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At least 145 records · Page 8

Residential electricity conservation in response to auto-generated, multi-featured, personalized eco-feedback designed for large scale applications with utilities

While past research has shown that providing residents with feedback about their electricity usage can reduce demand and its associated environmental burdens, some questions remain regarding what makes such feedback most effective. We followed the electricity usage of 36 residents who each received 14 feedback messages over 2 months. Using approaches borrowed from Natural-Language-Processing, feedbacks were generated automatically, using 10 features in random combinations. Unlike in previous studies, each resident received varying types of messages over time. In 504 observations, the average prompted reduction in electricity usage was 11 ± 3%, compared to a control group of 89 residents who received no messages. Feedback types prompting the largest reductions were self-comparisons with one’s own earlier usage (average reduction 14%) and messages of high variety from one feedback-cycle to the next (average reduction 16%). Comparisons with neighbors did not prompt higher reductions on average. Instead, they prompted reductions only when a resident’s recent usage happened to be higher than the average usage of neighbors, and increases when the reverse was true. Finally, this behavior was exhibited by all residents and is likely explained by a norm-conforming mean reversion of residents to their neighbors’ average usage, rather than an anti-conform “boomerang” behavior previously suggested in similar contexts.

42 ENGINEERING↗

Anomalous Hall effects in chiral superconductors

We report theoretical results for the electronic contribution to thermal and electrical transport for chiral superconductors belonging to even or odd-parity E 1 and E 2 representations of the tetragonal and hexagonal point groups. Chiral superconductors exhibit novel properties that depend on the topology of the order parameter and Fermi surface, and—as we highlight—the structure of the impurity potential. An anomalous thermal Hall effect is predicted and shown to be sensitive to the winding number, ν, of the chiral order parameter via Andreev scattering that transfers angular momentum from the chiral condensate to excitations that scatter off the random potential. For heat transport in a chiral superconductor with isotropic impurity scattering, i.e., point-like impurities, a transverse heat current is obtained for ν = ± 1, but vanishes for | ν | > 1. This is not a universal result. For finite-size impurities with radii of order or greater than the Fermi wavelength, R ≥ ℏ / p f, the thermal Hall conductivity is finite for chiral order with | ν | ≥ 2, and determined by a specific Fermi-surface average of the differential cross-section for electron-impurity scattering. Our results also provide quantitative formulae for analyzing and interpreting thermal transport measurements for superconductors predicted to exhibit broken time-reversal and mirror symmetries.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Deterministic Current–Induced Perpendicular Switching in Epitaxial Co/Pt Layers without an External Field

Current–induced spin–orbit torques (SOTs) have emerged as a powerful tool to control magnetic elements and non–uniform magnetic textures such as domain walls and skyrmions. SOT–induced switching of perpendicular magnetization generally requires an external field to break the rotational symmetry of the spin–orbit effective fields responsible for the deterministic reversal. The proposed mechanisms to eliminate this requirement often rely on complex multilayer structures that necessitate laborious optimization in the material and spin transport properties, making them less attractive for applications. Herein, current–induced, external field–free switching of an epitaxial MgO/Pt/Co trilayer with an extremely large perpendicular anisotropy in excess of 3 Tesla is reported. It is found that switching occurs due to the interplay of strong SOTs, local anisotropy fluctuations, and the Dzyaloshinkii–Moriya interaction inherent to this epitaxial system. Finally, given that these layers constitute the base stack of a magnetic tunnel junction, this switching mechanism offers the most technologically viable path toward devices such as field–free SOT–based magnetic random–access memories.

