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DOE OSTI2024

Predicting U.S. federal fleet electric vehicle charging patterns using internal combustion engine vehicle fueling transaction statistics

Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.

25 ENERGY STORAGE↗

DOE OSTI2014

Greenhouse Gas Source Attribution: Measurements Modeling and Uncertainty Quantification

In this project we have developed atmospheric measurement capabilities and a suite of atmospheric modeling and analysis tools that are well suited for verifying emissions of green- house gases (GHGs) on an urban-through-regional scale. We have for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate atmospheric CO 2 . This will allow for the examination of regional-scale transport and distribution of CO 2 along with air pollutants traditionally studied using CMAQ at relatively high spatial and temporal resolution with the goal of leveraging emissions verification efforts for both air quality and climate. We have developed a bias-enhanced Bayesian inference approach that can remedy the well-known problem of transport model errors in atmospheric CO 2 inversions. We have tested the approach using data and model outputs from the TransCom3 global CO 2 inversion comparison project. We have also performed two prototyping studies on inversion approaches in the generalized convection-diffusion context. One of these studies employed Polynomial Chaos Expansion to accelerate the evaluation of a regional transport model and enable efficient Markov Chain Monte Carlo sampling of the posterior for Bayesian inference. The other approach uses de- terministic inversion of a convection-diffusion-reaction system in the presence of uncertainty. These approaches should, in principle, be applicable to realistic atmospheric problems with moderate adaptation. We outline a regional greenhouse gas source inference system that integrates (1) two ap- proaches of atmospheric dispersion simulation and (2) a class of Bayesian inference and un- certainty quantification algorithms. We use two different and complementary approaches to simulate atmospheric dispersion. Specifically, we use a Eulerian chemical transport model CMAQ and a Lagrangian Particle Dispersion Model - FLEXPART-WRF. These two models share the same WRF assimilated meteorology fields, making it possible to perform a hybrid simulation, in which the Eulerian model (CMAQ) can be used to compute the initial condi- tion needed by the Lagrangian model, while the source-receptor relationships for a large state vector can be efficiently computed using the Lagrangian model in its backward mode. In ad- dition, CMAQ has a complete treatment of atmospheric chemistry of a suite of traditional air pollutants, many of which could help attribute GHGs from different sources. The inference of emissions sources using atmospheric observations is cast as a Bayesian model calibration problem, which is solved using a variety of Bayesian techniques, such as the bias-enhanced Bayesian inference algorithm, which accounts for the intrinsic model deficiency, Polynomial Chaos Expansion to accelerate model evaluation and Markov Chain Monte Carlo sampling, and Karhunen-Lo %60 eve (KL) Expansion to reduce the dimensionality of the state space. We have established an atmospheric measurement site in Livermore, CA and are collect- ing continuous measurements of CO 2 , CH 4 and other species that are typically co-emitted with these GHGs. Measurements of co-emitted species can assist in attributing the GHGs to different emissions sectors. Automatic calibrations using traceable standards are performed routinely for the gas-phase measurements. We are also collecting standard meteorological data at the Livermore site as well as planetary boundary height measurements using a ceilometer. The location of the measurement site is well suited to sample air transported between the San Francisco Bay area and the California Central Valley.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Drug-Inducible Gene Therapy Effectively Reduces Spontaneous Seizures in Kindled Rats but Creates Off-Target Side Effects in Inhibitory Neurons

