conduits is challenging when the conduit geometry coevolves with the flow. We theoretically show that the permeability (k) of a conduit whose walls are eroding due to fast phase change increases linearly with time because of a self-reinforcing mechanism. This simple result is surprising given complex feedbacks between flow, transport, and phase change. The theory is congruent with previous experimental observations of fracture dissolution in calcite. Supporting computational fracture dissolution experiments showed that k only slightly increases until the dissolution front reaches the narrowest conduit constriction, after which the linear evolution of k manifests. Finally, the theory holds across multiple scales and a broad range of Peclet and Damkohler numbers and thus advances the prediction of dynamic mass fluxes through expanding conduits in various geologic and environmental settings

58 GEOSCIENCES↗
DOE OSTI2018

The Influential Role of Sociocultural Feedbacks on Community-Managed Irrigation System Behaviors During times of Water Stress

Sociohydrological studies use interdisciplinary approaches to explore the complex interactions between physical and social water systems and increase our understanding of emergent and paradoxical system behaviors. The dynamics of community values and social cohesion, however, have received little attention in modeling studies due to quantification challenges. Social structures associated with community-managed irrigation systems around the world, in particular, reflect these communities' experiences with a multitude of natural and social shocks. Using the Valdez acequia (a communally-managed irrigation community in northern New Mexico) as a simulation case study, we evaluate the impact of that community's social structure in governing its responses to water availability stresses posed by climate change. Specifically, a system dynamics model (developed using insights from community stakeholders and multiple disciplines that captures biophysical, socioeconomic, and sociocultural dynamics of acequia systems) was used to generate counterfactual trajectories to explore how the community would behave with streamflow conditions expected under climate change. We found that earlier peak flows, combined with adaptive measures of shifting crop selection, allowed for greater production of higher value crops and fewer people leaving the acequia. The economic benefits were lost, however, if downstream water pressures increased. Even with significant reductions in agricultural profitability, feedbacks associated with community cohesion buffered the community's population and land parcel sizes from more detrimental impacts, indicating the community's resilience under natural and social stresses. In conclusion, continued exploration of social structures is warranted to better understand these systems' responses to stress and identify possible leverage points for strengthening community resilience.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

Abstract Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Watersheds dynamics following wildfires: Nonlinear feedbacks and implications on hydrologic responses

In recent years, wildfires in the western United States have occurred with increasing frequency and scale. Climate change scenarios in California predict prolonged periods of droughts with even greater potential for conditions amenable to wildfires. The Sierra Nevada Mountains provide 70% of water resources in California, yet how wildfires will impact watershed-scale hydrology is highly uncertain. In this work, we assess the impacts of wildfires perturbations on watershed hydrodynamics using a physically based integrated hydrologic model in a high-performance-computing framework. A representative Californian watershed, the Cosumnes River, is used to demonstrate how postwildfire conditions impact the water and energy balance. Results from the high-resolution model show counterintuitive feedbacks that occur following a wildfire and allow us to identify the regions most sensitive to wildfires conditions, as well as the hydrologic processes that are most affected. For example, whereas evapotranspiration generally decreases in the postfire simulations, some regions experience an increase due to changes in surface water run-off patterns in and near burn scars. Postfire conditions also yield greater winter snowpack and subsequently greater summer run-off as well as groundwater storage in the postfire simulations. Finally, comparisons between dry and wet water years show that climate is the main factor controlling the timing at which some hydrologic processes occur (such as snow accumulation) whereas postwildfire changes to other metrics (such as streamflow) show seasonally dependent impacts primarily due to the timing of snowmelt, illustrative of the integrative nature of hydrologic processes across the Sierra Nevada-Central Valley interface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Quantifying the role of land-atmosphere feedbacks in mediating near-surface temperature persistence

Weather persistence from month to month is an important source of predictability on the intraseasonal time-scale. Persistence in the lower troposphere is thought to be mainly due to dynamical feedbacks between the atmosphere and physical variables at or below the surface. Many recent improvements of dynamical seasonal forecasting models have been due to better initialisations and representations of the lower boundary. While many physical mechanisms that mediate persistence are well-understood, they have not previously been considered simultaneously in a causal framework. Here we use linear methods that are widely used in the field of causal inference in statistics to identify mediators between persistent near-surface temperature anomalies from one month to the next. The variables considered are soil temperature and soil moisture at four levels, and snow depth. In Northern Europe, the high persistence of temperature in summer is shown to be mediated mainly by soil temperature anomalies. The high persistence in winter is mediated by snow depth and soil moisture in addition to soil temperature. The annual cycles of persistence are also investigated for the mid- and high latitudes of the Northern Hemisphere, confirming the important role of soil temperature in summer. Snow depth is an important mediator when, and where, its interannual variability is large: in winter at midlatitudes and in spring and autumn at high latitudes. The novel contribution of this study is the introduction of a linear causal methodology and a holistic approach that allows quantification and comparison of the different roles of soil variables and snow depth in mediating near-surface temperature persistence.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