36 MATERIALS SCIENCE↗

Arctic Tipping Points Triggering Global Change (LDRD Final Report)

The Arctic is warming and feedbacks in the coupled Earth system may be driving the Arctic to tipping events that could have critical downstream impacts for the rest of the globe. In this project we have focused on analyzing sea ice variability and loss in the coupled Earth system Summer sea ice loss is happening rapidly and although the loss may be smooth and reversible, it has significant consequences for other Arctic systems as well as geopolitical and economic implications. Accurate seasonal predictions of sea ice minimum extent and long-term estimates of timing for a seasonally ice-free Arctic depend on a better understanding of the factors influencing sea ice dynamics and variation in this strongly coupled system. Under this project we have investigated the most influential factors in accurate predictions of September Arctic sea ice extent using machine learning models trained separately on observational data and on simulation data from five E3SM historical ensembles. Monthly averaged data from June, July, and August for a selection of ice, ocean, and atmosphere variables were used to train a random forest regression model. Gini importance measures were computed for each input feature with the testing data. We found that sea ice volume is most important earlier in the season (June) and sea ice extent became a more important predictor closer to September. Results from this study provide insight into how feature importance changes with forecast length and illustrates differences between observational data and simulated Earth system data. We have additionally performed a global sensitivity analysis (GSA) using a fully coupled ultra- low resolution configuration E3SM. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM Earth system model. We have found that parameter variations show significant impact on the Arctic climate state and atmospheric parameters related to cloud parameterizations are the most significant. We also find significant interactions between parameters from different components of E3SM. The results of this study provide invaluable insight into the relative importance of various parameters from the sea ice, atmosphere and ocean components of the E3SM (including cross-component parameter interactions) on various Arctic-focused quantities of interest (QOIs).

54 ENVIRONMENTAL SCIENCES↗

Ruddlesden–Popper Perovskites with Narrow Phase Distribution for Air-Stable Solar Cells

2D Ruddlesden-Popper perovskites have risen to prominence as stable and efficient photovoltaic materials because of their structural diversity, rich photophysics, and low moisture ingression. However, thin films processed from stoichiometric precursor solutions possess a broad phase distribution of different number of inorganic layers with random crystal orientation, crippling device performance. The effect of methylammonium chloride (MACI) and 3-amino4-phenolsulfonic acid (APSA) on the fabrication of perpendicularly oriented (PEA) 2 MA 4 Pb 5 I 16 films with narrow phase distribution using antisolvent and hot-casting processing techniques is investigated. MACI plays a critical role in suppressing parasitic n ≤ 2 and 3D-like phases. APSA performs the dual function of trap passivation and further narrowing phase polydispersity through strong coordination with Pb 2+ . Ex situ grazing-incident wide-angle X-Ray scattering (GIWAXS) and ultrafast spectroscopic characterization reveal uniformly mixed-phase distribution with disordered orientation in antisolvent treated films, while additive-assisted hot-casting treatment results in oriented, reverse-graded phase distribution, i.e., small-n on the film surface and large-n at the bottom. Arising thin films enable efficient p-i-n solar cells with an efficiency of 14.34%, and a V oc of 1.20 V, retaining 96% initial efficiency after 1440 h under ambient conditions (RH = 50-60%) without encapsulation.

36 MATERIALS SCIENCE↗

Mapping Composition Evolution through Synthesis, Purification, and Depolymerization of Random Heteropolymers

Random heteropolymers (RHPs) consisting of three or more comonomers have been routinely used to synthesize functional materials. While increasing the monomer variety diversifies the side-chain chemistry, this substantially expands the sequence space and leads to ensemble-level sequence heterogeneity. Most studies have relied on monomer composition and simulated sequences to design RHPs, but the questions remain unanswered regarding heterogeneities within each RHP ensemble and how closely these simulated sequences reflect the experimental outcomes. Here, we quantitatively mapped out the evolution of monomer compositions in four-monomer-based RHPs throughout a design-synthesis-purification-depolymerization process. By adopting a Jaacks method, we first determined 12 reactivity ratios directly from quaternary methacrylate RAFT copolymerization experiments to account for the influences of competitive monomer addition and the reversible activation/deactivation equilibria. The reliability of in silico analysis was affirmed by a quantitative agreement (<4% difference) between the simulated RHP compositions and the experimental results. Furthermore, we mapped out the conformation distribution within each ensemble in different solvents as a function of monomer chemistry, composition, and segmental characteristics via high-throughput computation based on self-consistent field theory (SCFT). These comprehensive studies confirmed monomer composition as a viable design parameter to engineer RHP-based functional materials as long as the reactivity ratios are accurately determined and the livingness of RHP synthesis is ensured.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Random vortex method for combusting flows