Over a third of patients with temporal lobe epilepsy (TLE) are not effectively treated with current anti-seizure drugs, spurring the development of gene therapies. The injection of adeno-associated viral vectors (AAV) into the brain has been shown to be a safe and viable approach. However, to date, AAV expression of therapeutic genes has not been regulated. Moreover, a common property of antiepileptic drugs is a narrow therapeutic window between seizure control and side effects. Therefore, a long-term goal is to develop drug-inducible gene therapies that can be regulated by clinically relevant drugs. In this study, a first-generation doxycycline-regulated gene therapy that delivered an engineered version of the leak potassium channel Kcnk2 (TREK-M) was injected into the hippocampus of male rats. Rats were electrically stimulated until kindled. EEG was monitored 24/7. Electrical kindling revealed an important side effect, as even low expression of TREK M in the absence of doxycycline was sufficient to cause rats to develop spontaneous recurring seizures. Treating the epileptic rats with doxycycline successfully reduced spontaneous seizures. Localization studies of infected neurons suggest seizures were caused by expression in GABAergic inhibitory neurons. In contrast, doxycycline increased the expression of TREK-M in excitatory neurons, thereby reducing seizures through net inhibition of firing. These studies demonstrate that drug-inducible gene therapies are effective in reducing spontaneous seizures and highlight the importance of testing for side effects with pro-epileptic stressors such as electrical kindling. These studies also show the importance of evaluating the location and spread of AAV-based gene therapies in preclinical studies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Spinor-helicity formalism for massive and massless amplitudes in five dimensions

Five-dimensional gauge and gravity theories are known to exhibit striking properties. D = 5 is the lowest dimension where massive tensor states appear naturally, providing a testing ground for perturbative insights into six-dimensional tensor theories. Five-dimensional supergravities are highly constrained and admit elegant geometric and algebraic formulations, with global symmetries manifest at the Lagrangian level. In this paper, we take a step towards the systematic investigation of amplitudes in five dimensions, and present a five-dimensional version of the spinor-helicity formalism, applicable to massless, massive and supersymmetric states. We give explicit representations for on-shell spinor and polarization variables such that the little-group symmetry and gauge redundancy are manifest. Massive self-dual tensor states are discussed in some detail, as well as all the on-shell supermultiplets that can appear in matter-coupled gauge and supergravity theories. As a byproduct of considering supersymmetry in the presence of central charge, we obtain massless ten-dimensional Majorana-Weyl spinors as products of five-dimensional massive spinors. We present compact expressions for superamplitudes at multiplicity three and four, including several novel superamplitudes that either do not straightforwardly uplift to six dimensions, or have not appeared in the six-dimensional literature. We discuss several examples of five-dimensional double-copy constructions in the context of gravitational theories with massive vectors and tensors, illustrating that the formalism we construct can also be used to considerably streamline the double-copy construction of $\mathcal{N}$ = 2 Maxwell-Einstein supergravities.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Feedback of the Dutch Historical Waste Management Program - 17085

This paper provides a feedback on the Dutch historical waste management program. Over the past decades, the High Flux Reactor located in Petten in the province of North Holland has tested numerous types of materials and fuels irradiation, for both medical and nuclear energy applications. A number of waste containers was accumulated on site, where the storage facility was initially built. Following Dutch political decisions in the 1980's, the contents of the waste storage facility in Petten must be transferred to another location. The consequences of this decision included the transportation of waste containers from Petten to the new location, which is located in the South-West of the Netherlands. This project was initiated and is now running. It includes a complex structure and can be described as follows: each historical-waste container will be opened, its contents segregated in three categories depending on the dose rate emission, characterized and repacked in new containers suitable for transportation and storage at the new site. The approach chosen is based on the final dose rate emitted by the repacked containers, which are sub-divided into 3 categories.. The lowest dose-emitting containers are transported directly to the new storage facility. The 2 other categories are first transported to a third party to be super compacted and subsequently transported to the new storage facility. So far, the segregation method is based on Co-60 gamma emission and nuclide vectors associated to the different materials. A pre-sorting has been established to fit as much as possible IAEA recommendations. Historical waste has then to be classified in families sharing common history. Thus, a set of canisters containing the pieces of the old reactor vessel, which was replaced in 1984 was the first challenge, followed by a set of canisters containing residues of instruments only and pieces coming from the top of the control rods of the reactor which were not directly irradiated in the reactor. So far, the two first sets of canisters are being treated and characterized as well as possible. Reporting and official documents have been filled up to comply to Dutch transport regulations and to define waste acceptance criteria for the storage facility. The first transports of containers with Low Level Waste occurred in October 2015, and another transport took place at the end of 2016. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

