Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “emerging technologies”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Simulating Discharge Curves of an All-Aqueous TRAB to Identify Pathways for Improving System Performance

Thermally regenerative ammonia batteries (TRABs) are an emerging technology that use low temperature heat (T < 150 °C) to recharge a flow battery that produces electrical power on demand. The all-aqueous copper TRAB can provide high power densities and thermal energy efficiencies relative to other devices that harvest energy from waste heat, but its performance is adversely impacted by the crossover of undesired species through the membrane and lower cell voltages compared to conventional batteries. In this work, we developed a numerical model to simulate discharge curves while accounting for crossover inefficiencies without tracking all electrolyte species through the membrane. The model was able to successfully reproduce discharge curves across a diverse range of battery conditions using a single fitting parameter to account for decay of electrode standard potential due to species crossover with minimal error (< 5%). The model was then used to simulate different design scenarios to estimate changes in energy output from alterations to the aspects of the battery electrolyte chemistry. Results from this study are used to identify pathways for improving future TRAB designs with respect to energy capacity and cost-effectiveness of the technology.

25 ENERGY STORAGE↗
DOE OSTI2023

Considerations for Electrochemical Phosphorus Precipitation: A Figures of Merit Approach

Electrochemical phosphorus precipitation (EPP) from wastewater is a promising emerging technology for recovering valuable nutrients. While there are significant advantages of EPP compared to traditional phosphorus recovery, large gaps in reported performance exist between EPP methods and between EPP and industrial methods. Herein we discuss Figures of Merit (FOM) to normalize and report EPP performance at low-to-intermediate technology readiness levels (TRLs). Here, the appropriate use of FOM in electrochemical engineering enables better comparison between technologies, enhanced understanding of electrochemical and mass transport phenomena, and faster scale-up and adoption of nascent technologies.¬ FOM specific to EPP are discussed along with important considerations and adaptations from traditional electrochemical engineering FOM. Importantly, this FOM approach may be adapted for many different electrochemical processes and technologies, aiding in the push toward and adoption of electrification in chemical processing.

42 ENGINEERING↗
DOE OSTI2020

DEPRECATED EVOLVE [SWR-20-79]

DEPRECATED This repository was archived by the owner on Jun 30, 2026. It is now read-only. EVOLVE is a combination of a dashboard developed in React, which enables user to visualize the net load evolution with emerging technologies such as PV, energy storage, and electric vehicles, and a Python backend processes data. A user can choose settings for solar, energy storage, and electric vehicles in the dashboard. Modules that are developed to model the impact of these DERs can also be used independently without being integrated into the dashboard. We used a RESTful API to expose these modules to the React front-end.

Duwadi, Kapil↗
DOE OSTI2021

WaterTAP v1.0.0

WaterTAP is a new open source library of water-specific models built on the IDAES Integrated Platform to support the design and optimization of integrated water treatment systems, improve existing systems and enable the analysis of new designs incorporating emerging technologies. WaterTAP enables advanced modeling and optimization for: conceptual and multiscale design, dynamics and model predictive control of water systems and networks.

Beattie, KeithS↗
DOE OSTI2025

Advancing Urban Water Resilience: Coproducing Knowledge through Civic–Academic Global Partnerships on Water and Climate

As extreme weather events become more pronounced, the vulnerabilities associated with the urban water supply and wastewater systems in megacities are intensified in multiple interconnected dimensions. These multifaceted water challenges can benefit from enhanced cross-sectoral collaboration and sharing of critical knowledge, which are essential for sustainable and adaptive water governance frameworks. In this context, the Megacity Alliance for Water and Climate (MAWAC)–Europe and North America Region (ENAR) Working Group convened a workshop in March 2023, followed by a subsequent workshop in London, United Kingdom, from 11 to 13 September 2024. These workshops aimed to investigate and devise solutions for the cascading hazards with water systems. The solutions examined various aspects focused on climate adaptation and mitigation, stormwater management, and the governance of water and wastewater systems. Additionally, discussions highlighted the importance of community engagement, economic considerations, equity, and effective communication in addressing these pressing challenges. Over the course of 3 days, experts from academia, government agencies, and industry engaged in meaningful discussions on digital modeling for integrated water management, climate-informed urban planning, and public–private–academic partnerships (Fig. 1). Case studies from cities such as New York, Los Angeles, London, Paris, and Chicago highlighted innovative governance strategies for managing water and wastewater systems, promoting water reuse, planning infrastructure, and fostering stakeholder-driven and stakeholder-informed adaptation. The workshop participants emphasized the need for data-driven decision-making, scalable governance models, and knowledge-sharing networks to enhance urban water governance for sustainability and resilience. This workshop report presents the key takeaways from the 3-day convening, providing a roadmap for integrating scientific research, policy frameworks, and emerging technologies to address water challenges faced by megacities.

