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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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All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2020

Optimized Highly Efficient SSCB Using Organic Substrate Packaging for Electric Vehicle Applications

Solid State Circuit Breakers (SSCBs) are an attractive protection solution for their arcless current interruption and fast actuation speeds over mechanical breakers. This paper proposes a Bidirectional SSCB (BSSCB) with a thermally defined and digitally controlled current time profile for fault protection in EV and other low-voltage DC systems. The paper proposes an organic packaging approach utilizing flex circuitry to develop a reliable, cost-effective power module for BSSCBs. The paper studies transient heat transfer in the power modules using finite element analysis (FEA). An RC themal ladder network is extracted to define a fusing curve. To demonstrate and verify the design, a 1kV/50 A SiC MOSFET BSSCB prototype is fabricated and tested, having a power density of 60 W/cm3 and 4x reduction in form factor over presently researched breakers. Also, given are results for 750 V/150 A operation showing interruption in 2.4 μs.

organic power packaging↗
DOE OSTI2025

Durable, Ultra-Thin Diaphragms for Liquid Alkaline Water Electrolysis

W. L. Gore & Associates assembled a world class team (De Nora Tech, LLC; New York University; and thyssenkrupp nucera) to develop durable, ultra-low resistance diaphragms that dramatically improve system efficiency, dynamic response, and operational flexibility. To enable the use of ultra-thin diaphragms, an optimized electrode package was chosen as part of an electrode down select study. Ultra-thin diaphragms based on microporous polymer scaffolds imbibed with zirconia nanoparticles were developed and tested. Performance and durability on par with incumbent materials was demonstrated. A transparent electrolyzer to directly observe in-situ diaphragm-bubble interactions was designed and built, and simultaneous optical and electrochemical performance tests were performed. A Pressurized Alkaline Water Electrolysis (PAWE) single cell test station was designed to support advanced R&D in high-pressure electrolysis, particularly for evaluating novel electrode and separator configurations under elevated temperature and pressure operating conditions. Due to early project termination, the original scope could not be fully achieved.

08 HYDROGEN↗
DOE OSTI2020

Analysis of Exposure during Transportation of Waste to the Nevada National Security Site (NNSS) - 20445

Safety is a priority for the U.S. Department of Energy (DOE) Environmental Management (EM) Nevada Program. This commitment to safety is integrated into requirements and work procedures at DOE sites across the U.S. that generate low-level radioactive waste, mixed low-level radioactive waste, and classified waste that is transported to the Nevada National Security Site (NNSS) for permanent disposal. Generators of this waste are responsible for ensuring it is packaged and transported in a safe and compliant manner. These generators and their contracted commercial carriers must comply with U.S. Department of Transportation (DOT) regulations; the Nevada National Security Site Waste Acceptance Criteria (NNSSWAC); and other applicable federal, state, and local regulations and requirements. This includes packaging and shipping the waste to meet DOT regulatory radiation dose limits to ensure public safety during transportation. NNSS waste generators have a solid safety record for complying with these stringent regulations as demonstrated by the fact that no shipments exceeding DOT dose rate limits were received at the NNSS Area 5 Radioactive Waste Management Complex (RWMC) during FY 2014 through FY 2018. A 2005 study conducted by the Desert Research Institute validated shipments to the NNSS comply with DOT radiation dose limits. In response to recent Nevada stakeholder requests for an updated study, an analysis was conducted to verify and provide evidence that the public continues to be safe from exposure to radioactive waste shipment to the NNSS. Radiation exposure from radioactive waste shipments in transit to the NNSS was evaluated. Three studies were performed: 1. Confirm compliance of waste shipments to the DOT radiation limits using dose rate surveys taken at the generator site and at the disposal site. 2. Estimate the dose to the public from waste shipments using the dosimetry results for disposal site personnel by comparing time of exposure for various individuals/scenarios. 3. Estimate the truck driver dose using the receipt dose rate surveys and time of exposure, and from dosimetry results. Study 1 confirmed compliance with the DOT dose rate limits for a sample of waste shipments (123 of 1,038) received during fiscal year (FY) 2017. Study 2 showed that the average annual radiation equivalent dose for disposal site personnel during FY 2017 was approximately 6.7% [0.415 mSv (41.5 millirem)] of the annual average dose that a person living in the U.S. receives from all sources [6.2 mSv (620 mrem)]. Maximum dose to a member of the public, a person resting/sleeping in an adjacent truck at a rest area to a waste shipment, was 0.0166 mSv (1.66 mrem), which is less than the dose received from a person taking a round trip from the east coast to the west coast of the continental U.S. on an airplane [0.07 mSv (7 mrem) per round trip]. Study 3 showed that the truck driver who made the most shipments of radioactive waste to the NNSS, who drove the most hours (estimated time of exposure 2,520 hours), during FY 2017 had a total dose of 0.15 mSv (15 mrem), which was obtained from their personal dosimeter. The annual dose recorded for disposal site personnel, the dose estimates for the public, and the truck driver dose were all a small percentage of the average radiation dose a person receives from all sources (e.g., background and medical radiation). At these levels, risk should only be described qualitatively. Radiation exposure from radioactive waste shipments to the NNSS were shown to be compliant with the DOT dose rate limits, safe, and very low (i.e., within background levels). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Solid-State Mixed-Potential Electrochemical Sensors for Natural Gas Leak Detection and Quality Control (Final Technical Report)

