Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Case Study”

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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Case Studies and Model Guidance for Establishing Solar Cybersecurity Working Groups

This tool outlines actions that states can take to establish state-level working groups, which can be a critical first step to establishing state cybersecurity programs and efforts to support cybersecurity in distributed energy resources. Specifically, these working groups can lead the development of cybersecurity task forces focused around distributed energy resources, formal recommendations for policy makers and regulators, and the creation of a response strategies and plans for cybersecurity impacts or incidents in solar photovoltaic systems.

14 SOLAR ENERGY↗
DOE OSTI2022

Building Blocks for Secure and Prosperous Defense Critical Supply Chains: A Case Study from Microelectronics

Securing defense-critical supply chains, built on resilient and sustainable microelectronics fabrication and deployment, is a national imperative and one that requires an investment in basic and applied research, development, and deployment into industries in (and out of) the Defense Industrial Base (DIB). Critical next steps include the development of pilots for revolutionary concepts in a new formal verification model and the Cyber-Physical Passport (CPP) concept for chain of custody. Critical infrastructure leadership should take action with a sense of urgency. Working in conjunction with the Under Secretary for Acquisition and Sustainment, the Assistant Secretary of Defense for Research and Engineering should establish pilot programs with the Defense Advanced Research Projects Agency and the service labs to build and test both concepts in legacy and new system development.. Additionally, the Pentagon may be aware that an existing Manufacturing Innovation Institute is piloting, with the semiconductor industry, cyber innovations to provide verifiable security properties and guarantees of physical functions to build a more cyber secure, resilient and efficient micro-electronics supply chain.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Offline Arterial Signal Timing Optimization based on Virtual Phase Link Model - A Real-world Case Study

Conventional signal timing for arterial usually takes bottom-up approaches. Engineers optimize each individual intersection first and then coordinate them by adjusting the offsets. This work is based on the Virtual Phase-Link (VPL) model, a street traffic model designed for online traffic model predictive control, to obtain a top-down offline arterial signal timing. We Studied the Shallowford Rd. in Chattanooga, TN and found that the inconsistency in intersection capacities along the arterial could lead to some intersections becoming bottlenecks. Signal timing is a significant factor that affect the intersection capacities. We realized that the VPL-based model can guarantee the consistency in intersections along an arterial. We therefore adopted the VPL-based model and developed an offline signal timing optimization approach. The proposed timing derived from the VPL-based offline signal timing optimization showed very good results in simulation. The Chattanooga Department of Transportation adopted the optimized timing obtained from the proposed approach and gave positive feedbacks to the research team. We also collected field experiment data, which demonstrated overall energy reductions and speed improvements on some sections of the Shallowford Rd. arterial. We will continue the experiment when the COVID-19 pandemic impact subsides to have a more robust quantitative evaluation.

Wang, Qichao↗
DOE OSTI2023

Case Study: Calorimetry

This chapter presents the use of adsorption microcalorimetry to study acid-base catalysis over mixed metal oxides. Understanding the acid-base properties of catalysts is of paramount importance for steering product selectivity in a wide variety of catalytic industrial processes. In particular, catalytic conversion of biomass into biofuels and chemicals demands comprehensive understanding of the acid-base properties of the catalyst to selectively convert functional groups. Herein, the use of probe molecules to measure the strength and density of acid and basic sites on mixed metal oxide surfaces is introduced. Further, the challenges when characterizing mixed metal oxides, presenting surface reconstruction, are explained. An example displays how combining adsorption calorimetry with ab initio calculations explains the activation of a reactant on the surface of a mixed metal oxide. Acid-base catalysis over nanoshapes is studied to unveil that different nanoshapes lead to different extents of surface reconstruction, which ultimately affect acid-base catalysis. The use of steady-state isotopic transient kinetic analysis (SSITKA), in combination with adsorption microcalorimetry, unveils that surface reconstruction of a set of perovskites impacts the density of active sites to a greater extent than the intrinsic reactivity of the active site. At the end of this chapter, fundamental insights into catalyst transient behavior during isomerization of n-butane, and the reaction mechanism for conversion of 4-methylpentan-2-ol, are presented.

