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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 217 records · Page 12

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning: Preprint

Distributed energy resources (DER) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline MPC controller.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2024

Hot, cold, or just right? An infrared biometric sensor to improve occupant comfort and reduce overcooling in buildings via closed-loop control

To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Engineering Nucleation Kinetics of Graphite Nodules in Inoculated Cast Iron for Reducing Porosity

Inoculation of cast iron with spherical graphite (SGI) controls nucleation kinetics of graphite nodules and is used for improving casting performance and eliminating solidification micro-porosity. In this study, thermodynamic simulations were used to design inoculants. Thermal and chemical stability of different potential heterogeneous nuclei formed in the melt above the liquidus temperature and in the mushy zone during solidification was predicted. Several inoculation treatments of SGI were performed in laboratory heats. An automated SEM/EDX analysis was applied to examine the graphite nodule size distribution and the family of the non-metallic inclusions in the experimental castings. The data were used to reconstruct a relative graphite nodule nucleation rate in the castings. It was shown that the graphite nodule nucleation kinetics in the castings were significantly different from those predicted by classical nucleation models. In inoculated SGI, the observed bi-modal distribution of graphite nodules was related to a continuous nucleation that occurred towards the end of solidification. The origin of the continuous nucleation and the possibility of engineering graphite nodules nucleation kinetics to control SGI casting soundness by inoculation are discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Failure-probability driven dose painting

Purpose: To demonstrate a data-driven dose-painting strategy based on the spatial distribution of recurrences in previously treated patients. The result is a quantitative way to define a dose prescription function, optimizing the predicted local control at constant treatment intensity. A dose planning study using the optimized dose prescription in 20 patients is performed.Methods: Patients treated at our center have five tumor subvolumes from the center of the tumor (PET positive volume) and out delineated. The spatial distribution of 48 failures in patients with complete clinical response after (chemo)radiation is used to derive a model for tumor control probability (TCP). The total TCP is fixed to the clinically observed 70% actuarial TCP at five years. Additionally, the authors match the distribution of failures between the five subvolumes to the observed distribution. The steepness of the dose–response is extracted from the literature and the authors assume 30% and 20% risk of subclinical involvement in the elective volumes. The result is a five-compartment dose response model matching the observed distribution of failures. The model is used to optimize the distribution of dose in individual patients, while keeping the treatment intensity constant and the maximum prescribed dose below 85 Gy.Results: The vast majority of failures occur centrally despite the small volumes of the central regions. Thus, optimizing the dose prescription yields higher doses to the central target volumes and lower doses to the elective volumes. The dose planning study shows that the modified prescription is clinically feasible. The optimized TCP is 89% (range: 82%–91%) as compared to the observed TCP of 70%.Conclusions: The observed distribution of locoregional failures was used to derive an objective, data-driven dose prescription function. The optimized dose is predicted to result in a substantial increase in local control without increasing the predicted risk of toxicity.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2017

Pore-scale water dynamics during drying and the impacts of structure and surface wettability

Plants and microbes secrete mucilage into soil during dry conditions, which can alter soil structure and increase contact angle. Structured soils exhibit a broad pore size distribution with many small and many large pores, and strong capillary forces in narrow pores can retain moisture in soil aggregates. Meanwhile, contact angle determines the water repellency of soils, which can result in suppressed evaporation rates. Although they are often studied independently, both structure and contact angle influence water movement, distribution, and retention in soils. Here drying experiments were conducted using soil micromodels patterned to emulate different aggregation states of a sandy loam soil. Micromodels were treated to exhibit contact angles representative of those in bulk soil (8.4° ± 1.9°) and the rhizosphere (65° ± 9.2°). Drying was simulated using a lattice Boltzmann single-component, multiphase model. In our experiments, micromodels with higher contact angle surfaces took 4 times longer to completely dry versus micromodels with lower contact angle surfaces. Microstructure influenced drying rate as a function of saturation and controlled the spatial distribution of moisture within micromodels. In conclusion, lattice Boltzmann simulations accurately predicted pore-scale moisture retention patterns within micromodels with different structures and contact angles.

