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NASA NTRS2008

Strategic Analysis Overview

NASA s Constellation Program employs a strategic analysis methodology in providing an integrated analysis capability of Lunar exploration scenarios and to support strategic decision-making regarding those scenarios. The strategic analysis methodology integrates the assessment of the major contributors to strategic objective satisfaction performance, affordability, and risk and captures the linkages and feedbacks between all three components. Strategic analysis supports strategic decision making by senior management through comparable analysis of alternative strategies, provision of a consistent set of high level value metrics, and the enabling of cost-benefit analysis. The tools developed to implement the strategic analysis methodology are not element design and sizing tools. Rather, these models evaluate strategic performance using predefined elements, imported into a library from expert-driven design/sizing tools or expert analysis. Specific components of the strategic analysis tool set include scenario definition, requirements generation, mission manifesting, scenario lifecycle costing, crew time analysis, objective satisfaction benefit, risk analysis, and probabilistic evaluation. Results from all components of strategic analysis are evaluated a set of pre-defined figures of merit (FOMs). These FOMs capture the high-level strategic characteristics of all scenarios and facilitate direct comparison of options. The strategic analysis methodology that is described in this paper has previously been applied to the Space Shuttle and International Space Station Programs and is now being used to support the development of the baseline Constellation Program lunar architecture. This paper will present an overview of the strategic analysis methodology and will present sample results from the application of the strategic analysis methodology to the Constellation Program lunar architecture.

Cirillo, William M.↗

DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗

DOE OSTI2021

Farmer characteristics and decision-making: A model for bioenergy crop adoption

We report the commercial development of biofuels and bioproducts depends on whether renewable biomass feedstock is available while not directly competing with the production of food. Farmers are one of the most important stakeholders in the biofuel supply chain and confront a range of uncertainties while entering the bioenergy market. Their decision-making process is extremely complex and rarely purely rational. Modeling farmer behavior requires considering a wide range of individual-level factors, socio-temporal dynamics, institutional settings, and their interactions. These characteristics make agent-based modeling a suitable framework for evaluating such systems. We developed a model to simulate farmer bioenergy crop adoption behavior across a 50-county study region in Nebraska, Kansas, and Colorado. The analysis considers adoption decisions for two bioenergy feedstocks, crop residues and energy crops. We examine the influence of individual and farm characteristics, market structure, social networks, and media influence on farmer adoption decisions. Our results indicate that different factors can have varied impacts on the speed of adoption for the crop residues and energy crops. Identifying levers that have the most impact on grower adoption can inform the design of interventions both from policy and private sector standpoints with important implications for the future the bioenergy industry.

09 BIOMASS FUELS↗

DOE OSTI2024

From Silos to Synergy: Identifying a Roadmap for Cross-Sector Research to Accelerate the Clean Energy Transition

The U.S. Department of Energy's blueprints for the transportation, buildings, and electricity sectors call for substantial reductions in greenhouse gas (GHG) emissions by 2050. These plans focus on zero-emission vehicles, investments in transit, energy-efficient buildings, and the widespread adoption and deployment of renewable energy technologies like solar photovoltaics (PV), energy storage and energy-efficient appliances. However, these sectors are often studied and modeled in isolation, overlooking how household decisions to adopt clean technologies in one sector influence others. This study, led by an interdisciplinary team at the National Renewable Energy Laboratory (NREL), explores opportunities for cross-sector collaboration to drive more effective and equitable decarbonization. Through discussions with 22 NREL researchers across transportation, building, solar, and grid sectors, the study highlights the need for integrated tools and models that capture interactions between these sectors. Key insights include the need for data standardization and interoperability to enable cross-sector analysis and decision-making. Strengthening utility partnerships is also critical to align energy policies with decarbonization goals and manage the increased demand for renewable energy. The study also emphasizes the importance of equity in the clean energy transition, calling for targeted incentives and support to ensure that low-income and underserved communities benefit from clean technologies like electric vehicles and energy-efficient appliances. To support these efforts, innovative funding mechanisms must be expanded to facilitate interdisciplinary research, such as city-specific decarbonization plans and federal projects like DOE"s Standard Scenarios. By encouraging collaboration and integrating cross-sector insights, this study aims to provide a roadmap to accelerate the clean energy transition and ensure it is both sustainable and inclusive.