Neutron Radiation, Radiation Measurement and the JLab APEX Experiment

The A-prime Experiment (APEX), which is approved to run at the Thomas Jefferson Accelerator Facility (JLab) Hall A, will search for a new vector boson that is hypothesized to be a possible force carrier that couples to dark matter. APEX results should be sensitive to the mass range of 65 MeV to 550 MeV, and high sensitivity will be achieved by means of a high intensity 100 muA beam on a 0.3 g/cm 2 Tungsten target resulting in very high luminosity. The experiment should be able to observe an A-prime with a coupling constant 1 x 10 7 times smaller than the electromagnetic coupling constant. To deal safely with such enormous intensity and luminosity, a full radiation analysis must be used. A Geant4 Monte Carlo simulation was performed to study the radiation environments expected in the JLab Hall A during the APEX experiment. We benchmarked our simulations with measurements carried out during past experiments such as the Pb Radius Experiment (PREX-I). Our PREX-I simulations agreed reasonably well with measurements performed by measuring the change in dark current in a Silicon Photomultipliers as a means of radiation damage. However, our simulations were initially significantly higher than the neutron dose measurements (using NP100) in Hall A. The apparent disagreement hints to several potential problems in NP100 measurements including the dead-time. Further, we identified radiation sources in the beam line and tested several shielding strategies to lower the radiation in Hall A. We characterized several thermal neutron detectors and also studied possibility of damage to electronics due to neutron radiation. We successfully demonstrated that the APEX experiment can be carried out in JLab Hall A without worrying about radiation damage to expensive electronics. The A-prime will appear as a narrow resonance in the invariant mass spectrum of e + e - pairs resulting from the electron beam scattering off the APEX target. In this search for the A-prime, it is critical to have a good mass resolution which is mainly dominated by the track measurement uncertainties. We studied a novel method to calibrate the HRS optics which will improve the accuracy of track measurements. Our results will be used to design HRS calibration runs for the APEX experiment.

Kaluarachchi, Maduka↗

DOE OSTI2021

Distributed Inference with Sparse and Quantized Communication

Here, we consider the problem of distributed inference where agents in a network observe a stream of private signals generated by an unknown state, and aim to uniquely identify this state from a finite set of hypotheses. We focus on scenarios where communication between agents is costly, and takes place over channels with finite bandwidth. To reduce the frequency of communication, we develop a novel event-triggered distributed learning rule that is based on the principle of diffusing low beliefs on each false hypothesis. Building on this principle, we design a trigger condition under which an agent broadcasts only those components of its belief vector that have adequate innovation, to only those neighbors that require such information. We prove that our rule guarantees convergence to the true state exponentially fast almost surely despite sparse communication, and that it has the potential to significantly reduce information flow from uninformative agents to informative agents. Next, to deal with finite-precision communication channels, we propose a distributed learning rule that leverages the idea of adaptive quantization. We show that by sequentially refining the range of the quantizers, every agent can learn the truth exponentially fast almost surely, while using just 1 bit to encode its belief on each hypothesis. For both our proposed algorithms, we rigorously characterize the trade-offs between communication-efficiency and the learning rate.

42 ENGINEERING↗

DOE OSTI2022

Rapidly Deployable Acoustic Monitoring and Localization System Based on a Low-Cost Wave Buoy Platform

The primary objective of this project is to develop a cost-effective, fit-for-purpose environmental monitoring system, “NoiseSpotter®,” that characterizes, classifies, and provides accurate location information for anthropogenic and natural sounds in near real-time. NoiseSpotter was developed to support the evaluation of potential acoustic effects of marine energy (ME) projects. By utilizing a compact array of three acoustic particle motion sensors, NoiseSpotter triangulates individual bearings to provide sound source localization to within 5% accuracy, allowing the ability to discern ME device sounds relative to other confounding sounds in the environment, while providing location estimates to nearby marine mammals for environmental mitigation purposes. The ME industry needs proven solutions to meet environmental impact assessment needs. The NoiseSpotter® includes off-the-shelf, modular components that are easy to assemble and disassemble. Its acoustic particle motion sensors are commercially available and the data logger and real-time telemetry system is designed to be plug-and-play. The entire system is relatively compact and can be deployed from small vessels. NoiseSpotter’s near real-time capability enables operational monitoring of ME sounds, particularly during early stages of technology adoption to facilitate mitigation of potential noise effects. Widespread adoption of the technology for acoustic monitoring of ME devices requires that it be cost effective; hence the anticipated commercial cost of system hardware is $35,000. This project contributes to reducing barriers to ME testing through support of scientific research focused on reducing or mitigating environmental risks and lowering costs and complexity of environmental monitoring. This project has developed an acoustic monitoring system, NoiseSpotter® (U.S. Patent No. 11,156,734 and U.S. Registered Trademark No. 6,442,313), to detect and characterize baseline noise and sounds from ME operations and support geolocation of detected sounds. The intended outcome of the project is to mitigate concerns about the potential for ME device noise to alter marine mammal or fish behavior. NoiseSpotter® enables cost-effective, near real-time acoustic monitoring of an operational ME device relative to ambient environmental noise and provides a technical basis for ME developers seeking to navigate the permitting process in an efficient manner.

