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DOE OSTI2021

Transformation Kinetics of Phosphorus and Nitrogen in Iron-Rich Sewage Sludges during Hydrothermal Treatment and Recovery of Nutrients from Process Water

Hydrothermal treatment (HT) is an emerging technique for sustainable sewage sludge management and resource recovery. Many sludges are rich in iron (Fe) due to the common addition of Fe salts in water resource recovery facilities. To develop guidance for reaction conditions targeting nutrient recovery, this study systematically investigated the influence of HT temperature, treatment time, and sludge source on the dynamic speciation evolution of phosphorus (P) and nitrogen (N) during HT of Fe-rich sewage sludge. Complementary chemical extraction and X-ray spectroscopy analyses were conducted to characterize the treatment products. For the sludge mixture (a blend of primary and waste activated sludges), P speciation did not change significantly within 4.5 h at 125 °C HT, while soluble and labile P was converted into insoluble P over time at 175 and 225 °C HT. Strengite (FePO 4 · 2 H 2 O) preferentially formed in the hydrochars with increasing treatment temperature and/or time, whereas 125 °C HT within 1.5 h favored the formation of vivianite (Fe 3 (PO 4 ) 2 ·8H 2 O). Organic P was completely decomposed into orthophosphate when the HT temperature reached up to 175 °C. Pyrrole-N was enriched in the hydrochars. Similar reaction pathways were observed during HT of anaerobically digested sludge, though some minor differences in Fe-associated P and organic P were observed. Meanwhile, HT of the two sludges released orthophosphate and ammonia into the process waters at 175 and 225 °C, which can be recovered by a sequential process involving struvite (MgNH 4 PO 4 ·6H 2 O) precipitation and air stripping. This study provides new insights into the transformation of P and N during HT of Fe-rich sludges as well as a modular design for maximum P and N recovery from the treatment products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Seismic moment tensor classification using elliptical distribution functions on the hypersphere

Discrimination of underground explosions from naturally occurring earthquakes and other anthropogenic sources is one of the fundamental challenges of nuclear explosion monitoring. In an operational setting, the number of events that can be thoroughly investigated by analysts is limited by available resources. The capability to rapidly screen out events that can be robustly identified as not being explosions is, therefore, of great potential benefit. Nevertheless, possible mis-classification of explosions as earthquakes currently limits the use of screening methods for verification of test-ban treaties. Moment tensors provide a physics-based classification tool for the characterization of different seismic sources and have enabled the advent of new techniques for discriminating between earthquakes and explosions. Following normalization and projection of their six-degree vectors onto the hypersphere, existing screening approaches use spherically symmetric metrics to determine whether any new moment tensor may have been an explosion. Here, we show that populations of moment tensors for both earthquakes and explosions are anisotropically distributed on the hypersphere. Distributions possessing elliptical symmetry, such as the scaled von Mises–Fisher distribution, therefore provide a better description of these populations than the existing spherically symmetric models. We describe a method that uses these elliptical distributions in combination with a Bayesian classifier to achieve successful classification rates of 99 per cent for explosions and 98 per cent for earthquakes using existing catalogues of events from the western United States. The 1983 May 5 Crowdie underground nuclear test and 2018 July 20 DAG-1 deep-borehole chemical explosion are the only two explosions out of 140 that are incorrectly classified. Application of the method to the 2006–2017 nuclear tests in the Democratic People’s Republic of Korea yields 100 per cent identification rates and we provide a simple routine MTid for general usage. The approach provides a means to rapidly assess the likelihood of an event being an explosion and can be built into monitoring workflows that rely on simultaneously assessing multiple different discrimination metrics.

