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DOE OSTI2022

Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) for Transportation Hubs (Final Report)

This report summarizes the work performed under the award number EE0008524. The project develops the Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) platform as the next-generation transportation solution based on autonomous electric vehicles (AEV) serving passenger trips from and to urban transportation hubs, to substantially reduce transportation energy consumption. Extensive data collection and analyses were first conducted to understand the demand patterns and energy consumption of hub-based on-road trips. Then, a data-driven framework that consists of an analytical module and a simulation module was proposed. For the analytical module, five planning + operation tools were developed to support the planning and energy-efficient operations of urban AEV services: the charging station planning that robotically allocates charging supplies based on the stationary charging demand distribution; the transit planning and demand adaptive scheduling model that efficiently generates\ candidate transit routes from hubs to other places and dynamically adjusts the transit time table to fit the current demand; the online energy-efficient routing that learns the energy-optimal paths from observations of link-level energy consumption in real-time; the hub-based ridesharing that matches trip requests together with account for the uncertainty of future trip demand and vehicle supply; and finally, the integrated demand prediction and anomaly detection pipeline that leverages the flight/train time table and support other planning/operation tools. To demonstrate the performance of these tools, a scalable high-performance agent-based simulator was built. We divided the urban space into multiple service zones where each zone was considered as an agent for passenger generation and vehicle charging. Two types of AEV agents were coded to model two types of mobility services: AEV taxi and AEV transit. For the AEV taxi, the team implemented the functions of pickup/drop-off passengers, energy-efficient routing, ridesharing, fleet rebalancing, and recharging. For the AEV bus, the team implemented the functions of demand-adaptive route scheduling, passenger boarding, and recharging. A high-performance computing framework was introduced to receive various profiling information (such as link energy updates, vehicle speed) from the simulator instances and communicate the operational commands back to the instances. The numerical experiments show that each of the proposed operational algorithms can reduce energy consumption and improve system efficiency. Furthermore, there exists the need to collectively consider multiple planning + operational strategies as multiple strategies can influence each other in terms of performance impacts. Recommendations for future work related to AEV planning and simulation are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

AI Driven Optimization of Public Transit

This project explores the application of AI-driven methods to optimize public transit operations for the Chattanooga Area Regional Transportation Authority (CARTA). By leveraging data analytics, machine learning, and predictive modeling, the initiative seeks to enhance system efficiency, improve rider experience, and support sustainability goals. This research, supported by the National Science Foundation and the U.S. Department of Energy, integrates real-time transit data with advanced computational tools to inform decision-making, optimize routes, and balance operational demands. The work exemplifies a forward-looking model for mid-sized cities aiming to modernize mobility systems through intelligent technology integration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Quantification of Load Flexibility in Residential Buildings Using Home Energy Management Systems

With increasing penetration of renewable energy resources, the flexibility of operating behind-the-meter (BTM) resources plays a key role in enhancing grid reliability and resilience. Residential buildings with home energy management systems (HEMS) can provide desired flexibility for the distribution system operator (DSO) while considering customer comfort and preferences. This paper discusses a methodology to quantify the flexibility of BTM resources of residential buildings using HEMS. First, we propose a model predictive control framework to formulate the flexibility band comprising nominal, upper, and lower demand profiles. Second, the paper proposes a dispatch method for HEMS to compute the control signals for each BTM resource (e.g., air conditioner, water heater, home battery system) upon receiving a flexibility service request from the DSO. The case study provides insight into the flexibility provided at the whole-home level with different user preferences and seasons. The results demonstrate that HEMS is capable of providing flexibility service at the request of the DSO while delivering primary services to the building occupants.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2023

Modeling the U.S. Western Electric Interconnection to Understand the Consequences of Hydrometeorological Extremes and Options for Risk Mitigation

Electricity grid operators around the world face a dual challenge; withstanding increasingly severe weather and the longer term impacts of climate change, while simultaneously decarbonizing. Extreme weather events such as heat waves and droughts are rising in both severity and frequency, which is threatening the reliability of electricity grids through increased demand, generation capacity losses, and equipment failures. Consequently, incorporating hydrometeorological stressors into computational power systems analysis is becoming an even more critical tool in long term planning and short term operations. However, there is a general lack of open-source customizable grid simulation software capable of exhaustively stress testing the grid under hydrometeorological uncertainty, and/or examining potential risk mitigation pathways. A related, persistent challenge for power system modelers is striking an appropriate balance between model fidelity (e.g. spatial scale and time resolution) and computational tractability (wall clock run-time). In this study, we are proposing a solution to this problem with open-source software that allows users to seamlessly customize the scale and track the accuracy of grid operations models. Our approach allows users to search over numerous model parameters (network topology, mathematical formulation, economic hurdle rates, and transmission line scaling) to identify model instantiations that accommodate experimental design. Further, we use this approach to demonstrate the importance of including extreme weather events in model validation and model selection. Focusing on the occurrence of heatwaves and droughts in the U.S. Western Interconnection, we examine role of extreme events in balancing tradeoffs between model fidelity and run-time at the model design stage.