16 TIDAL AND WAVE POWER↗

DOE OSTI2017

Considerations on the Use of Custom Accelerators for Big Data Analytics

Accelerators, including Graphic Processing Units (GPUs) for gen- eral purpose computation, many-core designs with wide vector units (e.g., Intel Phi), have become a common component of many high performance clusters. The appearance of more stable and reliable tools tools that can automatically convert code written in high-level specifications with annotations (such as C or C++) to hardware de- scription languages (High-Level Synthesis - HLS), is also setting the stage for a broader use of reconfigurable devices (e.g., Field Pro- grammable Gate Arrays - FPGAs) in high performance system for the implementation of custom accelerators, helped by the fact that new processors include advanced cache-coherent interconnects for these components. In this chapter, we briefly survey the status of the use of accelerators in high performance systems targeted at big data analytics applications. We argue that, although the progress in the use of accelerators for this class of applications has been sig- nificant, differently from scientific simulations there still are gaps to close. This is particularly true for the ”irregular” behaviors exhibited by no-SQL, graph databases. We focus our attention on the limits of HLS tools for data analytics and graph methods, and discuss a new architectural template that better fits the requirement of this class of applications. We validate the new architectural templates by mod- ifying the Graph Engine for Multithreaded System (GEMS) frame- work to support accelerators generated with such a methodology, and testing with queries coming from the Lehigh University Benchmark (LUBM). The architectural template enables better supporting the task and memory level parallelism present in graph methods by sup- porting a new control model and a enhanced memory interface. We show that out solution allows generating parallel accelerators, pro- viding speed ups with respect to conventional HLS flows. We finally draw conclusions and present a perspective on the use of reconfig- urable devices and Design Automation tools for data analytics.

Data Analytics↗

DOE OSTI2023

Angular Correlations in the β Decay of B 8 : First Tensor-Current Limits from a Mirror-Nucleus Pair

We present the first measurement of the α–β–ν angular correlation in the Gamow-Teller β + decay of 8 B. This was accomplished using the Beta-decay Paul Trap, expanding on our previous work on the β – decay of 8 Li. The 8 B result is consistent with the V – A electroweak interaction of the standard model and, on its own, provides a limit on the exotic right-handed tensor current relative to the axial-vector current of |C T /C A | 2 < 0.013 at the 95.5% confidence level. This represents the first high-precision angular correlation measurements in mirror decays and was made possible through the use of an ion trap. Here, by combining this 8 B result with our previous 8 Li results, we demonstrate a new pathway for increased precision in searches for exotic currents.

6 ≤ A ≤ 19↗

DOE OSTI2015

Theoretical & Experimental Research in Weak, Electromagnetic & Strong Interactions