58 GEOSCIENCES↗

DOE OSTI2024

Purple is the new green: biopigments and spectra of Earth-like purple worlds

ABSTRACT With more than 5500 detected exoplanets, the search for life is entering a new era. Using life on Earth as our guide, we look beyond green landscapes to expand our ability to detect signs of surface life on other worlds. While oxygenic photosynthesis gives rise to modern green landscapes, bacteriochlorophyll-based anoxygenic phototrophs can also colour their habitats and could dominate a much wider range of environments on Earth-like exoplanets. Here, we characterize the reflectance spectra of a collection of purple sulfur and purple non-sulfur bacteria from a variety of anoxic and oxic environments. We present models for Earth-like planets where purple bacteria dominate the surface and show the impact of their signatures on the reflectance spectra of terrestrial exoplanets. Our research provides a new resource to guide the detection of purple bacteria and improves our chances of detecting life on exoplanets with upcoming telescopes. Our biological pigment data base for purple bacteria and the high-resolution spectra of Earth-like planets, including ocean worlds, snowball planets, frozen worlds, and Earth analogues, are available online, providing a tool for modellers and observers to train retrieval algorithms, optimize search strategies, and inform models of Earth-like planets, where purple is the new green.

Coelho, Lígia Fonseca (ORCID:0000000150081249)↗

DOE OSTI2024

Multi-Factor-Coupled, Ahead-of-Time Aggregation of Power Flexibility Under Forecast Uncertainty

The increasing penetration of distributed energy resources (DERs) is significantly reshaping the role of distribution systems under active energy management. To aggregate the active-reactive power flexibility of DERs dispersed at the feeder and provide capacity support to the transmission system, it is essential to efficiently identify feasible substation power injection trajectories. This paper introduces a novel ahead-of-time flexibility characterization method to address it. First, a polyhedral non-feeder-level power flexibility region (PFR) is constructed, accounting for various time-dependent, power-coupled, and forecast error uncertainties. Then, a polyhedral feeder-level PFR is analytically derived through a coordinate transformation, which can reveal the uncertainty propagation path, i.e., how uncertainty applies to the feeder-level PFR. To facilitate the high-level application, a tractable chance-constrained Chebyshev centering optimization model is further developed to find a ball-shaped inner approximation of the feeder-level PFR. Finally, the proposed method is validated on a modified IEEE 123-bus test system. Here, both theoretical and experimental results show that, with appropriate robustness parameter settings, the proposed method can make the approximated PFR less conservative with abundant robustness against forecast error uncertainty.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Signatures of local nitrogen adaptation in the Brachypodium distachyon root microbiome

Plants associate with diverse microbiomes that impact their fitness, yet the contribution of the microbiome to plant adaptation is uncertain. As plant recruitment of its microbiome can be both highly variable and genetically determined, we hypothesized this recruitment process may be the result of adaptive evolution, and contributing to plant local adaptation. We investigated the evolution and adaptive benefit of plant–microbiome recruitment by characterizing the rhizosphere communities across a genotypic panel of Brachypodium distachyon in a common garden experiment. By linking microbial communities to their host genotype's historic environment, we identified signatures of selection on plant–microbiome recruitment. Plant–microbiome composition was significantly correlated with the host genotype's historic environment, with enrichment of microbial traits aligned to local resource conditions. For example, genotypes from low-nitrogen environments recruited communities enriched in nitrogen acquisition traits. In a complementary experiment evaluating plant nitrogen response, these same genotypes were well-adapted to low-nitrogen environments, contingent on the presence of key nitrogen-cycling microbes. These results suggest that local adaptation in plants may partially be mediated by recruitment of beneficial microbiomes. This perspective suggests that plant adaptation may be an emergent property of host–microbe interactions, where evolutionary responses favor traits that promote recruitment of locally beneficial microbiomes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

International Offshore Geologic Carbon Storage Story Map

The International Offshore Geologic Carbon Storage Story Map provides an overview of offshore geologic carbon storage (GCS) project maturity on a global scale. This story map reviews the history of CO2 storage in offshore sedimentary basins, basic geologic requirements, project timelines and lessons learned through defined project stage progression, areas previously studied for potential future domestic offshore GCS development, and regulatory considerations within different global regions. This resource is a distillation of information collected and recorded in the Offshore Geologic Carbon Storage Inventory Version 1.0 (https://edx.netl.doe.gov/dataset/offshore-gcs-data-inventory).

