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DOE OSTI2023

An automated and portable method for selecting an optimal GPU frequency

Power consumption poses a significant challenge in current and emerging graphics processing unit (GPU) enabled high-performance computing systems. In modern GPUs, dynamic voltage frequency scaling (DVFS) appears to be a reliable control to regulate power consumption and performance. However, the DVFS design space is large - hence, brute-force approaches are infeasible to select the optimal frequency. Furthermore, no single frequency can be universally optimal for applications with varying computational intensities. Thus, the application's complexity and the availability of a wide range of frequency settings are a challenge in selecting the optimal frequency configuration for a given GPU workload. To that end, this paper proposes a systematic approach that consists of three steps. The feature characterization study identifies the fine-grain GPU utilization metrics that influence the power consumption and execution time of a given workload. To understand the performance, power, and energy consumption behaviors of a workload across GPU's DVFS design space, we derived analytical power and performance models using the identified fine-grain features. Here, it is shown that the same set of GPU utilization metrics can estimate both the power consumption and execution time while being agnostic of changes to frequency and input sizes. Applying a power control with the single objective of reducing power may cause performance degradation, leading to more energy consumption. A multi-objective approach is proposed to select the optimal GPU DVFS configuration for a workload that reduces power consumption with negligible degradation in performance. The evaluation was conducted using SPEC ACCEL benchmarks and three real applications - NAMD LAMMPS, and LSTM on NVIDIA GV100, GA100, and AMD MI210 GPUs. On average, real applications showed 29.6% energy savings with a performance loss of 5.2% on GA100 and 22.6% energy savings with a performance loss of 4.7% on GV100. Moreover, the proposed models are portable to real applications, GPU architectures, and vendors, and require metric collection at only the default frequency rather than all supported DVFS configurations. Additionally, we conducted a comparison between our models and the GPU assembly instructions (PTX)-based static models. The results revealed a significant reduction in the average error rates, with a decrease from 19.7% to 3.1% for power models and from 29.4% to 5.2% for performance models.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

An Overview of Behind-the-Meter Solar-Plus-Storage Program Design: With Considerations for India

Indian consumers have been deploying behind-the-meter generation (predominantly diesel backup, and, more recently, photovoltaic) and storage systems (predominantly lead-acid and other kinds of batteries as uninterrupted power supplies) by the millions for decades) to address reliability issues within the Indian power system. The same energy storage systems could provide additional services to the consumer and distribution companies if properly regulated and designed from the outset to be grid interactive. Grid-connected distributed solar PV (DPV), or rooftop solar, has also seen wide deployment in India and features prominently in the Government of India’s plans for a transition to clean, reliable, and affordable energy for all. At the same time, many utilities and state governments, as well as the central government in India are currently funding-constrained for both operational and future capital expenditures in the power sector, and some perceive customer-sited resources as exacerbating existing financial challenges. In that context, behind-the-meter energy storage systems paired with distributed photovoltaic (DPV) - with the capability to act as both generation and load - represent a potentially unique and disruptive power sector technology capable of providing a range of important services to customers, utilities, and the broader power system in India. This report aims to offer a comprehensive, evidence-based approach to designing customer programs based on experience in the United States that can help regulators, utilities, and policymakers in India manage the range of challenges and opportunities that increased behind-the-meter energy storage deployment will bring to the power system, in particular when these systems are paired with DPV.

14 SOLAR ENERGY↗

DOE OSTI2025

Statistical relationships across epigenomes using large-scale hierarchical clustering

Recent advances in genomics and sequencing platforms have revolutionized our ability to create immense data sets, particularly for studying epigenetic regulation of gene expression. However, the avalanche of epigenomic data is difficult to parse for biological interpretation given nonlinear complex patterns and relationships. This attractive challenge in epigenomic data lends itself to machine learning for discerning infectivity and susceptibility. In this study, we explore over 3000 epigenomes of uninfected individuals and provide a framework to characterize the relationships among epigenetic modifiers, their modifiers, genetic loci, and specific immune cell types across all chromosomes using hierarchical clustering. Hierarchical clustering of epigenomic data revealed consistent epigenetic patterns across chromosomes, demonstrating that variation due to epigenetic modifiers is greater than variation between cell types. Gene Ontology and KEGG pathway analyses indicated significant enrichment of genes involved in chromatin remodeling, mRNA splicing, immune responses, and the regulation of microRNAs and snoRNAs. Epigenetic modifiers frequently formed biologically relevant clusters, including the cohesin complex, RNA Polymerase II transcription factors, and PRC2 complex members. These clustering behaviors remained consistent across all chromosomes, supported by entropy analysis and high Adjusted Rand Index scores, indicating robust cross-chromosomal similarity. Co-occurrence analysis further revealed specific sets of modifiers that consistently appeared together within clusters, reflecting shared biological functions and interactions. Validation using another dataset confirmed the reproducibility of these clustering patterns and modifier co-occurrence relationships, underscoring the reliability and generalizability of the methodology.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