The conducted research spans a wide range of topics in the theoretical, experimental and phenomenological aspects of elementary particle interactions. Theory projects involve topics in both the energy frontier and the intensity frontier. The experimental research involves energy frontier with the ATLAS Collaboration at the Large Hadron Collider (LHC). In theoretical research, novel ideas going beyond the Standard Model with strong theoretical motivations were proposed, and their experimental tests at the LHC and forthcoming neutrino facilities were outlined. These efforts fall into the following broad categories: (i) TeV scale new physics models for LHC Run 2, including left-right symmetry and trinification symmetry, (ii) unification of elementary particles and forces, including the unification of gauge and Yukawa interactions, (iii) supersummetry and mechanisms of supersymmetry breaking, (iv) superworld without supersymmetry, (v) general models of extra dimensions, (vi) comparing signals of extra dimensions with those of supersymmetry, (vii) models with mirror quarks and mirror leptons at the TeV scale, (viii) models with singlet quarks and singlet Higgs and their implications for Higgs physics at the LHC, (ix) new models for the dark matter of the universe, (x) lepton flavor violation in Higgs decays, (xi) leptogenesis in radiative models of neutrino masses, (xii) light mediator models of non-standard neutrino interactions, (xiii) anomalous muon decay and short baseline neutrino anomalies, (xiv) baryogenesis linked to nucleon decay, and (xv) a new model for recently observed diboson resonance at the LHC and its other phenomenological implications. The experimental High Energy Physics group has been, and continues to be, a successful and productive contributor to the ATLAS experiment at the LHC. Members of the group performed search for gluinos decaying to stop and top quarks, new heavy gauge bosons decaying to top and bottom quarks, and vector-like quarks produced in pairs and decaying to light quarks. Members of the OSU group played a leading role in the detailed optimization studies for the future ATLAS Inner Tracker (ITk), which will be installed during the Phase-II upgrade, replacing the current tracking system. The proposed studies aim to enhance the ATLAS discovery potential in the high-luminosity LHC era. The group members have contributed to the calibration of algorithms for identifying boosted vector bosons and b-jets, which will help expand the ATLAS reach in many searches for new physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Multi-scale experimental study of creep-fatigue failure initiation in a 709 Stainless Steel alloy using high resolution digital image correlation. Final report

This report discusses the efforts made towards the multi-scale study of the creep-fatigue response of stainless steel 709. Multiple experimental techniques, including digital image correlation (DIC) and electron backscatter diffraction (EBSD), were used in assessing the evolution of damage accumulation during fatigue, creep-fatigue, and thermomechanical fatigue of alloy 709. The role of microstructural features, hold times, temperature, and loading profiles were quantified and the interchangeability of temperature and time was investigated. Finally, a thermomechanical fatigue model was shown to predict well the failure of samples with varying loading profiles. Results indicate that strain accumulation in 709 steel happens primarily near grain boundaries (GBs) with the strains around GBs being inversely proportional to their measured residual burgers vector. Furthermore, hot-spots for strain accumulation were shown to be the locations of eventual microcrack nucleation. The introduction of hold times to the periodic loading cycle increases the damage accumulation rate and thus shortens the fatigue life of samples–this was true for both room temperature and high temperature. Thermomechanical cycling was shown to have little effect on the end life of samples, with the damage accumulation rate for in-phase and out-of-phase cycling being similar to isothermal fatigue. The interchangeability of time and temperature was shown to be possible within the studied load, time, and temperature ranges, since deformation mechanisms driving strain accumulation did not change with temperature (up to 650°C). The Neu-Sehitoglu thermomechanical fatigue model, with constants obtained from the literature and in part from our experimental results, was applied to predict failure of isothermal creep-fatigue samples. The main objectives of this work were to:(a) Perform high-resolution digital image correlation measurements (HiDIC) for alloy 709; (b) Quantify and assess damage accumulation at the microstructure under fatigue, TMF, and creep-fatigue conditions of 709; (c) Study of the role of hold times, i.e., adding a creep component, to: Room temperature cycling, High temperature cycling, Failure; (d) Study the mechanisms of thermomechanical fatigue in this alloy; (e) Investigate the existence of a time-temperature interchangeability criterion to aid in accelerated creep-fatigue testing; (f) Establish the validity of a combined creep-fatigue model for life prediction for 709. This report is structured with each subsequent section detailing the efforts, results, and conclusions related to each of the objectives described, mostly in chronological order.