Assessment↗

DOE OSTI2024

Evolving Competitive Markets in SAPP: Leveraging Competitive Wholesale Electricity Markets to Drive Renewable Generation Capacity in the Southern African Power Pool (SAPP)

The SADC region has significant natural resource potential to increase renewable energy generation, improve electricity reliability, and support economic development. This research finds an apparent lack of confidence from electricity infrastructure investors in SAPP wholesale electricity markets, which increases risk perception and lowers the likelihood of capital deployment. With respect to free market fundamentals, competitive market obstacles and renewable energy development obstacles are characterized. Stakeholders identified the top obstacles to well-functioning competitive markets as insufficient transmission infrastructure for interconnection and regional movement of electricity, dominance of national single-buyer markets, and lack of or weak nation-state regulatory frameworks. Stakeholders prioritized the top three obstacles for renewable energy development as a lack of viable commercial arrangements for variable renewable energy (VRE) balancing, lack of functional and consistent nation-level regulations, and higher project costs related to reliance on imported equipment. With respect to potential solution options, stakeholders prioritized the development of new cost allocation and finance methods to facilitate new transmission expansion, training to educate new or potential new market entrants on SAPP processes, as well as modeling and analysis of regional SAPP participation benefits disaggregated to the nation-state level. From these perspectives, this research identified strategy options for consideration including transitioning SAPP to a regional transmission operator (RTO) for operation and planning of cross-border transmission facilities and market administration, shifting operations of SAPP member transmission systems to Independent System Operators (ISOs), establishing a regional regulatory authority and enhancing market data transparency. Implementing these reforms is expected to be challenging, but not insurmountable, given the domestic political, legal, and jurisdictional complexities of the SADC region.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography–Mass Spectrometry Small Molecule Identification

Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this “molecular snapshot” is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Characterizing U.S. Heat Demand for Potential Application of Geothermal Direct Use: Preprint

In this paper, we assess the U.S. demand for low-temperature thermal energy at the county resolution for four major end-use sectors: residential buildings, commercial buildings, manufacturing facilities, and agricultural facilities. Existing, publicly available data on the U.S. thermal demand market are characterized by coarse spatial resolution, with assessments typically at the state-level or larger. For many uses, these data are sufficient; however, our research was motivated by an interest in assessing the potential demand for direct use (DU) of low-temperature (30 degrees to 150 degrees C) geothermal heat. The availability and quality of geothermal resources for DU applications are highly spatially heterogeneous; therefore, to assess the potential market for these resources, it is necessary to understand the spatial variation in demand for low-temperature resources at a local resolution. This paper presents the datasets and methods we used to develop county-level estimates of the thermal demand for the residential, commercial, manufacturing, and agricultural sectors. Although this analysis was motivated by an interest in geothermal energy deployment, the results are likely to have broader applications throughout the energy industry. The county-resolution thermal demand data developed in this study for four major U.S. sectors may have far-reaching implications for building technologies, industrial processes, and various distributed renewable energy thermal resources (e.g. biomass, solar).

15 GEOTHERMAL ENERGY↗

DOE OSTI2017

Characterizing U.S. Heat Demand Market for Potential Application of Geothermal Direct Use

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Reductive Analysis with Compiler-Guided Large Language Models for Input-Centric Code Optimizations

Input-centric program optimization aims to optimize code by considering the relations between program inputs and program behaviors. Despite its promise, a long-standing barrier for its adoption is the difficulty of automatically identifying critical features of complex inputs. This paper introduces a novel technique, reductive analysis through compiler-guided Large Language Models (LLMs), to solve the problem through a synergy between compilers and LLMs. It uses a reductive approach to overcome the scalability and other limitations of LLMs in program code analysis. The solution, for the first time, automates the identification of critical input features without heavy instrumentation or profiling, cutting the time needed for input identification by 44× (or 450× for local LLMs), reduced from 9.6 hours to 13 minutes (with remote LLMs) or 77 seconds (with local LLMs) on average, making input characterization possible to be integrated into the workflow of program compilations. Optimizations on those identified input features show similar or even better results than those identified by previous profiling-based methods, leading to optimizations that yield 92.6% accuracy in selecting the appropriate adaptive OpenMP parallelization decisions, and 20-30% performance improvement of serverless computing while reducing resource usage by 50-60%.