A high-capacity Sn metal anode for aqueous acidic batteries

Aqueous acidic batteries as a good choice to respond battery diversity, delivering safety, cost, environmental friendliness and high-power necessary for renewable energy storage. However, the practical adoption is greatly challenged by low-voltage and energy density due to the inadequate metal anode materials. Here we report an interfacial regulated Sn metal anode as the solution of the last piece of the puzzle. In this study, the ease of recycling, low potential, fast redox kinetics, and high capacity of Sn perfectly fit the battery system, and the Sn metal shedding critical issue is successfully suppressed by promoting uniform deposition for added interaction from alloying. Consequently, this reversible Sn anode with 442 mAh g -1 matches well to different types of cathodes. The as-assembled acidic batteries also demonstrate sufficient output voltage (up to 1.7 V), energy density (up to 312 Wh kg -1 based on both electrodes), kinetics (up to 24 C) and stability (up to 2400 cycles).

25 ENERGY STORAGE↗

DOE OSTI2024

Optimized In Situ Doping Strategy Stabling Single-Crystal Ultrahigh-Nickel Layered Cathode Materials

Single-crystal Ni-rich cathodes offer promising prospects in mitigating intergranular microcracks and side reaction issues commonly encountered in conventional polycrystalline cathodes. However, the utilization of micrometer-sized single-crystal particles has raised concerns about sluggish Li + diffusion kinetics and unfavorable structural degradation, particularly in high Ni content cathodes. Herein, we present an innovative in situ doping strategy to regulate the dominant growth of characteristic planes in the singlecrystal precursor, leading to enhanced mechanical properties and effectively tackling the challenges posed by ultrahigh-nickel layered cathodes. Compared with the traditional dry-doping method, our in situ doping approach possesses a more homogeneous and consistent modifying effect from the inside out, ensuring the uniform distribution of doping ions with large radius (Nb, Zr, W, etc). Further, this mitigates the generally unsatisfactory substitution effect, thereby minimizing undesirable coating layers induced by different solubilities during the calcination process. Additionally, the uniformly dispersed ions from this in situ doping are beneficial for alleviating the two-phase coexistence of H2/H3 and optimizing the Li + concentration gradient during cycling, thus inhibiting the formation of intragranular cracks and interfacial deterioration. Consequently, the in situ doped cathodes demonstrate exceptional cycle retention and rate performance under various harsh testing conditions. Our optimized in situ doping strategy not only expands the application prospects of elemental doping but also offers a promising research direction for developing high-energy-density single-crystal cathodes with extended lifetime.

25 ENERGY STORAGE↗

DOE OSTI2024

A Privacy Preserving Distributed Model Identification Algorithm for Power Distribution Systems

Distributed control/optimization is a promising approach for network systems due to its advantages over centralized schemes, such as robustness, cost-effectiveness, and improved privacy. However, distributed methods can have drawbacks, such as slower convergence rates due to limited knowledge of the overall network model. Additionally, ensuring privacy in the communication of sensitive information can pose implementation challenges. To address this issue, we propose a distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs. The proposed algorithm maintains the privacy of local agents by only communicating through dummy variables. We demonstrate the efficacy of our algorithm in the context of power distribution systems by applying it to the voltage regulation of a modified IEEE distribution system. The proposed algorithm is well-suited to the needs of power distribution controls and offers an effective solution to the challenges of distributed model identification in network systems.