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DOE OSTI2022

Current distribution monitoring enables quench and damage detection in superconducting fusion magnets

Abstract Fusion magnets made from high temperature superconducting ReBCO CORC ® cables are typically protected with quench detection systems that use voltage or temperature measurements to trigger current extraction processes. Although small coils with low inductances have been demonstrated, magnet protection remains a challenge and magnets are typically operated with little knowledge of the intrinsic performance parameters. We propose a protection framework based on current distribution monitoring in fusion cables with limited inter-cable current sharing. By employing inverse Biot-Savart techniques to distributed Hall probe arrays around CORC ® Cable-In-Conduit-Conductor (CICC) terminations, individual cable currents are recreated and used to extract the parameters of a predictive model. These parameters are shown to be of value for detecting conductor damage and defining safe magnet operating limits. The trained model is then used to predict cable current distributions in real-time, and departures between predictions and inverse Biot-Savart recreated current distributions are used to generate quench triggers. The methodology shows promise for quality control, operational planning and real-time quench detection in bundled CORC ® cables for compact fusion reactors.

47 OTHER INSTRUMENTATION↗

DOE OSTI2013

Visible light plasmonic heating of Au-ZnO for the catalytic reduction of CO 2

Plasmonic excitation of Au nanoparticles attached to the surface of ZnO catalysts using low power 532 nm laser illumination leads to significant heating of the catalyst and the conversion of CO 2 and H 2 reactants to CH 4 and CO products. Temperature-calibrated Raman spectra of ZnO phonons show that intensity-dependent plasmonic excitation can controllably heat Au–ZnO from 30 to ~600 °C and simultaneously tune the CH 4 : CO product ratio. The laser induced heating and resulting CH 4 : CO product distribution agrees well with predictions from thermodynamic models and temperature-programmed reaction experiments indicating that the reaction is a thermally driven process resulting from the plasmonic heating of the Au-ZnO. The apparent quantum yield for CO 2 conversion under continuous wave (cw) 532 nm laser illumination is 0.030%. The Au-ZnO catalysts are robust and remain active after repeated laser exposure and cycling. The light intensity required to initiate CO 2 reduction is low ( ~2.5 x 10 5 W m -2 ) and achievable with solar concentrators. Our results illustrate the viability of plasmonic heating approaches for CO 2 utilization and other practical thermal catalytic applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2016

New approaches to quantifying aerosol influence on the cloud radiative effect

The topic of cloud radiative forcing associated with the atmospheric aerosol has been the focus of intense scrutiny for decades. The enormity of the problem is reflected in the need to understand aspects such as aerosol composition, optical properties, cloud condensation, and ice nucleation potential, along with the global distribution of these properties, controlled by emissions, transport, transformation, and sinks. Equally daunting is that clouds themselves are complex, turbulent, microphysical entities and, by their very nature, ephemeral and hard to predict. Atmospheric general circulation models represent aerosol-cloud interactions at ever-increasing levels of detail, but these models lack the resolution to represent clouds and aerosol-cloud interactions adequately. There is a dearth of observational constraints on aerosol-cloud interactions. We develop a conceptual approach to systematically constrain the aerosol-cloud radiative effect in shallow clouds through a combination of routine process modeling and satellite and surface-based shortwave radiation measurements. We heed the call to merge Darwinian and Newtonian strategies by balancing microphysical detail with scaling and emergent properties of the aerosol-cloud radiation system.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Physics vs structure: A systematic benchmark of learning strategies for multi-zone building thermal dynamics

Recent advances in physics-informed and data-driven machine learning promise improved thermal models for advanced building control, yet there is limited quantitative evidence on when added physics structure and architectural complexity are beneficial. Here, this work presents a systematic benchmark of five representative system identification methods for modeling multi-zone building thermal dynamics: linear state-space models, multi-layer perceptrons, neural state-space models, neural ordinary differential equations, and physically-consistent neural networks. The methods are evaluated across multiple data regimes and zone coupling strategies. Using a high-fidelity multi-zone commercial building emulator, we examine short-term and long-term prediction accuracy, computational efficiency, and ease of development. Our results reveal critical trade-offs between prediction performance, model complexity, and physical consistency. We demonstrate that decoupled, nonlinear black-box models consistently outperform coupled physics-constrained architectures in both predictive accuracy and out-of-distribution robustness in majority of the test cases for the building type considered in the study. Our findings quantify the cost of complexity in building thermal modeling and provide concrete, actionable, scenario-based guidelines for selecting model classes for control-oriented applications.