Input-Centric Optimization↗

DOE OSTI2019

Solar Resource Calibration, Measurement, and Dissemination: Final Report FY 2016-FY 2018

The Solar Resource Calibration, Measurement, and Dissemination project supports the U.S. Department of Energy's Solar Energy Technologies Office (SETO) initiative to make solar energy cost-competitive with other forms of electricity by improving the tools and methods to measure and model solar radiation and therefore reduce uncertainty in predicting solar output and improve the bankability of solar projects. This project has three tasks that conduct research on advancing solar resource measurements and modeling: Task 1: Applied solar Radiation Measurements. Task 2: National Solar Radiation Data Base (NSRDB). Task 3: Knowledge Sharing. The National Renewable Energy Laboratory's (NREL's) Solar Radiation Research Laboratory (SRRL) activities ensure traceable solar measurements throughout the United States that are essential for project feasibility, due diligence, financing, and plant operations. The SRRL houses NREL's Baseline Measurement System (BMS), which provides a platform for improving the accuracy of solar measurements through research to improve the calibration and characterization of instruments and enable the development of new measurement technology and standards. The platform contains a variety of instruments measuring solar radiation and other meteorological parameters. Following best practices, BMS instruments are calibrated to ensure traceability to the World Radiometric Reference in compliance with ISO/IEC 17025 accreditation requirements. The BMS data sets are quality-controlled and made publicly available through the Measurement and Instrumentation Data Center website. The NSRDB is the most accessed public database of solar irradiance and associated weather parameters for use in energy modeling. It supports SETO's goals of reducing barriers to high penetrations of solar technologies by providing easy access to high-quality, foundational data essential for innovative product development and downstream modeling through the NSRDB website. Data are currently available for the contiguous United States and beyond for 20 years covering 1998-2017. The Physical Solar Model (PSM) that underpins the current NSRDB data sets is a unique physics-based model that has been developed at NREL and has opened the door to the use of next-generation satellite data sets for solar resource assessment and forecasting. Through the American Society for Testing Materials (ASTM) International and other international standards organizations, NREL continues to provide leadership in the development of standards that are relevant to the measurement and modeling of solar radiation for solar energy applications. Standards developed cover the calibration method of pyrheliometers (direct normal irradiance measurements), pyranometers (global horizontal irradiance measurements), spectroradiometers, ultraviolet radiometers, correct field deployment of these instruments, spectral distribution reference tables for photovoltaic modeling, and transfer of indoor calibrations. Further, NREL leads the International Energy Agency (IEA) Photovoltaic Power Systems Programme Task 16: Subtask 1, which focuses on the evaluation of current and emerging resource assessment methodologies. NREL and the IEA updated the Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, which is widely used by the solar industry.

14 SOLAR ENERGY↗

DOE OSTI2024

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Triphenylphosphine Oxide-Derived Anolyte for Application in Nonaqueous Redox Flow Battery

Recent advances in redox flow batteries have made them a viable option for grid-scale energy storage, however they exhibit low energy density. One way to boost energy density is by increasing the cell potential using a nonaqueous system. Molecular engineering has proven to be an effective strategy to develop redox-active compounds with extreme potentials but these are usually challenged by resource sustainability of the newly developed redox materials. Here, we investigate the utility of phosphine oxides as anolytes with extremely negative potentials. Specifically, we found that cyclic triphenylphosphine oxide (CPO), has a highly negative potential (–2.4 V vs Fc/Fc + ). Importantly, CPO is synthesized from triphenylphosphine oxide, a common industrial chemical waste with no commercial value. Structural and electrochemical characterization of the reduced radical anion showed that enhanced stability is due to cyclization or extended pi-conjugation. Importantly, mechanistic investigation into the decomposition of CPO under various solvents and electrochemical conditions allowed us to utilize an acetonitrile/DMF binary solvent system to enable a long-lived anolyte which exhibited no fade over 350 cycles. In conclusion, this work led to the development of a waste-derived cyclic phosphine oxide that exhibits excellent cycling stability making it an ideal anolyte toward the development of energy-dense RFBs.

