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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission Using a Hydrology‐Based Model

Abstract A combination of accelerated population growth and severe droughts has created pressure on food security and driven the development of irrigation schemes across sub‐Saharan Africa. Irrigation has been associated with increased malaria risk, but risk prediction remains difficult due to the heterogeneity of irrigation and the environment. While investigating transmission dynamics is helpful, malaria models cannot be applied directly in irrigated regions as they typically rely only on rainfall as a source of water to quantify larval habitats. By coupling a hydrologic model with an agent‐based malaria model for a sugarcane plantation site in Arjo, Ethiopia, we demonstrated how incorporating hydrologic processes to estimate larval habitats can affect malaria transmission. Using the coupled model, we then examined the impact of an existing irrigation scheme on malaria transmission dynamics. The inclusion of hydrologic processes increased the variability of larval habitat area by around two‐fold and resulted in reduction in malaria transmission by 60%. In addition, irrigation increased all habitat types in the dry season by up to 7.4 times. It converted temporary and semi‐permanent habitats to permanent habitats during the rainy season, which grew by about 24%. Consequently, malaria transmission was sustained all‐year round and intensified during the main transmission season, with the peak shifted forward by around 1 month. Lastly, we evaluated the spatiotemporal distribution of adult vectors under the effect of irrigation by resolving habitat heterogeneity. These findings could help larval source management by identifying transmission hotspots and prioritizing resources for malaria elimination planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Comparing Delay-, Distance-, and Cordon-Based Congestion Pricing Strategies Via Large-Scale Simulation

This study compares the impacts of delay-, distance-, and cordon-based congestion pricing strategies for Austin, Texas, using the POLARIS agent-based activity-based travel demand simulation model. This approach enables agent-level heterogeneity and realistic choice options (including destination, mode, and activity scheduling) for dynamic traffic assignment and congestion feedbacks across a major metro region, which are features lacking in past work. To ensure comparability, distance-based tolls were set to generate the same revenue as delay-based tolling of $3.5 M/day, averaging $1.17/resident/day or $0.42/vehicle-trip. Delay-based pricing delivers 44% lower network delay and 13% lower VHT compared to the no-toll baseline, levels unmatched by other pricing strategies. At the height of the AM peak, drivers pay up to $0.13/mile on average, though most links in the network remain untolled. Distance-based pricing is the most effective at reducing VMT (by 4%), but VHT reductions (of 6%) primarily stem from drivers selecting closer destinations, achieving only one-fourth the delay reduction of delay-based pricing. Across various implementations of delay- and distance-based pricing, the results suggest that spatial variations of tolls are far more important than temporal variations. Cordon tolls produce minimal impacts at the network-wide level, but offer substantial delay reductions inside the cordon. Other major findings include: 1) delay-based pricing increases trip-making during the PM peak period due to backward shifts in discretionary-activity start times by higher-income residents; and 2) tolls’ spatial impacts, including changes in network flows and tolls paid by residents, vary substantially between delay- and distance-based pricing strategies.

Agent-based modeling↗
DOE OSTI2020

EMIS Agent Simulation Model (Electricity Markets Investment Suite) [SWR-19-56]

The Electricity Markets Investment Suite Agent-based Simulation (EMIS-AS) model is an agent-based model developed at NREL for simulating annual investment and retirement decisions of heterogeneous investors in the electricity sector. EMIS-AS is designed to capture the evolution of the electricity generation portfolio resulting from the interactions of heterogeneous investors under different policy and market designs. EMIS-AS not only allows end-users to customize market products and rules, but also to capture investors' heterogeneous financing parameters, technology preferences, beliefs about the future (forecasts), ability to update those forecasts, and risk preferences under uncertainty.