14 SOLAR ENERGY↗

NASA NTRS2016

The Climate is A-Changin': Teaching Civic Competence for a Sustainable Climate

A central aim of social studies curriculum is to prepare young people for making "informed and reasoned decisions for the public good" concerning consequential problems like global climate change. By developing students' "vision of a good society" and exploring what actions and policies move our society in this direction, social studies teachers have an important role in preparing students for a world undergoing enormous environmental change. This article discusses elementary curriculum connections between building students' knowledge and understanding about "their community, nation and world" and global climate change. It also suggests ideas for building civic competency and climate literacy while creating opportunities for students to practice high-value skills like "data collection and analysis, collaboration, decision-making and problem-solving."

climate change↗

DOE OSTI2026

Retrofit Energy Analysis and Central Thermal modeling (REACT) v1.0

REACT is a website designed to simplify the analysis and decision-making process for retrofitting existing central plant heating and cooling systems with advanced heat pump technologies. The tool evaluates the technical and economic viability of replacing traditional boilers with various options including water-to-water or air-to-water heat pumps, which can provide efficient and lower-cost heating and cooling. It allows users to compare current central plant configurations with retrofit scenarios, assessing energy consumption, life-cycle costs, and environmental impact. Retrofitting traditional boiler and chiller systems with water-to-water or air-to-water heat pumps can significantly reduce energy consumption and lower lifecycle costs. The REACT provides: User-Friendly Tools: A user friendly web interface for quick, intuitive analysis accessible to non-experts. Advanced Modeling: A Python-powered engine for detailed parametric studies, optimization, and research applications. Comprehensive Analysis: Lifecycle cost evaluation, energy consumption modeling, and environmental impact assessment. Visual Insights: A variety of plots to visualize system performance and design trade-offs. The engine for the website (REACT) bases on several Python libraries, and the website will be hosted on an ETA server.

Kim, Donghun [Lawrence Berkeley National Laborator↗

DOE OSTI2023

Decision-Making Framework to Evaluate Opportunities for Recovery of Rare Earth Elements and Critical Minerals in Produced Water Networks

Oil and gas development activities require a significant amount of water. Given the environmental impacts associated with high consumption and subsequent disposal of this water, there is a need for strategies to support effective reuse of, treatment of, and/or resource recovery from this produced water. These streams can contain appreciable concentrations of rare earth elements (REE) and critical minerals (CM) (e.g., Lithium), which are critical for many important applications in electronics, technology, manufacturing, energy, and medicine [1]. Along with REE/CM present in produced water, other waste streams such as fly ash from coal-fired power plants and acid mine discharge present other potential sources for recovery of REE/CM [2-4]. And with increases in demand for REE/CM, the need for recovery from wastewater streams is becoming even more important. There is a need to develop effective decision-making tools to evaluate the economic and environmental potential for REE/CM recovery from produced water networks while supporting the needs of drilling and hydraulic fracturing activities. In this work, we present our progress in developing a decision-making framework to efficiently design and operate produced water networks for REE/CM recovery. Our approach considers REE/CM recovery from produced water streams alongside other potential wastewater sources. The models consider the location and capacity of storage and decentralized treatment processes as well as the design and operation of pipeline and transportation networks to connect different sources and effectively schedule inventories, reuse, and recovery opportunities. We focus on the evaluation of opportunities for REE/CM recovery with treatment constraints on composition and flow. We demonstrate this framework with an illustrative case study that features diverse production sites, storage facilities, transportation, and treatment sites. This proposed framework will be deployed in an open-source software package that is compatible with the PARETO framework to deliver analysis and decision-making tools that allow stakeholders to quantitatively evaluate potential opportunities for REE/CM recovery in produced water networks.

Pulsipher, Joshua↗

NASA NTRS2021

Southern Idaho Health & Air Quality: Monitoring Atmospheric Mixing Heights Post-Wildfire Through the Use of NASA Earth Observations

Wildfire smoke has long-lasting impacts on public and environmental health. Currently, agencies that monitor smoke base their decisions on an analysis of how fires burn, the direction the smoke moves from the fire source, and unverified estimates of mixing height. Mixing heights describe the maximum altitude to which a smoke plume rises. Satellite imagery provides more continuous and accurate coverage of mixing heights than current in situ methods. Thus, the team developed a software tool that processes and extracts mixing height observations from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Vertical Feature Mask granules. The team partnered with the National Oceanic and Atmospheric Administration’s National Weather Service, the Bureau of Land Management’s National Interagency Fire Center, and the National Park Service’s Fire Management Program Center to analyze historic fire events in southern Idaho. To do so, they used Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) to verify where a CALIPSO pass intersects thermal anomalies and smoke plumes. The software extracts features of relevance from the hdf file of each CALIPSO transect to locate layers of continuous aerosols. The maximum altitude at which the aerosol ends is recorded as the mixing height, along with a matching latitude and longitude. The satellite-derived values can be used to validate past mixing height predictions and evaluate the accuracy and systematic bias of different estimation methods. These results may allow agencies to make better comparisons and subsequent smoke pollution management, prevention, and public health decisions if the spatial and temporal differences between predictions and observations can be resolved.