Economics↗

NASA NTRS1989

The internet worm

In November 1988 a worm program invaded several thousand UNIX-operated Sun workstations and VAX computers attached to the Research Internet, seriously disrupting service for several days but damaging no files. An analysis of the work's decompiled code revealed a battery of attacks by a knowledgeable insider, and demonstrated a number of security weaknesses. The attack occurred in an open network, and little can be inferred about the vulnerabilities of closed networks used for critical operations. The attack showed that passwork protection procedures need review and strengthening. It showed that sets of mutually trusting computers need to be carefully controlled. Sharp public reaction crystalized into a demand for user awareness and accountability in a networked world.

Denning, Peter J.↗

NASA NTRS1997

Mars Surveyor '98 Landers MVACS Robotic Arm Control System Design Concepts

This paper describes the control system design concepts for the Mars Volatiles and Climate Surveyor (MVACS) Robotic Arm which supports the scientific investigations to be conducted as part of the Mars Surveyor '98 Lander project. Solutions are presented to some of the problems encountered in this demanding space application with its tight constraints on mass, power, volume, and computing resources. Problems addressed include 4-DOF forward and inverse kinematics, trajectory planning to minimize potential impact damage, joint drive train protection, Lander tilt prevention, hardware fault monitoring, and collision avoidance.

Bonitz, Robert G.↗

NASA NTRS1997

Mars Surveyor '98 MVACS Robotic Arm Control System Design Concepts

control manipulator Mars robot trajectory 4-DOF↗

NASA NTRS2005

Insect-Inspired Flight Control for Unmanned Aerial Vehicles

Flight-control and navigation systems inspired by the structure and function of the visual system and brain of insects have been proposed for a class of developmental miniature robotic aircraft called "biomorphic flyers" described earlier in "Development of Biomorphic Flyers" (NPO-30554), NASA Tech Briefs, Vol. 28, No. 11 (November 2004), page 54. These form a subset of biomorphic explorers, which, as reported in several articles in past issues of NASA Tech Briefs ["Biomorphic Explorers" (NPO-20142), Vol. 22, No. 9 (September 1998), page 71; "Bio-Inspired Engineering of Exploration Systems" (NPO-21142), Vol. 27, No. 5 (May 2003), page 54; and "Cooperative Lander-Surface/Aerial Microflyer Missions for Mars Exploration" (NPO-30286), Vol. 28, No. 5 (May 2004), page 36], are proposed small robots, equipped with microsensors and communication systems, that would incorporate crucial functions of mobility, adaptability, and even cooperative behavior. These functions are inherent to biological organisms but are challenging frontiers for technical systems. Biomorphic flyers could be used on Earth or remote planets to explore otherwise difficult or impossible to reach sites. An example of an exploratory task of search/surveillance functions currently being tested is to obtain high-resolution aerial imagery, using a variety of miniaturized electronic cameras. The control functions to be implemented by the systems in development include holding altitude, avoiding hazards, following terrain, navigation by reference to recognizable terrain features, stabilization of flight, and smooth landing. Flying insects perform these and other functions remarkably well, even though insect brains contains fewer than 10(exp -4) as many neurons as does the human brain. Although most insects have immobile, fixed-focus eyes and lack stereoscopy (and hence cannot perceive depth directly), they utilize a number of ingenious strategies for perceiving, and navigating in, three dimensions. Despite their lack of stereoscopy, insects infer distances to potential obstacles and other objects from image motion cues that result from their own motions in the environment. The concept of motion of texture in images as a source of motion cues is denoted generally as the concept of optic or optical flow. Computationally, a strategy based on optical flow is simpler than is stereoscopy for avoiding hazards and following terrain. Hence, this strategy offers the potential to design vision-based control computing subsystems that would be more compact, would weigh less, and would demand less power than would subsystems of equivalent capability based on a conventional stereoscopic approach.