Hydrologic models↗
DOE OSTI2022

ASPIRES: Airport Shuttle Planning and Improved Routing Event-driven Simulation

Most of the existing traffic simulation packages require significant calibration work to be able to reflect reality. To evaluate special operations including emerging technologies, a microscopic simulation that tracks detailed interactions of all the elements of the traffic systems is usually needed. This type of simulation is usually computationally demanding. This work developed an Airport Shuttle Planning and Improved Routing Event-driven Simulation (ASPIRES) package to simulate and evaluate current, potential, and future airport shuttle operations. Here, the simulation was driven by data and thus did not require much calibration effort. The discrete-event simulation nature of ASPIRES makes the simulation computationally efficient. Simulating 1 day of shuttle operations takes less than 2s. The study site of this work is the Dallas/Fort Worth International Airport in the U.S. The shuttle service that connects the five terminals of the airport and the rental car center was studied. Travel times, dwell times, and passenger arrivals were simulated using empirical distributions derived mainly from real data to capture the stochastic nature of the rental car center shuttle bus operations. Data on bus miles traveled, bus energy consumption, passenger wait times, and passengers left behind at stops were collected to study the trade-off between energy use and passenger experience. Electric bus and on-demand bus operations were also included. The simulation outputs can show passengers statistics at terminals, shuttles statistics, and charging station statistics. ASPIRES cannot be used to model a generic traffic system but is well-suited for fleet systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Optimizing Patient-Specific Medication Regimen Policies Using Wearable Sensors in Parkinson’s Disease

Effective treatment of Parkinson’s disease (PD) is a continual challenge for healthcare providers, and providers can benefit from leveraging emerging technologies to supplement traditional clinic care. We develop a data-driven reinforcement learning (RL) framework to optimize PD medication regimens through wearable sensors. We leverage a data set of n = 26 PD patients who wore wrist-mounted movement trackers for two separate six-day periods. Using these data, we first build and validate a simulation model of how individual patients’ movement symptoms respond to medication administration. We then pair this simulation model with an on-policy RL algorithm that recommends optimal medication types, timing, and dosages during the day while incorporating human-in-the-loop considerations on medication administration. The results show that the RL-prescribed medication regimens outperform physicians’ medication regimens, despite physicians having access to the same data as the RL agent. To validate our results, we assess our wearable-based RL medication regimens using n = 399 PD patients from the Parkinson’s Progression Markers Initiative data set. We show that the wearable-based RL medication regimens would lead to significant symptom improvement for these patients, even more so than training RL policies directly from this data set. In doing so, we show that RL models from even small data sets of wearable data can offer novel, generalizable clinical insights and medication strategies, which may outperform those derived from larger data sets without wearable data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Snapshot multifocal light field microscopy

Light field microscopy (LFM) is an emerging technology for high-speed wide-field 3D imaging by capturing 4D light field of 3D volumes. However, its 3D imaging capability comes at a cost of lateral resolution. In addition, the lateral resolution is not uniform across depth in the light field dconvolution reconstructions. To address these problems, here, we propose a snapshot multifocal light field microscopy (MFLFM) imaging method. The underlying concept of the MFLFM is to collect multiple focal shifted light fields simultaneously. We show that by focal stacking those focal shifted light fields, the depth-of-field (DOF) of the LFM can be further improved but without sacrificing the lateral resolution. Also, if all differently focused light fields are utilized together in the deconvolution, the MFLFM could achieve a high and uniform lateral resolution within a larger DOF. We present a house-built MFLFM system by placing a diffractive optical element at the Fourier plane of a conventional LFM. The optical performance of the MFLFM are analyzed and given. Both simulations and proof-of-principle experimental results are provided to demonstrate the effectiveness and benefits of the MFLFM. We believe that the proposed snapshot MFLFM has potential to enable high-speed and high resolution 3D imaging applications.