New Safety Feedback Control Design to Guarantee Adequate Frequency Performance in Microgrids

Safety analysis of power systems is concerned with the system's ability to maintain critical variables within specified limits following a disturbance. Frequency control adequacy has become increasingly important as the system inertia decreases due to the increase in renewable energy penetration. Various controllers for inverters have been proposed to improve the system frequency response and few are capable to ensure the safety of the response. In this article, a diesel-wind energy system is considered and modeled as a switching system between normal, faulted, and post-fault modes. Further, a safety feedback controller is designed as a supplementary signal for a wind turbine generator such that the speed of the diesel generator stays within a permissible range in the presence of a finite energy disturbance. Numerical results on the modified 33-bus microgrid system obtained of the proposed novel approach indicate that the suggested control configuration can guarantee adequate frequency response without excessive conservativeness.

barrier function↗
DOE OSTI2019

The double ITCZ syndrome in GCMs: A coupled feedback problem among convection, clouds, atmospheric and ocean circulations

The appearance of a spurious double ITCZ south of the equator in coupled general circulation models has been a stubborn problem ever since the beginning of coupled model development. This article reviews the past research in this area, with a focus on three possible major contributors to the double ITCZ biases: 1) the southeastern Pacific marine stratus clouds and associated warm sea surface temperature (SST) biases; 2) the extratropical shortwave absorption biases over Southern Ocean; and 3) convective parameterization. The negative biases in marine boundary layer clouds in the southeastern Pacific lead to warm SST biases. Through coupled atmosphere-ocean interactions, it contributes to the double ITCZ bias. Positive shortwave absorption biases over Southern Ocean is believed to be an extratropical contributor to the double ITCZ biases in models. Since the heat from excess shortwave absorption must be transported to the northern hemisphere by the Hadley circulation, the position of the ITCZ must shift southward. However, later research finds that reducing the extratropical shortwave absorption has little effect on the ITCZ position. One possibility is that some feedback mechanism involving subtropical low-level clouds as intermediary is missing. For convective parameterization, changes in various elements in convection schemes can have large impacts on the ITCZ simulation. It involves a complex chain of interactions among convection, large-scale atmospheric circulation, SST, and upper ocean circulation. In one scheme, changes in the scheme lead to the elimination of double ITCZ in all seasons, giving hope that we can finally take on the double ITCZ problem.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

LncRNA FIRRE/NF-kB feedback loop contributes to OGD/R injury of cerebral microglial cells

Stroke is one of the leading causes for serious long-term neurological disability. LncRNAs have been investigated to be dysregulated in ischemic stroke. However, the underlying mechanisms of some specific lncRNAs have not been clearly clarified. To determine lncRNA-mediated regulatory mechanism in ischemic stroke, we constructed OGD/R injury model of cerebral microglial cells. Microarray analysis was carried out and analyzed that lncRNA functional intergenic repeating RNA element (FIRRE) was associated with OGD/R injury. Based on the molecular biotechnology, we demonstrated that FIRRE could activate NF-kB signal pathway. Meanwhile, the activated NF-kB promoted FIRRE expression in OGD/R-treated cerebral microglial cells. Therefore, FIRRE and NF-kB formed a positive feedback loop to promote the transcription of NLRP3 inflammasome, thus contributed to the OGD/R injury of cerebral microglial cells. All findings in this study may help to explore novel and specific therapeutic target for ischemic stroke.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

miR-181d-5p-FOXP1 feedback loop modulates the progression of osteosarcoma

MicroRNAs (miRNAs) are playing more and more fundamental roles in the progress of human cancers. miR-181d-5p has not been fully elucidated in human cancers. In this study, we aimed to investigate the biological function and mechanism of miR-181d-5p in osteosarcoma (OS). At first, the expression conditions of miR-181d-5p were identified in OS tissues and cell lines. Downregulation of miR-181d-5p was identified to be a significant prognostic factor for patients with OS. Using the bioinformatics analysis, the putative target mRNA (FOXP1) of miR-181d-5p was found out. Similarly, the expression conditions and prognostic value of FOXP1 were identified in OS. To validate the tumor suppressive role of miR-181d-5p in OS, gain or loss of function assays were carried out in OS cell lines. The results indicated the anti-oncogenic function of miR-181d-5p in OS. Subsequently, the oncogenic function of FOXP1 in OS was identified by functional assays. Rescue assays manifested that the oncogenic function of FOXP1 can be partially reversed by miR-181d-5p. Combining with the result of luciferase reporter assay, we confirmed that miR-181d-5p can act as a tumor suppressor in OS via targeting FOXP1. Further mechanism experiments revealed that FOXP1 can suppressed the transcription activity of miR-181d-5p by acting as a transcription inhibitor. In conclusion, our study revealed that miR-181d-5p-FOXP1 feedback loop modulates cell proliferation and metastasis in osteosarcoma.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