Building thermal modeling↗

DOE OSTI2015

PUNCS: Towards Predictive Understanding of Nitrogen Cycling in Soils

In anoxic environments, the major nitrate/nitrite-consuming processes are respiratory ammonification (also known as dissimilatory nitrate reduction to ammonium) and denitrification (i.e., the formation of the gaseous products N 2 O and N 2 ). Respiratory ammonification oxidizes more carbon per mole of nitrate than denitrification and generates a cation (NH 4+ ), which is retained in soils and bioavailable for plants. Thus, these processes have profoundly different impacts on N retention and greenhouse gas (CO 2 , N 2 O) emissions. Microbes capable of respiratory ammonification or denitrification coexist but the environmental controls over these competing nitrate/nitrite-reducing processes are largely unknown. With the current level of understanding, predictions under what environmental conditions respiratory ammonification activity predominates leading to N-retention rather than N-loss are tenuous. Further, the diversity of genes encoding the ammonium-forming nitrite reductase NrfA is poorly defined hampering the development of tools to assess and monitor this activity in environmental systems. Incomplete denitrification leads to N 2 O, a gas implicated in ozone layer destruction and climate change. The conversion of the greenhouse gas N 2 O to benign N 2 is catalyzed by N 2 O reductase, the characteristic enzyme system of complete denitrifiers. Thus, efforts to estimate N 2 O conversion to N 2 have focused on the well-characterized denitrifier nosZ genes; however, our understanding of the diversity of genes and organisms contributing to N 2 O consumption is incomplete. This paucity of information limits the development of more accurate, predictive models for C- and N-fluxes and greenhouse gas emissions. A comprehensive analysis of the key catalyst of respiratory ammonification, ammonia-forming nitrite reductase NrfA, revealed the evolutionary history of nrfA and identified novel diagnostic features, allowing optimized primer design for nrfA monitoring. Further, a novel group of functional “atypical” nosZ genes was found indicating that a much broader diversity of genes and organisms contribute to consumption of N 2 O. The atypical nosZ genes are distributed in soil ecosystems and often outnumber their typical counterparts, emphasizing their potential role in N 2 O consumption in soils and possibly other environments. Kinetic studies revealed that organisms with atypical NosZ exhibit significantly higher affinity to N 2 O, indicating that the relative activity of bacteria with typical versus atypical NosZ control N 2 O emissions and determine a soil’s N 2 O sink capacity. Collectively, the discoveries made under the PUNCS project improve understanding of N- and associated C-cycling processes in soils, enable the design of enhanced monitoring tools, and allow a larger research community to generate comprehensive datasets required to generate Earth System Models with higher predictive power.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Grain Boundary Segregation Suppresses Local Short‐Range Ordering in Nanocrystalline High‐Entropy Alloys

Multi-principal-element alloys like high-entropy alloys (HEAs) have potential applications in many engineering fields due to their unique mechanical/functional properties. While HEAs are generally considered random solid solutions, recent studies revealed that they are prone to short-range-ordering (SRO) due to the complex multi-pair-wise interactions among the constituent elements. Meanwhile, SROs' evolution can sometimes be deleterious, and it is necessary to have control over their evolution. Examining the AlCoCrFe-Zr model alloy, long-range ordering occurs following the expectation of enthalpic predictions. Advanced characterization techniques—transmission electron microscopy, high-energy synchrotron X-ray diffraction/pair distribution function, and atom probe tomography, reveal that SRO is suppressed in as-milled and GB-decorated NC-(AlCoCrFe)100-xZrx (x = 0–1.5 atomic %). Warren-Cowley coefficient calculations are further used to validate the suppression of SRO. Besides the low segregation enthalpies of Cr, Fe, and Zr, and the high-mixing enthalpy of Cr and Fe, the short diffusion path to GBs due to high-GB density in the NC-HEAs and the higher energy state of the GBs than the matrix promotes GB-segregation that further alters the matrix chemistry and consequently disfavors SRO formation within the matrix. Despite the GB-segregation of Cr, Fe, and Zr, the matrices and GBs remain in a random solid solution.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Hamilton: Flexible, Open Source $10 Wireless Sensor System for Energy Efficient Building Operation