data-driven modeling↗

DOE OSTI2023

Observing fish interactions with marine energy turbines using acoustic cameras

Abstract Marine current energy converters such as tidal and riverine turbines have the potential to provide reliable, clean power. The risk of collision of fishes with marine energy turbines is not yet well understood, in part due to the challenges associated with observing fish at turbine sites. Turbidity and light availability can limit the effectiveness of optical sensors like video cameras, motivating the use of acoustic cameras for this task. However, challenges persist in collecting and interpreting data acquired from acoustic cameras. Given the limited number of turbine deployments to date, it is prudent to draw on the application of acoustic cameras to monitor fish in other scenarios. This article synthesizes their use for other fisheries applications to inform best practices and set realistic expectations for the results of acoustic camera monitoring at turbine sites. We discuss six key tasks performed with acoustic cameras: detecting objects, identifying objects as fish, counting fish, measuring fish, classifying fish taxonomically and analysing fish behavior. Specific challenges to monitoring fish at turbine sites are discussed. This article is intended to serve as a reference for researchers, regulators and marine energy developers on effective use of acoustic cameras to monitor fish at turbine sites. The studies detailed in this article provide evidence that, in some scenarios, acoustic cameras can be used to inform the risk of fish collision with marine energy turbines but doing so requires careful study design and data processing.

Cotter, Emma↗

DOE OSTI2024

Developing stable, simplified, functional consortia from Brachypodium rhizosphere for microbial application in sustainable agriculture

The rhizosphere microbiome plays a crucial role in supporting plant productivity and ecosystem functioning by regulating nutrient cycling, soil integrity, and carbon storage. However, deciphering the intricate interplay between microbial relationships within the rhizosphere is challenging due to the overwhelming taxonomic and functional diversity. Here we present our systematic design framework built on microbial colocalization and microbial interaction, toward successful assembly of multiple rhizosphere-derived Reduced Complexity Consortia (RCC). We enriched co-localized microbes from Brachypodium roots grown in field soil with carbon substrates mimicking Brachypodium root exudates, generating 768 enrichments. By transferring the enrichments every 3 or 7 days for 10 generations, we developed both fast and slow-growing reduced complexity microbial communities. Most carbon substrates led to highly stable RCC just after a few transfers. 16S rRNA gene amplicon analysis revealed distinct community compositions based on inoculum and carbon source, with complex carbon enriching slow growing yet functionally important soil taxa like Acidobacteria and Verrucomicrobia. Network analysis showed that microbial consortia, whether differentiated by growth rate (fast vs. slow) or by succession (across generations), had significantly different network centralities. Besides, the keystone taxa identified within these networks belong to genera with plant growth-promoting traits, underscoring their critical function in shaping rhizospheric microbiome networks. Furthermore, tested consortia demonstrated high stability and reproducibility, assuring successful revival from glycerol stocks for long-term viability and use. Our study represents a significant step toward developing a framework for assembling rhizosphere consortia based on microbial colocalization and interaction, with future implications for sustainable agriculture and environmental management.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

A Privacy Preserving Distributed Model Identification Algorithm for Power Distribution Systems: Preprint

data-driven modeling↗

DOE OSTI2021

BETO 2021 Peer Review - Cyanobacteria Photosynthetic Energy Platform (1.3.4.301)

Cyanobacteria Photosynthetic Energy Platform (WBS 1.3.4.301) The primary challenge in algal technology is low energy conversion efficiency, currently up to a few percentages from photon energy to biomass energy. Most incoming solar energy is lost in the processes of photosynthesis, through mechanisms that are not fully understood. There is large room for improvement. This project develops cyanobacteria genetic tools to improve photosynthetic efficiency and biomass productivity through manipulation of energy regulation mechanisms. Increasing algal biomass productivity by 20% translates to $2 reduction of MFSP. Genetic manipulation is an essential tool in understanding and improving algal productivity. While genetic manipulation is challenging for most algae, model cyanobacteria, with in-house mutant library and genetic engineering toolboxes, allow for rapid hypothesis testing and transfer of lessons to other cyanobacteria and eukaryotic algae. We developed Energome (energy-ome) concept to guide novel engineering strategies and unleash unused potential in photosynthesis. Energome consists of cellular energy management mechanisms. For example, we found that cyanobacteria use futile cycles around glycogen and sucrose to dissipate ATP. By genetic modification, the model cyanobacterium Synechocystis 6803 showed higher energy levels, increased photosynthetic capacity, and 20% improvement in biomass yield in simulated outdoor light conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation

The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Harnessing plant‐based platform for low‐cost cellulosic sugar recovery from bioenergy crops

