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At least 505 records · Page 28

Quantum simulation of real-space dynamics

Quantum simulation is a prominent application of quantum computers. While there is extensive previous work on simulating finite-dimensional systems, less is known about quantum algorithms for real-space dynamics. We conduct a systematic study of such algorithms. In particular, we show that the dynamics of a d-dimensional Schrödinger equation with η particles can be simulated with gate complexity O ~ (ηdFpoly(log(g'/ϵ))), where ϵ is the discretization error, g' controls the higher-order derivatives of the wave function, and F measures the time-integrated strength of the potential. Compared to the best previous results, this exponentially improves the dependence on ϵ and g' from poly(g'/ϵ) to poly(log(g'/ϵ)) and polynomially improves the dependence on T and d, while maintaining best known performance with respect to η. For the case of Coulomb interactions, we give an algorithm using η 3 (d + η)Tpoly(log(ηdTg'/(Δϵ)))/Δ one- and two-qubit gates, and another using η 3 (4d) d/2 Tpoly(log(ηdTg'/(Δϵ)))/Δ one- and two-qubit gates and QRAM operations, where T is the evolution time and the parameter Δ regulates the unbounded Coulomb interaction. We give applications to several computational problems, including faster real-space simulation of quantum chemistry, rigorous analysis of discretization error for simulation of a uniform electron gas, and a quadratic improvement to a quantum algorithm for escaping saddle points in nonconvex optimization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Analysis of degradation in residential battery energy storage systems for rate-based use-cases

This article examines the impact of residential battery energy storage (BES) systems' operational modes on the life (i.e. usable energy capacity) of the battery under several climatic conditions and battery chemistries. The sharp increase in residential BES installations has been a result of decreasing costs of batteries, increase in rate structures motivated applications such as solar self-consumption and time-of-use energy management, and customers purchasing these systems for backup power. While these different modes of operations provide a combination of increased bill savings, reliability of supply, and energy sustainability to the customer, their operational characteristics vary significantly between use-cases. Functional life of BES systems is known to be strongly dependent on their operating conditions. In this article, we analyze the operation of residential BES systems under different rate-based use-cases, for different battery chemistries and cell designs, and under different environmental conditions. This is conducted by simulating the control of BES operations using rate-based cycling algorithms and analyzing the prognosis of multiple battery lifetime models that consider complex nonlinear dependencies of operational stress factors such as state-of-charge, depth-of-discharge, and temperature on degradation. Significant variations in battery life are observed owing to the differences in characteristics of the uses-cases coupled with environmental conditions and battery chemistries. Such differences lead to the conclusion that choice of residential BES technologies and chemistries should account for their intended use-cases.

25 ENERGY STORAGE↗

Modeling and Analysis of Clean Energy and Storage Technologies (CRADA Final Report, Project 1)

The goal of this project is to provide Southern Company Services, Inc. ("Participant") with custom scripts that can be used to create an average PV energy production profile, calculate lifetime energy value, calculate capacity value, and calculate the resultant financial metrics considering those value streams. Secondly, a fuel-cell model will be added to the public version of System Advisor Model (SAM). This standalone technology will incorporate PV and battery storage, allowing the participant to model the interaction of these three technologies. By adding this capability to a public version of SAM, a broad audience will be able to consider the system performance and financial benefits of installing a fuel cell as a baseline generator with PV. Thirdly, automated dispatch algorithms will be developed and added to SAM. These algorithms will enable Southern Company to dispatch a DC-connected front-of-the-meter battery system while considering price signals and PV clipping behavior. By adding these capabilities to a public version of SAM, users will be able to consider more complex and realistic ways of dispatching a battery system.