Anwar, Mahammad Bashar↗
DOE OSTI2024

Parking Strategies and Outcomes for Shared Autonomous Vehicle Fleet Operations

Parking spots are a premium commodity, especially in dense downtown settings, so this study examines the service impacts of shared autonomous vehicles (SAVs) parking in legal on- or off-street locations when idle across Travis County in Austin, Texas. Here, using an agent-based activity-based travel demand model with dynamic traffic simulation, two restricted-parking strategies for SAVs were simulated. SAVs either found the nearest available parking spot or the lowest-cost spot (via a tradeoff of parking fees and distance-based costs). Two comparisons were conducted to analyze the impacts of these strategies. First, two restricted parking strategies were compared, where SAVs park without competition with private human-driven vehicles (HVs) for parking locations. Second, a more realistic analysis compared two SAV parking strategies with a scenario where SAVs remain idle in place. Private HVs in all scenarios and strategies of this comparison park at the closest designated location unless they opt for private parking. Using a supply of 8,400 aggregated parking locations in Austin, this study simulated fleet performance under different trip demands, with SAV fares of $\$0.62$ per kilometer ($\$1$ per mile) plus a $\$1$ fixed pickup fee with dynamic ridesharing permitted. Parking costs were negligible in both SAV parking search strategies applied to the Austin network because of the region’s provision of mostly free parking. Requiring SAVs to park on designated on- and off-street parking locations and parking lots (restricted parking) also increased parking costs for HV drivers by up to 22% since SAVs occupied some free parking spaces, especially in the least-cost parking search strategy.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Exploring impacts of electricity tariff on charging infrastructure planning: An activity-based approach

In the past decade, electric vehicles (EVs) have gained popularity for their efficiency and environmental benefits. Advances in battery technology and charging equipment have yielded long-range EVs and fast-charging. However, many major cities lack adequate charging infrastructure for daily EV use. This study addresses this gap by integrating activity-based modeling, charging behavior simulation, and charging infrastructure optimization. The research utilizes the POLARIS agent-based transportation model to accurately capture user activities, trip patterns, and traffic flows. Additionally, the study investigates the impact of fixed and spatiotemporal electricity rate distributions on optimal charging infrastructure deployment. The framework is applied to the Chicago regional area network and analyzed under various EV ownership scenarios. Further, the results reveal significant impacts of the charging pricing strategy on user decision-making and charging demand distribution. There is also a need for consistent pricing policies in charging infrastructure planning and operational phases to avoid drops in service quality.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Zooming in on virtual commutes: Telecommuting impacts on mobility and sustainability

Motivated by a societal shift towards remote work and rapid advancement in information and communication technologies, this study examines the impact of telecommuting on urban mobility across the nine counties of the San Francisco Bay Area, California. Utilizing a behaviorally realistic integrated agent-based transportation model and recent data on telecommuting patterns, we simulate the impact of multiple telecommuting scenarios on transportation system outcomes. This provides a comprehensive picture of the impact of remote work on travel costs and accessibility of all travelers, factoring in changes in mode use and congestion. We analyze how telecommuting influences broader societal factors such as transportation energy consumption. Our findings indicate that increased telecommuting reduces overall person miles traveled and transportation energy consumption. Furthermore, telecommuting results in externality benefits by improving accessibility and reducing commute times for non-telecommuters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

dGen (Distributed Generation Market Demand) Model Data: Alpha Release

Open sourced data needed to run the basic alpha release version of the dGen model. Includes a pre-generated agent file of 100,000 agents in pickle file format along with the base schema and table data in parquet format that are needed to create a postgreSQL database for the model to interact with.

14 SOLAR ENERGY↗
DOE OSTI2021

Integrated Water-Power System Resilience Analysis in a Southeastern Idaho Irrigation District: Minidoka Case Study

This study investigates the joint water–power system resilience of an irrigation district in southeastern Idaho. Irrigation districts face difficulties in the delivery of water to farmers under drought conditions, during equipment failures, or unplanned infrastructure disruptions. The resilience of interconnected water and power systems can be better analyzed and understood through an integrated approach, using a model that connects the dependencies between the two halves of the system. Using a multi-agent system model capturing both water and power system components, as well as their linkages, we capture the interdependencies of these systems and highlight opportunities for improvement when faced with disruptions. Through simulation scenarios, we examine the system resilience using system performance, quantified as the percentage of met demand of the power and water system, when subjected to drought water year, an unforeseen water demand increase, power outage and dam failure. Scenario results indicate that the effects of low flow years are mostly felt in the power system; unexpected increases in water demand marginally impact irrigation system performance; dams and pumps present vulnerabilities of the system, causing substantial unmet demand during disruptions. Noting the interdependencies between the water–power system halves while leveraging an integrated simulation allows for an insightful analysis of the system impacts during disruptions.