The random vortex method (RVM) of Chorin was developed by the recirculating flows. The RVM method models turbulence from first principles, tracking the vorticity and obtaining the interaction of vorticity with the bulk flow field. A computer program was produced called MIMOC, Modeling the Interface Motion of Combustion, which can be used to calculate the reacting flow field behind a rearward facing step. Several comparisons between experimental data and calculations were made. The RVM methood computes qualitatively good results, but the quantitative agreement as yet is not completely satisfactory. Much of the difficulty may be caused by the treatment of boundary conditions and the techniques used for obtaining statistical averages of velocities and turbulence quantities. For the rearward facing step the computed reattachment length equals the experimental value. However, the reverse velocity in the recirculation zone is over predicted by 300 percent. In the calculations, a uniform entrance velocity was assumed with no boundary layer at the step lip. This high velocity may be overdriving the reverse flow region.

Marek, C. J.↗

Non-volatile, high density, high speed, Micromagnet-Hall effect Random Access Memory (MHRAM)

The micromagnetic Hall effect random access memory (MHRAM) has the potential of replacing ROMs, EPROMs, EEPROMs, and SRAMs because of its ability to achieve non-volatility, radiation hardness, high density, and fast access times, simultaneously. Information is stored magnetically in small magnetic elements (micromagnets), allowing unlimited data retention time, unlimited numbers of rewrite cycles, and inherent radiation hardness and SEU immunity, making the MHRAM suitable for ground based as well as spaceflight applications. The MHRAM device design is not affected by areal property fluctuations in the micromagnet, so high operating margins and high yield can be achieved in large scale integrated circuit (IC) fabrication. The MHRAM has short access times (less than 100 nsec). Write access time is short because on-chip transistors are used to gate current quickly, and magnetization reversal in the micromagnet can occur in a matter of a few nanoseconds. Read access time is short because the high electron mobility sensor (InAs or InSb) produces a large signal voltage in response to the fringing magnetic field from the micromagnet. High storage density is achieved since a unit cell consists only of two transistors and one micromagnet Hall effect element. By comparison, a DRAM unit cell has one transistor and one capacitor, and a SRAM unit cell has six transistors.

Wu, Jiin C.↗

Observable measurement-induced transitions

One of the main postulates of quantum mechanics is that measurements destroy quantum coherence (wave function collapse). Recently it was discovered that in a many-body system dilute local measurements still preserve some coherence across the entire system. As the measurement density is increased, a phase transition occurs that is characterized by the disentanglement of different parts of the system. Unfortunately, this transition is impossible to observe experimentally for macroscopic systems because it requires an exponentially costly full tomography of the many-body wave function or a comparison with the simulation on an oracle classical computer. In this work we report the discovery of another measurement-induced phase transition that can be observed experimentally if quantum dynamics can be reversed. On one side of this phase transition the quantum information encoded in some part of the Hilbert space is fully recovered after the time inversion. On the other side, all quantum information is corrupted. This transition also manifests itself as the change in the behavior of the probability to observe the same measurement outcome in the process that consists of identical blocks repeated many times. In each block the unitary evolution is followed by the measurement. On one side of the transition the probability decreases exponentially with the number of repetitions, on the other it tends to a constant as the number of repetitions is increased. We confirm the existence of the proposed phase transition through numerical simulations of realistic quantum circuits and analytical calculations using an effective random-matrix theory model.