14 SOLAR ENERGY↗

Inferring adversarial behaviour in cyber‐physical power systems using a Bayesian attack graph approach

Abstract Highly connected smart power systems are subject to increasing vulnerabilities and adversarial threats. Defenders need to proactively identify and defend new high‐risk access paths of cyber intruders that target grid resilience. However, cyber‐physical risk analysis and defense in power systems often requires making assumptions on adversary behaviour, and these assumptions can be wrong. Thus, this work examines the problem of inferring adversary behaviour in power systems to improve risk‐based defense and detection. To achieve this, a Bayesian approach for inference of the Cyber‐Adversarial Power System (Bayes‐CAPS) is proposed that uses Bayesian networks (BNs) to define and solve the inference problem of adversarial movement in the grid infrastructure towards targets of physical impact. Specifically, BNs are used to compute conditional probabilities to queries, such as the probability of observing an event given a set of alerts. Bayes‐CAPS builds initial Bayesian attack graphs for realistic power system cyber‐physical models. These models are adaptable using collected data from the system under study. Then, Bayes‐CAPS computes the posterior probabilities of the occurrence of a security breach event in power systems. Experiments are conducted that evaluate algorithms based on time complexity, accuracy and impact of evidence for different scales and densities of network. The performance is evaluated and compared for five realistic cyber‐physical power system models of increasing size and complexities ranging from 8 to 300 substations based on computation and accuracy impacts.

Sahu, Abhijeet↗

Automatic Waveform Quality Control for Surface Waves Using Machine Learning

Surface-wave seismograms are widely used by researchers to study Earth’s interior and earthquakes. To extract information reliably and robustly from a suite of surface waveforms, the signals require quality control screening to reduce artifacts from signal complexity and noise. This process has usually been completed by human experts labeling each waveform visually, which is time consuming and tedious for large data sets. We explore automated approaches to improve the efficiency of waveform quality control processing by investigating logistic regression, support vector machines, K-nearest neighbors, random forests (RF), and artificial neural networks (ANN) algorithms. To speed up signal quality assessment, we trained these five machine learning (ML) methods using nearly 400,000 human-labeled waveforms. The ANN and RF models outperformed other algorithms and achieved a test accuracy of 92%. We evaluated these two best-performing models using seismic events from geographic regions not used for training. The results show that the two trained models agree with labels from human analysts but required only 0.4% of the time. Although the original (human) quality assignments assessed general waveform signal-to-noise, the ANN or RF labels can help facilitate detailed waveform analysis. Our investigations demonstrate the capability of the automated processing using these two ML models to reduce outliers in surface-wave-related measurements without human quality control screening.

58 GEOSCIENCES↗

Analysis of Fast Charging Station Network for Electrified Ride-Hailing Services

Today's electric vehicle (EV) owners charge their vehicles mostly at home and seldom use public direct current fast charger (DCFCs), reducing the need for a large deployment of DCFCs for private EV owners. However, due to the emerging interest among transportation network companies to operate EVs in their fleet, there is great potential for DCFCs to be highly utilized and become economically feasible in the future. This paper describes a heuristic algorithm to emulate operation of EVs within a hypothetical transportation network company fleet using a large global positioning system data set from Columbus, Ohio. DCFC requirements supporting operation of EVs are estimated using the Electric Vehicle Infrastructure Projection tool. Operation and installation costs were estimated using real-world data to assess the economic feasibility of the recommended fast charging stations. Results suggest that the hypothetical transportation network company fleet increases daily vehicle miles traveled per EV with less overall down time, resulting in increased demand for DCFC. Sites with overhead service lines are recommended for hosting DCFC stations to minimize the need for trenching underground service lines. A negative relationship was found between cost per unit of energy and fast charging utilization, underscoring the importance of prioritizing utilization over installation costs when siting DCFC stations. Although this preliminary analysis of the impacts of new mobility paradigms on alternative fueling infrastructure requirements has produced several key results, the complexity of the problem warrants further investigation.