Thakoor, Sarita↗

NASA NTRSDate not supplied

Noise Impact Analysis for Urban Air Mobility in Dallas-Fort Worth Metroplex

This study supports re-design of airspace and procedures for management of Urban Air Mobility (UAM) fleet operations in a given metropolitan area. A goal of UAM is to maintain geographic separation of UAM flight tracks from densely populated areas in order to reduce the potential negative community impact of UAM and manage flight trajectory in anticipation of allowable noise levels and current land use. A UAM route inventory and a demographic-data-optimized topographic map are developed for the Dallas-Fort Worth metroplex to identify routes or baseline route segments for reduction of noise impact. Noise contours are computed for baseline route and alternative route design given the desired UAM demand level. The impact of UAM fleet noise is predicted based on predicted number of people and noise-sensitive facilities located inside the noise contours.

Urban Air Mobility↗

NASA NTRSDate not supplied

Noise Impact Analysis for Urban Air Mobility in Dallas-Fort Worth Metroplex

Urban Air Mobility↗

DOE OSTI2018

Hybrid memory module bridge network and buffers

Systems, apparatuses, and methods for implementing a hybrid memory module bridge network and buffers are disclosed. A system includes one or more host processors and multiple memory modules. Each memory module includes a relatively low pin count, high-bandwidth serial link to one or more other memory modules to perform inter-memory data transfers without consuming host-memory bandwidth. In one embodiment, a first memory module acts as a cache and a second memory module acts as the main memory for the system. The traffic between the host and the first memory module utilizes a first interface, and the cache traffic between the first and second memory modules utilizes a second interface. Cache line fill and writeback transfers between the first and second memory modules occur in parallel with timing-critical cache demand accesses from the host, in a latency-tolerant and buffered manner, without interfering with the cache demand accesses.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

An Integrated Approach to Scaling Task-Based Runtime Systems for Next Generation Engineering problems

The need to scale next-generation industrial engineering problems to the largest computational platforms presents unique challenges. Such problems may have complex coupled multi-physics models, as well as computationally and communication intensive algorithms not often seen in standard academic problems, while also needing high-demand I/O for analysis purposes. In such cases even codes with good existing scaling properties may need significant changes, addressed in a cross-cutting way to solve such problems at extreme scales. These challenges relate to system components that were not previously problematic on less complex problems and/or at smaller computational scales. This paper illustrates these challenges for Uintah, a highly scalable asynchronous many-task runtime system being applied to the modeling of a 1000 MWe ultra-supercritical coal boiler. In order to model this formidable, production engineering problem, not only was an integrated approach needed that built upon existing scalable components in areas such as complex stencil calculations and linear algebra, but the significant challenges of high demand I/O, complex task graphs, and infrastructure inefficiencies also had to be addressed. This integrated approach allowed this problem to run on 256K CPU cores on Mira, with good weak scaling to 512K CPU cores and similar strong scaling, except for radiation. The need for strong scaling of a new ray tracing-based radiation model required substantial Uintah infrastructure improvements before 119K CPU cores and 7.5K GPUs on Titan could be used, with scaling out to 256K CPU cores and 16K GPUs. The resulting code demonstrates not only excellent overall scalability but a significant improvement in overall performance for the full-scale, production boiler problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Scalable parallel distance field construction for large-scale applications

Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory machines. Anew distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking overtime, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from real-world applications. We demonstrate its efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. In conclusion, our work greatly extends the usability of distance fields for demanding applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Radiation Safety Information Computational Center (RSICC) [Slides]

This presentation touches on the Radiation Safety Information Computational Center (RSICC). This Lecture provides general mission, statistics, and strategies. A general background is provided of the tool. Licensee agreements and export control agreements are discussed. The presentation finishes with a look at the most in demand and requested software packages.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

The Future of Food Demand: Understanding Differences in Global Economic Models

Understanding the capacity of agricultural systems to feed the world population under climate change requires a good prospective vision on the future development of food demand. This paper reviews modeling approaches from ten global economic models participating to the AgMIP project, in particular the demand function chosen and the set of parameters used. We compare food demand projections at the horizon 2050 for various regions and agricultural products under harmonized scenarios. Depending on models, we find for a business as usual scenario (SSP2) an increase in food demand of 59-98% by 2050, slightly higher than FAO projection (54%). The prospective for animal calories is particularly uncertain with a range of 61-144%, whereas FAO anticipates an increase by 76%. The projections reveal more sensitive to socio-economic assumptions than to climate change conditions or bioenergy development. When considering a higher population lower economic growth world (SSP3), consumption per capita drops by 9% for crops and 18% for livestock. Various assumptions on climate change in this exercise do not lead to world calorie losses greater than 6%. Divergences across models are however notable, due to differences in demand system, income elasticities specification, and response to price change in the baseline.