Measurement-induced phase transitions↗

Modified Spectral Fatigue Methods for S-N Curves With MIL-HDBK-5J Coefficients

The rainflow method is used for counting fatigue cycles from a stress response time history, where the fatigue cycles are stress-reversals. The rainflow method allows the application of Palmgren-Miner's rule in order to assess the fatigue life of a structure subject to complex loading. The fatigue damage may also be calculated from a stress response power spectral density (PSD) using the semi-empirical Dirlik, Single Moment, Zhao-Baker and other spectral methods. These methods effectively assume that the PSD has a corresponding time history which is stationary with a normal distribution. This paper shows how the probability density function for rainflow stress cycles can be extracted from each of the spectral methods. This extraction allows for the application of the MIL-HDBK-5J fatigue coefficients in the cumulative damage summation. A numerical example is given in this paper for the stress response of a beam undergoing random base excitation, where the excitation is applied separately by a time history and by its corresponding PSD. The fatigue calculation is performed in the time domain, as well as in the frequency domain via the modified spectral methods. The result comparison shows that the modified spectral methods give comparable results to the time domain rainflow counting method.

Irvine, Tom↗

Efficient 3-D velocity model building using joint inline and crossline plane-wave wave-equation migration velocity analyses

SUMMARY Wave-equation migration velocity analysis (WEMVA) is an image-domain inversion method for velocity model building. Automatic plane-wave WEMVA (PWEMVA) calculates the moveouts of plane-wave common-image gathers (CIGs) by searching a best-fitting parabola with semblance analysis and backprojects residual CIG moveouts into wavefield wave paths with a reflection tomographic kernel. However, 3-D PWEMVA is very computationally expensive because 3-D reflection tomographic inversion requires at least five 3-D reverse-time migrations per iteration and stores two types of source wavefields at model boundaries. We develop a joint inline and crossline PWEMVA method for efficient 3-D velocity model building. We alternatively implement the inline and crossline PWEMVAs with a constraint for each other, in which we iteratively construct the 3-D velocity model update through 1-D spline interpolation of 2-D gradients. The inline and crossline joint inversion is practical since PWEMVA only inverts for low-wavenumber velocity perturbations along wave paths, and the method can take less than 1 per cent of the computational cost of full 3-D PWEMVA. To construct unaliased plane waves for our joint inline and crossline PWEMVA, we develop a 3-D data interpolation method in the frequency–wavenumber (FK) domain to recover regularly and randomly missing traces. The method minimizes the misfit on sufficiently localized data subsets with iterative optimal step lengths and a gradient preconditioner that iteratively selects dominant dips along different azimuths. In numerical experiments, we use a 3-D synthetic seismic data set and a land 3-D field seismic data set acquired at the Farnsworth CO2-EOR (enhanced oil recovery) field to demonstrate the efficacy of our velocity model building and data interpolation methods.

Liu, Xuejian↗

"Electrostructural Phase Changes" In Charged Particulate Clouds: Planetary and Astrophysical Implications