33 ADVANCED PROPULSION SYSTEMS↗

Intrepid MCMC: Metropolis-Hastings with exploration

In engineering examples, one often encounters the need to sample from unnormalized distributions with complex shapes that may also be implicitly defined through a physical or numerical simulation model, making it computationally expensive to evaluate the associated density function. For such cases, MCMC has proven to be an invaluable tool. Random-walk Metropolis Methods (also known as Metropolis-Hastings (MH)), in particular, are highly popular for their simplicity, flexibility, and ease of implementation. However, most MH algorithms suffer from significant limitations when attempting to sample from distributions with multiple modes (particularly disconnected ones). Here, in this paper, we present Intrepid MCMC - a novel MH scheme that utilizes a simple coordinate transformation to significantly improve the mode-finding ability and convergence rate to the target distribution of random-walk Markov chains while retaining most of the simplicity of the vanilla MH paradigm. Through multiple examples, we showcase the improvement in the performance of Intrepid MCMC over vanilla MH for a wide variety of target distribution shapes. We also provide an analysis of the mixing behavior of the Intrepid Markov chain, as well as the efficiency of our algorithm for increasing dimensions. A thorough discussion is presented on the practical implementation of the Intrepid MCMC algorithm. Finally, its utility is highlighted through a Bayesian parameter inference problem for a two-degree-of-freedom oscillator under free vibration.

97 - MATHEMATICS AND COMPUTING↗

Shortening the Design and Certification Cycle for Additively Manufactured Materials by Improved Mesoscale Simulations and Validation Experiments: Fiscal Year 2019 Status Report.

This report outlines the fiscal year (FY) 2019 status of an ongoing multi-year effort to develop a general, microstructurally-aware, continuum-level model for representing the dynamic response of material with complex microstructures. This work has focused on accurately representing the response of both conventionally wrought processed and additively manufactured (AM) 304L stainless steel (SS) as a test case. Additive manufacturing, or 3D printing, is an emerging technology capable of enabling shortened design and certification cycles for stockpile components through rapid prototyping. However, there is not an understanding of how the complex and unique microstructures of AM materials affect their mechanical response at high strain rates. To achieve our project goal, an upscaling technique was developed to bridge the gap between the microstructural and continuum scales to represent AM microstructures on a Finite Element (FE) mesh. This process involves the simulations of the additive process using the Sandia developed kinetic Monte Carlo (KMC) code SPPARKS. These SPPARKS microstructures are characterized using clustering algorithms from machine learning and used to populate the quadrature points of a FE mesh. Additionally, a spall kinetic model (SKM) was developed to more accurately represent the dynamic failure of AM materials. Validation experiments were performed using both pulsed power machines and projectile launchers. These experiments have provided equation of state (EOS) and flow strength measurements of both wrought and AM 304L SS to above Mbar pressures. In some experiments, multi-point interferometry was used to quantify the variation is observed material response of the AM 304L SS. Analysis of these experiments is ongoing, but preliminary comparisons of our upscaling technique and SKM to experimental data were performed as a validation exercise. Moving forward, this project will advance and further validate our computational framework, using advanced theory and additional high-fidelity experiments.

36 MATERIALS SCIENCE↗

Measurement of Neutron Multiplicity in Charged Current Neutrino Interactions on Oxygen