Mitigation of methane emissions are a critical factor to limiting the impact of the natural gas industry on global climate change. Throughout the period of 2020-2024, the University of New Mexico and its commercialization partner and subcontractor, SensorComm Technologies, Inc. (SCT), have worked together to develop a low-cost Artificial Intelligence (AI)-driven Internet of Things (IoT)-based multi-gas sensor platform for methane emissions detection. In the final year of the project, we extended this work to include hydrogen detection in support of a transition to a hydrogen economy where hydrogen could be transported through existing natural gas infrastructure. Mixed potential electrochemical sensors were first prototyped by ceramic additive manufacturing and then transitioned to conventional ceramic manufacturing tape casting and screen-printing technologies in preparation for mass production. Demonstrated limits of detection of 5 ppm of methane in natural gas and 1 ppm of hydrogen were measured. These limits of detection are among the lowest of solid-state electrochemical sensors that have been reported in the literature or available in the industry. Machine learning algorithms were developed to identify natural gas mixtures with > 98% accuracy level and quantify methane concentrations at 97% accuracy. The presence of hydrogen could also be identified, and its concentration quantified at these accuracy levels. These algorithms were optimized for running on portable computing hardware which enabled > 1 Hz processing rates. A portable packaged IoT system was integrated with the electrochemical sensor in collaboration with SCT. The package consists of readout electronics with < 1 mV resolution, sensor temperature control, and data transmission over cellular wireless and/or Wi-Fi networks. Field testing was performed in two rounds at Colorado State University’s Methane Emissions Technology Evaluation Center (CSU METEC). The first round of testing demonstrated successful measurements of methane from an underground natural gas leak of 20 standard liters per minute (SLPM), which agreed with previously published literature using more sophisticated and expensive analytical equipment. The second round of testing showed that an above ground leak of 2 SLPM of hydrogen could be detected at 32 ft. This project has resulted in six published peer reviewed journal articles, over ten presentations at professional conferences, and one full patent application filed in 2023. Future work on this project includes increased sensitivity, higher production yields, and applications in the hydrogen safety and flare emissions monitoring spaces.

03 NATURAL GAS↗
DOE OSTI2023

Impact of genotype‐calling methodologies on genome‐wide association and genomic prediction in polyploids

Abstract Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost‐efficient genotyping using next‐generation sequencing. However, accurate genotype calling from next‐generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome‐wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype‐calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum , and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype‐calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost‐efficiently implement GWAS and genomic prediction in polyploid crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Energy cost savings and expected payback for Re-tuning the controls of US Army buildings