Sensors for improving building performance are rapidly populating the market, driven in part by the drive to reduce greenhouse gas emissions resulting from energy production as well as improve the interior environment for healthy and more productive spaces. UC Berkeley has led wireless sensor development over the past 25 years (e.g., Telos mote), with the Hamilton (named after Alexander Hamilton on the US $10 bill) as the most recent. The Hamilton sensor was designed as a low-cost high-performance sensor that is modular and interoperable. The objective of the Hamilton project was to create, evaluate and establish the technological foundations for secure and easy to deploy building energy efficiency applications utilizing pervasive, low-cost wireless sensors integrated with traditional Building Management Systems (BMS), consumer-sector building components, and powerful data analytics. The project included iterative hardware design, incorporating a high-performance database (BTrDb, http://btrdb.io/), creating and iterating the development of secure data middleware (BOSSwave, WAVE/WAVEMQ), working with and pushing the development of an open-source tiny operating system RiotOS, and implementing and improving protocols such as Thread/OpenThread and TCP/IP. The hardware benefited from careful design to drive down the cost; the design included a System-on-a-Chip (SoC), chip antenna, single crystal and five passive components. Careful design of the operating system created a low-power design to enable a long life with small batteries. The hardware included several sensors: temperature, radiant temperature, relative humidity, magnetometer, accelerometer, and light, with an optional occupancy (Passive InfraRed) sensor. The project was the basis of several applications, both internal to the research team and other researchers and professionals at other institutions. Several applications used the sensor hardware as the basis for other complex devices. Other applications used the sensors to improve building performance through interoperating with the building Heating Ventilation and Air-Conditioning (HVAC) system, such as using occupancy and/or distributed temperature sensing to reduce HVAC zone energy while still providing thermal comfort and to reduce peak loads in small commercial buildings. We demonstrated cloud-based energy analytics, implemented a schedule and a Model Predictive Controller in a small commercial building to optimize HVAC energy, occupancy and electricity price. Initial integration of these technological innovations was performed through the creation of execution containers containing the WAVE agent and various driver, proxy, or building system function logic. The research added to the understanding of efficient sensor hardware, secure middleware, time-series data management (high performance database), efficient communication protocols, and interoperating with applications and building systems. The project showed the technical effectiveness and economic feasibility of creating a low-cost, modular, and easy-to-deploy sensor. Through conversations with multiple end users, the research team discovered that many customers wanted data management and services in addition to the sensors. HamiltonIOT developed packages of sensors, border router, and data services to provide a seamless “plug-and-play” sensor deployment. Some customers were willing to pay for higher quality sensors (such as light); some customers wanted a robust enclosure (waterproof).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2012

GeoPhysical Analysis Code

GPAC is a code that integrates open source libraries for element formulations, linear algebra, and I/O with two main LLNL-written components: (i) a set of standard finite, discrete, and discontinuous displacement element physics solvers for resolving Darcy fluid flow, explicit mechanics, implicit mechanics, fault rupture and earthquake nucleation, and fluid-mediated fracturing, including resolution of physcial behaviors both implicity and explicity, and (ii) a MPI-based parallelization implementation for use on generic HPC distributed memory architectures. The resultant code can be used alone for linearly elastic problems; ploblems involving hydraulic fracturing, where the mesh topology is dynamically changed; fault rupture modeling and seismic risk assessment; and general granular materials behavior. The key application domain is for low-rate stimulation and fracture control in subsurface reservoirs (e.g., enhanced geothermal sites and unconventional shale gas stimulation). GPAC also has interfaces to call external libraries for , e.g., material models and equations of state; however, LLNL-developed EOS and material models will not be part of the current release. CPAC's secondary applications include modeling fault evolution for predicting the statistical distribution of earthquake events and to capture granular materials behavior under different load paths.