LncRNA cancer susceptibility candidate 15 accelerates the breast cancer cells progression via miR-153–3p/KLF5 positive feedback loop

Highlights: • CASC15 expression level was aberrantly high-expressed in breast cancer tissue specimens and cells. • Loss-of-functional experiments showed that knockdown of CASC15 suppressed the malignant behaviors of breast cancer cells. • CASC15 functioned as ceRNA of miR-153–3p, besides, miR-153–3p targeted the 3‘-UTR of KLF5 mRNA. • KLF5 binds with the promoter region of CASC15 and activates the transcription. Emerging literature have illustrated the vital regulatory roles of long noncoding RNAs (lncRNAs) on the breast cancer tumorigenesis. Although series of researches have been proceeded on the pathogenesis, there are still much of unsolved mysteries worth investigating. This study uncovered that CASC15 expression level was aberrantly high-expressed in breast cancer tissue specimens and cells. Functionally, the loss-of-functional experiments showed that knockdown of CASC15 suppressed the malignant behaviors of breast cancer cells, such as proliferation, invasion and tumor growth in vitro and vivo. Mechanically, we confirmed that CASC15 functioned as a competing endogenous RNA (ceRNA) of miR-153–3p, besides, miR-153–3p targeted the 3‘-UTR of KLF5 mRNA utilizing the bioinformatics online tools, luciferase reporter assay and RNA immunoprecipitation. Interestingly, we confirmed that the transcription factor KLF5 binds with the promoter region of CASC15 and activates the transcription. In conclusion, we validated the positive feedback loop of KLF5/CASC15/miR-153–3p/KLF5 in the acceleration of breast cancer malignant behaviors and tumorigenesis, suggesting the important biologic roles of CASC15 on the breast cancer tumorigenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

The positive feedback between lncRNA TNK2-AS1 and STAT3 enhances angiogenesis in non-small cell lung cancer

Highlights: • TNK2-AS1 promotes NSCLC. • TNK2-AS1 interacts with STAT3. • STAT3 induces TNK2-AS1 expression. Evidence has shown the importance of long non-coding RNAs (lncRNAs) during angiogenesis and lung cancer progression. However, the potential functions of TNK2-AS1 in non-small cell lung cancer (NSCLC) remain elusive. By lncRNA profiling, we identified TNK2-AS1 as a novel oncogenic lncRNA in NSCLC. TNK2-AS1 was significantly upregulated in NSCLC and correlated with poor prognosis. We found that TNK2-AS1 could increase viability and migration of NSCLC cells in vitro. TNK2-AS1 also promoted NSCLC xenograft tumor growth and metastasis in vivo. TNK2-AS1 could interact with STAT3 to increase its protein stability by protecting it from proteasome-mediated degradation. STAT3 could also bind TNK2-AS1 promoter to trigger its transcription. The positive feedback loop between STAT3 and TNK2-AS1 therefore augmented STAT3 signaling by elevating VEGFA expression to facilitate angiogenesis. Collectively, our work has elucidated a novel mechanism of TNK2-AS1-mediated angiogenesis by enforcing STAT3/VEGFA signaling and may provide a potential target for therapeutic intervention.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology

Accurate occupancy prediction can improve facility control and energy efficiency of buildings. In recent years, buildings’ exiting WiFi infrastructures have been widely studied in the research of occupancy and energy conservation. However, using WiFi to assess occupancy is challenging due to that occupancy information is often characterized stochastically and varies with time and easily disturbed by building components. To overcome such limitations, this study utilizes WiFi probe technology to actively scan WiFi connection requests and responses between access points and network devices of building occupants. With captured signals, this study proposed a Markov based feedback recurrent neural network (M-FRNN) algorithm to model and predict the occupancy profiles. One on-site experiment was conducted to collect ground truth data using camera-based video analysis and the results were used to validate the M-FRNN occupancy prediction model over a 9-day measurement period. From the results, the M-FRNN based occupancy model using WiFi probes shows best accuracies can reach 80.9%, 89.6%, and 93.9% with a tolerance of 2, 3, and 4 occupants respectively. In conclusion, this study demonstrated that WiFi data coupled with stochastic machine learning system can provide a viable alternative to determine a building's occupancy profile.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Machine learning-based ethylene and carbon monoxide estimation, real-time optimization, and multivariable feedback control of an experimental electrochemical reactor