The price of pure cellulase enzyme for the recovery of fermentable cellulosic sugars is one of the major challenges that limit the commercialization of second-generation biofuels and bio-based products. This work shows a means to greatly reduce the cost of cellulases. The abundant capacity of plants to synthesize and hyperaccumulate transgenic proteins in their plastids has been demonstrated for two gene constructs using either NPT II or Tet C to regulate the expression of bacterial cellulase Cel6A. Previously, the expression of Cel6A to 20–35% of total soluble leaf protein in tobacco was shown in two consecutive field trials. Moving forward, the key challenge was the extraction of active enzymes from the transgenic tobacco leaves hyperaccumulating bacterial cellulases in a cost-effective manner. The study showed that unpurified crude extracts of NPT II and Tet C transgenic tobacco leaves recovered ~23% and ~29% w/w cellulosic sugars from energycane bagasse, respectively. A supplementation of as low as 25% of purified commercial cellulase improved the glucose recovery by 2.9 times (~85% w/w) compared to 100% crude extract, which is comparable to the glucose recovery obtained by commercial cellulases, thereby suggesting a reduced requirement of commercial cellulases leading to a cost reduction of 75% for biorefineries. Assuming a stable 40% cellulase yield in total soluble protein under field conditions and multiple harvests (one to three) a year, the study estimates that the potential cost for saccharification of 1 t of lignocellulosic biomass can be reduced to 67–200 USD by using crude leaf extracts of transgenic tobacco.

biofuel↗

DOE OSTI2025

Uncertainty quantification of a physics-informed model based on sparse identification of a Thermal Energy Distribution System

Integrated energy systems (IES)s are crucial for enhancing the economy and efficiency of power generation sources (e.g., nuclear energy) necessary to unleash American energy dominance. These systems can be integrated with thermal energy storage (TES) and intermittent renewable energies to optimize overall energy use, peak-load regulation, and demand-side responses. However, the stabilization of energy generation, transport, and utilization introduces operational complexities that exceed the challenges of managing each sub-component individually. Currently, though IESs rely on human operators for efficiency and stability, reducing human error risk and enhancing performance through automation is highly desirable. Recent advances at Idaho National Laboratory have demonstrated successful control of the Thermal Energy Distributed System (TEDS). However, the automatic control system depends on a deterministic Sparse Identification of Nonlinear Dynamics with Control (SINDyC) model, which are trained based on simulation data from physics-based simulations. Because of uncertainties in physics-based simulation, SINDyC model results in large discrepancies against experimental data and cannot be reliably used in automatic control. In this paper, we present an innovative approach to address these discrepancies by quantifying uncertainties and developing a more robust model. We first generated trajectories by using first-principles physics codes to encapsulate the experiment. Next, we trained thousands of models by randomly sampling these trajectories. We then collapsed all those models into one probabilistic SINDyC by fitting a multivariate Gaussian distribution onto the resulting coefficient’s distribution. Despite its simplicity, our approach successfully produced 95% confidence intervals that captured the experimental trajectories. It even did so with a higher probability and better U-pooling score across six of the seven relevant quantities of interest (QoIs), as compared to other classical approaches. In conclusion, ongoing research is focusing on generating new experimental trajectories to validate this approach, and on employing Bayesian calibration to refine parametric uncertainties and guide future model development efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Mapping protein dynamics at high spatial resolution with temperature-jump X-ray crystallography

Understanding and controlling protein motion at atomic resolution is a hallmark challenge for structural biologists and protein engineers because conformational dynamics are essential for complex functions such as enzyme catalysis and allosteric regulation. Time-resolved crystallography offers a window into protein motions, yet without a universal perturbation to initiate conformational changes the method has been limited in scope. Here we couple a solvent-based temperature jump with time-resolved crystallography to visualize structural motions in lysozyme, a dynamic enzyme. We observed widespread atomic vibrations on the nanosecond timescale, which evolve on the submillisecond timescale into localized structural fluctuations that are coupled to the active site. An orthogonal perturbation to the enzyme, inhibitor binding, altered these dynamics by blocking key motions that allow energy to dissipate from vibrations into functional movements linked to the catalytic cycle. Because temperature jump is a universal method for perturbing molecular motion, the method demonstrated here is broadly applicable for studying protein dynamics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Power-Sharing between Grid-Forming and Grid-Following Inverters

Power-sharing between the grid-forming and the grid-following inverter in a small microgrid is a challenging task. Specifically, the decentralized droop-based power-sharing is difficult to achieve when the grid-forming inverter regulates the voltage and the frequency. This article proposes controllers for the grid-forming and the grid-following inverters, which ensure proportional power-sharing as well as regulate the voltage and the frequency. Both controllers use active power-frequency and reactive power-voltage droops. Additional secondary loops and secondary forward paths are added to the controller of the grid-forming inverter and the grid-following inverter, respectively, to ensure simultaneous voltage and frequency regulation and power-sharing. The efficacy of the proposed controllers for power-sharing is verified through experiment results in a multi-inverter testbed.