DEVELOP Project Summary↗

DOE OSTI2026

Dataset for Top Model Decision Tree: Selecting Segmentation Models for Reliable Quantitative Analysis in Low- and Ultralow-Dose CryoEM

Motivation Multiple deep learning model architectures can be used to segment bacterial membranes in cryoEM images. However, an AI-based tool advancement is often presented with only a single segmentation model for broad use, and this single model may show inconsistent results across datasets from different users. Here, we present the Top Model Decision Tree, a model screening framework to screen for the best model to generate bacterial inner and outer membrane masks based on user priorities. We use pre-trained segmentation models from YOLOv11, YOLO26, U-Net, Detectron2 and SAM3 fine-tuned on bacterial inner and outer membranes imaged with cryoEM. Run the Framework This notebook must be opened in Google Colab. Mount Google Drive and run with a GPU-based runtime. Open the notebook and follow steps to git clone in folders and files within this repository. There will be a repeating top_model_decision_tree.ipynb (notebook clone) that will not be used. Save your .png binary mask files and .csv table outputs within your Google Drive or download before closing the notebook. The models and all analysis/training scripts are available at [GitHub: https://github.com/Lynnicia/CryoEM_membranes_top_model_decision_tree and https://github.com/Sireesiru/Semantic-Segmentation-of-bacterial-cell-envelope-using-U-Nets.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Search for low-mass hidden-valley dark showers with non-prompt muon pairs in proton-proton collisions at $\sqrt{s}=13$ TeV

A search for signatures of a dark analog to quantum chromodynamics is performed. The analysis targets long-lived dark mesons that decay into standard-model particles, with a high branching fraction of the dark mesons decaying into muons. The dark mesons are formed by the hadronisation of dark partons, which are produced by a decay of the Higgs boson. The search is performed using a data set corresponding to an integrated luminosity of 41.6 fb −1 , which was collected in proton-proton collisions at $\sqrt{s}=13$ TeV by the CMS experiment at the CERN LHC in 2018 using non-prompt muon triggers. The search is based on resonant muon pair signatures. Machine-learning techniques are employed in the analysis, utilising boosted decision trees to discriminate between signal and background. No significant excess is observed above the standard model expectation. Upper limits on the branching fraction of the Higgs boson decaying to dark partons are determined to be as low as 10−4 at 95% confidence level, surpassing and extending the existing limits on models with dark $\tilde{ω}$ mesons for mean proper decay lengths of less than 500 mm and for $\tilde{ω}$ masses down to 0.3 GeV. First limits are set for extended dark-shower models with two dark flavours that contain dark photons, probing their masses down to 0.33 GeV.

Beyond Standard Model↗

DOE OSTI2019

Public charging infrastructure for plug-in electric vehicles: What is it worth?

A lack of charging infrastructure is an important barrier to the growth of the plug-in electric vehicle (PEV) market. Public charging infrastructure has tangible and intangible value, such as reducing range anxiety or building confidence in the future of the PEV market. Quantifying the value of public charging infrastructure can inform analysis of investment decisions and can help predict the impact of charging infrastructure on future PEV sales. Estimates of willingness to pay (WTP) based on stated preference surveys are limited by consumers' lack of familiarity with PEVs. As an alternative, we focus on quantifying the tangible value of public PEV chargers in terms of their ability to displace gasoline use for PHEVs and to enable additional electric (e-) vehicle miles for BEVs, thereby mitigating the limitations of shorter range and longer recharging time. Simulation studies provide data that can be used to quantify e-miles enabled by public chargers and the value of additional e-miles can be inferred from econometric estimates of WTP for increased vehicle range. Functions are synthesized that estimate the WTP for public charging infrastructure by plug-in hybrid and battery electric vehicles, conditional on vehicle range, annual vehicle travel, pre-existing charging infrastructure, energy prices, vehicle efficiency, and household income. A case study based on California's public charging network in 2017 indicates that, to the purchaser of a new BEV with a 100-mile range and home recharging, existing public fast chargers are worth about $1500 for intraregional travel, and fast chargers along intercity routes are valued at over $6500.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Pipe Parity Analysis of Seawater Desalination in the United States: Exploring Costs, Energy, and Reliability via Case Studies and Scenarios of Emerging Technology