He, Kuan↗
DOE OSTI2023

Statistical methods for design and testing of 3D-printed polymers

Different statistical methods are used in various fields to qualify processes and products, especially in emerging technologies like Additive Manufacturing (AM) or 3D printing. Since several statistical methods are being employed to ensure quality production of the 3D-printed parts, an overview of these methods used in 3D printing for different purposes is presented in this paper. The advantages and challenges, to understanding the importance it brings for design and testing optimization of 3D-printed parts are also discussed. The application of different metrology methods is also summarized to guide future researchers in producing dimensionally-accurate and good-quality 3D-printed parts. Here, this review paper shows that the Taguchi Methodology is the commonly-used statistical tool in optimizing mechanical properties of the 3D-printed parts, followed by Weibull Analysis and Factorial Design. In addition, key areas such as Artificial Intelligence (AI), Machine Learning (ML), Finite Element Analysis (FEA), and Simulation require more research for improved 3D-printed part qualities for specific purposes. Future perspectives are also discussed, including other methods that can help further improve the overall quality of the 3D printing process from designing to manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Phase I Final Technical Report on Energy-Efficient Reconfigurable Universal Accelerator Interconnect

It is well known that application specific computing systems, optimally designed and configured for a given workload, offer much higher energy-efficiency and throughput than general purpose systems. In modern computing systems, heterogeneous computing systems have emerged that exploit the energy and performance benefits of combining various different domain-specific processor architectures. Application domains such as high-performance computing and machine learning now process terabyte-sized data sets, requiring enormous processing and memory resources. These applications have very high-power consumption due to bottlenecks in the electrical interconnection between processing units. This project aims to reduce both communication energy and latency by integrating universally available accelerators with silicon photonics. It also aims to increase system throughput by exploiting emerging technologies in silicon photonic reconfigurable interconnects; this will allow the system to balance itself in real time to accommodate changes in workloads and data flows.

2.5D/3D integration↗
DOE OSTI2020

Workshop on Improving Holdup Monitoring in the US

The characterization and quantification of nuclear deposits, or residual nuclear material retained in process equipment, generally referred to as holdup, continues to challenge nuclear processing facilities in DOE program areas such as nuclear criticality safety, material control and accountability, environmental management. The efficiency and effectiveness of nuclear operations in these facilities depends heavily on the results from nondestructive techniques designed to measure nuclear materials in situ. The traditional methods used to measure holdup were established more than 30 years ago and assume unrealistic conditions for most measurement cases. These blind, in situ measurements present unique challenges due to the following attributes: unique, non-ideal geometries; unknown deposit thicknesses; lack of representative calibration standards; large number of holdup deposit locations; and poor accessibility for many measurement locations. These difficulties and the poor assumptions made to handle them have resulted in measurement uncertainties that are often too large for many programs to effectively utilize. In an effort to address and identify solutions to the current holdup challenges facing the US nuclear industry, Oak Ridge National Laboratory hosted the Technical Workshop on Improving Holdup Monitoring in the US. The two-day workshop held August 21–22, 2019, was attended by 35 holdup measurement practitioners, program managers, stakeholders from US Department of Energy sites, the Nuclear Regulatory Commission, and representatives from commercial industry. The primary objectives were to identify critical elements of a successful and defensible holdup measurement program; identify technical challenges, needs, and potential improvements associated with measurement of holdup; and share best practices and lessons learned from recent accumulation events. Fourteen presentations were delivered on holdup topics including necessary elements for effective program management, measurement needs, emerging technologies and other measurement solutions, and lessons learned from recent events. These presentations highlighted best practices related to accumulation monitoring, considerations and proactive steps to facilitate holdup in new facility designs, imaging solutions to improve holdup assumptions, and new software developed in support of holdup measurements. Breakout sessions were also employed to determine additional needs and identify potential solutions to common holdup challenges. The primary discussions focused on causes and prevention of material accumulations, needs for uranium and plutonium measurements, and reduction of measurement uncertainty, all of which were explicitly identified as critical needs during the 2018 Workshop on Technical and Programmatic Needs for a Sustainable NDA Program for the US DOE.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