Sadeque, Fahmid↗

DOE OSTI2024

Alternative Pozzolans for Replacement of Fly Ash in Grout: Literature Review for Continuous Improvement of Cement Waste Forms

The Department of Energy (DOE) is currently responsible for treating radioactive and mixed waste, performing environmental restoration, and closing contaminated tanks and facilities resulting from nuclear weapons production during the Cold War. Cementitious reagents are the most widely used materials for (1) chemically stabilizing and encapsulating radionuclides and hazardous metals and (2) solidifying radioactive wastewater. Cementitious grouts and flowable concretes are also the most widely used materials for tank and facility closures and are used extensively for physical as well as chemical stabilization in environmental restoration projects. Ambient temperature radioactive waste cementation is a widely used technology for producing waste forms for final disposal. The current practice of designing and testing waste forms is based on a mid to late 20th century technology approach (i.e., materials, characterization, and test methods) for generating parameters for risk assessments. DOE technology development is needed to address both current, long-term, and emerging issues in this area as new waste streams come online and as regulations, performance knowledge, and risk assessment methodology continue to evolve. Consequently, the use of cementation as a means of treating chemically challenging radioactive liquid waste streams and reactive debris requires an enduring effort.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Conformation-specific synthetic intrabodies modulate mTOR signaling with subcellular spatial resolution

Subcellular compartmentalization is integral to the spatial regulation of mechanistic target of rapamycin (mTOR) signaling. However, the biological outputs associated with location-specific mTOR signaling events are poorly understood and challenging to decouple. Here, we engineered synthetic intracellular antibodies (intrabodies) that are capable of modulating mTOR signaling with genetically programmable spatial resolution. Epitope-directed phage display was exploited to generate high affinity synthetic antibody fragments (Fabs) against the FKBP12–Rapamycin binding site of mTOR (mTOR FRB ). We determined high-resolution crystal structures of two unique Fabs that discriminate distinct conformational states of mTOR FRB through recognition of its substrate recruitment interface. By leveraging these conformation-specific binders as intracellular probes, we uncovered the structural basis for an allosteric mechanism governing mTOR complex 1 (mTORC1) stability mediated by subtle structural adjustments within mTOR FRB . Furthermore, our results demonstrated that synthetic binders emulate natural substrates by employing divergent yet complementary hydrophobic residues at defined positions, underscoring the broad molecular recognition capability of mTOR FRB . Intracellular signaling studies showed differential time-dependent inhibition of S6 kinase 1 and Akt phosphorylation by genetically encoded intrabodies, thus supporting a mechanism of inhibition analogous to the natural product rapamycin. Finally, we implemented a feasible approach to selectively modulate mTOR signaling in the nucleus through spatially programmed intrabody expression. These findings establish intrabodies as versatile tools for dissecting the conformational regulation of mTORC1 and should be useful to explore how location-specific mTOR signaling influences disease progression.

Science & Technology - Other Topics↗

DOE OSTI2023

Cybersecurity for Distributed Energy Resources: All Hazards Approach for Grid Modernization

Distributed energy resources (DER) sit at the intersection of IoT and critical infrastructure. As we work towards clean energy and decarbonization targets, high rates of growth of DER are expected, making them an important component of generation and grid services. They have been a part of the explosion of internet connected devices developed to solve real-world problems and make life easier by providing clean, local energy and enable the smart management of grid services. However, the quick-to-market, low-cost drivers for DER, combined with a historically relative low-impact has meant that cybersecurity has not been a top priority. So as DER penetration increases, will DER be a part of the growing challenge of securing the grid, or part of the solution for necessary grid modernization? In this talk, we will discuss the factors that have led to our current position and what makes cybersecurity for DER unique. A wide range of research is conducted at national labs, in partnership with industry and academia, to inform everything from standards and regulation, to the development of cyber-physical anomaly detection through use of digital twins, and even advanced threat analytics to SBOM and HBOM tracking. Even with this multi-faceted approach to the challenges, the question remains: is this too little too late or are we doing enough to secure the grid for the next 30 years?

14 SOLAR ENERGY↗