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton gadolinium-doped water Cherenkov detector located 100~meters downstream in the Booster Neutrino Beam (BNB) at the Fermi National Accelerator Laboratory (Fermilab). Its primary goals are to (1) measure the neutron yield from $\nu_\mu$ interactions as a function of momentum transfer $Q^2$ so that neutrino-nucleus interaction models can be better constrained, and (2) demonstrate the power of novel, fast-timing detectors with the first deployment of Large Area Picosecond PhotoDetectors (LAPPDs). Current knowledge of neutrino-nucleus interactions fall short in modeling the topologies of such interactions, leading to inaccurate reconstruction of event kinematics such as particle energy, direction, and vertex. As a consequence, the accuracy of cross section measurements is impacted, which is necessary for precise physics measurements. Neutrons are an indication of inelasticity and affect the determination of the energy of the parent neutrino. Quantifying the neutron yield is a step towards reducing the associated uncertainties, and thus improving our understanding of these complex interactions and benefiting the next generation of long-baseline neutrino experiments. ANNIE will make use of LAPPDs to measure neutron multiplicity of CC-0$\pi$ $\nu_\mu$ interactions on oxygen, making it the first experiment to deploy an array of these photodetectors. Because the LAPPD is a novel photodetection technology, much customization is required to integrate it into existing electronics. The first half of this thesis covers the significant progress made towards the first deployment of the LAPPD system. From its test stand at Fermilab, the LAPPD system was systematically tested and put together until deployment readiness was achieved. I present my contributions to the design, fabrication, and testing of the waterproof housing and cables, and the commissioning of the LVHV board that powers the LAPPD and its readout electronics. These efforts brought the LAPPD system significantly closer to deployment, and eventually first data. The second half of this thesis presents the vertex and energy reconstruction algorithms developed to analysis the beam data with PMT-only information. While much progress has been made towards the deployment of LAPPDs, with several in the detector tank, efforts to integrate the LAPPD datastream are in progress. Thus, I developed a ring edge detection technique using PMT data to fit the muon vertex and determine its energy. The analysis in this thesis finds average neutron yields of $\Bar{n}_{data} = 0.452 \pm 0.039 (\text{stat}) \pm 0.27 (\text{sys})$ for a selection of muon neutrino candidates in the fiducial volume of ANNIE and corrected with an averaged neutron detection efficiency. An equivalent analysis for simulated beam data results in an average neutron yield of $\Bar{n}_{MC} = 0.582 \pm 0.018 (\text{stat}) \pm 0.25 (\text{sys})$. Future work includes application of efficiency corrections at a positional level, quantification of all systematic uncertainties, and neutron multiplicity measurements with other event topologies.

43 PARTICLE ACCELERATORS↗

First Principles Nonadiabatic Excited-State Molecular Dynamics in NWChem

Computational simulation of non-adiabatic molecular dynamics is an indispensable tool for understanding complex photoinduced processes such as internal conversion, energy transfer, charge separation, and spatial localization of excitons, to name a few. Here, we report an implementation of the fewest-switches surface hopping algorithm in the NWChem computational chemistry program. Here the surface hopping method is combined with linear-response time-dependent density functional theory calculations of adiabatic excited state potential energy surfaces. To treat quantum transitions between arbitrary electronic Born{Oppenheimer states, we have implemented both numerical and analytical differentiation schemes for derivative non-adiabatic couplings. A numerical approach for the time-derivative non-adiabatic couplings together with an analytical method for calculating non-adiabatic coupling vectors is an efficient combination for surface hopping approaches. Additionally, electronic decoherence schemes and a state reassigned unavoided crossings algorithm are also implemented to improve the accuracy of the simulated dynamics and to handle trivial unavoided crossings. We apply our code to study the ultrafast decay of photoexcited benzene, including a detailed analysis of the potential energy surface, population decay time scales, and vibrational coordinates coupled to the excitation dynamics. The development presented in this work is a baseline for future implementations of more sophisticated frameworks for simulating of non-adiabatic molecular dynamics in NWChem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Differential methods for assessing sensitivity in biological models

Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to determine which method is best suited for a particular model. In this paper, we explain a variety of differential sensitivity methods and assess their value in some typical biological models. First, we explain the mathematical basis for three numerical methods: adjoint sensitivity analysis, complex perturbation sensitivity analysis, and forward mode sensitivity analysis. We then carry out four instructive case studies. (a) The CARRGO model for tumor-immune interaction highlights the additional information that differential sensitivity analysis provides beyond traditional naive sensitivity methods, (b) the deterministic SIR model demonstrates the value of using second-order sensitivity in refining model predictions, (c) the stochastic SIR model shows how differential sensitivity can be attacked in stochastic modeling, and (d) a discrete birth-death-migration model illustrates how the complex perturbation method of differential sensitivity can be generalized to a broader range of biological models. Finally, we compare the speed, accuracy, and ease of use of these methods. We find that forward mode automatic differentiation has the quickest computational time, while the complex perturbation method is the simplest to implement and the most generalizable.

