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Solid State Circuit Breaker Device and Method without Current Limiter
Full text linkA Solid-State Circuit Breaker Device and Method Without Current Limiting Inductor. According to an exemplary embodiment of this disclosure, described is a high-density, high-efficiency megawatt (MW) medium-voltage (MV) solid-state circuit breaker (SSCB) for, example only, aviation hybrid electric propulsion applications. The SSCB is based on the mature silicon (Si) insulated gate bipolar transistor (IGBT) devices. With reduced IGBT gate voltage, the disclosed SSCB can limit the peak fault current without the fault current limiting inductor. Thus, the specific power density of the SSCB is substantially improved compared with the traditional design
Modeling Future Demands for Child Development Center (CDC) Funding
No full textThe U.S. Marine Corps installations provide childcare services to support military members and their families run by the Childcare Development Center (CDC), which serves infants and children through age five. The growing number of infants and young children continues to strain an already overtaxed CDC system. Many locations have developed excessive waiting lists. Excessive waiting lists have caused military members to become disheartened about the availability of childcare while in service, leading to negative effects on readiness, job performance, and retention. The purpose of this research is to develop a reusable, extensible, replicable set of simulated stochastic predictive models that could be used to inform future resourcing to avoid and mitigate this disincentive and model the needed funding to allocate during the year of execution. The research methodology applies uncertainty-based intelligence and decision analysis. We will propose novel, reusable, extensible, adaptable, and comprehensive advanced analytical processes employing quantitative analytical methods assuming uncertainty with Monte Carlo stochastic risk simulation, predictive analytics, and Poisson queuing models on arrival rates of children and infants versus service rates and exit rates of the children who are already in the system. Machine learning and artificial intelligence methods can also be deployed when required (e.g., Logit, maximum likelihood estimates, random forest, and bagging with bootstrapping). The accuracy and precision of the estimates will be analyzed using historical waitlist data. The idea is to determine the probability a new recruit’s family will be placed on the waitlist and the length of waiting time, balanced against a desired probability and length of wait by having added funding. This portfolio allocation analysis will provide actionable intelligence to decision-makers to determine the desired quality and waitlist threshold while balancing the budget requirements.Approved for public release; distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Naval Postgraduate School, Naval Research ProgramHQMC Programs & Resources (P&R
Investigating Cost Variance in Defense Acquisition Projects: A Data Analytics Approach
No full textDefense acquisition projects are critical to national security, encompassing the development and procurement of military systems, equipment, and technologies. These projects are often complex, large-scale, span several years, and involve multiple stakeholders. Cost variance— the difference between the planned budget and the actual expenditure— is a common issue, frequently leading to budget overruns. This project aims to investigate the causes of cost variance in defense acquisition projects. By analyzing historical data, the research seeks to uncover patterns and common factors that contribute to cost deviations. The study employs qualitative and quantitative methods to examine the extent and nature of cost variances across diverse types of defense projects, ranging from advanced weapons systems to information technology.Approved for public release; distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Naval Postgraduate School, Naval Research ProgramN8 - Integration of Capabilities & Resource
Faces of NPS: Lapin, Lisa
Full text linkFaces of NPS features Interviews spotlighting the students, faculty, staff and alumni of our Nation's premier defense education and research institution
Call for Papers and Panels Due 18 November: Accelerating Warfi ghting Capabilities: NPS 23rd Annual Acquisition ResearchSymposium & Innovation Summit
Full text linkAn Acquisition Research Program Blog entry on this dat
Optimal Deployment and Employment of Naval Air Connectors
Full text linkThe Navy Air Logistics Office (NALO) is tasked with supporting fleet operations and readiness. They accomplish this through planning air cargo and passenger transport missions that fulfill demands around the world. These missions are executed by the Fleet Logistics Support Wing (FLSW), which is a U.S. Navy Reserve wing stationed at Naval Air Station Joint Reserve Base Fort Worth, Texas. FSLW comprises 11 transportation squadrons spread over 10 bases in different locations in the continental U.S. and Hawaii. Based on aircargo demand data provided by the sponsor, we have analyzed the FLSW’s current operational practices and squadron allocation, with the objective of identifying potential improvements in operations and deployment (i.e., re-allocation). We have addressed two main questions: (1) Given the current deployment of aircraft in bases, can we improve the employment of aircraft, that is, the assignment of aircraft from bases to missions? (2) Can we improve the deployment of aircraft, that is, the allocation of aircraft to bases? We have developed two variants of a mixed-integer program (MIP) that address these two questions. One variant is called employment model, and the other is called deployment model. The employment model, which optimizes assignments of aircraft to missions, is run twice: once with respect to the current FLSW deployment of aircraft, and once with respect to a redeployment of aircraft in bases, which results from running the deployment optimization model. With these models, we identify inefficiencies in the current operations and propose directions for improvement.Approved for public release; distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Naval Postgraduate School, Naval Research ProgramOPNAV N98 – Air Warfar