climate change↗

NASA NTRS2003

Software for Allocating Resources in the Deep Space Network

TIGRAS 2.0 is a computer program designed to satisfy a need for improved means for analyzing the tracking demands of interplanetary space-flight missions upon the set of ground antenna resources of the Deep Space Network (DSN) and for allocating those resources. Written in Microsoft Visual C++, TIGRAS 2.0 provides a single rich graphical analysis environment for use by diverse DSN personnel, by connecting to various data sources (relational databases or files) based on the stages of the analyses being performed. Notable among the algorithms implemented by TIGRAS 2.0 are a DSN antenna-load-forecasting algorithm and a conflict-aware DSN schedule-generating algorithm. Computers running TIGRAS 2.0 can also be connected using SOAP/XML to a Web services server that provides analysis services via the World Wide Web. TIGRAS 2.0 supports multiple windows and multiple panes in each window for users to view and use information, all in the same environment, to eliminate repeated switching among various application programs and Web pages. TIGRAS 2.0 enables the use of multiple windows for various requirements, trajectory-based time intervals during which spacecraft are viewable, ground resources, forecasts, and schedules. Each window includes a time navigation pane, a selection pane, a graphical display pane, a list pane, and a statistics pane.

Wang, Yeou-Fang↗

DOE OSTI2019

Engine-Aftertreatment in Closed-Loop Modeling for Heavy Duty Truck Emissions Control

An engine-aftertreatment computational model was developed to support in-loop performance simulations of tailpipe emissions and fuel consumption associated with a range of heavy-duty (HD) truck drive cycles. For purposes of this study, the engine-out exhaust dynamics were simulated with a combination of steady-state engine maps and dynamic correction factors that accounted for recent engine operating history. The engine correction factors were approximated as dynamic first-order lags associated with the thermal inertia of the major engine components and the rate at which engine-out exhaust temperature and composition vary as combustion heat is absorbed or lost to the surroundings. The aftertreatment model included catalytic monolith components for diesel exhaust oxidation, particulate filtration, and selective catalytic reduction of nitrogen oxides (NOx) with urea. Both the engine and aftertreatment models have been calibrated with dynamometer measurements from a commercial 2010-certificated 15-L Cummins diesel engine. The fuel consumption engine map with the reduced data is attached in the appendix. Simulations with the combined engine and aftertreatment models above appear to reveal important trends among the fuel efficiency, emissions control, power demand for HD trucks under realistic drive cycle conditions. Thus, this type of computational simulation appears to have significant value in choosing among options for HD vehicle design and operation.

Gao, Zhiming↗

DOE OSTI2024

Integrating HPC, AI, and Workflows for Scientific Data Analysis: Report from Dagstuhl Seminar 23352

The Dagstuhl Seminar 23352, titled “Integrating HPC, AI, and Workflows for Scientific Data Analysis,” held from August 27 to September 1, 2023, was a significant event focusing on the synergy between High-Performance Computing (HPC), Artificial Intelligence (AI), and scientific workflow technologies. The seminar recognized that modern Big Data analysis in science rests on three pillars: workflow technologies for reproducibility and steering, AI and Machine Learning (ML) for versatile analysis, and HPC for handling large data sets. These elements, while crucial, have traditionally been researched separately, leading to gaps in their integration. The seminar aimed to bridge these gaps, acknowledging the challenges and opportunities at the intersection of these technologies. The event highlighted the complex interplay between HPC, workflows, and ML, noting how ML has increasingly been integrated into scientific workflows, thereby enhancing resource demands and bringing new requirements to HPC architectures, like support for GPUs and iterative computations. The seminar also addressed the challenges in adapting HPC for large-scale ML tasks, including in areas like deep learning, and the need for workflow systems to evolve to leverage ML in data analysis fully. Moreover, the seminar explored how ML could optimize scientific workflow systems and HPC operations, such as through improved scheduling and fault tolerance. A key focus was on identifying prestigious use cases of ML in HPC and understanding their unique, unmet requirements. The stochastic nature of ML and its impact on the reproducibility of data analysis on HPC systems was also a topic of discussion.

97 MATHEMATICS AND COMPUTING↗