59 BASIC BIOLOGICAL SCIENCES↗

A Butterfly-Accelerated Volume Integral Equation Solver for Broad Permittivity and Large-Scale Electromagnetic Analysis

In this work, a butterfly-accelerated volume integral equation (VIE) solver is proposed for fast and accurate electromagnetic (EM) analysis of scattering from heterogeneous objects. The proposed solver leverages the hierarchical off-diagonal butterfly (HOD-BF) scheme to construct the system matrix and obtain its approximate inverse, used as a preconditioner. Complexity analysis and numerical experiments validate the O(N log 2 N) construction cost of the HOD-BF-compressed system matrix and O(N log 1.5 N) inversion cost for the preconditioner, where N is the number of unknowns in the high-frequency EM scattering problem. For many practical scenarios, the proposed VIE solver requires less memory and computational time to construct the system matrix and obtain its approximate inverse compared to a H matrix-accelerated VIE solver. The accuracy and efficiency of the proposed solver have been demonstrated via its application to the EM analysis of large-scale canonical and real-world structures comprising of broad permittivity values and involving millions of unknowns.

42 ENGINEERING↗

Locally purified maximally mixed states at scale: Entanglement pruning and symmetries

Locally Purified Density Operators (LPDOs) are state-of-the-art tensor network ansatze candidates that efficiently represent mixed quantum states at scale. However, given their non-uniqueness, their representational complexity is generally sub-optimal in practical computations. Here, in this work we perform a comprehensive numerical and analytical analysis and resolve this issue in the experimentally relevant limit where noise depolarizes the density operator into a maximally mixed state. To resolve the sub-optimality issue, we analyze two numerical tools, one analytic method, and detail the relations between them. The numerical tools used are fidelity-preserving truncations and isometric gauge transformations leveraging Riemannian optimizations over entropic objective functions. In addition, by invoking the injectivity and symmetry constraints of the maximally mixed LPDO, we also present analytical closed-form expressions for the disentangler and discuss their relation to numerical optimizers. Further, away from the maximally mixed state, our simulations highlight how the truncation threshold smoothly interpolate, as a function of depolarization, between established matrix product results and our new results. Our work shows how, by minimizing the resources required to represent key states of practical interest in experiment, the efficiency of tensor network algorithms can be substantially increased. This paves the path for uncovering tensor network’s fundamental scalability limits and latent potential in representing the wide locus of mixed quantum states that are accessible on near-term quantum devices.

Gangapuram, Amit Jamadagni [Oak Ridge National Lab↗

Journey to Time-Variable Moment Tensors through Inversion of Acoustic and Seismoacoustic Data

We explore the capability of acoustic and seismoacoustic datasets to directly resolve a complex, time-variable source consisting of a buried mechanism, represented as a moment tensor, and a spall mechanism, represented as a vertical force at the surface. Traditionally, each component of a resolved moment tensor assumes one underlying source time function, which likely fails to capture the full evolution of a dynamic source, such as an explosion followed by slip on near-source joints or development of spallation. Specifically, we expand previous work to resolve a time-variable moment tensor using single-modality and joint-modality inversion frameworks through analysis of infrasound and seismoacoustic data recorded as part of the Source Physics Experiment Phase II: Dry Alluvium Geology (DAG). We investigate the impact of including signals from seismic-to-air coupling that are local to each infrasound sensor in comparison to mainly atmosphere-propagating acoustic signals, which occur from coupling of the wavefield from the subsurface to the atmosphere directly above the source. Additionally, we assess the ability of our inversion algorithm to fit observed infrasound data using a variety of time-variable source mechanisms. First, we consider the buried moment tensor source alone, which assumes that the determined Green’s functions incorporate effects from spallation or that the impact from spallation is minimal. Second, we examine the estimated buried moment tensor and vertical surface spallation as terms that must both be resolved in the inversion. Third, we assess the ability for an estimated vertical surface spallation source to fit the acoustic data on its own. Finally, we compare results from the joint inversion of both seismic geophone and infrasound acoustic data for the buried-only source compared to buried and spallation sources. Our results are a preliminary investigation into the applications of the inversion technique to recorded datasets and show the technique has limited capabilities using acoustic data alone. Instead, this method shows promise for seismic and seismoacoustic datasets to resolve the time-variable mechanisms of a buried source.