Source record↗

DOE OSTI2012

Integrated Experimental and Model-based Analysis Reveals the Spatial Aspects of EGFR Activation Dynamics

The epidermal growth factor receptor (EGFR) belongs to the ErbB family of receptor tyrosine kinases, and controls a diverse set of cellular responses relevant to development and tumorigenesis. ErbB activation is a complex process involving receptor-ligand binding, receptor dimerization, phosphorylation, and trafficking (internalization, recycling and degradation), which together dictate the spatio-temporal distribution of active receptors within the cell. The ability to predict this distribution, and elucidation of the factors regulating it, would help to establish a mechanistic link between ErbB expression levels and the cellular response. Towards this end, we constructed mathematical models for deconvolving the contributions of receptor dimerization and phosphorylation to EGFR activation, and to examine the dependence of these processes on sub-cellular location. We collected experimental datasets for EGFR activation dynamics in human mammary epithelial cells, with the specific goal of model parameterization, and used the data to estimate parameters for several alternate models. Model-based analysis indicated that: 1) signal termination via receptor dephosphorylation in late endosomes, prior to degradation, is an important component of the response, 2) less than 40% of the receptors in the cell are phosphorylated at any given time, even at saturating ligand doses, and 3) receptor dephosphorylation rates at the cell surface and early endosomes are comparable. We validated the last finding by measuring EGFR dephosphorylation rates at various times following ligand addition both in whole cells, and in endosomes using ELISAs and fluorescent imaging. Overall, our results provide important information on how EGFR phosphorylation levels are regulated within cells. Further, the mathematical model described here can be extended to determine receptor dimer abundances in cells co-expressing various levels of ErbB receptors. This study demonstrates that an iterative cycle of experiments and modeling can be used to gain mechanistic insight regarding complex cell signaling networks.

kinetic modeling↗

DOE OSTI2023

Adaptive Deep Reinforcement Learning Algorithm for Distribution System Cyber Attack Defense With High Penetration of DERs

With grid modernization, smart inverters are increasingly used to execute advanced controls for distribution network reliability. However, this also increases the cyber-attack space. Here this paper focuses on the defense approaches to restore the system to normal operation circumstances in the presence of cyber-attacks. A unique deep reinforcement learning (DRL) method is developed to minimize voltage violations and reduce power losses for impacted feeders. The defense problem is reformulated as a Markov decision-making process to dynamically control DERs while minimizing load shedding. This is achieved via an improved soft actor-critic (SAC)-based DRL algorithm, which can govern DER set points and load-shedding scenarios in discrete and continuous modes via the auto-tune entropy and Gaussian policy features. Numerical comparison results on the modified IEEE 123-node system with other control approaches, such as Volt-VAR (VV), Volt-Watt (VW), and model predictive control (MPC) show that the proposed method can eliminate voltage violations and provide feasible control actions that perform complete mitigation of cyber-threats.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Investigation of an Intermittent Binary Control Strategy for Distributed Aerodynamic Control Devices for Load Alleviation in Wind Turbine Blades

A study was conducted of an intermittent binary control strategy for trailing edge flaps and leading edge spoilers installed on wind turbine blades for the purpose of load alleviation. Cost estimation models were developed for the systems to predict overall impact on levelized cost of energy over the lifecycle of the turbine system. Aeroelastic simulations of turbines with the control strategy implemented showed improved levelized cost for some, but not all cases.

17 WIND ENERGY↗

DOE OSTI2015

Atmospheric Flow Development and Associated Changes in Turbulent Sensible Heat Flux over a Patchy Mountain Snow Cover

In this study, the small-scale boundary layer dynamics and the energy balance over a fractional snow cover are numerically investigated. The atmospheric boundary layer flows over a patchy snow cover were calculated with an atmospheric model (Advanced Regional Prediction System) on a very high spatial resolution of 5 m. The numerical results revealed that the development of local flow patterns and the relative importance of boundary layer processes depend on the snow patch size distribution and the synoptic wind forcing. Energy balance calculations for quiescent wind situations demonstrated that well-developed katabatic winds exerted a major control on the energy balance over the patchy snow cover, leading to a maximum in the mean downward sensible heat flux over snow for high snow-cover fractions. This implies that if katabatic winds develop, total melt of snow patches may decrease for low snow-cover fractions despite an increasing ambient air temperature, which would not be predicted by most hydrological models. In contrast, stronger synoptic winds increased the effect of heat advection on the catchment’s melt behavior by enhancing the mean sensible heat flux over snow for lower snow-cover fractions. A sensitivity analysis to grid resolution suggested that the grid size is a critical factor for modeling the energy balance of a patchy snow cover. The comparison of simulation results from coarse (50 m) and fine (5 m) horizontal resolutions revealed a difference in the spatially averaged turbulent heat flux over snow of 40%–70% for synoptic cases and 95% for quiescent cases.

17 WIND ENERGY↗