The Value of Motorcycle Electrification in Mexico City

Local and city governments worldwide are looking to adopt emerging technologies and mobility approaches, such as electric vehicles, as they pursue their environmental and economic goals. Commercially available electric vehicles include buses, automobiles, motorcycles, and other two-wheelers. Electrifying transportation is critical to achieve urban sustainability, but it also raises challenges for private and public actors. Jurisdictions across countries are seeking to answer big questions, including how the transportation sector can be electrified, what impacts are priorities, and how such impacts can be obtained.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Data Science and Computation for Rapid and Dynamic Compression Experiment Workflows at Experimental Facilities, September 8-11, 2020. Workshop Report

The application of high pressure to materials has enabled discoveries in scientific fields such as planetary science, materials science, and materials synthesis. Recent advances in X-ray user light sources and other facilities, co-location and integration of user facilities with high-pressure drivers, availability of high-performance computing (HPC) platforms, and the development of new data science techniques have created opportunities for, and challenges in, advancing data analytics for rapid and dynamic compression experiments. To address these challenges, harness the emerging technology now available, and expedite scientific discovery, Los Alamos National Laboratory (LANL) hosted a virtual workshop entitled “Data Science and Computation for Rapid and Dynamic Compression Workflows at Experimental Facilities” from September 8 to 11, 2020. The workshop included 95 registered scientists and analytics experts from 15 universities, 9 United States (US) national laboratories, 5 US and European X-ray light sources, neutron sources such as the Los Alamos Neutron Science Center (LANSCE), other big science facilities such as the National Ignition Facility (NIF), and an industry representative. The workshop included 31 invited talks and 4 lightning talks by students and postdocs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

PV CAMPER Annual Report 30 December 2020

The objective of the Photovoltaic Collaborative to Advance Multi-climate and Performance Research (PVCAMPER) is to: 1) Build and maintain a multi-climate research platform to enable pioneering photovoltaic research; 2) Validate the performance of emerging technologies in specific climates; 3) Help accelerate the world’s transition to a solar-intensive economy. Our focus in achieving those goals is to foster collaborative research and to build an international organization dedicated to improving data quality, minimizing measurement uncertainty and exchanging best practices related to PV performance.

14 SOLAR ENERGY↗
DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Energy Efficiency and Performance Evaluation of Self-Contained, Medium Temperature, Reach-In Refrigerated Display Cases

This project was part of an effort by Commonwealth Edison Company (ComEd) to evaluate the energy and peak demand saving potential of emerging technologies in the Chicago area. This document focuses on the assessment of energy-efficient, medium-temperature, self-contained refrigerated display cases utilizing environmentally friendly refrigerants. The results of this evaluation will be considered by CLEAResult to develop a new energy efficiency rebate measure for ComEd's incentive programs. This rebate measure will become an addition to the Technical Reference Manual (TRM). In 2016, the United Nations passed the Kigali Montreal Protocol Amendment which placed restrictions on certain types of refrigerants. In compliance with this amendment, the US Environmental Protection Agency (EPA) placed a ban on the manufacture of refrigeration systems using hydrofluorocarbons including R134a starting in January 2020. Although the ban has halted manufacture, the EPA continues to allow the use of these refrigerants. Therefore, it is critical to provide incentives for replacing these refrigerants with other environmentally friendly and energy-efficient alternatives. The energy-efficient refrigerator cases evaluated here (referred to as EE Case A and B) contain environmentally-friendly refrigerants in compliance with the EPA hydrofluorocarbon ban. These consist of natural refrigerant propane (R290), and HFC drop-in hydrofluoroolefin R513a, respectively. These cases also contain other energy-efficient components including efficient lighting, more robust evaporator and condenser fans and different-sized heat exchangers. EE Case A is also built with materials that better insulate the case, which improves energy efficiency. To ascertain the energy efficiency contribution of these design components, the consumption of the evaporator and condenser fan motors, compressor, and lighting/controller were evaluated individually. The medium-temperature, self-contained reach-in refrigerated display case was selected due to its widespread use in convenience stores and small supermarkets. Self-contained refrigeration has also seen increased use in restaurants due to curbside pickup during the COVID-19 pandemic. For this study, the refrigerated display cases' performance was evaluated in a controlled environmental chamber at representative indoor dry-bulb and humidity conditions found in supermarkets within ComEd's service territory climate zone. The test method used in this project was foundationally inspired by the ANSI/ASHRAE 72-2018 method of testing. However, modifications to the ANSI/ASHRAE test methodology were implemented to better represent customer operation of the units. In addition to the indoor supermarket conditions, the cases were also evaluated at the "upper target," or environmental conditions used in ANSI/ASHRAE 72-2018. The case total power and case components were metered to obtain their daily power (kW) and energy consumption(kWh). The cases were filled with thermal filler mass to replicate thermal mass of product loading. Additionally, product simulators were used to provide product temperature information. Door actuators were mounted to each of the cases' three doors to replicate regular door openings and effects of shopper traffic.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Manipulating Quantum Systems: An Assessment of Atomic, Molecular, and Optical Physics in the United States