47 OTHER INSTRUMENTATION↗

Computer Modeling and Simulation: Increasing Reliability by Disentangling Verification and Validation

Verification and validation (V&V) of computer codes and models used in simulations are two aspects of the scientific practice of high importance that recently have been discussed widely by philosophers of science. While verification is predominantly associated with the correctness of the way a model is represented by a computer code or algorithm, validation more often refers to the model’s relation to the real world and its intended use. Because complex simulations are generally opaque to a practitioner, the Duhem problem can arise with verification and validation due to their entanglement; such an entanglement makes it impossible to distinguish whether a coding error or the model’s general inadequacy to its target should be blamed in the case of a failure. I argue that a clear distinction between computer modeling and simulation has to be made to disentangle verification and validation. Drawing on that distinction, I suggest to associate modeling with verification and simulation, which shares common epistemic strategies with experimentation, with validation. To explain the reasons for their entanglement in practice, I propose a Weberian ideal–typical model of modeling and simulation as roles in practice. I examine an approach to mitigate the Duhem problem for verification and validation that is generally applicable in practice and is based on differences in epistemic strategies and scopes. Based on this analysis, I suggest two strategies to increase the reliability of simulation results, namely, avoiding alterations of verified models at the validation stage as well as performing simulations of the same target system using two or more different models. In response to Winsberg’s claim that verification and validation are entangled I argue that deploying the methodology proposed in this work it is possible to mitigate inseparability of V&V in many if not all domains where modeling and simulation are used.

97 MATHEMATICS AND COMPUTING↗

Development of Multiresolution Capabilities for the Holistic Energy Resource Optimization Network (HERON) tool A progress update

INL researchers work on technoeconomic analyses for integrated energy systems (IES) using the Framework for Optimization of ResourCes and Economics (FORCE). Within FORCE, researchers use the Holistic Energy Resource Optimization Network (HERON) tool to conduct optimization of grid portfolios under uncertain market conditions. These optimizations determine optimal capacities for all IES components and strategies for resource dispatch which maximize some economic metric (e.g., net present value). Resource dispatch occurs on finer timescales (typically hours) and thus are asked to respond to a given time series (e.g. hourly load demand profiles for a grid, or pre-determined electricity prices). Volatile and complex bidding dynamics as well as poorly forecasted weather events within deregulated markets add uncertainty to the time series; FORCE can address this uncertainty by training a reduced order model on historical time series and generate unique synthetic time series which represent individual scenarios or realizations of the market. The IES configuration can be simulated under these different sampled realizations and a stochastic optimization is conducted which optimizes the expected value of the desired economic metric. The training of a synthetic time series generator is limited by the chosen time resolution; dynamics can occur on different time scales. Seasonal demand trends can dominate faster dynamical events (such as power outages from certain sectors or severe weather events) which might not get captured correctly by the trained model. In this report, we investigate different ways of addressing the training and generation of time series on multiple time scales using three main algorithms: wavelet decomposition, dynamic mode decomposition, and generative adversarial networks for time series. We demonstrate a time series analysis that yields information on not just the frequency space but also temporal space: where a fast Fourier transform can provide what frequencies dominate, the new algorithms can provide when the frequencies dominate as well. These analyses can help improve IES optimization by allowing researchers to couple simulations at different timescales when it is most needed - seasonal, day-ahead, and real time optimization - with greater computational efficiency. Future work will include implementation of a subset of the proposed algorithms into the FORCE toolset and application of these analyses into multiple timescale optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Identification and selection of optimal reference genes for qPCR-based gene expression analysis in Fucus distichus under various abiotic stresses