Electrochemical reduction of CO 2 gas is a novel CO 2 utilization technique that has the potential to mitigate the global climate crisis caused by anthropogenic CO 2 emissions, and enable the large-scale storage of energy generated from renewable sources in the form of carbon-based chemicals and fuels. However, due to the complexity of the electrochemical reactions, the explicit first-principles models for CO2 reduction are not available yet, and there has been a limited effort to develop process modeling, optimization and control of CO 2 electrochemical reactors. To this end, a rotating cylinder electrode (RCE) reactor has been constructed at UCLA to understand the mass transfer and reaction kinetics effects separately on the productivity. In the RCE reactor, the applied potential strongly influences the reaction energetics and the electrode rotation speed affects the hydrodynamic boundary layer and modifies the film mass transfer coefficient, which involves convective and diffusive transport. Further, the present work aims to develop a multi-input multi-output (MIMO) control scheme for the RCE reactor that integrates techniques from artificial and recurrent neural network modeling, nonlinear optimization, and process controller design. Specifically, production rates of two products from the experimental reactor, ethylene and carbon monoxide, are controlled by manipulating two inputs, applied potential and catalyst rotation speed. Process dynamics and controllability are analyzed, a feedback control strategy is designed and the controllers are tuned accordingly. The experimental electrochemical cell is employed to gather data for process modeling and implement the multivariable control system. Finally, the experimental results are presented which demonstrate excellent closed-loop performance by the control system and regulation of the outputs at three different set-points including an economically-optimal set-point.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation

Microstructure sensitive design has a critical impact on the performance of engineering materials. The safety and performance requirements of critical components, as well as the cost of material and machining of Titanium components, make dovetailing of the microstructure imperative. This paper addresses the optimization of several microstructure design problems for Titanium components under specific design constraints using a feedback-aware data-driven solution methodology. In this study, the microstructure is modeled with an orientation distribution function (ODF) that measures the volumes of different crystallographic orientations. Two algorithms are used to sample the entire microstructure space followed by machine learning-aided identification of a minimal subset of ODF dimensions which is subsequently explored by targeted sampling. Conventional optimization methods lead to a unique microstructure rather than yielding a comprehensive space of optimal or near-optimal microstructures. Multiple solutions are crucial for the deployment of materials design for manufacturing as traditional manufacturing processes can only generate a limited set of microstructures. Our data sampling-based methodology not only outperforms or is on par with other optimization techniques in terms of the optimal property value, but also provides numerous near-optimal solutions, 3–4 orders of magnitude more than previous methods. Consequently, the proposed framework delivers a spectrum of optimal solutions in the microstructure space which can accelerate materials development and reduce manufacturing costs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of real-time density feedback control on MAST-U in L-mode

In this paper we report on the development and demonstration of density feedback control for MAST-U. Sinusoidal perturbations are used to measure the frequency response from a deuterium gas valve (actuator) to line-integrated core electron density measured by the interferometer (sensor). In the frequency range relevant for control design, only two system-identification experiments were needed to regress a first-order dynamic model. This control-oriented model informs the offline design of a proportional integral controller with the established loop-shaping controller design method. After offline verification of the controller implementation, control is demonstrated by experimentally tracking a staircase reference for the line-integrated electron density. This paper demonstrates the efficiency of controller design using system-identification and loop-shaping, providing reliable density control for MAST-U.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Soil temperature mitigation due to vegetation biophysical feedbacks

Understanding the interactions between temperature regimes and vegetation cover is one of the key scientific topics in global environmental change and land surface process research. Recently, the prominent global greening trend has inspired considerable interest in examining climate feedbacks to vegetation changes. However, the impacts of recent widespread vegetation cover changes on soil temperature have been less documented and have received insufficient attention. Here, a high-resolution regional climate model was used to examine the potential impact of vegetation cover changes over the past 37 years, obtained as satellite observational data, on regional air and soil temperatures across the Heilong-Amur River Basin. Our sensitivity experiments indicated that the 1.85% increase in the regional fractional vegetation cover (FVC) from 1982 to 2018 cooled the air temperature by 0.045°C and the soil temperature by 0.19°C and that this cooling effect would continue in the near future, from 2016-2018 to 2051-2053. We found that soil temperatures were more sensitive to vegetation cover changes than air temperatures, particularly in mid-high latitude forest ecosystems; this finding helps to explain the nonsignificant decrease in soil temperatures in forest ecosystems over the past 40 years detected in the ERA5-Land reanalysis. Further, our results also suggest that vegetation restoration in forest ecosystems could mitigate the effects of climate change on permafrost environments by increasing the number of soil freezing days.

54 ENVIRONMENTAL SCIENCES↗