Polo Garzon, Felipe↗
DOE OSTI2024

Community Public Mobility Using On-Demand, Low-Speed Electric Vehicles: A Case Study in Downtown St. Louis, Missouri

Legacy fixed route transit systems designed to serve commuters struggle to provide efficient and effective service for short neighborhood trips and for population groups unable to access and egress transit stops using active modes (e.g., elderly, disabled). Neighborhood on-demand transit (ODT) services using low-speed electric vehicles (LSEV) are an innovative technological solution that can help fill this gap in service (e.g., short, high-frequency trips) for diverse populations and trip types. This study evaluated user characteristics and travel behavior for a neighborhood ODT service (using LSEVs) in downtown St. Louis, Missouri using responses from a community survey (n=244), ridership data, and vehicle trajectory information. A comparative analysis between neighborhood ODT, fixed route transit, and transportation network companies (TNC) was also conducted from the perspectives of total travel time, cost, and greenhouse gas emissions. Ultimately, the goal of the analysis was to motivate and inform holistic public mobility systems where different services are optimized to meet specific community needs. Findings indicate that the neighborhood ODT was effective at reaching diverse populations (elderly (20%), lower income (27%), and households with limited access to private vehicles (34%)). ODT reduced total travel time by 32% compared to fixed route transit, produced 2.4 - 4.3 times less greenhouse gas emissions per passenger mile (compared to transit and TNCs), and was more affordable (free to users) than alternative options ($1 for transit, $10-12 for TNCs). Overall satisfaction rates were high, with 80% of respondents rating the service a 4 or 5 out of 5.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Carbonated Brine Injection as a Low-Risk CO2 Storage Strategy: A Case Study for Inyan Kara Sandstone Reservoir

Carbonated brine storage is a low-risk geologic CO2 sequestration strategy compared to traditional supercritical CO2 injection and storage. Our study provides an evaluation methodology for CBI that can be used for other potential pilot injections in suitable fields and opens the opportunity to work with industry for CBI using existing Class II wells to gain CO2 tax credits.

Xiong, Wei↗
DOE OSTI2020

Leveraging Machine Learning Capabilities for the Characterization of Irradiated Uranium: A Case Study of Analysis Methods for Nuclear Safeguards and Nuclear Forensics

Nondestructively determining the initial enrichment of irradiated uranium is a complex and laborious multivariable problem due to the presence of fission products. This work demonstrates the capabilities of machine learning to analyze gamma-ray spectral data to determine initial enrichment without knowledge of the decay time of the sample. The approach developed is agnostic to the particular scenario and is applicable to a wide variety of applications in nuclear forensics and nuclear safeguards. We irradiated 5 mg uranium standard reference materials at discrete enrichment values ranging from 0.02% to 97% 235 U (weight percent) in UT Austin’s Nuclear Engineering Teaching Laboratory TRIGA Mark II 1.1 MW research reactor, allowed each to decay for 8 hours, and then measured each sample via gamma-ray spectrometry for 50 hours post-irradiation yielding 1,400 individual gamma-ray spectra discretized into 8,192 energy bins. We then trained decision trees models to analyze individual gamma-ray spectra and estimate the associated initial enrichment without knowledge of the time since end of irradiation. We evaluated the performance of the models with a reserved test set not used for training or calibrating the model. A decision tree model constructed with this procedure achieved a mean absolute error in initial enrichment determination of 2.3% (weight percent 235 U). Next, we implemented a principal component analysis pre-processing routine of the gamma-ray spectrometry data to reduce the dimensionality of the dataset from 8,192 channels in the spectrum to 10 principal components while retaining over 99% of the inherent variance in the data. Decision tree models constructed with these data demonstrated decreased mean absolute error in enrichment determination, reduced computational time, and decreased complexity. A single decision tree model constructed with this procedure achieved a mean absolute error in initial enrichment determination of 0.05% (weight percent 235 U). Furthermore, we analyzed these models with learning curves to ensure that overfitting did not occur. The capabilities provided by these models can be naturally extended to other application-focused measurements in the fields of nuclear safeguards, nuclear forensics, and nuclear non-proliferation.