As climate change-induced variables exacerbate water scarcities, the use of seawater reverse osmosis (SWRO) membrane desalination technology for treating seawater could potentially provide a long-term drought-proof source of drinking water. This study carried out a technoeconomic assessment (TEA) for three SWRO desalination plants in the U.S. and one in Israel to set baselines for the cost and energy consumption for seawater desalination. In addition, a breakeven curve for implementing SWRO desalination was estimated in relation to the cost of water conservation measures to meet drought-induced reduction of the traditional water supply. The results show that the cost of SWRO water production scales with the plant capacity while energy intensity is not dramatically different across the plants. The higher cost in some U.S. plants is due to high capital investment, including land acquisition and permitting. Variations in plant capacity utilization have the greatest impact on the levelized cost of water (LCOW) over the plant service life, suggesting the importance of reducing fouling and maintenance-related downtime. Scenario analysis of fixed labor cost reduction through process automation indicates investments in automation and sensing technology could result in long-term savings. Breakeven analysis shows the decision to adopt SWRO is highly dependent on the local cost associated with water conservation to meet water supply reduction. Moreover, a small reduction in SWRO cost can influence a shift toward the adoption of SWRO over water conservation measures. Incorporation of future water demand, water conservation potential, and water stress data around the nation indicates SWRO desalination could be an important contributor to the future municipal drinking water portfolio in the U.S.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Blinding multiprobe cosmological experiments

ABSTRACT The goal of blinding is to hide an experiment’s critical results – here the inferred cosmological parameters – until all decisions affecting its analysis have been finalized. This is especially important in the current era of precision cosmology, when the results of any new experiment are closely scrutinized for consistency or tension with previous results. In analyses that combine multiple observational probes, like the combination of galaxy clustering and weak lensing in the Dark Energy Survey (DES), it is challenging to blind the results while retaining the ability to check for (in)consistency between different parts of the data. We propose a simple new blinding transformation, which works by modifying the summary statistics that are input to parameter estimation, such as two-point correlation functions. The transformation shifts the measured statistics to new values that are consistent with (blindly) shifted cosmological parameters while preserving internal (in)consistency. We apply the blinding transformation to simulated data for the projected DES Year 3 galaxy clustering and weak lensing analysis, demonstrating that practical blinding is achieved without significant perturbation of internal-consistency checks, as measured here by degradation of the χ2 between the data and best-fitting model. Our blinding method’s performance is expected to improve as experiments evolve to higher precision and accuracy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Improving I/O-aware Workflow Scheduling via Data Flow Characterization and trade-off Analysis

The scientific computing paradigm has transitioned from compute-intensive to I/O-intensive and memory-intensive in the past decade, especially when data-driven science has become common practice. Numerous empirical I/O-aware scheduling optimizations have been developed by incorporating I/O capacity and bandwidth as constraints into scheduling. Unfortunately, there is a lack of data flow (I/O) characterization tool and an understanding of trade-offs between concurrency, locality, and I/O bandwidth. To bridge the gap, this work 1) presents a set of descriptors to characterize, organize, and visualize I/O profiles, including flow size, I/O bandwidth, and operation count, which group data flows by I/O types, tasks, and files; 2) proposes an I/O Roofline model-based trade-off analysis to find the optimal trade-off between flow operational intensity, concurrency, and flow performance. The I/O descriptors generate useful insights into complicated I/O behaviors, suggesting distinct concurrency, storage, and scheduling to be used by types, tasks, and files. The proposed trade-off analysis guides scheduling decisions that generate resource assignment with the best flow parallelism. We evaluate our I/O-aware scheduling methodology on a highly I/O-intensive workflow–1000 Genomes. The experimental results demonstrate speedups of up to 2.4× compared to the state-of-the- art methods.