There is empirical evidence that freely-suspended triboelectrostatically charged particulate clouds of dielectric materials undergo rapid conversion from (nominally) monodispersed "aerosols" to a system of well-defined grain aggregates after grain motion or fluid turbulence ceases within the cloud. In United States Microgravity Laboratory Space Shuttle experiments USML-1 and USML-2, it was found that ballistically-energized grain dispersions would rapidly convert into populations of filamentary aggregates after natural fluid (air) damping of grain motion. Unless continuously disrupted mechanically, it was impossible to maintain a non-aggregated state for the grain clouds of sand-size materials. Similarly, ground- based experiments with very fine dust-size material produced the same results: rapid, impulsive "collapse" of the dispersed grains into well-defined filamentary structures. In both ground-based and microgravity experiments, the chains or filaments were created by long-range dipole electrostatic forces and dipole-induced dielectric interactions, not by monopole interactions. Maintenance of the structures was assisted by short-range static boundary adhesion forces and van der Waals interactions. When the aggregate containers in the USML experiments were disturbed after aggregate formation, the quiescently disposed filaments would rearrange themselves into fractal bundles and tighter clusters as a result of enforced encounters with one another. The long-range dipole interactions that bring the grains together into aggregates are a product of randomly-distributed monopole charges on the grain surfaces. In computer simulations, it has been shown that when the force vectors of all the random charges (of both sign) on a grain are resolved mathematically by assuming Coulombic interaction between them, the net result is a dipole moment on individual grains, even though the grains are electrically neutral insofar as there is no predominance, on their surface, of one charge sign over another. The random charges of both sign derive from natural grain-to-grain interactions that produce triboelectrification via charge exchange every time grain surfaces make contact with one another. The conversion from a random distribution of grains (upon which there are randomly distributed charges) into an organization of electrostatically-ordered aggregates, can be regarded (within the framework of granular-material science) as an "electrical or Coulombic phase change" of the particulate cloud. It is not totally dissimilar from the more normal phase-change concept in which, for example, a gas with long free-path-molecules suddenly becomes a solid as a result of structural ordering of the molecules (notably, also the result of electronic forces, albeit at a different scale). In both the gas-to-solid case, and the aerosol-to-aggregate case, the same materials and charges are present before and after the phase change, but their arrangement now has a higher degree of order and a lower-energy configuration. An input of energy into the system is required to reverse the situation. The aggregates in the USML experiments were observed to undergo at least two phase changes as noted above. The point about phase changes, and by implication, the "electrostructural" reorganizations in particulate clouds, is the following: (a) they can occur very rapidly, almost spontaneously, above a critical cloud density, (b) in going from a higher energy state to a lower energy state, they convert to a denser system, (c) energy must be required to reverse the situation, implying that energy is released during the high-to-low energy phase change. In applying this information to natural particulate clouds, some inferences can be made (it is stressed that reference is still to dielectric materials attracted by dipole forces). There are several natural settings to which the USML observations apply, and to which the phase-change implications likewise apply. Dense clouds of triboelectrically-charged, kinetically-energized grains are to be found in volcanic eruptions (particularly on earth), aeolian dust storms (particularly on Mars), meteorite impact ejecta curtains (on all planets), in "immature" debris rings around planets (e.g., that from which our own Moon may have condensed), and in gravitationally collapsing protoplanetary dust/planetesimal debris disks where dielectric granules are being increasingly brought into collisional relationships with one another (increasing both charge exchange and physical proximity). It is noted that in many of these cases, the degree of electrical charging on the grains is likely to be much higher than that in the USML experiments where charging was not enhanced above the "normal", naturally encountered level for the particular materials (quartz, glass, and various silicate minerals). Application of the phase-change concept suggests that volcanic, aeolian, and impact debris clouds may, under certain circumstances, undergo rapid, impulsive, or even catastrophic collapse into a denser state that will lead to rapid precipitation or fall-out of suspended particulates. Although this idea has been suggested previously by the author , the phase change concept possibly permits some new insights into cloud-system behavior. For example, in a protoplanetary debris disk, the work of gravity may suddenly be enhanced by electrostatically driven collapse of the system when materials reach a critical intergranular spacing or grain number density. This might reduce the rate of planet formation by orders of magnitude (considering the ratio of g-forces to electrostatic ones for very small grains in close proximity), and indeed, it might drive the collapse into a system configuration that would perhaps not be created by gravity alone. Additional information is contained in the original.

Marshall, J. R.↗

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - Simulated Marine Hydrokinetic Tidal Turbine