36 MATERIALS SCIENCE↗

DOE OSTI2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. Furthermore, we observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Implementing Superresolution of Nonstationary Tides with Wavelets: An Introduction to CWT_Multi

Abstract Tides are often nonstationary due to nonastronomical influences. Investigating variable tidal properties implies a trade-off between separating adjacent frequencies (using long analysis windows) and resolving their time variations (short analysis windows). Previous continuous wavelet transform (CWT) tidal methods resolved tidal species. Here, we present CWT_Multi, a MATLAB code that 1) uses CWT linearity (via the “response coefficient method”) to implement superresolution, i.e., resolving tidal constituents beyond the Rayleigh criterion; 2) provides a Munk–Hasselmann constituent selection criterion appropriate for superresolution; and 3) introduces an objective, time-variable form of inference (“dynamic inference”) based on time-varying data properties. CWT_Multi resolves tidal species on time scales of days, and multiple constituents per species with fortnightly filters. It outputs astronomical phase lags and admittances, analyzes multiple records, and provides power spectra of the signal(s), residual(s), and reconstruction(s); confidence limits; and signal-to-noise ratios. Artificial data and water levels from the Lower Columbia River Estuary (LCRE) and San Francisco Bay Delta (SFBD) are used to test CWT_Multi and compare it to harmonic analysis programs NS_Tide and UTide. CWT_Multi provides superior reconstruction, detiding, dynamic analysis utility, and time resolution of constituents (but with broader confidence limits). Dynamic inference resolves closely spaced constituents (like K 1 , S 1 , and P 1 ) on fortnightly time scales, quantifying impacts of diel power peaking (with a 24-h period, like S 1 ) on water levels in the LCRE. CWT_Multi also helps quantify the impacts of high flows and a salt barrier closing on tidal properties in the SFBD. On the other hand, CWT_Multi does not excel at prediction, and results depend on analysis details, as for any method applied to nonstationary data. Significance Statement Ocean tides, especially in coastal and estuarine systems, are often nonstationary, in the sense that the mean and standard deviation of tidal properties vary over time, usually in response to some nontidal process. We introduce here a MATLAB code, CWT_Multi, that uses wavelet transforms to resolve both tidal species and constituents on time scales from a few days to months. Our code accommodates multiple scalar time series and has typical tidal analysis features like constituent selection and inference, plus two forms of uncertainty analyses. It is flexible, allowing the user to adapt analysis properties to diverse datasets. CWT_Multi is applicable to many problems involving time-variable tides, including sea level rise, compound flooding, sediment transport, and wetland habitat analyses. Application to vector data is a straightforward extension, but further development of our uncertainty analysis is merited. Because nonstationary tidal analysis is rapidly advancing, we also define the features of a “well-formed” analysis code.

Lobo, Matthew↗

DOE OSTI2019

Mixed Integer Nonlinear Programming Approaches to Enhance Resiliency and Response Strategies in Critical Infrastructure

Motivation. Critical infrastructures are large, complex engineered systems that must be operated robustly under abnormal conditions resulting from natural hazards or intentional acts. For example, electric power systems must be robust to line faults, water utilities must rapidly mitigate contamination incidents, and computing networks must adapt to adversarial intrusions to protect critical information. Problem. It is difficult for decision-makers within resiliency analysis in critical infrastructure to optimize designs and develop effective response strategies that can account for uncertainties. Facing incomplete information and the sheer scope that a natural hazard or attack vector may incorporate, response can be ineffective without reliable, scalable decision support tools. These problems are intrinsically nonlinear and involve discrete decisions, and unfortunately, existing off-the-shelf mathematical programming methods cannot support optimization-based decision-making of these nonlinear at scale. Method/Approach and Results. This project emphasized development of fundamental optimization strategies that supported real- time mitigation and response for critical infrastructures. In particular, the project developed multi- tree approaches based on piecewise outer-approximations for solution of mixed-integer nonlinear programming (MINLP) problems. These techniques alternate between an MILP or MISOCP relaxation to obtain a lower bound and candidate discrete solutions and an NLP subproblem to obtain upper bounds. Using tailored relaxations based on problem structure, these methods were used to solve several key applications in resilience and response of critical infrastructure. This work resulted in two open-source, copyrighted software packages: CORAMIN (https://github.com/Coramin/Coramin) -- an object-oriented mathematical programming framework that supports tailored multi-tree algorithms for solution of large- scale mixed-integer nonlinear programming; and EGRET (Electrical Grid Research and Engineering Toolkit) (https://github.com/grid- parity-exchange/Egret) -- a declarative mathematical programming framework built upon CORAMIN and Pyomo for formulation and solution of resilience and operations problems in power grid systems. Furthermore, these tools resulted in several important published results, including the following: The first known global optimization approach that could solve the unit-commitment problem with nonlinear power flow constraints on medium-sized test problems; Improved parallel optimization-based bounds tightening and strengthening of relaxations of AC power flow constraints; and, Optimization-based approaches for improved grid resilience and use of demand response to improve grid resilience with reduction in capital requirements. Result Implications. This project developed first-of-a-kind algorithms for decision-making in critical infrastructure resilience operations and planning, as well as a next-generation toolkit for MINLP researchers. These approaches leveraged high-performance computing architectures to solve some of the largest, most challenging nonlinear discrete optimization problems to global optimality, and these successes were captured in open-source software to enable optimization-based decision-making, and efficient solution of MINLP formulations for electric power transmission grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

