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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS2005

Aspects of Numerical Simulation of Circulation Control Airfoils

The mass-averaged compressible Navier-Stokes equations are solved for circulation control airfoils. Numerical solutions are computed with a multigrid method that uses an implicit approximate factorization smoother. The effects of flow conditions (e.g., free-stream Mach number, angle of attack, momentum coefficient) and mesh on the prediction of circulation control airfoil flows are considered. In addition, the impact of turbulence modeling, including curvature effects and modifications to reduce eddy viscosity levels in the wall jet (i.e., Coanda flow), is discussed. Computed pressure distributions are compared with available experimental data.

Swanson, R. C.↗
NASA NTRS1971

Heat and momentum transfer model studies applicable to once-through, forced convection potassium boiling

A number of heat transfer and fluid flow mechanisms that control once-through, forced convection potassium boiling are studied analytically. The topics discussed are: (1) flow through tubes containing helical wire inserts, (2) motion of droplets entrained in vapor flow, (3) liquid phase distribution in boilers, (4) temperature distributions in boiler tube walls, (5) mechanisms of heat transfer regime change, and (6) heat transfer in boiler tubes. Whenever possible, comparisons of predicted and actual performances are made. The model work presented aids in the prediction of operating characteristics of actual boilers.

Sabin, C. M.↗
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↗
NASA NTRS2012

Piezocomposite Actuator Arrays for Correcting and Controlling Wavefront Error in Reflectors

Three reflectors have been developed and tested to assess the performance of a distributed network of piezocomposite actuators for correcting thermal deformations and total wave-front error. The primary testbed article is an active composite reflector, composed of a spherically curved panel with a graphite face sheet and aluminum honeycomb core composite, and then augmented with a network of 90 distributed piezoelectric composite actuators. The piezoelectric actuator system may be used for correcting as-built residual shape errors, and for controlling low-order, thermally-induced quasi-static distortions of the panel. In this study, thermally-induced surface deformations of 1 to 5 microns were deliberately introduced onto the reflector, then measured using a speckle holography interferometer system. The reflector surface figure was subsequently corrected to a tolerance of 50 nm using the actuators embedded in the reflector's back face sheet. Two additional test articles were constructed: a borosilicate at window at 150 mm diameter with 18 actuators bonded to the back surface; and a direct metal laser sintered reflector with spherical curvature, 230 mm diameter, and 12 actuators bonded to the back surface. In the case of the glass reflector, absolute measurements were performed with an interferometer and the absolute surface was corrected. These test articles were evaluated to determine their absolute surface control capabilities, as well as to assess a multiphysics modeling effort developed under this program for the prediction of active reflector response. This paper will describe the design, construction, and testing of active reflector systems under thermal loads, and subsequent correction of surface shape via distributed peizeoelctric actuation.

piezoelectric actuation↗
NASA NTRS2022

Uncertainty Models for the Hybrid Parametric Variation Method of Uncertainty Quantification; Analysis