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset is part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with other energy technologies. This dataset contains inputs and outputs from simulations of a floating marine hydrokinetic turbine over approximately half a tidal cycle (~6.6 hours). Inflow conditions were derived from field measurements in Alaska’s Cook Inlet and represent a tidal environment in which the current speed ramps from near 0 m/s to a peak of 3 m/s and back. The original acoustic doppler current profiler dataset is publicly available on the Marine and Hydrokinetic Data Repository. In a full tidal cycle, the flow reverses and the rotor would reorient; this reversal was not modeled. In the Cook Inlet campaign , turbulence intensity was similar in both directions. Two inflow cases are included. In the first case, labeled “raw” in the files, the measured current time series was used directly in the InflowWind module of OpenFAST. Speed and direction were applied as a function of time and elevation, uniformly in the horizontal direction. With full spatial coherence, this approach captures high turbulent variability and results in pronounced power fluctuations, so it is considered a conservative, near-worst-case representation of loading. In the second case, labeled “average” in the files, a 30-minute moving average was applied to extract the slowly varying mean speed. The residual fluctuations about this mean were used to generate spatially varying, full-field turbulence inputs with TurbSim, giving a more physically realistic representation of the inflow across the rotor disk. Two random realizations were used to produce distinct inflow conditions for two OpenFAST simulations representing a two-turbine array. The same turbulence intensity is applied across the full time series, producing larger fluctuations at the start and end, where the mean speed is low. The second case is the more appropriate framework for performance and power assessment but overpredicts turbulence at lower flow speeds and underpredicts it at higher speeds. As the floating platform moves and the rotor changes its x-position, Taylor’s frozen turbulence hypothesis used by InflowWind assumes a constant rather than a time-varying mean velocity, introducing some inaccuracy in the velocity plane sampling. The turbine modeled is the 500-kW Reference Model 1, a horizontal-axis two-bladed hydrokinetic turbine on a four-column floating semisubmersible substructure . Simulations were performed using OpenFAST v4.1 with the Reference Open Source Controller (ROSCO) v2.10. All input files required to reproduce the simulations are included. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel . This unit supports up to 2.5 MW, but NLR has only a single 1.25-MW stack. The datasets report hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. The system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operating current of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The simulated tidal turbine time series data was translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz. Each zip file represents a single tidal electrolysis experiment and is named: {technology}_{inflow method}_{number of 500 kW tidal turbines connected} For instance, “tidal-500kW-RM1_average_2.zip” is a 6-hour experiment using the 500-kW tidal reference model, scaled by 2x (1-MW) to better match the electrolyzer maximum of 1.25MW, fed with the 30-minute moving average current case. Each zip folder contains the following files: A .csv file of raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wave power. A .csv file combines all tidal profiles as "combined_tidal_experiments.csv." A separate experiment, “characterization_200.zip,” shows the MC250 electrolyzer steady-state response with 30-minute load steps over 5 hours and is accessible with this entry.

08 HYDROGEN↗

Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks

The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. This suite included a network of MEDLI2 Instrumented Sensor Plugs (MISPs). Each MISP was comprised of a cylinder made of Thermal Protection System (TPS) material with 1-3 embedded thermocouples (TCs), and it was flush mounted into the heatshield or backshell. Data from these in-depth TCs were used to reconstruct the aeroheating environment of the vehicle throughout entry. Surface heating was posed as an inverse problem, with the goal of estimating the surface heating by minimizing an objective function of the difference between MISP temperature measurements during flight and the temperature predictions derived from the Fully Implicit Ablation and Thermal response (FIAT) program. Given an aerothermal environment, FIAT calculates the material response and provides in-depth temperatures throughout the TPS material. To achieve the reverse, an internal tool called FIAT_Opt runs through multiple different environments until the output temperature at the TC depth closely matches the flight data. 95% confidence intervals on the reconstructed surface heating were obtained using Monte Carlo analysis, in which uncertainties in the thermocouple depth and the TPS material properties (e.g., density, thermal conductivity, heat capacity, emissivity) based on flight-lot material testing were included. A variance decomposition method using Sobol indices was employed to assess the sensitivity of the reconstructed peak heating to the TC placement and material property uncertainties. Variance decomposition was found to require tens of thousands of FIAT_Opt runs in order for the Sobol indices to converge. With a single FIAT_Opt run taking on the order of 40 minutes, the required number of computations would take months to complete, even if using multiple CPUs. To mitigate this problem, three machine learning models (ridge regression with cross-validation, random forest regression, and a deep neural network) were trained and tested using the 2000 Monte Carlo runs that were already completed. A subset of 1600 runs were used to train the model (i.e., training set), while the remaining 400 runs were used as the test set. The predictions from the deep neural network (DNN) on the test set showed nearly perfect agreement to the actual values computed with FIAT_Opt (R2 > 0.99). Using the DNN as a surrogate model, the variance decomposition using 50,000 runs was completed within minutes. The resulting Sobol indices showed that the reconstructed peak surface heating was most sensitive to the uncertainties in the thermal conductivity (ST = 0.37) and heat capacity (ST = 0.26). This method can be leveraged to provide requirements for material property measurements needed to improve the accuracy of surface heating prediction and ultimately lead to the reduction of design margins in the future. This presentation will include background on the MEDLI2 suite; the method used for inverse heating estimation; the way that material property uncertainties were accounted for using Monte Carlo analysis; a brief background on variance decomposition; the motivation for using machine learning in this context; how a neural network was trained on the data to enable variance decomposition in a fraction of the time; and the variance decomposition results for one of the MISPs.