25 ENERGY STORAGE↗

DOE OSTI2020

The Cost of Saving Electricity: A Multi-Program Cost Curve for Programs Funded by U.S. Utility Customers

This study analyzed the cost performance of electricity efficiency programs implemented by 116 investor-owned utilities between 2009 and 2015 in 41 states, representing about three-quarters of the total spending on U.S. efficiency programs. We applied our typology to characterize efficiency programs along several dimensions (market sector, technology, delivery approach, and intervention strategy) and report the costs incurred by utilities and other program administrators to achieve electricity savings as a result of the programs. Such cost performance data can be used to compare relative costs of different types of efficiency programs, evaluate efficiency options alongside other electricity resources, benchmark local efficiency programs against regional and national cost estimates, and assess the costs of meeting state efficiency policies. The savings-weighted average cost of saved electricity for the period was $0.025/kilowatt-hour (kWh). The cost of saved electricity for programs that targeted residential customers was $0.021/kWh, compared to $0.025/kWh for programs for commercial and industrial customers. Ultimately, we developed an aggregate program savings “cost curve” for the actual electricity efficiency resource during the period that provides insights into the relative costs of various types of efficiency programs and the savings contribution of each program type to the efficiency resource at a national level.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2002

[Earth and Space Sciences Project Services for NASA HPCC]

This grant supported the effort to characterize the problem domain of the Earth Science Technology Office's Computational Technologies Project, to engage the Beowulf Cluster Computing Community as well as the High Performance Computing Research Community so that we can predict the applicability of said technologies to the scientific community represented by the CT project and formulate long term strategies to provide the computational resources necessary to attain the anticipated scientific objectives of the CT project. Specifically, the goal of the evaluation effort is to use the information gathered over the course of the Round-3 investigations to quantify the trends in scientific expectations, the algorithmic requirements and capabilities of high-performance computers to satisfy this anticipated need.

Merkey, Phillip↗

NASA NTRS2005

[Earth Science Technology Office's Computational Technologies Project]

Fischer, James↗

NASA NTRS2010

Method and Circuit for In-Situ Health Monitoring of Solar Cells in Space

This innovation represents a method and circuit realization of a system designed to make in-situ measurements of test solar-cell operational parameters on orbit using readily available high-temperature and high-ionizing-radiation- tolerant electronic components. This innovation enables on-orbit in-situ solar-array health monitoring and is in response to a need recognized by the U.S. Air Force for future solar arrays for unmanned spacecraft. This system can also be constructed out of commercial-grade electronics and can be embedded into terrestrial solar power system as a diagnostics instrument. This innovation represents a novel approach to I-V curve measurement that is radiation and temperature hard, consumes very few system resources, is economical, and utilizes commercially available components. The circuit will also operate at temperatures as low as 55 C and up to +225 C, allowing it to reside close to the array in direct sunlight. It uses a swept mode transistor functioning as a resistive load while utilizing the solar cells themselves as the biasing device, so the size of the instrument is small and there is no danger of over-driving the cells. Further, this innovation utilizes nearly universal spacecraft bus resources and therefore can be readily adapted to any spacecraft bus allowing for ease of retrofit, or designed into new systems without requiring the addition of infrastructure. One unique characteristic of this innovation is that it effects the measurement of I-V curves without the use of large resistor arrays or active current sources normally used to characterize cells. A single transistor is used as a variable resistive load across the cell. This multi-measurement instrument was constructed using operational amplifiers, analog switches, voltage regulators, MOSFETs, resistors, and capacitors. The operational amplifiers, analog switches, and voltage regulators are silicon-on-insulator (SOI) technology known for its hardness to the effects of ionizing radiation. The SOI components used can tolerate temperatures up to 225 C, which gives plenty of thermal headroom allowing this circuit to perhaps reside in the solar cell panel itself where temperatures can reach over 100 C.

Krasowski, Michael J.↗