Drescher, Adam↗
DOE OSTI2023

Maximum-impact Adversary Design for Network-based Control System: A Case Study on Grid-interactive Efficient Buildings

The Internet of Things (IoT) technology has dramatically improved the efficiency of today's building operation and management. By connecting controllable devices into a communication network, control signals can be easily passed to the devices, and operating status can be acquired from measurable ends with minimal effort. However, this all-connected configuration could also expose the network-based control system (NBCS) to malicious actions, such as cyberattacks. One of the common NBCSs is the building automation system. With the promotion of grid-interactive efficient buildings (GEBs), there has been increasing attention on securing the buildings from the network perspective. This research proposes a maximum-impact adversary design framework so that the adversary can provide the most adversarial impact on the controlled system while remaining stealthy. The proposed framework is numerically demonstrated on a network-based building energy and control system. The building energy system is built in a Modelica-based simulation environment and controlled by the state-of-the-art ASHRAE Guideline 36 control sequences. The control commands at the supervisory level, generated from the Guideline 36 controller, are assumed to be sent to local devices through communication networks using the BACnet protocol. Simulation results show that the proposed maximum-impact adversary on such a system can stealthily affect the building system's performance to its maximum extent. It is anticipated that results can be used by researchers and practitioners in the building automation industry to design efficient and robust cyber-attack detection algorithms, especially for stealthy attacks.

Chu, Mengyuan↗
DOE OSTI2023

Performance Versus Maintainability: A Case Study of Scream on Frontier

The Simple Cloud-Resolving E3SM Atmosphere Model (Scream) won the inaugural ACM Gordon Bell Prize for Climate Modeling. While most of Scream is portable Kokkos code, the Gordon-Bell runs did include tuning specifically for Frontier, the exascale computer at Oak Ridge National Laboratory. Production science runs use the same high-level configuration of Scream, but the tuned kernels do not meet the software standards necessary to merge into the production code base. This work describes experiments to refactor these kernels to meet the maintainability requirements of the production Scream code base while preserving high performance.

White, Trey↗
DOE OSTI2024

The PARADIGM Project: Case Study in Balancing Experiment Uncertainty with Design simplicity

Accurate nuclear data are required for simulations of many applications including nuclear criticality safety. Actinide nuclear data at intermediate energies (from 1 to 100s of keV) are imprecise and inaccurate, because of scarce differential data, and an insufficient theory approach to capture the structures expected in the data to yield evaluated nuclear data, and lack of integral data for proper validation. This is a known deficiency but has proved challenging to address. More specifically, only 5% of integral experiments in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) benchmark suite address intermediate energies (Fig. 1). Associated calculated effective multiplication factor, k eff , values for these experiments are far outside the experimental uncertainties and are 25× further from experiment than for fast energies. These differences could either stem from systematic biases in nuclear data, experiments or both. The goal of the PARADIGM (PARallel Approach of Differential and InteGral Measurements) project is to significantly reduce (by more than tens of percent) the uncertainties of intermediate energy actinide nuclear data. The PARADIGM project designed and intends to execute LANSCE (Los Alamos Neutron Science CEnter) and NCERC (National Criticality Experiments Research Center) intermediate experiments in parallel. They will specifically address a high priority nuclear data need—reducing bias and uncertainty in intermediate plutonium nuclear data. The two experiment will achieve that by informing each other and nuclear theory. By doing all these steps in parallel, the timeline to deliver improved nuclear data to users will significantly be reduced. This work will focus on the integral experiment final design and the balance of design and modeling simplicity while minimizing experiment uncertainty.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