Hannah Alpert↗

A petabyte size electronic library using the N-Gram memory engine

A model library containing petabytes of data is proposed by Triada, Ltd., Ann Arbor, Michigan. The library uses the newly patented N-Gram Memory Engine (Neurex), for storage, compression, and retrieval. Neurex splits data into two parts: a hierarchical network of associative memories that store 'information' from data and a permutation operator that preserves sequence. Neurex is expected to offer four advantages in mass storage systems. Neurex representations are dense, fully reversible, hence less expensive to store. Neurex becomes exponentially more stable with increasing data flow; thus its contents and the inverting algorithm may be mass produced for low cost distribution. Only a small permutation operator would be recalled from the library to recover data. Neurex may be enhanced to recall patterns using a partial pattern. Neurex nodes are measures of their pattern. Researchers might use nodes in statistical models to avoid costly sorting and counting procedures. Neurex subsumes a theory of learning and memory that the author believes extends information theory. Its first axiom is a symmetry principle: learning creates memory and memory evidences learning. The theory treats an information store that evolves from a null state to stationarity. A Neurex extracts information data without a priori knowledge; i.e., unlike neural networks, neither feedback nor training is required. The model consists of an energetically conservative field of uniformly distributed events with variable spatial and temporal scale, and an observer walking randomly through this field. A bank of band limited transducers (an 'eye'), each transducer in a bank being tuned to a sub-band, outputs signals upon registering events. Output signals are 'observed' by another transducer bank (a mid-brain), except the band limit of the second bank is narrower than the band limit of the first bank. The banks are arrayed as n 'levels' or 'time domains, td.' The banks are the hierarchical network (a cortex) and transducers are (associative) memories. A model Neurex was built and studied. Data were 50 MB to 10 GB samples of text, data base, and images: black/white, grey scale, and high resolution in several spectral bands. Memories at td, S(m(sub td)), were plotted against outputs of memories at td-1. S(m(sub td)) was Boltzman distributed, and memory frequencies exhibited self-organized criticality (SOC); i.e., 'l/f(sup beta)' after long exposures to data. Whereas output signals from level n may be encoded with B(sub output) = O(-log(2)f(sup beta)) bits, and input data encoded with B(sub input) = O((S(td)/S(td-1))(sup n)), B(sup output)/B(sub input) is much less than 1 always, the Neurex determines a canonical code for data and it is a lossless data compressor. Further tests are underway to confirm these results with more data types and larger samples.

Bugajski, Joseph M.↗

Snowex 2017 Community Snow Depth Measurements: A Quality-Controlled, Georeferenced Product

Snow depth was one of the core ground measurements required to validate remotely-sensed data collected during SnowEx Year 1, which occurred in Colorado. The use of a single, common protocol was fundamental to produce a community reference dataset of high quality. Most of the nearly 100 Grand Mesa and Senator Beck Basin SnowEx ground crew participants contributed to this crucial dataset during 6-25 February 2017. Snow depths were measured along ~300 m transects, whose locations were determined according to a random-stratified approach using snowfall and tree-density gradients. Two-person teams used snowmobiles, skis, or snowshoes to travel to staked transect locations and to conduct measurements. Depths were measured with a 1-cm incremented probe every 3 meters along transects. In shallow areas of Grand Mesa, depth measurements were also collected with GPS snow-depth probes (a.k.a. MagnaProbes) at ~1-m intervals. During summer 2017, all reference stake positions were surveyed with <10 cm accuracy to improve overall snow depth location accuracy. During the campaign, 193 transects were measured over three weeks at Grand Mesa and 40 were collected over two weeks in Senator Beck Basin, representing more than 27,000 depth values. Each day of the campaign depth measurements were written in waterproof field books and photographed by National Snow and Ice Data Center (NSIDC) participants. The data were later transcribed and prepared for extensive quality assessment and control. Common issues such as protocol errors (e.g., survey in reverse direction), notebook image issues (e.g., halo in the center of digitized picture), and data-entry errors (sloppy writing and transcription errors) were identified and fixed on a point-by-point basis. In addition, we strove to produce a georeferenced product of fine quality, so we calculated and interpolated coordinates for every depth measurement based on surveyed stakes and the number of measurements made per transect. The product has been submitted to NSIDC in csv format. To educate data users, we present the study design and processing steps that have improved the quality and usability of this product. Also, we will address measurement and design uncertainties, which are different in open vs. forest areas.