There is some level of uncertainty in every finite element model (FEM), which flows to a level of uncertainty in predicted results. The purpose of uncertainty quantification (UQ) is to provide statistical bounds on prediction accuracy based on model uncertainty. This is distinct from model updating, which attempts to modify models to improve their accuracy. UQ does not improve the accuracy of models, but accepts that the models are inherently inaccurate and attempts to quantify the impact of that inaccuracy on predicted results. Previously, an alternate method for UQ, called the Hybrid Parametric Variation (HPV) method, was applied to Space Launch System (SLS) Hurty/Craig-Bampton (HCB) components to predict system-level statistics for launch vehicle attitude control transfer functions and core stage section loads due to buffet. The HPV method combines a parametric variation of the HCB fixed-interface (FI) modal frequencies with a nonparametric variation (NPV) method that randomly varies the HCB mass and stiffness matrices as Wishart random matrix distributions using random matrix theory (RMT). Alternatively, the most common method for modeling uncertainty in the structural dynamics community is a parametric approach, which varies physical parameters in the model. However, there are several disadvantages associated with the parametric method. Determining a reduced set of parameters that have a significant impact on the system response can be time consuming, and the selected parameter probability distributions are rarely reliably known. Therefore, in practice, the parameters are surrogates for the actual errors, and the link to parameter uncertainty is unknown. Another major drawback is that the uncertainty that can be represented is limited to the form of the nominal FEM. It is the experience of the authors that based on numerous aerospace programs, almost all FEM errors are in form rather than parameter values. This hypothesis is supported by the observation of the authors that it is almost never possible to ‘tune’ a FEM to match modal test results by only modifying model parameters. Model-form uncertainty cannot be directly represented by FEM input parameters nor included in a parametric approach. However, model-form uncertainty can be modeled using RMT, where a probability distribution is developed for the matrix ensemble of interest. The major advantage of the NPV method is that it covers errors in model form. The HPV method anchors uncertainty at the HCB component level to component modal test results by matching the HCB and test modes based on mode descriptions or other methods, and then applying differing levels of frequency variation. The specific variations depend on the confidence to which a component FEM has been validated through modal testing. The NPV method is layered on the frequency variation to match modal test self-orthogonality and cross-orthogonality (XO) results. Once the component uncertainty models are identified, they are assembled, and the uncertainty is propagated to the system level using a Monte Carlo (MC) analysis approach that generates statistics for system-level predictions This provides a UQ method that can be traced to test data, which can be updated as additional data and improved correlated models become available. The purpose of this paper is to collect and present all of the theory for HPV that has been previously published in reports and papers and to present examples of its application. Specifically, component uncertainty models based on the dispersion of corresponding mass and stiffness matrices using proposed test/analysis correlation metrics are investigated. The first example is purely academic so that the true answers are known, and the validity of the HPV method and the corresponding uncertainty models can be determined. The purpose of this paper is to collect and present all of the theory for HPV that has been previously published in reports and papers and to present examples of its application. Specifically, component uncertainty models based on the dispersion of corresponding mass and stiffness matrices using proposed test/analysis correlation metrics are investigated. The first example is purely academic so that the true answers are known, and the validity of the HPV method and the corresponding uncertainty models can be determined. The second example is an application to a component that is design specific to the SLS. Based on this work and other assessments, the HPV method provides another tool to the toolset used for complex system UQ analysis. From experience gathered to date using the HPV method, additional design specific applications must be investigated to provide further confidence in the validity of the HPV method of UQ analysis.

Uncertainty quantification↗
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↗
DOE OSTI2024

Maps of plant functional type (PFT), PFT fractional cover, and uncertainty derived from AVIRIS-NG data, 2019, Seward Peninsula

Remote sensing maps of plant functional type (PFT), PFT fractional cover (fCover), and fCover uncertainty maps derived from NASA’s Airborne Visible / Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG). The AVIRIS-NG imaging spectroscopy data (380 ~ 2510 nm) was collected as a part of the collaboration between NASA’s Arctic-Boreal Vulnerability Experiment (ABoVE; Miller et al., 2019) and DOE’s Next Generation Ecosystem Experiment in the Arctic (NGEE-Arctic). This package includes maps for flights over Teller, Kougarok, and Council watersheds created using eight AVIRIS-NG imagery collected in July, 2019. The map data and metadata are provided as image (ENVI, *.png) and text (*.txt, *hdr) formats. Additional supporting map quicklooks are provided as GIS *.kml files. Detailed description of the methods for each map are provided in Yang et al., (2023). These datasets are provided in support of Yang et al., (In revision) “Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra”.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Characterization of Thermal, Hydraulic, and Gas Diffusion Properties in Variably Saturated Sand Grades

Core Ideas This study provides physical, hydraulic, and thermal properties of five sand grades. An extended three‐region thermal conductivity model is proposed. A gas diffusivity‐based variable tortuosity parameter is examined. Useful parametric functions and descriptive models were tested. Detailed characterization of partially saturated porous media is important for understanding and predicting vadose zone transport processes. While basic properties (e.g., particle‐ and pore‐size distributions and soil‐water retention) are, in general, essential prerequisites for characterizing most porous media transport properties, key transport parameters such as thermal conductivity and gas diffusivity are particularly important to describe temperature‐induced heat transport and diffusion‐controlled gas transport processes, respectively. Despite many experimental and numerical studies focusing on a specific porous media characteristic, a single study presenting a wide range of important characteristics, together with the best‐performing functional relationships, can seldom be found. This study characterized five differently textured sand grades (Accusand no. 12/20, 20/30, 30/40, 40/50, and 50/70) in relation to physical properties, water retention, hydraulic conductivity, thermal conductivity, and gas diffusivity. We used measured basic properties and transport data to accurately parameterize the characteristic functions (particle‐ and pore‐size distributions and water retention) and descriptive transport models (thermal conductivity, saturated hydraulic conductivity, and gas diffusivity). An existing thermal conductivity model was improved to describe the distinct three‐region behavior in observed thermal conductivity–water saturation relations. Applying widely used parametric models for saturated hydraulic conductivity and soil‐gas diffusivity, we characterized porous media tortuosity in relation to grain size. Strong relations among average particle diameter, characteristic pore diameter from soil‐water retention measurements, and saturated hydraulic conductivity were found. Thus, the results of this work are useful toward better describing, linking, and predicting mass transfer and pore network properties in variably saturated porous media.