Quantitative gene expression analysis is an important tool in the scientist’s belt. The identification of evenly expressed reference genes is necessary for accurate quantitative gene expression analysis, whether by traditional RT-PCR (reverse-transcription polymerase chain reaction) or by qRT-PCR (quantitative real-time PCR; qPCR). In the Stramenopiles (the major line of eukaryotes that includes brown algae) there is a noted lack of known reference genes for such studies, largely due to the absence of available molecular tools. Here we present a set of nine reference genes (Elongation Factor 1 alpha (EF1A), Elongation Factor 2 alpha (EF2A), Elongation Factor 1 beta (EF1B), 14-3-3 Protein, Ubiquitin Conjugating Enzyme (UBCE2), Glyceraldehyde-3-phosphate Dehydrogenase (GAPDH), Actin Related Protein Complex (ARP2/3), Ribosomal Protein (40s; S23), and Actin) for the brown alga Fucus distichus . These reference genes were tested on adult sporophytes across six abiotic stress conditions (desiccation, light and temperature modification, hormone addition, pollutant exposure, nutrient addition, and wounding). Suitability of these genes as reference genes was quantitatively evaluated across conditions using standard methods and the majority of the tested genes were evaluated favorably. However, we show that normalization genes should be chosen on a condition-by-condition basis. We provide a recommendation that at least two reference genes be used per experiment, a list of recommended pairs for the conditions tested here, and a procedure for identifying a suitable set for an experimenter’s unique design. With the recent expansion of interest in brown algal biology and accompanied molecular tools development, the variety of experimental conditions tested here makes this study a valuable resource for future work in basic biology and understanding stress responses in the brown algal lineage.

59 BASIC BIOLOGICAL SCIENCES↗

Methods for Assessing Opportunities for Ring Dam Pumped Storage Hydropower

There is growing interest in new pumped storage hydropower (PSH) deployment to provide a range of grid flexibility, reliability, and resiliency services under an evolving and uncertain future power sector. The National Laboratory of the Rockies develops open PSH resource assessment and cost modeling tools to help evaluate PSH deployment opportunities, and this report describes expansions to those tools to consider an additional PSH system configuration - ring-dam reservoirs built on flat topographical features that are constructed from roller-compacted concrete material. This reservoir type is common among current PSH proposals and requires new methods to identify sites with this reservoir geometry throughout the United States and characterize the associated dam cost. Cost characterization for ring dam reservoirs required collecting historical dam cost data for earthen, rockfill, and roller-compacted concrete dams and regressing equations that relate costs between alternative materials. The ring dam site identification algorithm follows a 5-step procedure to identify circular geometry reservoirs. Once ring dam reservoirs are identified, they are then paired with potential dry-gully reservoirs, and the full set of potential paired reservoirs is cost-optimized to produce a least-cost set of potential PSH sites with no overlapping reservoirs. The resulting analysis found 1,663 ring-dam to dry-gully systems in the contiguous United States that are lower cost than any overlapping dry-gully to dry-gully systems, 29 in Alaska, and none in Hawaii or Puerto Rico. These systems constitute 1.5 TW of capacity in the contiguous United States and nearly 29 GW in Alaska, demonstrating that under suitable topography and head, ring-dam systems can provide cost-effective PSH opportunities. The greatest density of these opportunities are found in the intermountain west where there are mesas and flat land at bases of mountain ranges, but continued work could incorporate additional site characteristics or consider more complex reservoir shapes to find additional PSH deployment opportunities.

13 HYDRO ENERGY↗