Brucker, L.↗

Folding and translocation of the undecamer of poly-L-leucine across the water-hexane interface. A molecular dynamics study

The undecamer of poly-L-leucine at the water-hexane interface is studied by molecular dynamics simulations. This represents a simple model relevant to folding and insertion of hydrophobic peptides into membranes. The peptide, initially placed in a random coil conformation on the aqueous side of the system, rapidly translocates toward the hexane phase and undergoes interfacial folding into an alpha-helix in the subsequent 36 ns. Folding is nonsequential and highly dynamic. The initially formed helical segment at the N-terminus of the undecamer becomes transiently broken and, subsequently, reforms before the remainder of the peptide folds from the C-terminus. The formation of intramolecular hydrogen bonds during the folding of the peptide is preceded by a dehydration of the participating polar groups, as they become immersed in hexane. Folding proceeds through a short-lived intermediate, a 3(10)-helix, which rapidly interconverts to an alpha-helix. Both helices contribute to the equilibrium ensemble of folded structures. The helical peptide is largely buried in hexane, yet remains adsorbed at the interface. Its preferred orientation is parallel to the interface, although the perpendicular arrangement with the N-terminus immersed in hexane is only slightly less favorable. In contrast, the reversed orientation is highly unfavorable, because it would require dehydration of C-terminus carbonyl groups that do not participate in intramolecular hydrogen bonding. For the same reason, the transfer of the undecamer from the interface to the bulk hexane is also unfavorable. The results suggest that hydrophobic peptides fold in the interfacial region and, simultaneously, translocate into the nonpolar side of the interface. It is further implied that peptide insertion into the membrane is accomplished by rotating from the parallel to the perpendicular orientation, most likely in such a way that the N-terminus penetrates the bilayer.

NASA Discipline Exobiology↗

Electrochemical Random-Access Memory: Progress, Perspectives, and Opportunities

Non-von Neumann computing using neuromorphic systems based on analogue synaptic and neuronal elements has emerged as a potential solution to tackle the growing need for more efficient data processing, but progress toward practical systems has been stymied due to a lack of materials and devices with the appropriate attributes. Recently, solid state electrochemical ion-insertion, also known as electrochemical random access memory (ECRAM) has emerged as a promising approach to realize the needed device characteristics. ECRAM is a three terminal device that operates by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions. This mechanism can be considered as a gate-controlled bulk modulation of dopants and/or phases in the channel. Early work demonstrating that ECRAM can achieve nearly ideal analogue synaptic characteristics has sparked tremendous interest in this approach. More recently, the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered two-dimensional materials, organic and coordination polymers, and that the changes in conductance can span orders of magnitude has further attracted interest in ECRAM as the basis for analogue synaptic elements for inference accelerators as well as for dynamical devices that can emulate a wide range of neuronal characteristics for implementation in analogue spiking neural networks. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to tens of nanometers, all while achieving reliable electronic-state tuning at scaled-up rates and endurances, and with minimal energy dissipation and noise. Further, in this review, we discuss the history, basic concepts, recent progress, as well as the challenges and opportunities for different types of ECRAM, broadly grouped by their primary mobile ionic charge carrier, including Li, protons, and oxygen vacancies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