Chamindu Deepagoda, T. K. K.↗
DOE OSTI2014

Simplified continuum solvent model with a smooth cavity based on volumetric data

We present a continuum solvent model (CSM) with a smooth cavity for the application in grid-based electronic structure methods. The cavity is identified with the inherently smooth distribution function of a binary mixture at infinite dilution. We obtain a cavity model based on atomic van der Waals radii and one free parameter controlling the overall size. This single parameter is sufficient to adequately reproduce experimental partial molar volumes. The CSM based on this cavity is of similar accuracy in the prediction of aqueous solvation Gibbs energies of small neutral molecules and ions as other CSMs with a smooth cavity. We apply the model to systems in non-aqueous solution, i.e., spiropyran/merocyanin energetics, a proton transfer reaction in dimethyl sulfoxide, and the electrostatic screening of charged gold clusters in an ionic liquid.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Surface-mediated spontaneous emulsification of the acylated peptide, semaglutide

Acylated peptides composed of glucagon-like peptide-1 receptor agonists modified with a fatty acid side chain are an important class of therapeutics for type 2 diabetes and obesity but are susceptible to an unusual physical instability in the presence of hydrophobic surfaces, i.e., spontaneous emulsification, also known as ouzo formation in practice. In this work, light scattering, small-angle X-ray scattering, and circular dichroism measurements are used to characterize the physical properties of the semaglutide colloidal phase, including size distribution, shape, secondary structure, internal structure, and internal composition, as a function of solution physico-chemical conditions. The existence and size of the colloids formed are successfully predicted by a classical Rayleigh model, which identifies the parameters controlling their size and formation. Colloid formation is found to be catalyzed by hydrophobic surfaces, and formation rates are modeled as an autocatalytic reaction, enabling the formation of a master curve for various surfaces that elucidates the mechanism. Surfaces differ due to differences in surface wettability, which can be correlated with Hansen solubility parameters. This work provides insights into this unusual colloidal phenomenon and guides the peptide synthesis process and drug product formulation in the pharmaceutical industry.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Fuzzy-Logic Subsumption Controller for Home Energy Management Systems

Home Energy Management Systems (HEMS) are controllers that manage and coordinate the generation, storage, and loads in a home. These controllers are increasingly necessary to ensure that increasing penetrations of distributed energy resources are used effectively and do not disrupt the operation of the grid. In this paper, we propose a novel approach to HEMS design based on behavioral control methods, which do not require accurate models or predictions and are very responsive to changing conditions. We develop a proof-of-concept behavioral HEMS controller and show by simulation on an example home energy system that it capable of making context-dependent tradeoffs between goals under challenging conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2019

Cloud Influence on ERA5 and AMPS Surface Downwelling Longwave Radiation Biases in West Antarctica

The surface downwelling longwave radiation component (LW[down arrow]) is crucial for the determination of the surface energy budget and has significant implications for the resilience of ice surfaces in the polar regions. Accurate model evaluation of this radiation component requires knowledge about the phase, vertical distribution, and associated temperature of water in the atmosphere, all of which control the LW[down arrow] signal measured at the surface. In this study, we examine the LW[down arrow] model errors found in the Antarctic Mesoscale Prediction System (AMPS) operational forecast model and the ERA5 reanalysis model relative to observations from the AWARE campaign at McMurdo Station and the West Antarctic Ice Sheet (WAIS) Divide. The errors are calculated separately for observed clear-sky conditions, ice-cloud occurrences, and liquid-bearing cloud layer (LBCL) occurrences. The analysis results show a tendency in both models at each site to underestimate the LW[down arrow] during clear sky conditions, high error variability (standard deviations > 20 W/m[exp2]) during any type of cloud occurrence, and negative LW biases when LBCLs are observed (bias magnitudes > 15 W/m[exp2] in tenuous LBCL cases; > 43 W/m[exp2] in optically thick/opaque LBCLs instances). We suggest that a generally dry and liquid-deficient atmosphere responsible for the identified LW[down arrow] biases in both models is the result of excessive ice formation and growth, which could stem from model initial and lateral boundary conditions, microphysics scheme, aerosol representation, and/or limited vertical resolution.

Israel Silber↗