13 HYDRO ENERGY↗
DOE OSTI2024

Dataset for: Price Controls for Scarcity Events in Real-Time and Transactive Energy Systems

Real time pricing (RTP) is often promoted as a mechanism to improve the economic efficiency of the electricity system. However, many regulators have been hesitant to adopt RTP due to concerns about exposing customers to extreme price swings. To balance these concerns, this paper proposes a methodology for establishing price controls, based on the supply of demand-side flexibility in the system. As an illustrative example, we measure price responsiveness using an agent-based simulation model that is representative of the ERCOT market. The model is composed of a distribution feeder that has 250 customers with active agents controlling their HVAC systems in response to the historical ERCOT RTP with an artificially added high-price event. These agents are subjected to increasing electricity prices during the event, which we then use to create a supply curve for demand-side resources in our modeled scarcity event. We set potential price caps at points on the supply curve where customers’ have exhausted their flexible capacity. Using historical prices, we examine the systemic costs of these price caps, and present regulatory options for recouping them. Utilities and regulators interested in limiting consumer risk from dynamic pricing can utilize these methods to develop rate structures and encourage conservation. The attached data upload allows for the duplication or modification of the analysis performed in this study.

Kerby, Jessica R↗
DOE OSTI2021

Projecting the number of new HIV infections to formulate the "Getting to Zero" strategy in Illinois, USA

Objectives: Getting to Zero (GTZ) initiatives focus on expanding use of antiretroviral treatment (ART) and pre-exposure prophylaxis (PrEP) to eliminate new HIV infections. Computational models help inform policies for implementation of ART and PrEP continuums. Such models, however, vary in their design, and may yield inconsistent predictions. Using multiple approaches can help assess the consistency in results obtained from varied modeling frameworks, and can inform optimal implementation strategies. Methods: A study using three different modeling approaches is conducted. Two approaches use statistical time series analysis techniques that incorporate temporal HIV incidence data. A third approach uses stochastic stimulation, conducted using an agent-based network model (ABNM). All three approaches are used to project HIV incidence among a key population, young Black MSM (YBMSM), over the course of the GTZ implementation period (2016-2030). Results: All three approaches suggest that simultaneously increasing PrEP and ART uptake is likely to be more effective than increasing only one, but increasing ART and PrEP by 20% points may not eliminate new HIV infections among YBMSM. The results further suggest that a 20% increase in ART is likely to be more effective than a 20% increase in PrEP. All three methods consistently project that increasing ART and PrEP by 30% simultaneously can help reach GTZ goals. Conclusions: Increasing PrEP and ART uptake by about 30% might be necessary to accomplish GTZ goals. Such scale-up may require addressing psychosocial and structural barriers to engagement in HIV and PrEP care continuums. ABNMs and other flexible modeling approaches can be extended to examine specific interventions that address these barriers and may provide important data to guide the successful intervention implementation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Automobile and Technology Lifecycle-Based Assignment (ATLAS) v2.0.12

ATLAS is a comprehensive vehicle transaction and technology adoption microsimulator. ATLAS evolves the fleet mix of individual households by simulating the transaction (vehicle addition, disposal, and replacement) and choice (vehicle type, vintage, powertrain, and tenure) decisions in response to the co-evolving demographics, land use, and vehicle technology simulations. Different from the existing vehicle models that are either static or aggregated (e.g. stock model), ATLAS is fully disaggregated and dynamic following a sequential and circumstantial decision-making trajectory. This fine-grained approach not only enhances the realism of the simulation but also provides a nuanced understanding of the dynamics inherent in vehicle fleet evolution. ATLAS outputs are fully compatible with subsequent agent-based transportation modeling system and can enable distributional effect analysis regarding the fleet turnover among heterogeneous populations. ATLAS expands the typical new sale focused vehicle choice modeling to including used vehicle transactions that are of increasing interests to understanding the vehicle adoption behavior among lower income households.

Jin, Ling↗
DOE OSTI2022

Simulation-Based Model for Facility-Scale Eagle Presence Mapping

This talk presents the recent progress in building a predictive movement model for predicting conflict between soaring raptors and wind turbines. In particular, it details the recently developed numerical tool called Stochastic Soaring Raptor Simulator for simulating updraft-subsidized paths for soaring raptors through variety of wind conditions and the associated directional intent.

agent-based model↗
DOE OSTI2021

Distributed Solar and Storage Adoption Modeling

The National Renewable Energy Laboratory (NREL) is analyzing the rapidly increasing role of energy storage in the electrical grid through 2050 through its Storage Futures Study. In one phase of the study, NREL used the laboratory's Distributed Generation Market (dGen) model to examine the various future distributed storage capacity adoption scenarios, results, and implications.