An Approach to Quad Meshing Based On Cross Valued Maps and the Ginzburg-Landau Theory

A generalization of vector fields, referred to as N-direction fields or cross fields when N=4, has been recently introduced and studied for geometry processing, with applications in quadrilateral (quad) meshing, texture mapping, and parameterization. We make the observation that cross field design for two-dimensional quad meshing is related to the well-known Ginzburg-Landau problem from mathematical physics. This identification yields a variety of theoretical tools for efficiently computing boundary-aligned quad meshes, with provable guarantees on the resulting mesh, for example, the number of mesh defects and bounds on the defect locations. The procedure for generating the quad mesh is to (i) find a complex-valued "representation" field that minimizes the Dirichlet energy subject to a boundary constraint, (ii) convert the representation field into a boundary-aligned, smooth cross field, (iii) use separatrices of the cross field to partition the domain into four sided regions, and (iv) mesh each of these four-sided regions using standard techniques. Under certain assumptions on the geometry of the domain, we prove that this procedure can be used to produce a cross field whose separatrices partition the domain into four sided regions. To solve the energy minimization problem for the representation field, we use an extension of the Merriman-Bence-Osher (MBO) threshold dynamics method, originally conceived as an algorithm to simulate motion by mean curvature, to minimize the Ginzburg-Landau energy for the optimal representation field. Lastly, we demonstrate the method on a variety of test domains.

Computational Geometry↗

DOE OSTI2014

Inflationary tensor fossils in large-scale structure

Inflation models make specific predictions for a tensor-scalar-scalar three-point correlation, or bispectrum, between one gravitational-wave (tensor) mode and two density-perturbation (scalar) modes. This tensor-scalar-scalar correlation leads to a local power quadrupole, an apparent departure from statistical isotropy in our Universe, as well as characteristic four-point correlations in the current mass distribution in the Universe. So far, the predictions for these observables have been worked out only for single-clock models in which certain consistency conditions between the tensor-scalar-scalar correlation and tensor and scalar power spectra are satisfied. Here we review the requirements on inflation models for these consistency conditions to be satisfied. We then consider several examples of inflation models, such as non-attractor and solid-inflation models, in which these conditions are put to the test. In solid inflation the simplest consistency conditions are already violated whilst in the non-attractor model we find that, contrary to the standard scenario, the tensor-scalar-scalar correlator probes directly relevant model-dependent information. We work out the predictions for observables in these models. For non-attractor inflation we find an apparent local quadrupolar departure from statistical isotropy in large-scale structure but that this power quadrupole decreases very rapidly at smaller scales. The consistency of the CMB quadrupole with statistical isotropy then constrains the distance scale that corresponds to the transition from the non-attractor to attractor phase of inflation to be larger than the currently observable horizon. Solid inflation predicts clustering fossils signatures in the current galaxy distribution that may be large enough to be detectable with forthcoming, and possibly even current, galaxy surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

Background A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Methods and Findings Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Conclusions Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.

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