The Headquarters Department of the Army (HQDA) sponsored a study to determine the national impact of deploying the Re-tuning™ methodology in 5 buildings types that account for over 40% of the Army’s conditioned building stock. Re-tuning is a systematic process that improves operational efficiency and reduces energy consumption at no- or low-cost through the building automation system (BAS) by correcting operational problems that plague buildings. The study relied on successful demonstration of the re-tuning methodology at 4 pilot US Army installations that informed a holistic effort that included simulating 12 individual re-tuning energy efficiency measures (EEMs) and 6 packages of EEMs in 5 selected Army building prototype models that represent 311 million ft2 (28.9 million m2) of the Army’s conditioned floor space. Both the baseline buildings and the packages were customized to capture the expected outcomes of re-tuning a diverse set of buildings. The study highlighted the benefit of individual re-tuning EEMs and economics of implementing packages of EEMs in applicable Army buildings across 16 climates and 2 building vintages. The average whole-building energy savings ranged from 10.8% to 40.5% by building type, with the company operations facility (COF) building having the highest value proposition from re-tuning. In addition, the study reveals that all large office (LO) buildings in the Army are economical to re-tune. The total modeled cost savings potential for re-tuning the five building types across the US Army is $204/1,000 sf ($220/100 m2), or $64M annually. This cost savings represents 5.6% of all the Army’s energy expenditures.

Fernandez, Nicholas EP↗
DOE OSTI2020

SAS PDF: pair distribution function analysis of nanoparticle assemblies from small-angle scattering data

SASPDF, a method for characterizing the structure of nanoparticle assemblies (NPAs), is presented. The method is an extension of the atomic pair distribution function (PDF) analysis to the small-angle scattering (SAS) regime. The PDFgetS3 software package for computing the PDF from SAS data is also presented. An application of the SASPDF method to characterize structures of representative NPA samples with different levels of structural order is then demonstrated. The SASPDF method quantitatively yields information such as structure, disorder and crystallite sizes of ordered NPA samples. The method was also used to successfully model the data from a disordered NPA sample. The SASPDF method offers the possibility of more quantitative characterizations of NPA structures for a wide class of samples.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

eDNAjoint: An R package for interpreting paired or semi‐paired environmental DNA and traditional survey data in a Bayesian framework

Abstract Environmental DNA (eDNA) sampling is increasingly used in surveys of species distribution as a potentially sensitive and efficient monitoring method. Yet access to modelling tools designed specifically for interpreting this new data type lags behind its ubiquity. While occupancy modelling software has dominated the analytical landscape for eDNA data analysis of single species, this type of model may not always be the most appropriate. The rate of eDNA detection often corresponds to species density, rather than just occupancy, and researchers often have access to observations from non‐genetic sampling methods at the same sites. To provide users access to a modelling framework designed to maximize the use of all available data, we developed an R package, eDNAjoint . The package provides an easy‐to‐use interface for fitting a ‘joint’ model that integrates data from paired or semi‐paired eDNA and traditional surveys in a Bayesian framework. The model can be used to estimate parameters like the probability of a false positive eDNA detection and mean catch rate at a site, and the package allows access to multiple model variations and Bayesian prior customization. Additional functionality can be used for model selection, summarising posteriors and comparing the relative sensitivities of the two survey methods. We demonstrate the use of eDNAjoint by fitting a variation of the model with site‐level covariates that scale the sensitivity of eDNA sampling relative to traditional sampling. The example workflow uses binary eDNA and seine count data for the endangered tidewater goby ( Eucyclogobius newberryi ) from a study by Schmelzle and Kinziger (2016). This use case includes a prior sensitivity analysis and an evaluation of the relationship between detection rates and environmental variables. eDNAjoint has the potential to greatly increase the range of users who will be able to rigorously analyse eDNA and traditional survey data in a Bayesian framework, understand if and how eDNA can improve monitoring practices, and gain confidence in the interpretability of eDNA data.

Keller, Abigail G. [Department of Environment Scie↗
DOE OSTI2021

Investigating Wireless Quantum Key Distribution for Advanced Reactor Communications

Remote operation of small modular reactor (SMR) facilities is an appealing prospect for streamlined operation costs, safety concerns, and user convenience. The ability to guarantee security of communication channels between offsite users and nuclear reactor facilities is critical to enabling remote operations, given the sensitive nature of reactor state data. This report proposes a configuration implementing quantum key distribution (QKD) and advanced encryption standard (AES) protocols to establish secure reactor data feeds. The model outlined, which utilizes the Argonne-developed SeQUeNCe software package and custom MATLAB code, determines free-space channel losses for microwave-band communications in a variety of weather conditions and simulates QKD via ground-satellite links. This report demonstrates the feasibility of repeatable, real-time key generation and distribution for this configuration towards enabling secure wireless reactor communications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Microtopographic and Depth Controls on Active Layer Chemistry in Arctic Polygonal Ground: Supporting Data