Predicting the Accuracy of Cost, Schedule, and Performance Projections for Acquisition Projects
No full textNPS NRP Executive SummaryDefense acquisition projects are critical to national security. Cost variance is a significant problem in defense acquisition projects because it directly impacts budgeting, scheduling, and overall program success (Christensen, 1993). The objective of this research is to investigate the causes of cost variance in defense acquisition projects. Specifically, the goal of this research is to identify the primary drivers of cost variance in defense acquisition projects; assess the impact of these variances on project outcomes and overall defense budgeting; and propose actionable recommendations for minimizing cost variance in future projects. The methodology used for this research follows a data science approach consisting of data collection and understanding, data preparation, data analysis and modeling, and model validation (Hastie, Tibshirani, & Friedman, 2009). The research demonstrates that funding and cost variance categories are strong indicators of cost variance while service component, commodity prime categories and Joint Area categories are not. Further, text analytics revealed that the top three reasons for cost variances in the F-35 Joint Strike Fighter Program were revised estimates, adjustments for current and prior escalation, and revised escalation indices. Similarly, for the DDG 51 Arleigh Burke Class Destroyer Program, the primary reasons for cost variances were revised estimates, revised escalation indices, and adjustments for current and prior escalation. Our main recommendation is to: 1) develop a robust interactive dashboard to support the analysis of cost variance data and generate insights, leveraging text analytics and the proof-of-concept prototype developed in this study, 2) identify additional data sources that capture a broader range of predictor factors influencing cost variance, and 3) use these factors to develop predictive models using statistical and machine learning techniques, such as multiple regression, decision trees, and neural networks, to improve cost variance forecasting.Approved for public release. Distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)N8 - Integration of Capabilities & Resource
Leveraging AI to Learn, Optimize, and Wargame Strategic Laydown and Dispersal of USN Operating Forces (Continuation)
No full textNPS NRP Executive SummaryAccording to the Office of the Chief of Naval Operations (OPNAV) Instruction 3111.17C, the Secretary of the Navy (SECNAV), based on recommendations from the Chief of Naval Operations (CNO), disperses units of the Navy’s operating forces to locations in a deliberate manner that directly supports Department of Defense (DoD) guidance and policy. The current strategic, laydown, and dispersal (SLD) process is a manual, labor-intensive process and does not easily evaluate competing, alternative plans. Artificial intelligence and machine learning (AI/ML) tools are studied to digitize, standardize, and automate many components of the current SLD process. In this continuous Phase III project, the Naval Postgraduate School (NPS) team continued, developed, and designed a research prototype with the integrated databases and AI/ML tools in the NPS classified environment or the online secret-level research prototype (OSRP). Our achievements also include implementation of an AI/ML Retrieval Augmented Generation (RAG) pipeline that integrates structured data recommendations with unstructured data with large language models (LLMs) for interpretation and justification of SLD decision making. The resulting OSRP can be used continuously as a platform and repository to accumulate and analyze historical SLD human decision data, meanwhile provide recommendations for future SLD decisions.Approved for public release. Distribution is unlimited.This research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)N3/N5 - Plans & Strateg
Military Operations Research Society (MORS) Oral History Project Interview of Dr. Susan M. Sanchez
No full textInterviewers: W. David Kelton, Ms. Tammy McNeley, and Dr. Bob SheldonDr. Susan M. Sanchez is a Distinguished Professor Emerita in the Operations Research (OR) Department at the Naval Postgraduate School (NPS). She joined the OR Department at NPS in 2000. Dr. Sanchez was designated a Titan of Simulation by the Winter Simulation Conference community. She received the Institute for Operations Research and the Management Sciences (INFORMS) Award for the Advancement of Women in Operations Research and the Management Sciences, the INFORMS Koopman Prize, the Distinguished Service Award from the INFORMS Simulation Society, and selection as an INFORMS Fellow
DROUGHT IS HERE: HOW CAN THE UNITED STATES GOVERNMENT COORDINATE AN EFFECTIVE RESPONSE?
Full text linkThis thesis explores the growing threat of drought in the United States, particularly in the context of climate change. It examines how federal authorities, resources, and coordination mechanisms address drought response and recovery. The research investigates key gaps in the existing federal framework and assesses case studies from the Klamath and Colorado River Basins, as well as water crises in Flint, Michigan, and Jackson, Mississippi. The findings reveal that the current federal response to drought is fragmented and lacks the coordination typical of rapid-onset disasters. The study concludes that a comprehensive, adaptive federal response is urgently needed to address the increasing frequency, severity, and duration of droughts. Recommendations include establishing a national drought response plan, leveraging Stafford Act authorities and the National Response Framework, and investing in drought-resistant infrastructure and hazard mitigation to build resilience in vulnerable communities.Distribution Statement A. Approved for public release: Distribution is unlimited.Civilian, Department of Homeland Securit