Guo, Luanzheng [BATTELLE (PACIFIC NW LAB)]↗

DOE OSTI2023

HUMAN-ARTIFICIAL INTELLIGENCE TEAMING FOR THE U.S. NAVY: DEVELOPING A HOLISTIC RESEARCH ROADMAP

With the ever-increasing deluge of data and demand for warfighters to make decisions upon its analysis, U.S. defense strategy has prioritized the development of artificial intelligence (AI)/machine learning (ML) systems that can analyze multi-source data streams and suggest courses of action. However, there is a history of systems that have failed to be adopted by the warfighter due not only to unsolved technical challenges, but also a lack of usability or contributions to mission effectiveness, perceived or otherwise. To avoid this, research into human-AI teaming shows promise for developing AI systems that work with frontline operators. A recent National Academies of Sciences, Engineering, and Medicine report (NASEM, 2022) presented 57 research objectives in this area; however, the U.S. Navy requires a more-focused set of priorities, as it is impossible to tackle every priority. A workshop involving 23 human factors scientists, computer scientists, and active-duty sailors was organized at the Naval Information Warfare Center Pacific, resulting in a set of five research priorities spanning near-, mid-, and far-term time frames. This panel will summarize the results of this workshop, with a focus on the big questions both going into this workshop and coming out of it. The panel participants come from government, academia, and industry, providing perspective from the different kinds of organizations required to accomplish these research goals.

Wong, Jason↗

DOE OSTI2022

North Carolina Water Utility Builds Resilience with Distributed Energy Resources

As the frequency and duration of grid outages increase, backup power systems are becoming more important for ensuring that critical infrastructure continues to provide essential services. Most facilities rely on diesel generators, which may be ineffective during long outages owing to limited fuel supplies and high generator failure rates. Distributed energy resources such as solar, storage, and combined-heat-and-power systems, coupled with on-site biofuel production, offer an alternative source of on-site generation that can provide both cost savings and resilience (i.e., the ability to respond to catastrophic events with longer-term consequences). A mixed-integer linear program minimizes costs and maximizes resilience at a wastewater treatment plant in Wilmington, North Carolina. We find that the plant can reduce life-cycle energy costs by 3.1% through the installation of a hybrid combined-heat-and-power, photovoltaic, and storage system. When paired with existing diesel generators, this system can sustain full load for seven days while saving $664,000 over 25 years and reducing diesel fuel use by 48% compared with the diesel-only solution. This analysis informed a decision by the Cape Fear Public Utility Authority to allocate funds for the implementation of a combined-heat-and-power system at the wastewater treatment plant in fiscal year 2023. Finally, the benefits of deploying hybrid combined-heat-and-power technologies and the utilization of on-site biofuel production extend, on a national scale, to thousands of wastewater treatment facilities and other types of critical infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

LLM integration into EPICS

The utilization of large language models (LLMs) such as ChatGPT has seen a remarkable increase in various fields over the past few years. These models have demonstrated their versatility and capability in understanding and generating human-like text, making them invaluable tools in numerous applications. In this project, we explore the integration of a LLM into the Experimental Physics and Industrial Control System (EPICS). The primary focus of this integration is to employ the LLM for advanced image processing and spatial analysis on images obtained from the beamlines. By leveraging the capabilities of the LLM, we aim to enhance the accuracy and efficiency of image interpretation, enabling more precise data analysis and decision-making within the EPICS framework. This integration not only showcases the potential of LLMs in scientific and industrial applications but also sets the stage for future advancements in automated control systems.

Adams, Ethan↗

DOE OSTI2024

From Text to Maps: LLM-Driven Extraction and Geotagging of Epidemiological Data

Epidemiological datasets are essential for public health analysis and decision-making, yet they remain scarce and often difficult to compile due to inconsistent data formats, language barriers, and evolving political boundaries. Traditional methods of creating such datasets involve extensive manual effort and are prone to errors in accurate location extraction. To address these challenges, we propose utilizing large language models (LLMs) to automate the extraction and geotagging of epidemiological data from textual documents. Our approach significantly reduces the manual effort required, limiting human intervention to validating a subset of records against text snippets and verifying the geotagging reasoning, as opposed to reviewing multiple entire documents manually to extract, clean, and geotag. Additionally, the LLMs identify information often overlooked by human annotators, further enhancing the dataset’s completeness. Our findings demonstrate that LLMs can be effectively used to semi-automate the extraction and geotagging of epidemiological data, offering several key advantages: (1) comprehensive information extraction with minimal risk of missing critical details; (2) minimal human intervention; (3) higher-resolution data with more precise geotagging; and (4) significantly reduced resource demands compared to traditional methods.

Harrod, Karly↗