agent-based modeling↗
DOE OSTI2022

Bayesian State-Space Modeling Framework for Understanding and Predicting Golden Eagle Movements Using Telemetry Data

Predicting raptor movements through a wind power plant under given atmospheric and topographical conditions is a crucial first step in the overall goal of quantifying the risk of turbine-related collisions and mortalities. Extracting behavioral traits of golden eagles (Aquila chrysaetos) from telemetry data requires the fusion of noisy and sparse movement data (location, heading, velocity) with a stochastic mathematical representation of the eagles' decision-making processes. In this study, we framed this problem in a Bayesian state-space framework where both observations and decision-making are assumed to be stochastic processes connected through hidden states (mode of flight, intent), and the unknown model parameters are assumed to be random variables that are calibrated using the available telemetry data. This framework allowed for rigorous consideration of underlying uncertainties while allowing for both data and prior biological knowledge to contribute to a probabilistic and predictive agent-based movement model. We implemented and applied the Bayesian framework to understand movement behavior of 23 GPS-tagged golden eagles travelling in the western US for years 2019 and 2020. Our preliminary findings show that the Bayesian state-space framework provides a robust inverse modeling apparatus to decode eagle behavioral characteristics from telemetry data. This study was primarily aimed at verifying and validating the framework with selected golden eagle tracks (both long- and short-ranged), with future research aimed at extending the framework to include multi-mode flight, consideration of atmospheric data and uplift mechanisms, eagle-to-eagle interaction, and eagle-to-turbine interaction.

Bayesian modeling↗
DOE OSTI2024

Reducing Sample Size While Improving Equity in Vaccine Clinical Trials: A Machine Learning-Based Recruitment Methodology with Application to Improving Trials of Hepatitis C Virus Vaccines in People Who Inject Drugs

Despite the availability of direct-acting antivirals that cure individuals infected with the hepatitis C virus (HCV), developing a vaccine is critically needed in achieving HCV elimination. HCV vaccine trials have been performed in populations with high incidence of new HCV infection such as people who inject drugs (PWID). Developing strategies of optimal recruitment of PWID for HCV vaccine trials could reduce sample size, follow-up costs and disparities in enrollment. We investigate trial recruitment informed by machine learning and evaluate a strategy for HCV vaccine trials termed PREDICTEE—Predictive Recruitment and Enrichment method balancing Demographics and Incidence for Clinical Trial Equity and Efficiency. PREDICTEE utilizes a survival analysis model applied to trial candidates, considering their demographic and injection characteristics to predict the candidate’s probability of HCV infection during the trial. The decision to recruit considers both the candidate’s predicted incidence and demographic characteristics such as age, sex, and race. We evaluated PREDICTEE using in silico methods, in which we first generated a synthetic candidate pool and their respective HCV infection events using HepCEP, a validated agent-based simulation model of HCV transmission among PWID in metropolitan Chicago. We then compared PREDICTEE to conventional recruitment of high-risk PWID who share drugs or injection equipment in terms of sample size and recruitment equity, with the latter measured by participation-to-prevalence ratio (PPR) across age, sex, and race. Comparing conventional recruitment to PREDICTEE found a reduction in sample size from 802 (95%: 642–1010) to 278 (95%: 264–294) with PREDICTEE, while also reducing screening requirements by 30%. Simultaneously, PPR increased from 0.475 (95%: 0.356–0.568) to 0.754 (95%: 0.685–0.834). Even when targeting a dissimilar maximally balanced population in which achieving recruitment equity would be more difficult, PREDICTEE is able to reduce sample size from 802 (95%: 642–1010) to 304 (95%: 288–322) while improving PPR to 0.807 (95%: 0.792–0.821). PREDICTEE presents a promising strategy for HCV clinical trial recruitment, achieving sample size reduction while improving recruitment equity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Evaluating Material Circular Efficacy of Waste-Management Scenarios Using PV ICE (PV in the Circular Economy) Tool

To meet the nation’s clean energy needs, it is projected that at least 1 TW of solar photovoltaics (PV) will be installed by 2050. To make this transition to solar energy sustainable, planning for a circular economy that maximizes reuse, repair, and recycle is crucial. The objective of this poster is to evaluate the circular efficacy of waste-management interventions, while considering social factors, using PV ICE, a modeling tool that quantifies future mass flows of PV based on dynamic, real-world data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