Polygonal ground is a signature characteristic of the Arctic, and permafrost thaw can potentially generate substantial feedbacks to Arctic ecosystems and climate. This study describes the first comprehensive spatial examination of active layer biogeochemistry that extends across high- and low-centered polygons and their features, including depth. Water chemistry measurements were made on active layer water samples collected near Barrow, Alaska during summer, 2012. Several significant differences in chemistry were observed between high- and low-centered polygons suggesting polygon types may be useful for landscape-scale geochemical classification. However, differences were found for polygon features (centers and troughs) for analytes that were not significant for type, suggesting that finer scale features control biogeochemistry in a different way than polygon type. Depth variations were also significant, demonstrating important multi-dimensional aspects of polygonal ground biogeochemistry. These results have major implications for understanding how polygonal ground ecosystems function, and how they may respond to future change. This data package includes one *.csv data file and one *.pdf user guide. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Determination of the spent fuel decay heat with the VERA core simulator

VERA core simulator capabilities to predict spent fuel decay heat have been explored. The comparison with the Serpent2 and NEWT/TRITON sequence from the SCALE package, performed on the NPP Krško fuel test cases, has enabled independent verification of the successful implementation of the VERA depletion/decay calculation sequence applied on the extended nuclide set. Since the VERA is a 3-D core simulator, the focus in the rest of the paper was on the investigation of the 3-D effects and non-linearity of the soluble boron concentration averaging process, which are difficult to assess with most of available computational tools. A difference in the decay heat prediction between the 3-D and 2-D model approaches was analyzed. In addition, the impact of the Inconel grids on the decay heat was determined. Averaging the concentration of soluble boron over the fuel irradiation period has been found to be a viable approach, since it results in slight decay heat overprediction, which is considered conservative for most applications. Finally, demonstration on the Watts Bar Unit 1 cycle 1 depletion has shown formidable VERA capabilities to accurately predict spent fuel decay heat.

Kromar, Marjan↗
DOE OSTI2025

Spotlight: efficient automated global optimization in rietveld analysis of diffraction data

Performing reliable Rietveld analysis on tens or hundreds of powder diffraction datasets from parametric or time-resolved experiments often poses a bottleneck in extracting meaningful results from the data. While automated analysis of data has recently been demonstrated, high temperature annealing studies, during which phase transformations occur and lattice parameters may change due to repartitioning of elements, are prime examples where automation by a simple phase identification from a database of room temperature structures or automation by sequential refinements is likely to fail. To enable reliable, efficient, automated Rietveld analysis, we present a Python package named Spotlight , building on established Rietveld packages such as MAUD, GSAS , or GSAS-II , which extends the refinement of best fit parameters to a global optimization using an ensemble of optimizers leveraging hierarchical parallel execution on high-performance computing clusters. Spotlight further enables the efficient design of refinement plans through the iterative automated machine-learning of a surrogate for the refinement on which the global optimizations are performed until results from the surrogate converge to the response surface data. We demonstrate Spotlight with the analysis of uranium molybdenum and Ti–6Al–4V datasets, as well as in two open-source tutorials analyzing aluminium oxide and lead sulphate.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structure, Function, and Application of Self–Healing Adhesives from Mistletoe Viscin

Berries from the European Mistletoe (Viscum album) possess a sticky tissue called viscin that facilitates adhesion and germination onto host trees. Recent studies of viscin have demonstrated its adhesive capacity on a range of natural and synthetic surfaces including wood, skin, metals, and plastic. Yet, the underlying mechanisms remain poorly understood. Here, an investigation of the adhesive performance of mistletoe viscin is performed, demonstrating its hygroscopic nature and ability to self-heal following adhesive failure. It is identified that adhesion originates from a water-soluble adhesive component that can be extracted, isolated, and characterized independently. Lap shear mechanical testing indicates that the mistletoe adhesive extract (MAE) outperforms native viscin tissue, as well as gum arabic and arabinogalactan—common plant-based adhesives. Furthermore, humidity uptake experiments reveal that MAE can reversibly absorb nearly 100% of its mass in water from the atmosphere. In-depth spectroscopic and mass spectrometry investigations reveal a composition consisting primarily of an atypical arabinogalactan, with additional sugar alcohols. Finally, several proof-of-concept applications are demonstrated using MAE for hygro-responsive reversible adhesion between various surfaces including skin, plastic, PDMS, and paper, revealing that MAE holds potential as a biorenewable and reusable adhesive for applications in cosmetics, packaging, and potentially, tissue engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2025

HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network

Machine learning (ML) is emerging as a promising tool for modeling hydro-ecological processes due to the increasing availability of large environmental data. However, the use of ML requires sufficient programming knowledge due to a lack of a graphical user interface (GUI). In this study, we introduced a GUI package, named HydroEcoLSTM, with the long short-term memory network (LSTM) as the core model, that allows non-ML experts to utilize their domain knowledge to construct complex ML models. We demonstrated the functionalities of HydroEcoLSTM with two practical examples, including (1) predictions of streamflow in both gauged and ungauged catchments and (2) predictions of multiple outputs (i.e., streamflow and isotope transport from two catchments). The simulation results obtained in both case experiments are satisfactory. In the first example, the average Nash–Sutcliffe Efficiency (NSE) for streamflow simulation during the testing period is 0.79 while the application of the trained model in two assumed ungauged catchments also achieves the average NSE of 0.68. In the second example, the average NSE for streamflow and instream isotope simulation during the testing period is 0.71. Ultimately, applications of HydroEcoLSTM with real-world examples demonstrate its potential use for practical applications and research without requiring extensive coding skills.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

An Innovative Fault Impact Analysis Framework for Enhancing Building Operations

The main goal of this paper is to rank building faults based on their impacts on the building energy penalty and occupant thermal comfort penalty considering multiple faults and fault occurrence rates. A fault impact analysis framework is created by incorporating the fault model library with the whole building energy performance simulation (e.g., EnergyPlus used in this study). The fault occurrence rate is introduced as a 'meta' parameter in the simulation. This framework involves three essential aspects of conducting a fault impact analysis: fault constructing, fault simulation, and fault impact analysis. A parametric sensitivity analysis was used to determine and rank the criticality of the faults considering the fault concurrence frequency, by using the deep-learning based response surface model (i.e., the multi-layer perceptron regression). The proposed fault analysis framework with ranking was tested and demonstrated for the DOE's prototype medium-sized office in four different climate zones (i.e., Atlanta, Chicago, Miami, and San Francisco) with 12,000 EnergyPlus fault simulations. A total of 129 fault modes from 41 groups of fault models were simulated for the medium-sized office case. The results demonstrate the proposed framework is robust and scalable for the fault impact analysis. The top critical fault for the medium-sized office is the fault of HVAC-Left-ON for the packaged rooftop unit, regarding the site energy, source energy, and HVAC energy. Excluding the fault of HVAC-Left-ON, the top critical faults vary significantly among the four climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Collective mode Brownian dynamics: A method for fast relaxation of statistical ensembles

Sampling equilibrium configurations of correlated systems of particles with long relaxation times (e.g., polymeric solutions) using conventional molecular dynamics and Monte Carlo methods can be challenging. This is especially true for systems with complicated, extended bond network topologies and other interactions that make the use and design of specialized relaxation protocols infeasible. We introduce a method based on Brownian dynamics simulations that can reduce the computational time it takes to reach equilibrium and draw decorrelated samples. Importantly, the method is completely agnostic to the particle configuration and the specifics of interparticle forces. In particular, we develop a mobility matrix that excites non-local, collective motion of N particles and can be computed efficiently in $\mathcal{O}(N)$ time. Particle motion in this scheme is computed by integrating the overdamped Langevin equation with an Euler–Maruyama scheme, in which Brownian displacements are drawn efficiently using a low-rank representation of the mobility matrix in position and wave space. We demonstrate the efficacy of the method with various examples from the realm of soft condensed matter and release a massively parallel implementation of the code as a plugin for the open-source package HOOMD-blue [J. A. Anderson et al., J. Comput. Phys. 227, 5342 (2008) and J. Glaser et al., Comput. Phys. Commun. 192, 97 (2015)] which runs on graphics processing units

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