The AMO2020 decadal report took a forward-looking perspective on how the different elements that constitute AMO science provide a broad interdisciplinary driver for capitalizing on future opportunities in the scientific community. Goals for this project were to produce a report that (1) displays AMO science as a vital field that relates and unifies broad scientific endeavors; (2) discusses how AMO is supplying these and other fields with emerging technologies and fulfilling national needs; (3) identifies new opportunities, compelling scientific questions, and themes that have arisen from recent advances and accomplishments in the AMO field; (4) explains how AMO science meets workforce, educational, and other societal needs; and (5)makes recommendations for a strategy to fully realize the potential at the frontiers of AMO science. The study also compared the trajectory of AMO science in the US in the context of the international community of AMO science, in terms of cooperation, collaboration, and competition. The study resulted in a report composed in a style accessible to the non-scientist reader in order to understand how AMO science will lead the way in these fields and what AMO researchers want to learn in the coming decades and why.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Plant Single-Cell Solutions for Energy and the Environment (Second Workshop Report)

Plants are important sources of energy and materials, and they collectively represent a critical component of Earth’s ecosystem. With increasing environmental stresses due to climate change and intensive agricultural practices, the need for resilient plants is greater than ever before. To secure plant resources for bioenergy, biomaterials, food, and ecosystem adaptation, a deeper understanding of the fundamental biology of plants at a cellular level is urgently needed. Plants contain a multitude of specialized cell types that compose tissues and organs. Pathogens often target specific cell types within plants, and the response of one cell to a particular stimulus is likely to be distinct from its neighbor because of underlying molecular and contextual differences. Understanding how these responses are distributed among cells, the main goal of single-cell approaches, will substantially enhance our ability to use targeted engineering for improving plant productivity and resilience. Furthermore, single-cell approaches are necessary to understand the interactions between plants and other ecosystem members such as fungi, bacteria, and archaea. Unlocking these gene-response mechanisms at a cellular level can improve our ability to adapt plants to environmental stresses, increasing their utility as feedstocks for biomaterials and bioenergy. Recent advances in high-throughput sequencing, mass spectrometry, microfluidics and miniaturization, artificial intelligence and machine learning, and bioinformatics have greatly improved our ability to detect and understand processes at a cellular level. In mammalian systems, single-cell transcriptomics has already led to many advances, such as newly identified cell types and cell-targeted treatment of diseases, and mass spectrometry-based single-cell proteomics has recently been demonstrated as a promising emerging technology. However, plant single-cell omics has lagged behind mammalian approaches due to the high cost of the technologies relative to available resources and to the innate biological features of plants, including the complexity of the cell wall and polyploidy. To better understand how single-cell methods could enable plant science, Lawrence Berkeley National Laboratory (Berkeley Lab) hosted a workshop on April 29, 2021, that brought together a diverse group of leaders in plant and/or single-cell biology. Attendees represented federal research programs and domestic and international academic institutions. During the workshop, three presenters described the current state of research in both experimental and computational approaches. While the focus of the workshop was on factors preventing plant biology researchers from fully adopting single-cell methodologies, workshop participants agreed that most barriers could be overcome with focused, strategic investment and coordinated efforts among institutions leading to significant scientific discoveries that would be difficult to obtain using more conventional technologies.

59 BASIC BIOLOGICAL SCIENCES↗